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Ogunbawo AR, Hidalgo J, Mulim HA, Carrara ER, Ventura HT, Souza NO, Lourenco D, Oliveira HR. Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle. Front Genet 2025; 16:1549284. [PMID: 40370699 PMCID: PMC12075139 DOI: 10.3389/fgene.2025.1549284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2024] [Accepted: 04/07/2025] [Indexed: 05/16/2025] Open
Abstract
Background Identifying genomic regions associated with traits of interest and their biological processes provides valuable insights into the phenotypic variability of these traits. This study aimed to identify candidate genes and genomic regions associated with 16 traits currently evaluated by the Brazilian Association of Zebu Breeders (ABCZ). These traits include reproductive traits such as age at first calving (AFC), stayability (STAY), and scrotal circumference at 365 (SC365) and 450 days (SC450). Growth traits include birthweight (BW), expected progeny difference for weight at 120days of age (EPD120), as well as weight at 120 (W120), 210 (W210), 365 (W365), and 450 days of age (W450). Carcass traits include body conformation (BC), finishing score (FS), marbling (MARB), muscularity (MUSC), finishing precocity (FP), and ribeye area (REA). Methods A dataset containing 304,782 Nellore cattle genotyped with 437,650 SNPs (after quality control) was used for this study. The Algorithm for Proven and Young (APY), implemented in the PREGSF90 software, was used to compute theG A P Y - 1 matrix using 36,000 core animals (which explained 98% of the variance in the genomic matrix). Subsequently, the SNP solutions were estimated by back-solving the Genomic Estimated Breeding Values (GEBVs) predicted by ABCZ using the single-step GBLUP method. Genomic regions were identified using sliding windows of 175 consecutive SNPs, and the top 1% genomic windows, ranked based on their proportion of the additive genetic variance, were used to annotate positional candidate genes and genomic regions associated with each of the 16 traits. Results The top 1% windows for all traits explained between 2.779% (STAY) to 3.158% (FP) of the additive genetic variance, highlighting the polygenic nature of these traits. Functional analysis of the candidate genes and genomic regions provided valuable insights into the genetic architecture underlying these traits in Nellore cattle. For instance, our results revealed genes with important functions for each trait, such as SERPINA14 (plays a key role for the endometrial epithelium) identified for AFC, HSPG2 (associated with morphological development and tissue differentiation) identified for BW, among others. Conclusion We identified genomic regions and candidate genes, some of which have been previously reported in the literature, while others are novel discoveries that warrant further investigation. These findings contribute to gene prioritization efforts, facilitating the identification of functional candidate genes that can enhance genomic selection strategies for economically important traits in Nellore cattle.
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Affiliation(s)
- Adebisi R. Ogunbawo
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| | - Jorge Hidalgo
- Department of Animal and Dairy Sciences, University of Georgia, Athens, GA, United States
| | - Henrique A. Mulim
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| | - Eula R. Carrara
- Department of Animal and Dairy Sciences, University of Georgia, Athens, GA, United States
| | | | - Nadson O. Souza
- Brazilian Association of Zebu Breeders, Uberaba, Minas Gerais, Brazil
| | - Daniela Lourenco
- Department of Animal and Dairy Sciences, University of Georgia, Athens, GA, United States
| | - Hinayah R. Oliveira
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
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Zhu J, Liu F, Ye T, Li Q, Liu H, Liu S, Zhang T, Guo D, Zhu J, Lou B. Genome-wide association study and transcriptomic analysis reveal the crucial role of sting1 in resistance to visceral white-nodules disease in Larimichthys polyactis. Front Immunol 2025; 16:1562307. [PMID: 40356894 PMCID: PMC12066304 DOI: 10.3389/fimmu.2025.1562307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2025] [Accepted: 04/04/2025] [Indexed: 05/15/2025] Open
Abstract
Introduction Larimichthys polyactis is a promising marine fishery species, but visceral white-nodules disease (VWND) caused by Pseudomonas plecoglossicida causes significant losses. However, genetic resistance mechanisms to VWND remain elusive in this species. Methods This study combined genome-wide association study (GWAS) and transcriptome analysis to unravel resistance loci and transcriptional regulation in L. polyactis. Results As a result, GWAS on 946 infected fish genotyped by 100 K lipid chips identified 22 suggestive significantly associated single-nucleotide polymorphisms (SNPs), annotated 60 candidate genes, where DNA-sensing pathway were enriched. RNA-seq on liver tissues of resistant, sensitive, and control groups found immune-related pathways enriched in the comparisons of RL vs CL and RL vs SL, and autophagy-related pathways enriched in the comparisons of SL vs CL and RL vs SL. Then, the integration of GWAS and transcriptome analysis identified seven key genes associated with resistance to VWND. Among the genes, the expression levels of mRNA for genes related to the cyclic GMP-AMP synthase-stimulator of interferon genes (STING) signaling pathway, as well as the protein levels of STING1, were significantly upregulated in RL. Collectively, integrating KEGG pathway analysis, gene and protein expression analysis revealed that the importance of STING1 for VWND resistance. Discussion These findings deepen the available knowledge on molecular mechanisms of host genetic resistance to VWND and provide an important foundation for the selection and breeding of VWND-resistant L. polyactis.
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Affiliation(s)
- Jiajie Zhu
- Key Laboratory of Applied Marine Biotechnology by the Ministry of Education, School of Marine Sciences, Ningbo University, Ningbo, China
| | - Feng Liu
- State Key Laboratory for Quality and Safety of Agro-Products / Institute of Hydrobiology, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Ting Ye
- State Key Laboratory for Quality and Safety of Agro-Products / Institute of Hydrobiology, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Qian Li
- National Engineering Research Center for Marine Aquaculture, Zhejiang Ocean University, Zhoushan, China
| | - Haowen Liu
- College of Life Sciences, China Jiliang University, Hangzhou, China
| | - Sifang Liu
- College of Life Sciences, China Jiliang University, Hangzhou, China
| | - Tianle Zhang
- College of Life Sciences, China Jiliang University, Hangzhou, China
| | - Dandan Guo
- State Key Laboratory for Quality and Safety of Agro-Products / Institute of Hydrobiology, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Junquan Zhu
- Key Laboratory of Applied Marine Biotechnology by the Ministry of Education, School of Marine Sciences, Ningbo University, Ningbo, China
| | - Bao Lou
- State Key Laboratory for Quality and Safety of Agro-Products / Institute of Hydrobiology, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
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Zhang J, Deng L, Deng L. Protein structural domain-disease association prediction based on heterogeneous networks. BMC Genomics 2025; 23:869. [PMID: 40211147 PMCID: PMC11987217 DOI: 10.1186/s12864-024-11117-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 12/02/2024] [Indexed: 04/12/2025] Open
Abstract
BACKGROUND Domains can be viewed as portable units of protein structure, folding, function, evolution, and design. Small proteins are often found to be composed of only a single domain, while most large proteins consist of multiple domains for achieving various composite cellular functions. A dysfunction in domains may affect the function of proteins in some disease. Inferring the disease-related domains will help our understanding of the mechanism of human complex diseases. RESULTS In this study, we firstly build a global heterogeneous information network based on structural-based domains, proteins, and diseases. Then the topological features of the network are extracted according to the meta-paths between domain and disease nodes. Finally, we train a binary classifier based on the XGBOOST (eXtreme Gradient Boosting) algorithm to predict the potential associations between domains and diseases. The results show that the binary classification model using the XGBOOST algorithm performs significantly better than models using other machine learning algorithms, achieving an AUC (Area Under Curve) score of 0.94 in the leave-one-out cross-validation experiment. CONCLUSIONS We develop a method to build a binary classifier using the topological features based on meta-paths and predict the potential associations between domains and diseases. Based on its predictive performance in independent test sets, the method is proved to be powerful. Moreover, representing domains and diseases through integrating more multi-omic data will further optimize predictive performance.
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Affiliation(s)
- Jingpu Zhang
- School of Computer and Data Science, Henan University of Urban Construction, 467000, Pingdingshan, China
| | - Lianping Deng
- School of Computer Science and Engineering, Central South University, 410075, Changsha, China
| | - Lei Deng
- School of Computer Science and Engineering, Central South University, 410075, Changsha, China.
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Li T, Yang Z, Ang Y, Zhao Y, Zhang Y, Liu Z, Sun H, Chang Y, Du M, Cheng X, Sun J, Liu E. Genome-wide association study identifies elite alleles of FLA2 and FLA9 controlling flag leaf angle in rice. BMC Genomics 2025; 26:280. [PMID: 40119348 PMCID: PMC11927237 DOI: 10.1186/s12864-025-11487-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2024] [Accepted: 03/13/2025] [Indexed: 03/24/2025] Open
Abstract
BACKGROUND In hybrid rice seed production, rice varieties with a small flag leaf angle (FLA) experience obstacles to cross-pollination at the early heading stage, and farmers usually need to remove flag leaves to achieve artificial pollination. Therefore, the cultivation of rice varieties with large FLAs can not only save a substantial amount of labour in the leaf-cutting process during artificial pollination but also accelerate the mechanization of hybrid rice seed production. RESULTS In this study, 431 rice accessions were included in a genome-wide association study (GWAS) to identify quantitative trait loci (QTLs) and the superior haplotypes for rice FLA in 2022 and 2023. The aim of the study was to identify new QTLs and provide germplasm resources for the genetic improvement of rice FLA. The population exhibited rich phenotypic variation in FLA in both years. The FLA GWAS was performed with more than 3 million single-nucleotide polymorphisms (SNPs), and eight QTLs associated with FLA were detected; of these, six QTLs located on rice chromosomes 1, 2, 8 and 9 were novel and detected in both years. In addition, these QTLs were analysed by haplotype analysis and functional annotation, and FLA2 and FLA9, which encode xyloglucan fucosyltransferase and cytokinin-O-glucosyltransferase 2, respectively, were identified as candidate genes for FLA regulation in rice. Quantitative real-time polymerase chain reaction (qRT‒PCR) results validated FLA2 and FLA9 as candidate genes. The results of this study showed that the elite alleles of FLA2 and FLA9 can increase FLA in rice. Excellent parents for FLA improvement were predicted through pyramiding breeding. CONCLUSIONS A total of six new QTLs and two candidate genes (FLA2 and FLA9) were identified by a GWAS of 431 rice accessions over two years. The elite alleles and excellent parents predicted in our study can provide important information for the functional analysis of rice FLA-related genes and improvement through pyramiding breeding.
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Affiliation(s)
- Tianhu Li
- College of Agronomy, Anhui Agricultural University, Hefei, 230000, China
| | - Zhen Yang
- College of Agronomy, Anhui Agricultural University, Hefei, 230000, China
| | - Yang Ang
- College of Agronomy, Anhui Agricultural University, Hefei, 230000, China
| | - Yingying Zhao
- College of Agronomy, Anhui Agricultural University, Hefei, 230000, China
| | - Yanan Zhang
- College of Agronomy, Anhui Agricultural University, Hefei, 230000, China
| | - Zhengbo Liu
- College of Agronomy, Anhui Agricultural University, Hefei, 230000, China
| | - Hao Sun
- College of Agronomy, Anhui Agricultural University, Hefei, 230000, China
| | - Yinping Chang
- College of Agronomy, Anhui Agricultural University, Hefei, 230000, China
| | - Mingyu Du
- College of Agronomy, Anhui Agricultural University, Hefei, 230000, China
| | - Xianping Cheng
- College of Agronomy, Anhui Agricultural University, Hefei, 230000, China
| | - Jinghan Sun
- College of Agronomy, Anhui Agricultural University, Hefei, 230000, China
| | - Erbao Liu
- College of Agronomy, Anhui Agricultural University, Hefei, 230000, China.
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Li H, Zeng J, Snyder MP, Zhang S. Modeling gene interactions in polygenic prediction via geometric deep learning. Genome Res 2025; 35:178-187. [PMID: 39562137 PMCID: PMC11789630 DOI: 10.1101/gr.279694.124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Accepted: 11/14/2024] [Indexed: 11/21/2024]
Abstract
Polygenic risk score (PRS) is a widely used approach for predicting individuals' genetic risk of complex diseases, playing a pivotal role in advancing precision medicine. Traditional PRS methods, predominantly following a linear structure, often fall short in capturing the intricate relationships between genotype and phenotype. In this study, we present PRS-Net, an interpretable geometric deep learning-based framework that effectively models the nonlinearity of biological systems for enhanced disease prediction and biological discovery. PRS-Net begins by deconvoluting the genome-wide PRS at the single-gene resolution and then explicitly encapsulates gene-gene interactions leveraging a graph neural network (GNN) for genetic risk prediction, enabling a systematic characterization of molecular interplay underpinning diseases. An attentive readout module is introduced to facilitate model interpretation. Extensive tests across multiple complex traits and diseases demonstrate the superior prediction performance of PRS-Net compared with a wide range of conventional PRS methods. The interpretability of PRS-Net further enhances the identification of disease-relevant genes and gene programs. PRS-Net provides a potent tool for concurrent genetic risk prediction and biological discovery for complex diseases.
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Affiliation(s)
- Han Li
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, China
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China
| | - Jianyang Zeng
- School of Engineering, Research Center for Industries of the Future, Westlake University, Hangzhou, 310030, Zhejiang, China;
| | - Michael P Snyder
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, California 94304, USA;
| | - Sai Zhang
- Department of Epidemiology, University of Florida, Gainesville, Florida 32603, USA;
- Departments of Biostatistics & Biomedical Engineering, UF Genetics Institute, University of Florida, Gainesville, Florida 32603, USA
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Feng GJ, Xu Q, Zhao QG, Han BX, Yan SS, Zhu J, Pei YF. The genetic architecture of age at menarche and its causal effects on other traits. J Hum Genet 2024; 69:645-653. [PMID: 39147824 DOI: 10.1038/s10038-024-01287-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 07/19/2024] [Accepted: 07/31/2024] [Indexed: 08/17/2024]
Abstract
Age at menarche (AAM) is a sign of puberty of females. It is a heritable trait associated with various adult diseases. However, the genetic mechanism that determines AAM and links it to disease risk is poorly understood. Aiming to uncover the genetic basis for AAM, we conducted a joint association study in up to 438,089 women from 3 genome-wide association studies of European and East Asian ancestries. A series of bioinformatical analyses and causal inference were then followed to explore in-depth annotations at the associated loci and infer the causal relationship between AAM and other complex traits/diseases. This largest meta-analysis identified a total of 21 novel AAM associated loci at the genome wide significance level (P < 5.0 × 10-8), 4 of which were European ancestry-specific loci. Functional annotations prioritized 33 candidate genes at newly identified loci. Significant genetic correlations were observed between AAM and 67 complex traits. Further causal inference demonstrated the effects of AAM on 13 traits, including forced vital capacity (FVC), high blood pressure, age at first live birth, etc, indicating that earlier AAM causes lower FVC, worse lung function, hypertension and earlier age at first (last) live birth. Enrichment analysis identified 5 enriched tissues, including the hypothalamus middle, hypothalamo hypophyseal system, neurosecretory systems, hypothalamus and retina. Our findings may provide useful insights that elucidate the mechanisms determining AAM and the genetic interplay between AAM and some traits of women.
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Affiliation(s)
- Gui-Juan Feng
- The First People's Hospital of Lianyungang, Jiangsu, PR China
- Department of Epidemiology and Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, PR China
| | - Qian Xu
- Department of Epidemiology and Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, PR China
| | - Qi-Gang Zhao
- Department of Epidemiology and Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, PR China
| | - Bai-Xue Han
- Department of Epidemiology and Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, PR China
| | - Shan-Shan Yan
- Department of Epidemiology and Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, PR China
| | - Jie Zhu
- Department of Gynaecology and Obstetrics, Suzhou Ninth Hospital Affiliated to Soochow University, 2666 Lu‑dang Rd., Wujiang District, Suzhou, 215200, Jiangsu, China.
| | - Yu-Fang Pei
- Department of Epidemiology and Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu, PR China.
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Akin S, Cekin N. Preeclampsia and STOX1 (storkhead-box protein 1): Molecular evaluation of STOX1 in preeclampsia. Gene 2024; 927:148742. [PMID: 38969244 DOI: 10.1016/j.gene.2024.148742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 06/13/2024] [Accepted: 07/01/2024] [Indexed: 07/07/2024]
Abstract
Preeclampsia (PE) is clinically defined as a part of pregnancy characterized by hypertension and multiple organ failure. PE is broadly categorized into two types: "placental" and "maternal". Placental PE is associated with fetal growth restriction and adverse maternal and neonatal outcomes. STOX1 (Storkhead box 1), a transcription factor, discovered through a complete transcript analysis of the PE susceptibility locus of 70,000 bp on chromosome 10q22.1. So far, studies investigating the relationship between STOX1 and PE have focused on STOX1 overexpression, STOX1 isoform imbalance, and STOX1 variations that could have clinical consequence. Initially, the Y153H variation of STOX was associated with the placental form of PE. Additionally, studies focusing on the maternal and fetal interface have shown that NODAL and STOX1 variations play a role together in the unsuccessful remodeling of the spiral arteries. Research specifically addressing the overexpression of STOX1 has shown that its disruption of cellular hemoastasis, leading to impaired hypoxia response, disruption of the cellular antioxidant system, and nitroso/redox imbalance. Furthermore, functional studies have been conducted showing that the imbalance between STOX1 isoforms contributes to the pathogenesis of placental PE. Research indicates that STOX1B competes with STOX1A and that the overexpression of STOX1B reverses cellular changes that STOX1A induces to the pathogenesis of PE. In this review, we aimed at elucidating the relationship between STOX1 and PE as well as function of STOX1. In conclusion, based on a comprehensive literature review, numerous studies support the role of STOX1 in the pathogenesis of PE.
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Affiliation(s)
- Seyda Akin
- Sivas Cumhuriyet University, Faculty of Medicine, Department of Medical Biology, 58140 Sivas, Turkey.
| | - Nilgun Cekin
- Sivas Cumhuriyet University, Faculty of Medicine, Department of Medical Biology, 58140 Sivas, Turkey.
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Wang S, Ojewunmi OO, Kamiza A, Ramsay M, Morris AP, Chikowore T, Fatumo S, Asimit JL. Accounting for heterogeneity due to environmental sources in meta-analysis of genome-wide association studies. Commun Biol 2024; 7:1512. [PMID: 39543362 PMCID: PMC11564974 DOI: 10.1038/s42003-024-07236-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Accepted: 11/07/2024] [Indexed: 11/17/2024] Open
Abstract
Meta-analysis of genome-wide association studies (GWAS) across diverse populations offers power gains to identify loci associated with complex traits and diseases. Often heterogeneity in effect sizes across populations will be correlated with genetic ancestry and environmental exposures (e.g. lifestyle factors). We present an environment-adjusted meta-regression model (env-MR-MEGA) to detect genetic associations by adjusting for and quantifying environmental and ancestral heterogeneity between populations. In simulations, env-MR-MEGA has similar or greater association power than MR-MEGA, with notable gains when the environmental factor has a greater correlation with the trait than ancestry. In our analysis of low-density lipoprotein cholesterol in ~19,000 individuals across twelve sex-stratified GWAS from Africa, adjusting for sex, BMI, and urban status, we identify additional heterogeneity beyond ancestral effects for seven variants. Env-MR-MEGA provides an approach to account for environmental effects using summary-level data, making it a useful tool for meta-analyses without the need to share individual-level data.
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Affiliation(s)
- Siru Wang
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
| | - Oyesola O Ojewunmi
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Abram Kamiza
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- The African Computational Genomic (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda
- Malawi Epidemiology and Intervention Research Unit, Lilongwe, Malawi
| | - Michele Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, UK
| | - Tinashe Chikowore
- MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Segun Fatumo
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
- The African Computational Genomic (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
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Biagetti G, Thompson E, O'Brien C, Damrauer S. The Role of Genetics in Managing Peripheral Arterial Disease. Ann Vasc Surg 2024; 108:279-286. [PMID: 38960093 DOI: 10.1016/j.avsg.2024.04.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 04/26/2024] [Indexed: 07/05/2024]
Abstract
BACKGROUND Genome wide association studies (GWAS) have allowed for a rapid increase in our understanding of the underlying genetics and biology of many diseases. By capitalizing on common genetic variation between individuals, GWAS can identify DNA variants associated with diseases of interest. A variety of statistical methods can be applied to GWAS results which allows for risk factor identification, stratification, and to identify potential treatments. Peripheral artery disease (PAD) is a common vascular disease that has been shown to have a strong genetic component. This article provides a review of the modern literature and our current understanding of the role of genetics in PAD. METHODS All available GWAS studies on PAD were reviewed. A literature search involving these studies was conducted and relevant articles applying the available GWAS data were summarized to provide a comprehensive review of our current understanding of the genetic component in PAD. RESULTS The largest available GWAS on PAD has identified 19 genome wide significant loci, with factor V Leiden and genes responsible for circulating lipoproteins being implicated in the development of PAD. Mendelian randomization (MR) studies have identified risk factors and causal associations with smoking, diabetes, and obesity and many other traits; protein-based MR has also identified circulating lipid and clotting factor levels associated with the incidence of PAD. Polygenic risk scores may allow for improved prediction of disease incidence and allow for early identification of at-risk patients but more work needs to be done to validate this approach. CONCLUSIONS Genetic epidemiology has allowed for an increased understanding of PAD in the past decade. Genome-wide association studies have led to improved detection of genetic contributions to PAD, and further genetic analyses have validated risk factors and may provide options for improved screening in at-risk populations. Ongoing biobank studies of chronic limb threatening ischemia patients and the increasing ancestral diversity in biobank enrollment will allow for even further exploration into the pathogenesis and progression of PAD.
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Affiliation(s)
- Gina Biagetti
- Division of Vascular Surgery and Endovascular Therapy, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Elizabeth Thompson
- Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Ciaran O'Brien
- Division of Vascular Surgery and Endovascular Therapy, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Scott Damrauer
- Division of Vascular Surgery and Endovascular Therapy, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Corporal Michael Crescenz VA Medical Center, Philadelphia, PA; Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA; Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA.
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10
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Nandi S, Varotariya K, Luhana S, Kyada AD, Saha A, Roy N, Sharma N, Rambabu D. GWAS for identification of genomic regions and candidate genes in vegetable crops. Funct Integr Genomics 2024; 24:203. [PMID: 39470821 DOI: 10.1007/s10142-024-01477-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2024] [Revised: 09/24/2024] [Accepted: 10/14/2024] [Indexed: 11/01/2024]
Abstract
Genome-wide association Studies (GWAS), initially developed for human genetics, have been highly effective in plant research, particularly for vegetable crops. GWAS is a robust tool for identifying genes associated with key traits such as yield, nutritional value, disease resistance, adaptability, and bioactive compound biosynthesis. Unlike traditional methods, GWAS does not require prior biological knowledge and can accurately pinpoint loci, minimizing false positives. The process involves developing a diverse panel, rigorous phenotyping and genotyping, and sophisticated statistical analysis using various models and software tools. By scanning the entire genome, GWAS identifies specific loci or single nucleotide polymorphisms (SNPs) linked to target traits. When a causal SNP variant is not directly genotyped, GWAS identifies SNPs in linkage disequilibrium (LD) with the causal variant, mapping the genetic interval. The method begins with careful panel selection, phenotyping, and genotyping, controlling for environmental effects and utilizing Best Linear Unbiased Prediction (BLUP). High-correlation, high-heritability traits are prioritized. Various genotyping methods address confounders like population structure and kinship. Bonferroni correction (BC) prevents false positives, and significant associations are shown in Manhattan plots. Candidate genes are identified through LD analysis and fine mapping, followed by functional validation. GWAS offers critical insights for enhancing vegetable crop breeding efficiency and precision, driving breakthroughs through advanced methods.
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Affiliation(s)
- Swagata Nandi
- Division of Vegetable Science, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Kishor Varotariya
- Division of Vegetable Science, ICAR-Indian Institute of Horticultural Research, Bengaluru, 560089, India.
| | - Sohamkumar Luhana
- Division of Vegetable Science, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Amitkumar D Kyada
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Ankita Saha
- Division of Vegetable Science, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Nabanita Roy
- Division of Vegetable Science, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Neha Sharma
- Division of Vegetable Science, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
| | - Dharavath Rambabu
- Division of Vegetable Science, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India
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11
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Kong L, Chen Y, Shen Y, Zhang D, Wei C, Lai J, Hu S. Progress and Implications from Genetic Studies of Bipolar Disorder. Neurosci Bull 2024; 40:1160-1172. [PMID: 38206551 PMCID: PMC11306703 DOI: 10.1007/s12264-023-01169-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 10/05/2023] [Indexed: 01/12/2024] Open
Abstract
With the advancements in gene sequencing technologies, including genome-wide association studies, polygenetic risk scores, and high-throughput sequencing, there has been a tremendous advantage in mapping a detailed blueprint for the genetic model of bipolar disorder (BD). To date, intriguing genetic clues have been identified to explain the development of BD, as well as the genetic association that might be applied for the development of susceptibility prediction and pharmacogenetic intervention. Risk genes of BD, such as CACNA1C, ANK3, TRANK1, and CLOCK, have been found to be involved in various pathophysiological processes correlated with BD. Although the specific roles of these genes have yet to be determined, genetic research on BD will help improve the prevention, therapeutics, and prognosis in clinical practice. The latest preclinical and clinical studies, and reviews of the genetics of BD, are analyzed in this review, aiming to summarize the progress in this intriguing field and to provide perspectives for individualized, precise, and effective clinical practice.
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Affiliation(s)
- Lingzhuo Kong
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
| | - Yiqing Chen
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
| | - Yuting Shen
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
| | - Danhua Zhang
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
| | - Chen Wei
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China
| | - Jianbo Lai
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China.
- The Key Laboratory of Mental Disorder Management in Zhejiang Province, Hangzhou, 310003, China.
- Brain Research Institute of Zhejiang University, Hangzhou, 310003, China.
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, 310003, China.
- Department of Neurobiology, NHC and CAMS Key Laboratory of Medical Neurobiology, School of Brain Science and Brian Medicine, and MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University School of Medicine, Hangzhou, 310003, China.
| | - Shaohua Hu
- Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310003, China.
- The Key Laboratory of Mental Disorder Management in Zhejiang Province, Hangzhou, 310003, China.
- Brain Research Institute of Zhejiang University, Hangzhou, 310003, China.
- Zhejiang Engineering Center for Mathematical Mental Health, Hangzhou, 310003, China.
- Department of Neurobiology, NHC and CAMS Key Laboratory of Medical Neurobiology, School of Brain Science and Brian Medicine, and MOE Frontier Science Center for Brain Science and Brain-machine Integration, Zhejiang University School of Medicine, Hangzhou, 310003, China.
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12
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Chatzigeorgiou C, Barrett JH, Martin J, Morgan AW, Mackie SL. Estimating overdiagnosis in giant cell arteritis diagnostic pathways using genetic data: genetic association study. Rheumatology (Oxford) 2024; 63:2307-2313. [PMID: 38048604 PMCID: PMC11292050 DOI: 10.1093/rheumatology/kead643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 09/12/2023] [Accepted: 10/02/2023] [Indexed: 12/06/2023] Open
Abstract
OBJECTIVES GCA can be confirmed by temporal artery biopsy (TAB) but false negatives can occur. GCA may be overdiagnosed in TAB-negative cases, or if neither TAB nor imaging is done. We used HLA genetic association of TAB-positive GCA as an 'unbiased umpire' test to estimate historic overdiagnosis of GCA. METHODS Patients diagnosed with GCA between 1990 and 2014 were genotyped. During this era, vascular imaging alone was rarely used to diagnose GCA. HLA region variants were jointly imputed from genome-wide genotypic data of cases and controls. Per-allele frequencies across all HLA variants with P < 1.0 × 10-5 were compared with population control data to estimate overdiagnosis rates in cases without a positive TAB. RESULTS Genetic data from 663 GCA patients were compared with data from 2619 population controls. TAB-negative GCA (n = 147) and GCA without TAB result (n = 160) had variant frequencies intermediate between TAB-positive GCA (n = 356) and population controls. For example, the allele frequency of HLA-DRB1*04 was 32% for TAB-positive GCA, 29% for GCA without TAB result, 27% for TAB-negative GCA and 20% in population controls. Making several strong assumptions, we estimated that around two-thirds of TAB-negative cases and one-third of cases without TAB result may have been overdiagnosed. From these data, TAB sensitivity is estimated as 88%. CONCLUSIONS Conservatively assuming 95% specificity, TAB has a negative likelihood ratio of around 0.12. Our method for utilizing standard genotyping data as an 'unbiased umpire' might be used as a way of comparing the accuracy of different diagnostic pathways.
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Affiliation(s)
| | | | - Javier Martin
- Institute of Parasitology and Biomedicine Lopez-Neyra, CSIC, Granada, Spain
| | - Ann W Morgan
- School of Medicine, University of Leeds, Leeds, UK
- NIHR Leeds Biomedical Research Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
- NIHR Leeds Medicines and In Vitro Diagnostics Co-operative, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Sarah L Mackie
- School of Medicine, University of Leeds, Leeds, UK
- NIHR Leeds Biomedical Research Centre, Leeds Teaching Hospitals NHS Trust, Leeds, UK
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13
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Zhu X, Yang Y, Lorincz-Comi N, Li G, Bentley AR, de Vries PS, Brown M, Morrison AC, Rotimi CN, Gauderman WJ, Rao DC, Aschard H. An approach to identify gene-environment interactions and reveal new biological insight in complex traits. Nat Commun 2024; 15:3385. [PMID: 38649715 PMCID: PMC11035594 DOI: 10.1038/s41467-024-47806-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 04/10/2024] [Indexed: 04/25/2024] Open
Abstract
There is a long-standing debate about the magnitude of the contribution of gene-environment interactions to phenotypic variations of complex traits owing to the low statistical power and few reported interactions to date. To address this issue, the Gene-Lifestyle Interactions Working Group within the Cohorts for Heart and Aging Research in Genetic Epidemiology Consortium has been spearheading efforts to investigate G × E in large and diverse samples through meta-analysis. Here, we present a powerful new approach to screen for interactions across the genome, an approach that shares substantial similarity to the Mendelian randomization framework. We identify and confirm 5 loci (6 independent signals) interacted with either cigarette smoking or alcohol consumption for serum lipids, and empirically demonstrate that interaction and mediation are the major contributors to genetic effect size heterogeneity across populations. The estimated lower bound of the interaction and environmentally mediated heritability is significant (P < 0.02) for low-density lipoprotein cholesterol and triglycerides in Cross-Population data. Our study improves the understanding of the genetic architecture and environmental contributions to complex traits.
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Affiliation(s)
- Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
| | - Yihe Yang
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Noah Lorincz-Comi
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Gen Li
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Michael Brown
- Human Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - W James Gauderman
- Division of Biostatistics, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Dabeeru C Rao
- Center for Biostatistics and Data Science, Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, F-75015, Paris, France
- Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
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14
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Liu L, Ma Y, Zhao H, Guo L, Guo Y, Liu CM. Genome-wide association studies identified OsTMF as a gene regulating rice seed germination under salt stress. FRONTIERS IN PLANT SCIENCE 2024; 15:1384246. [PMID: 38601316 PMCID: PMC11004275 DOI: 10.3389/fpls.2024.1384246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 03/15/2024] [Indexed: 04/12/2024]
Abstract
Introduction Salt tolerance during seed germination is an important trait for direct seeding and low-cost rice production. Nevertheless, it is still not clear how seed germination under salt stress is regulated genetically. Methods In this study, genome-wide association studies (GWAS) were performed to decipher the genetic basis of seed germination under salt stress using 541 rice varieties collected worldwide. Results and discussion Three quantitative trait loci (QTLs) were identified including qGRG3-1 on chromosome 3, qGRG3-2 on chromosome 5, and qGRG4 on chromosome 4. Assessment of candidate genes in these loci for their responses to salt stress identified a TATA modulatory factor (OsTMF) in qGRG3-2. The expression of OsTMF was up-regulated in both roots and shoots after exposure to salt stress, and OsTMF knockout mutants exhibited delayed seed germination under salt stress. Haplotype analysis showed that rice varieties carrying OsTMF-Hap2 displayed elevated salt tolerance during seed germination. These results provide important knowledge and resources to improve rice seed germination under salt stress in the future.
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Affiliation(s)
- Lifeng Liu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
- State Key Laboratory of Plant Environmental Resilience, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Yanling Ma
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Heng Zhao
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Lin Guo
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yan Guo
- State Key Laboratory of Plant Environmental Resilience, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Chun-Ming Liu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
- School of Advanced Agricultural Sciences, Peking University, Beijing, China
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15
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Coban-Akdemir Z, Song X, Ceballos FC, Pehlivan D, Karaca E, Bayram Y, Mitani T, Gambin T, Bozkurt-Yozgatli T, Jhangiani SN, Muzny DM, Lewis RA, Liu P, Boerwinkle E, Hamosh A, Gibbs RA, Sutton VR, Sobreira N, Carvalho CM, Shaw CA, Posey JE, Valle D, Lupski JR. The impact of the Turkish population variome on the genomic architecture of rare disease traits. GENETICS IN MEDICINE OPEN 2024; 2:101830. [PMID: 39669594 PMCID: PMC11613692 DOI: 10.1016/j.gimo.2024.101830] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 02/03/2024] [Accepted: 02/07/2024] [Indexed: 12/14/2024]
Abstract
Purpose The variome of the Turkish (TK) population, a population with a considerable history of admixture and consanguinity, has not been deeply investigated for insights on the genomic architecture of disease. Methods We generated and analyzed a database of variants derived from exome sequencing data of 773 TK unrelated, clinically affected individuals with various suspected Mendelian disease traits and 643 unaffected relatives. Results Using uniform manifold approximation and projection, we showed that the TK genomes are more similar to those of Europeans and consist of 2 main subpopulations: clusters 1 and 2 (N = 235 and 1181, respectively), which differ in admixture proportion and variome (https://turkishvariomedb.shinyapps.io/tvdb/). Furthermore, the higher inbreeding coefficient values observed in the TK affected compared with unaffected individuals correlated with a larger median span of long-sized (>2.64 Mb) runs of homozygosity (ROH) regions (P value = 2.09e-18). We show that long-sized ROHs are more likely to be formed on recently configured haplotypes enriched for rare homozygous deleterious variants in the TK affected compared with TK unaffected individuals (P value = 3.35e-11). Analysis of genotype-phenotype correlations reveals that genes with rare homozygous deleterious variants in long-sized ROHs provide the most comprehensive set of molecular diagnoses for the observed disease traits with a systematic quantitative analysis of Human Phenotype Ontology terms. Conclusion Our findings support the notion that novel rare variants on newly configured haplotypes arising within the recent past generations of a family or clan contribute significantly to recessive disease traits in the TK population.
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Affiliation(s)
- Zeynep Coban-Akdemir
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
| | - Xiaofei Song
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL
| | | | - Davut Pehlivan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
- Section of Neurology, Department of Pediatrics, Baylor College of Medicine, Houston, TX
| | - Ender Karaca
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
- Department of Pathology, Baylor University Medical Center, Dallas, TX
- Texas A&M School of Medicine, Texas A&M University, Dallas, TX
| | - Yavuz Bayram
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
- Division of Genomic Diagnostics, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Tadahiro Mitani
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
| | - Tomasz Gambin
- Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland
| | - Tugce Bozkurt-Yozgatli
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
- Department of Biostatistics and Bioinformatics, Institute of Health Sciences, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | | | - Donna M. Muzny
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX
| | - Richard A. Lewis
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
- Department of Pediatrics, Baylor College of Medicine, Houston, TX
- Department of Ophthalmology, Cullen Eye Institute, Baylor College of Medicine, Houston, TX
| | - Pengfei Liu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
| | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX
| | - Ada Hamosh
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Richard A. Gibbs
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX
| | - V. Reid Sutton
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
- Texas Children’s Hospital, Houston, TX
| | - Nara Sobreira
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Claudia M.B. Carvalho
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
- Pacific Northwest Research Institute, Seattle, WA
| | - Chad A. Shaw
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
- Baylor Genetics, Houston, TX
| | - Jennifer E. Posey
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
| | - David Valle
- McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - James R. Lupski
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX
- Department of Pediatrics, Baylor College of Medicine, Houston, TX
- Texas Children’s Hospital, Houston, TX
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16
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Choi Y, Cha J, Choi S. Evaluation of penalized and machine learning methods for asthma disease prediction in the Korean Genome and Epidemiology Study (KoGES). BMC Bioinformatics 2024; 25:56. [PMID: 38308205 PMCID: PMC10837879 DOI: 10.1186/s12859-024-05677-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 01/26/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND Genome-wide association studies have successfully identified genetic variants associated with human disease. Various statistical approaches based on penalized and machine learning methods have recently been proposed for disease prediction. In this study, we evaluated the performance of several such methods for predicting asthma using the Korean Chip (KORV1.1) from the Korean Genome and Epidemiology Study (KoGES). RESULTS First, single-nucleotide polymorphisms were selected via single-variant tests using logistic regression with the adjustment of several epidemiological factors. Next, we evaluated the following methods for disease prediction: ridge, least absolute shrinkage and selection operator, elastic net, smoothly clipped absolute deviation, support vector machine, random forest, boosting, bagging, naïve Bayes, and k-nearest neighbor. Finally, we compared their predictive performance based on the area under the curve of the receiver operating characteristic curves, precision, recall, F1-score, Cohen's Kappa, balanced accuracy, error rate, Matthews correlation coefficient, and area under the precision-recall curve. Additionally, three oversampling algorithms are used to deal with imbalance problems. CONCLUSIONS Our results show that penalized methods exhibit better predictive performance for asthma than that achieved via machine learning methods. On the other hand, in the oversampling study, randomforest and boosting methods overall showed better prediction performance than penalized methods.
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Affiliation(s)
- Yongjun Choi
- Department of Applied Artificial Intelligence, College of Computing, Hanyang University, 55 Hanyang-daehak-ro, Sangnok-gu, Ansan, 15588, South Korea
| | - Junho Cha
- Department of Applied Artificial Intelligence, College of Computing, Hanyang University, 55 Hanyang-daehak-ro, Sangnok-gu, Ansan, 15588, South Korea
| | - Sungkyoung Choi
- Department of Applied Artificial Intelligence, College of Computing, Hanyang University, 55 Hanyang-daehak-ro, Sangnok-gu, Ansan, 15588, South Korea.
- Department of Mathematical Data Science, College of Science and Convergence Technology, Hanyang University, 55 Hanyang-daehak-ro, Sangnok-gu, Ansan, 15588, South Korea.
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Ge F, Arif M, Yan Z, Alahmadi H, Worachartcheewan A, Shoombuatong W. Review of Computational Methods and Database Sources for Predicting the Effects of Coding Frameshift Small Insertion and Deletion Variations. ACS OMEGA 2024; 9:2032-2047. [PMID: 38250421 PMCID: PMC10795160 DOI: 10.1021/acsomega.3c07662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 11/30/2023] [Accepted: 12/04/2023] [Indexed: 01/23/2024]
Abstract
Genetic variations (including substitutions, insertions, and deletions) exert a profound influence on DNA sequences. These variations are systematically classified as synonymous, nonsynonymous, and nonsense, each manifesting distinct effects on proteins. The implementation of high-throughput sequencing has significantly augmented our comprehension of the intricate interplay between gene variations and protein structure and function, as well as their ramifications in the context of diseases. Frameshift variations, particularly small insertions and deletions (indels), disrupt protein coding and are instrumental in disease pathogenesis. This review presents a succinct review of computational methods, databases, current challenges, and future directions in predicting the consequences of coding frameshift small indels variations. We analyzed the predictive efficacy, reliability, and utilization of computational methods and variant account, reliability, and utilization of database. Besides, we also compared the prediction methodologies on GOF/LOF pathogenic variation data. Addressing the challenges pertaining to prediction accuracy and cross-species generalizability, nascent technologies such as AI and deep learning harbor immense potential to enhance predictive capabilities. The importance of interdisciplinary research and collaboration cannot be overstated for devising effective diagnosis, treatment, and prevention strategies concerning diseases associated with coding frameshift indels variations.
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Affiliation(s)
- Fang Ge
- State
Key Laboratory of Organic Electronics and lnformation Displays &
lnstitute of Advanced Materials (IAM), Nanjing University of Posts
& Telecommunications, 9 Wenyuan Road, Nanjing 210023, China
- Center
for Research Innovation and Biomedical Informatics, Faculty of Medical
Technology, Mahidol University, Bangkok 10700, Thailand
| | - Muhammad Arif
- College
of Science and Engineering, Hamad Bin Khalifa
University, Doha 34110, Qatar
| | - Zihao Yan
- School
of Computer Science and Engineering, Nanjing
University of Science and Technology, 200 Xiaolingwei, Nanjing 210094, China
| | - Hanin Alahmadi
- College
of Computer Science and Engineering, Taibah
University, Madinah 344, Saudi Arabia
| | - Apilak Worachartcheewan
- Department
of Community Medical Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
| | - Watshara Shoombuatong
- Center
for Research Innovation and Biomedical Informatics, Faculty of Medical
Technology, Mahidol University, Bangkok 10700, Thailand
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Tiniakos DG, Anstee QM, Brunt EM, Burt AD. Fatty Liver Disease. MACSWEEN'S PATHOLOGY OF THE LIVER 2024:330-401. [DOI: 10.1016/b978-0-7020-8228-3.00005-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
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19
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Liu J, Xu L, Ding X, Ma Y. Genome-Wide Association Analysis of Reproductive Traits in Chinese Holstein Cattle. Genes (Basel) 2023; 15:12. [PMID: 38275594 PMCID: PMC10815438 DOI: 10.3390/genes15010012] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2023] [Revised: 12/15/2023] [Accepted: 12/17/2023] [Indexed: 01/27/2024] Open
Abstract
This study was to explore potential SNP loci for reproductive traits in Chinese Holstein cattle and identify candidate genes. Genome-wide Association Study based on mixed linear model was performed on 643 Holstein cattle using GeneSeek Bovine 50 K SNP chip. Our results detected forty significant SNP loci after Bonferroni correction. We identified five genes (VWC2L, STAT1, PPP3CA, LDB3, and CTNNA3) as being associated with pregnancy ratio of young cows, five genes (PAEP, ACOXL, EPAS1, GLRB, and MARVELD1) as being associated with pregnancy ratio of adult cows, and nine genes (PDE1B, SLCO1A2, ARHGAP26, ADAM10, APBB1, MON1B, COQ9, CDC42BPB, MARVELD1, and HPSE2) as being associated with daughter pregnancy rate. Our study may provide valuable insights into identifying genes related to reproductive traits and help promote the application of molecular breeding in dairy cows.
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Affiliation(s)
- Jiashuang Liu
- Tianjin Key Laboratory of Animal Molecular Breeding and Biotechnology, Tianjin Engineering Research Center of Animal Healthy Farming, Institute of Animal Science and Veterinary, Tianjin Academy of Agricultural Sciences, Tianjin 300381, China;
- College of Animal Science and Veterinary Medicine, Tianjin Agricultural University, Tianjin 300384, China;
| | - Lingyang Xu
- Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China;
| | - Xiangbin Ding
- College of Animal Science and Veterinary Medicine, Tianjin Agricultural University, Tianjin 300384, China;
| | - Yi Ma
- Tianjin Key Laboratory of Animal Molecular Breeding and Biotechnology, Tianjin Engineering Research Center of Animal Healthy Farming, Institute of Animal Science and Veterinary, Tianjin Academy of Agricultural Sciences, Tianjin 300381, China;
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20
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Long L, He H, Shen Q, Peng H, Zhou X, Wang H, Zhang S, Qin S, Lu Z, Zhu Y, Tian J, Chang J, Miao X, Shen N, Zhong R. Birthweight, genetic risk, and gastrointestinal cancer incidence: a prospective cohort study. Ann Med 2023; 55:62-71. [PMID: 36503347 PMCID: PMC9754019 DOI: 10.1080/07853890.2022.2146743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND The epidemiologic studies investigating the association of birthweight and genetic factors with gastrointestinal cancer remain scarce. The study aimed to prospectively assess the interactions and joint effects of birthweight and genetic risk levels on gastrointestinal cancer incidence in adulthood. METHODS A total of 254,997 participants were included in the UK Biobank study. We used multivariate restricted cubic splines and Cox regression models to estimate the hazard ratios (HRs) and 95% confidential intervals (CI) for the association between birthweight and gastrointestinal cancer risk, then constructed a polygenic risk score (PRS) to assess its interaction and joint effect with birthweight on the development of gastrointestinal cancer. RESULTS We documented 2512 incident cases during a median follow-up of 8.88 years. Compare with participants reporting a normal birthweight (2.5-4.5 kg), multivariable-adjusted HR of gastrointestinal cancer incidence for participants with high birthweight (≥4.5 kg) was 1.17 (95%CI: 1.01-1.36). Such association was remarkably observed in pancreatic cancer, with an HR of 1.82 (95%CI: 1.26-2.64). No statistically significant association was observed between low birth weight and gastrointestinal cancers. Participants with high birthweight and high PRS had the highest risk of gastrointestinal cancer (HR: 2.95, 95%CI: 2.19-3.96). CONCLUSION Our findings highlight that high birthweight is associated with a higher incidence of gastrointestinal cancer, especially for pancreatic cancer. Benefits would be obtained from birthweight control, particularly for individuals with a high genetic risk.KEY MESSAGESThe epidemiologic studies investigating the association of birthweight and genetic factors with gastrointestinal cancer remain scarce.This cohort study of 254,997 adults in the United Kingdom found an association of high birthweight with the incidence of gastrointestinal cancer, especially for pancreatic cancer, and also found that participants with high birthweight and high polygenic risk score had the highest risk of gastrointestinal cancer.Our data suggests a possible effect of in utero or early life exposures on adulthood gastrointestinal cancer, especially for those with a high genetic risk.
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Affiliation(s)
- Lu Long
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Heng He
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China
| | - Qian Shen
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hongxia Peng
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xiaorui Zhou
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Haoxue Wang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shanshan Zhang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shifan Qin
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zequn Lu
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Zhu
- School of Public Health, Wuhan University, Wuhan, China
| | - Jianbo Tian
- School of Public Health, Wuhan University, Wuhan, China
| | - Jiang Chang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoping Miao
- School of Public Health, Wuhan University, Wuhan, China
| | - Na Shen
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, HUST, Wuhan, China
- Na Shen Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, HUST, Wuhan, 430030, China
| | - Rong Zhong
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- CONTACT Rong Zhong Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
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Chen J, Guo S, Hu X, Wang R, Jia D, Li Q, Yin X, Liao X, Hu Z, Wang P, Ren C, Dong S, Chen C, Chen S, Xu J, Pei J. Whole-genome and genome-wide association studies improve key agricultural traits of safflower for industrial and medicinal use. HORTICULTURE RESEARCH 2023; 10:uhad197. [PMID: 38023481 PMCID: PMC10673658 DOI: 10.1093/hr/uhad197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 09/26/2023] [Indexed: 12/01/2023]
Abstract
Safflower (Carthamus tinctorius) is widely cultivated around the world for its seeds and flowers. The presence of linoleic acid (LA) in its seeds and hydroxysafflor yellow A (HSYA) in its flowers are the crucial traits that enable safflower to be used for industrial and medicinal purposes. Understanding the genetic control of these traits is essential for optimizing the quality of safflower and its breeding. To further this research, we present a chromosome-scale assembly of the genome of the safflower variety 'Chuanhonghua 1', which was achieved using an integrated strategy combining Illumina, Oxford Nanopore, and Hi-C sequencing. We obtained a 1.17-Gb assembly with a contig N50 of 1.08 Mb, and all assembled sequences were assigned to 12 pseudochromosomes. Safflower's evolution involved the core eudicot γ-triplication event and a whole-genome duplication event, which led to large-scale genomic rearrangements. Extensive genomic shuffling has occurred since the divergence of the ancestor of dicotyledons. We conducted metabolite and transcriptome profiles with time- and part-dependent changes and screened candidate genes that significantly contribute to seed lipid biosynthesis. We also analyzed key gene families that participate in LA and HSYA biosynthesis. Additionally, we re-sequenced 220 safflower lines and carried out a genome-wide association study using high-quality SNP data for eight agronomic traits. We identified SNPs related to important traits in safflower. Besides, the candidate gene HH_034464 (CtCGT1) was shown to be involved in the biosynthesis of HSYA. Overall, we provide a high-quality reference genome and elucidate the genetic basis of LA and HSYA biosynthesis in safflower. This vast amount of data will benefit further research for functional gene mining and breeding in safflower.
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Affiliation(s)
- Jiang Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, College of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Shuai Guo
- State Key Laboratory of Southwestern Chinese Medicine Resources, College of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Xueli Hu
- Industrial Crops Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Rui Wang
- State Key Laboratory of Southwestern Chinese Medicine Resources, College of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Donghai Jia
- Institute of Economic Crops, Xinjiang Academy of Agricultural Sciences, Urumchi 830091, China
| | - Qiang Li
- Institute of Economic Crops, Xinjiang Academy of Agricultural Sciences, Urumchi 830091, China
| | - Xianmei Yin
- State Key Laboratory of Southwestern Chinese Medicine Resources, College of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Xuejiao Liao
- State Key Laboratory of Southwestern Chinese Medicine Resources, College of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Zunhong Hu
- Industrial Crops Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Peiqi Wang
- Industrial Crops Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
| | - Chaoxiang Ren
- State Key Laboratory of Southwestern Chinese Medicine Resources, College of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Shuai Dong
- State Key Laboratory of Southwestern Chinese Medicine Resources, College of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Chao Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, College of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Shilin Chen
- State Key Laboratory of Southwestern Chinese Medicine Resources, College of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Jiang Xu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Jin Pei
- State Key Laboratory of Southwestern Chinese Medicine Resources, College of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
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22
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Tong X, Chen D, Hu J, Lin S, Ling Z, Ai H, Zhang Z, Huang L. Accurate haplotype construction and detection of selection signatures enabled by high quality pig genome sequences. Nat Commun 2023; 14:5126. [PMID: 37612277 PMCID: PMC10447580 DOI: 10.1038/s41467-023-40434-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 07/27/2023] [Indexed: 08/25/2023] Open
Abstract
High-quality whole-genome resequencing in large-scale pig populations with pedigree structure and multiple breeds would enable accurate construction of haplotype and robust selection-signature detection. Here, we sequence 740 pigs, combine with 149 of our previously published resequencing data, retrieve 207 resequencing datasets, and form a panel of worldwide distributed wild boars, aboriginal and highly selected pigs with pedigree structures, amounting to 1096 genomes from 43 breeds. Combining with their haplotype-informative reads and pedigree structure, we accurately construct a panel of 1874 haploid genomes with 41,964,356 genetic variants. We further demonstrate its valuable applications in GWAS by identifying five novel loci for intramuscular fat content, and in genomic selection by increasing the accuracy of estimated breeding value by 36.7%. In evolutionary selection, we detect MUC13 gene under a long-term balancing selection, as well as NPR3 gene under positive selection for pig stature. Our study provides abundant genomic variations for robust selection-signature detection and accurate haplotypes for deciphering complex traits in pigs.
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Affiliation(s)
- Xinkai Tong
- National Key Laboratory for Swine genetic improvement and production technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, NanChang, Jiangxi Province, PR China
- College of Life Sciences, Jiangxi Normal University, NanChang, Jiangxi Province, PR China
| | - Dong Chen
- National Key Laboratory for Swine genetic improvement and production technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, NanChang, Jiangxi Province, PR China
| | - Jianchao Hu
- National Key Laboratory for Swine genetic improvement and production technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, NanChang, Jiangxi Province, PR China
| | - Shiyao Lin
- National Key Laboratory for Swine genetic improvement and production technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, NanChang, Jiangxi Province, PR China
| | - Ziqi Ling
- National Key Laboratory for Swine genetic improvement and production technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, NanChang, Jiangxi Province, PR China
| | - Huashui Ai
- National Key Laboratory for Swine genetic improvement and production technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, NanChang, Jiangxi Province, PR China
| | - Zhiyan Zhang
- National Key Laboratory for Swine genetic improvement and production technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, NanChang, Jiangxi Province, PR China.
| | - Lusheng Huang
- National Key Laboratory for Swine genetic improvement and production technology, Ministry of Science and Technology of China, Jiangxi Agricultural University, NanChang, Jiangxi Province, PR China.
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23
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Wang N, Zhang W, Wang X, Zheng Z, Bai D, Li K, Zhao X, Xiang J, Liang Z, Qian Y, Wang W, Shi Y. Genome-Wide Association Study of Xian Rice Grain Shape and Weight in Different Environments. PLANTS (BASEL, SWITZERLAND) 2023; 12:2549. [PMID: 37447110 PMCID: PMC10347298 DOI: 10.3390/plants12132549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 06/29/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023]
Abstract
Drought is one of the key environmental factors affecting the growth and yield potential of rice. Grain shape, on the other hand, is an important factor determining the appearance, quality, and yield of rice grains. Here, we re-sequenced 275 Xian accessions and then conducted a genome-wide association study (GWAS) on six agronomic traits with the 404,411 single nucleotide polymorphisms (SNPs) derived by the best linear unbiased prediction (BLUP) for each trait. Under two years of drought stress (DS) and normal water (NW) treatments, a total of 16 QTLs associated with rice grain shape and grain weight were detected on chromosomes 1, 2, 3, 4, 5, 7, 8, 11, and 12. In addition, these QTLs were analyzed by haplotype analysis and functional annotation, and one clone (GSN1) and five new candidate genes were identified in the candidate interval. The findings provide important genetic information for the molecular improvement of grain shape and weight in rice.
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Affiliation(s)
- Nansheng Wang
- College of Agronomy, Anhui Agricultural University, Hefei 230000, China; (N.W.); (W.Z.); (X.W.); (Z.Z.); (D.B.); (K.L.); (X.Z.); (J.X.); (Z.L.); (Y.Q.)
| | - Wanyang Zhang
- College of Agronomy, Anhui Agricultural University, Hefei 230000, China; (N.W.); (W.Z.); (X.W.); (Z.Z.); (D.B.); (K.L.); (X.Z.); (J.X.); (Z.L.); (Y.Q.)
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Xinchen Wang
- College of Agronomy, Anhui Agricultural University, Hefei 230000, China; (N.W.); (W.Z.); (X.W.); (Z.Z.); (D.B.); (K.L.); (X.Z.); (J.X.); (Z.L.); (Y.Q.)
| | - Zhenzhen Zheng
- College of Agronomy, Anhui Agricultural University, Hefei 230000, China; (N.W.); (W.Z.); (X.W.); (Z.Z.); (D.B.); (K.L.); (X.Z.); (J.X.); (Z.L.); (Y.Q.)
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Di Bai
- College of Agronomy, Anhui Agricultural University, Hefei 230000, China; (N.W.); (W.Z.); (X.W.); (Z.Z.); (D.B.); (K.L.); (X.Z.); (J.X.); (Z.L.); (Y.Q.)
| | - Keyang Li
- College of Agronomy, Anhui Agricultural University, Hefei 230000, China; (N.W.); (W.Z.); (X.W.); (Z.Z.); (D.B.); (K.L.); (X.Z.); (J.X.); (Z.L.); (Y.Q.)
| | - Xueyu Zhao
- College of Agronomy, Anhui Agricultural University, Hefei 230000, China; (N.W.); (W.Z.); (X.W.); (Z.Z.); (D.B.); (K.L.); (X.Z.); (J.X.); (Z.L.); (Y.Q.)
| | - Jun Xiang
- College of Agronomy, Anhui Agricultural University, Hefei 230000, China; (N.W.); (W.Z.); (X.W.); (Z.Z.); (D.B.); (K.L.); (X.Z.); (J.X.); (Z.L.); (Y.Q.)
| | - Zhaojie Liang
- College of Agronomy, Anhui Agricultural University, Hefei 230000, China; (N.W.); (W.Z.); (X.W.); (Z.Z.); (D.B.); (K.L.); (X.Z.); (J.X.); (Z.L.); (Y.Q.)
| | - Yingzhi Qian
- College of Agronomy, Anhui Agricultural University, Hefei 230000, China; (N.W.); (W.Z.); (X.W.); (Z.Z.); (D.B.); (K.L.); (X.Z.); (J.X.); (Z.L.); (Y.Q.)
| | - Wensheng Wang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Yingyao Shi
- College of Agronomy, Anhui Agricultural University, Hefei 230000, China; (N.W.); (W.Z.); (X.W.); (Z.Z.); (D.B.); (K.L.); (X.Z.); (J.X.); (Z.L.); (Y.Q.)
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24
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Lee S, Yang HK, Lee HJ, Park DJ, Kong SH, Park SK. Cross-phenotype association analysis of gastric cancer: in-silico functional annotation based on the disease-gene network. Gastric Cancer 2023; 26:517-527. [PMID: 36995485 DOI: 10.1007/s10120-023-01380-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 03/02/2023] [Indexed: 03/31/2023]
Abstract
BACKGROUND A gene or variant has pleiotropic effects, and genetic variant identification across multiple phenotypes can provide a comprehensive understanding of biological pathways shared among different diseases or phenotypes. Discovery of genetic loci associated with multiple diseases can simultaneously support general interventions. Several meta-analyses have shown genetic associations with gastric cancer (GC); however, no study has identified associations with other phenotypes using this approach. METHODS Here, we applied disease network analysis and gene-based analysis (GBA) to examine genetic variants linked to GC and simultaneously associated with other phenotypes. We conducted a single-nucleotide polymorphism (SNP) level meta-analysis and GBA through a systematic genome-wide association study (GWAS) linked to GC, to integrate published results for the SNP variants and group them into major GC-associated genes. We then performed disease network and expression quantitative trait loci (eQTL) analyses to evaluate cross-phenotype associations and expression levels of GC-related genes. RESULTS Seven genes (MTX1, GBAP1, MUC1, TRIM46, THBS3, PSCA, and ABO) were associated with GC as well as blood urea nitrogen (BUN), glomerular filtration rate (GFR), and uric acid (UA). In addition, 17 SNPs regulated the expression of genes located on 1q22, 24 SNPs regulated the expression of PSCA on 8q24.3, and rs7849820 regulated the expression of ABO on 9q34.2. Furthermore, rs1057941 and rs2294008 had the highest posterior causal probabilities of being a causal candidate SNP in 1q22, and 8q24.3, respectively. CONCLUSIONS These findings identified seven GC-associated genes exhibiting a cross-association with GFR, BUN, and UA.
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Affiliation(s)
- Sangjun Lee
- Department of Preventive Medicine, Seoul National University College of Medicine, 103 Daehak-Ro, Jongro-Gu, Seoul, 03080, Korea
- Cancer Research Institute, Seoul National University, Seoul, Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, Korea
| | - Han-Kwang Yang
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Hyuk-Joon Lee
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Do Joong Park
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Seong-Ho Kong
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
| | - Sue K Park
- Department of Preventive Medicine, Seoul National University College of Medicine, 103 Daehak-Ro, Jongro-Gu, Seoul, 03080, Korea.
- Cancer Research Institute, Seoul National University, Seoul, Korea.
- Integrated Major in Innovative Medical Science, Seoul National University College of Medicine, Seoul, Korea.
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25
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Ortiz GG, Torres-Mendoza BMG, Ramírez-Jirano J, Marquez-Pedroza J, Hernández-Cruz JJ, Mireles-Ramirez MA, Torres-Sánchez ED. Genetic Basis of Inflammatory Demyelinating Diseases of the Central Nervous System: Multiple Sclerosis and Neuromyelitis Optica Spectrum. Genes (Basel) 2023; 14:1319. [PMID: 37510224 PMCID: PMC10379341 DOI: 10.3390/genes14071319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/15/2023] [Accepted: 06/20/2023] [Indexed: 07/30/2023] Open
Abstract
Demyelinating diseases alter myelin or the coating surrounding most nerve fibers in the central and peripheral nervous systems. The grouping of human central nervous system demyelinating disorders today includes multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSD) as distinct disease categories. Each disease is caused by a complex combination of genetic and environmental variables, many involving an autoimmune response. Even though these conditions are fundamentally similar, research into genetic factors, their unique clinical manifestations, and lesion pathology has helped with differential diagnosis and disease pathogenesis knowledge. This review aims to synthesize the genetic approaches that explain the differential susceptibility between these diseases, explore the overlapping clinical features, and pathological findings, discuss existing and emerging hypotheses on the etiology of demyelination, and assess recent pathogenicity studies and their implications for human demyelination. This review presents critical information from previous studies on the disease, which asks several questions to understand the gaps in research in this field.
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Affiliation(s)
- Genaro Gabriel Ortiz
- Department of Philosophical and Methodological Disciplines and Service of Molecular Biology in Medicine Hospital, Civil University Health Sciences Center, University of Guadalajara, Guadalajara 44340, Jalisco, Mexico
- Department of Neurology, High Specialty Medical Unit, Western National Medical Center of the Mexican Institute of Social Security, Guadalajara 44329, Jalisco, Mexico
| | - Blanca M G Torres-Mendoza
- Department of Philosophical and Methodological Disciplines and Service of Molecular Biology in Medicine Hospital, Civil University Health Sciences Center, University of Guadalajara, Guadalajara 44340, Jalisco, Mexico
- Neurosciences Division, Western Biomedical Research Center, Mexican Social Security Institute (Instituto Mexicano del Seguro Social, IMSS), Guadalajara 44340, Jalisco, Mexico
| | - Javier Ramírez-Jirano
- Neurosciences Division, Western Biomedical Research Center, Mexican Social Security Institute (Instituto Mexicano del Seguro Social, IMSS), Guadalajara 44340, Jalisco, Mexico
| | - Jazmin Marquez-Pedroza
- Neurosciences Division, Western Biomedical Research Center, Mexican Social Security Institute (Instituto Mexicano del Seguro Social, IMSS), Guadalajara 44340, Jalisco, Mexico
- Coordination of Academic Activities, Western Biomedical Research Center, Mexican Social Security Institute (Instituto Mexicano del Seguro Social, IMSS), Guadalajara 44340, Jalisco, Mexico
| | - José J Hernández-Cruz
- Department of Neurology, High Specialty Medical Unit, Western National Medical Center of the Mexican Institute of Social Security, Guadalajara 44329, Jalisco, Mexico
| | - Mario A Mireles-Ramirez
- Department of Neurology, High Specialty Medical Unit, Western National Medical Center of the Mexican Institute of Social Security, Guadalajara 44329, Jalisco, Mexico
| | - Erandis D Torres-Sánchez
- Department of Medical and Life Sciences, University Center of la Cienega, University of Guadalajara, Ocotlan 47820, Jalisco, Mexico
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26
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Wang L, Yeo S, Lee M, Endah S, Alhuda NA, Yue GH. Combination of GWAS and F ST-based approaches identified loci associated with economic traits in sugarcane. Mol Genet Genomics 2023:10.1007/s00438-023-02040-2. [PMID: 37289230 DOI: 10.1007/s00438-023-02040-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 05/28/2023] [Indexed: 06/09/2023]
Abstract
Sugarcane is a globally important plant for both sugar and biofuel production. Although conventional breeding has played an important role in increasing the productivity of sugarcane, it takes a long time to achieve breeding goals such as high yield and resistant to diseases. Molecular breeding, including marker-assisted breeding and genomic selection, can accelerate genetic improvement by selecting elites at the seedling stage with DNA markers. However, only a few DNA markers associated with important traits were identified in sugarcane. The purpose of this study was to identify DNA markers associated with sugar content, stalk diameter, and sugarcane top borer resistance. The sugarcane samples with trait records were genotyped using the restriction site-associated DNA sequencing (RADseq) technology. Using FST analysis and genome-wide association study (GWAS), a total of 9, 23 and 9 DNA variants (single nucleotide polymorphisms (SNPs)/insertions and deletions (indels)) were associated with sugar content, stalk diameter, and sugarcane top borer resistance, respectively. The identified genetic variants were on different chromosomes, suggesting that these traits are complex and determined by multiple genetic factors. These DNA markers identified by both approaches have the potential to be used in selecting elite clones at the seeding stage in our sugarcane breeding program to accelerate genetic improvement. Certainly, it is essential to verify the reliability of the identified DNA markers associated with traits before they are used in molecular breeding in other populations.
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Affiliation(s)
- Le Wang
- Temasek Life Sciences Laboratory, National University of Singapore, 1 Research Link, Singapore, 117604, Singapore
| | - Shadame Yeo
- Temasek Life Sciences Laboratory, National University of Singapore, 1 Research Link, Singapore, 117604, Singapore
| | - May Lee
- Temasek Life Sciences Laboratory, National University of Singapore, 1 Research Link, Singapore, 117604, Singapore
| | - S Endah
- Research and Development, PT Gunung Madu Plantations, KM 90 Terusan Nunyai, Central Lampung, Lampung, 34167, Indonesia
| | - N A Alhuda
- Research and Development, PT Gunung Madu Plantations, KM 90 Terusan Nunyai, Central Lampung, Lampung, 34167, Indonesia
| | - G H Yue
- Temasek Life Sciences Laboratory, National University of Singapore, 1 Research Link, Singapore, 117604, Singapore.
- Department of Biological Sciences, National University of Singapore, 14 Science Drive, Singapore, 117543, Singapore.
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27
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Byington N, Grimsrud G, Mooney MA, Cordova M, Doyle O, Hermosillo RJM, Earl E, Houghton A, Conan G, Hendrickson TJ, Ragothaman A, Carrasco CM, Rueter A, Perrone A, Moore LA, Graham A, Nigg JT, Thompson WK, Nelson SM, Feczko E, Fair DA, Miranda-Dominguez O. Polyneuro risk scores capture widely distributed connectivity patterns of cognition. Dev Cogn Neurosci 2023; 60:101231. [PMID: 36934605 PMCID: PMC10031023 DOI: 10.1016/j.dcn.2023.101231] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 03/06/2023] [Accepted: 03/13/2023] [Indexed: 03/17/2023] Open
Abstract
Resting-state functional connectivity (RSFC) is a powerful tool for characterizing brain changes, but it has yet to reliably predict higher-order cognition. This may be attributed to small effect sizes of such brain-behavior relationships, which can lead to underpowered, variable results when utilizing typical sample sizes (N∼25). Inspired by techniques in genomics, we implement the polyneuro risk score (PNRS) framework - the application of multivariate techniques to RSFC data and validation in an independent sample. Utilizing the Adolescent Brain Cognitive Development® cohort split into two datasets, we explore the framework's ability to reliably capture brain-behavior relationships across 3 cognitive scores - general ability, executive function, learning & memory. The weight and significance of each connection is assessed in the first dataset, and a PNRS is calculated for each participant in the second. Results support the PNRS framework as a suitable methodology to inspect the distribution of connections contributing towards behavior, with explained variance ranging from 1.0 % to 21.4 %. For the outcomes assessed, the framework reveals globally distributed, rather than localized, patterns of predictive connections. Larger samples are likely necessary to systematically identify the specific connections contributing towards complex outcomes. The PNRS framework could be applied translationally to identify neurologically distinct subtypes of neurodevelopmental disorders.
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Affiliation(s)
- Nora Byington
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States.
| | - Gracie Grimsrud
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States
| | - Michael A Mooney
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, United States; Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97239, United States
| | - Michaela Cordova
- Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego, San Diego, CA 92120, United States
| | - Olivia Doyle
- Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, United States
| | - Robert J M Hermosillo
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States
| | - Eric Earl
- Data Science and Sharing Team, National Institute of Mental Health, Bethesda, MD 20892, United States
| | - Audrey Houghton
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States
| | - Gregory Conan
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States
| | - Timothy J Hendrickson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States
| | | | - Cristian Morales Carrasco
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States
| | - Amanda Rueter
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States
| | - Anders Perrone
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States
| | - Lucille A Moore
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States
| | - Alice Graham
- Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, United States
| | - Joel T Nigg
- Department of Psychiatry, Oregon Health & Science University, Portland, OR 97239, United States; Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR 97239, United States
| | - Wesley K Thompson
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK 74136, United States
| | - Steven M Nelson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States; Department of Pediatrics, University of Minnesota, Minneapolis, MN 55414, United States
| | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States; Department of Pediatrics, University of Minnesota, Minneapolis, MN 55414, United States
| | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States; Department of Pediatrics, University of Minnesota, Minneapolis, MN 55414, United States; Institute of Child Development, University of Minnesota, Minneapolis, MN 55414, United States
| | - Oscar Miranda-Dominguez
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55414, United States; Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN 55414, United States; Department of Pediatrics, University of Minnesota, Minneapolis, MN 55414, United States
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Pearson SM, Griffiths AG, Maclean P, Larking AC, Hong SW, Jauregui R, Miller P, McKenzie CM, Lockhart PJ, Tate JA, Ford JL, Faville MJ. Outlier analyses and genome-wide association study identify glgC and ERD6-like 4 as candidate genes for foliar water-soluble carbohydrate accumulation in Trifolium repens. FRONTIERS IN PLANT SCIENCE 2023; 13:1095359. [PMID: 36699852 PMCID: PMC9868827 DOI: 10.3389/fpls.2022.1095359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 12/09/2022] [Indexed: 06/17/2023]
Abstract
Increasing water-soluble carbohydrate (WSC) content in white clover is important for improving nutritional quality and reducing environmental impacts from pastoral agriculture. Elucidation of genes responsible for foliar WSC variation would enhance genetic improvement by enabling molecular breeding approaches. The aim of the present study was to identify single nucleotide polymorphisms (SNPs) associated with variation in foliar WSC in white clover. A set of 935 white clover individuals, randomly sampled from five breeding pools selectively bred for divergent (low or high) WSC content, were assessed with 14,743 genotyping-by-sequencing SNPs, using three outlier detection methods: PCAdapt, BayeScan and KGD-FST. These analyses identified 33 SNPs as discriminating between high and low WSC populations and putatively under selection. One SNP was located in the intron of ERD6-like 4, a gene coding for a sugar transporter located on the vacuole membrane. A genome-wide association study using a subset of 605 white clover individuals and 5,757 SNPs, identified a further 12 SNPs, one of which was associated with a starch biosynthesis gene, glucose-1-phosphate adenylyltransferase, glgC. Our results provide insight into genomic regions underlying WSC accumulation in white clover, identify candidate genomic regions for further functional validation studies, and reveal valuable information for marker-assisted or genomic selection in white clover.
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Affiliation(s)
- Sofie M. Pearson
- School of Natural Sciences, Massey University, Palmerston North, New Zealand
- Resilient Agriculture, AgResearch Grasslands, Palmerston North, New Zealand
| | | | - Paul Maclean
- Resilient Agriculture, AgResearch Grasslands, Palmerston North, New Zealand
| | - Anna C. Larking
- Resilient Agriculture, AgResearch Grasslands, Palmerston North, New Zealand
| | - S. Won Hong
- Resilient Agriculture, AgResearch Grasslands, Palmerston North, New Zealand
| | - Ruy Jauregui
- Resilient Agriculture, AgResearch Grasslands, Palmerston North, New Zealand
| | - Poppy Miller
- Resilient Agriculture, AgResearch Grasslands, Palmerston North, New Zealand
| | | | - Peter J. Lockhart
- School of Natural Sciences, Massey University, Palmerston North, New Zealand
| | - Jennifer A. Tate
- School of Natural Sciences, Massey University, Palmerston North, New Zealand
| | - John L. Ford
- Grasslands, PGG Wrightson Seeds Limited, Palmerston North, New Zealand
| | - Marty J. Faville
- Resilient Agriculture, AgResearch Grasslands, Palmerston North, New Zealand
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Essential Role of Multi-Omics Approaches in the Study of Retinal Vascular Diseases. Cells 2022; 12:cells12010103. [PMID: 36611897 PMCID: PMC9818611 DOI: 10.3390/cells12010103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 12/19/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022] Open
Abstract
Retinal vascular disease is a highly prevalent vision-threatening ocular disease in the global population; however, its exact mechanism remains unclear. The expansion of omics technologies has revolutionized a new medical research methodology that combines multiple omics data derived from the same patients to generate multi-dimensional and multi-evidence-supported holistic inferences, providing unprecedented opportunities to elucidate the information flow of complex multi-factorial diseases. In this review, we summarize the applications of multi-omics technology to further elucidate the pathogenesis and complex molecular mechanisms underlying retinal vascular diseases. Moreover, we proposed multi-omics-based biomarker and therapeutic strategy discovery methodologies to optimize clinical and basic medicinal research approaches to retinal vascular diseases. Finally, the opportunities, current challenges, and future prospects of multi-omics analyses in retinal vascular disease studies are discussed in detail.
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30
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Gompert Z, Flaxman SM, Feder JL, Chevin LM, Nosil P. Laplace's demon in biology: Models of evolutionary prediction. Evolution 2022; 76:2794-2810. [PMID: 36193839 DOI: 10.1111/evo.14628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 08/23/2022] [Accepted: 08/30/2022] [Indexed: 01/22/2023]
Abstract
Our ability to predict natural phenomena can be limited by incomplete information. This issue is exemplified by "Laplace's demon," an imaginary creature proposed in the 18th century, who knew everything about everything, and thus could predict the full nature of the universe forward or backward in time. Quantum mechanics, among other things, has cast doubt on the possibility of Laplace's demon in the full sense, but the idea still serves as a useful metaphor for thinking about the extent to which prediction is limited by incomplete information on deterministic processes versus random factors. Here, we use simple analytical models and computer simulations to illustrate how data limits can be captured in a Bayesian framework, and how they influence our ability to predict evolution. We show how uncertainty in measurements of natural selection, or low predictability of external environmental factors affecting selection, can greatly reduce predictive power, often swamping the influence of intrinsic randomness caused by genetic drift. Thus, more accurate knowledge concerning the causes and action of natural selection is key to improving prediction. Fortunately, our analyses and simulations show quantitatively that reasonable improvements in data quantity and quality can meaningfully increase predictability.
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Affiliation(s)
| | | | - Jeffrey L Feder
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, 46556, USA
| | - Luis-Miguel Chevin
- CEFE, Univ Montpellier, Montpellier, France.,CNRS, EPHE, IRD, Univ Paul Valéry Montpellier 3, Montpellier, France
| | - Patrik Nosil
- CEFE, Univ Montpellier, Montpellier, France.,CNRS, EPHE, IRD, Univ Paul Valéry Montpellier 3, Montpellier, France
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31
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Tappu R, Haas J, Lehmann DH, Sedaghat-Hamedani F, Kayvanpour E, Keller A, Katus HA, Frey N, Meder B. Multi-omics assessment of dilated cardiomyopathy using non-negative matrix factorization. PLoS One 2022; 17:e0272093. [PMID: 35980883 PMCID: PMC9387871 DOI: 10.1371/journal.pone.0272093] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 07/11/2022] [Indexed: 11/19/2022] Open
Abstract
Dilated cardiomyopathy (DCM), a myocardial disease, is heterogeneous and often results in heart failure and sudden cardiac death. Unavailability of cardiac tissue has hindered the comprehensive exploration of gene regulatory networks and nodal players in DCM. In this study, we carried out integrated analysis of transcriptome and methylome data using non-negative matrix factorization from a cohort of DCM patients to uncover underlying latent factors and covarying features between whole-transcriptome and epigenome omics datasets from tissue biopsies of living patients. DNA methylation data from Infinium HM450 and mRNA Illumina sequencing of n = 33 DCM and n = 24 control probands were filtered, analyzed and used as input for matrix factorization using R NMF package. Mann-Whitney U test showed 4 out of 5 latent factors are significantly different between DCM and control probands (P<0.05). Characterization of top 10% features driving each latent factor showed a significant enrichment of biological processes known to be involved in DCM pathogenesis, including immune response (P = 3.97E-21), nucleic acid binding (P = 1.42E-18), extracellular matrix (P = 9.23E-14) and myofibrillar structure (P = 8.46E-12). Correlation network analysis revealed interaction of important sarcomeric genes like Nebulin, Tropomyosin alpha-3 and ERC-protein 2 with CpG methylation of ATPase Phospholipid Transporting 11A0, Solute Carrier Family 12 Member 7 and Leucine Rich Repeat Containing 14B, all with significant P values associated with correlation coefficients >0.7. Using matrix factorization, multi-omics data derived from human tissue samples can be integrated and novel interactions can be identified. Hypothesis generating nature of such analysis could help to better understand the pathophysiology of complex traits such as DCM.
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Affiliation(s)
- Rewati Tappu
- Institute for Cardiomyopathies Heidelberg (ICH), Heart Center Heidelberg, University of Heidelberg, Heidelberg, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Heidelberg/Mannheim, Mannheim, Germany
| | - Jan Haas
- Institute for Cardiomyopathies Heidelberg (ICH), Heart Center Heidelberg, University of Heidelberg, Heidelberg, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Heidelberg/Mannheim, Mannheim, Germany
| | - David H. Lehmann
- Institute for Cardiomyopathies Heidelberg (ICH), Heart Center Heidelberg, University of Heidelberg, Heidelberg, Germany
| | - Farbod Sedaghat-Hamedani
- Institute for Cardiomyopathies Heidelberg (ICH), Heart Center Heidelberg, University of Heidelberg, Heidelberg, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Heidelberg/Mannheim, Mannheim, Germany
| | - Elham Kayvanpour
- Institute for Cardiomyopathies Heidelberg (ICH), Heart Center Heidelberg, University of Heidelberg, Heidelberg, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Heidelberg/Mannheim, Mannheim, Germany
| | - Andreas Keller
- Department of Clinical Bioinformatics, Medical Faculty, Saarland University, Saarbrücken, Germany
| | - Hugo A. Katus
- Institute for Cardiomyopathies Heidelberg (ICH), Heart Center Heidelberg, University of Heidelberg, Heidelberg, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Heidelberg/Mannheim, Mannheim, Germany
| | - Norbert Frey
- Institute for Cardiomyopathies Heidelberg (ICH), Heart Center Heidelberg, University of Heidelberg, Heidelberg, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Heidelberg/Mannheim, Mannheim, Germany
| | - Benjamin Meder
- Institute for Cardiomyopathies Heidelberg (ICH), Heart Center Heidelberg, University of Heidelberg, Heidelberg, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Heidelberg/Mannheim, Mannheim, Germany
- Department of Genetics, Stanford University School of Medicine, Palo Alto, California, United States of America
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Lee S, Yang HK, Lee HJ, Park DJ, Kong SH, Park SK. Systematic review of gastric cancer-associated genetic variants, gene-based meta-analysis, and gene-level functional analysis to identify candidate genes for drug development. Front Genet 2022; 13:928783. [PMID: 36081994 PMCID: PMC9446437 DOI: 10.3389/fgene.2022.928783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 07/25/2022] [Indexed: 12/05/2022] Open
Abstract
Objective: Despite being a powerful tool to identify novel variants, genome-wide association studies (GWAS) are not sufficient to explain the biological function of variants. In this study, we aimed to elucidate at the gene level the biological mechanisms involved in gastric cancer (GC) development and to identify candidate drug target genes. Materials and methods: We conducted a systematic review for GWAS on GC following the PRISMA guidelines. Single nucleotide polymorphism (SNP)-level meta-analysis and gene-based analysis (GBA) were performed to identify SNPs and genes significantly associated with GC. Expression quantitative trait loci (eQTL), disease network, pathway enrichment, gene ontology, gene-drug, and chemical interaction analyses were conducted to elucidate the function of the genes identified by GBA. Results: A review of GWAS on GC identified 226 SNPs located in 91 genes. In the comprehensive GBA, 44 genes associated with GC were identified, among which 12 genes (THBS3, GBAP1, KRTCAP2, TRIM46, HCN3, MUC1, DAP3, EFNA1, MTX1, PRKAA1, PSCA, and ABO) were eQTL. Using disease network and pathway analyses, we identified that PRKAA, THBS3, and EFNA1 were significantly associated with the PI3K-Alt-mTOR-signaling pathway, which is involved in various oncogenic processes, and that MUC1 acts as a regulator in both the PI3K-Alt-mTOR and P53 signaling pathways. Furthermore, RPKAA1 had the highest number of interactions with drugs and chemicals. Conclusion: Our study suggests that PRKAA1, a gene in the PI3K-Alt-mTOR-signaling pathway, could be a potential target gene for drug development associated with GC in the future. Systematic Review Registration: website, identifier registration number.
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Affiliation(s)
- Sangjun Lee
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, South Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, South Korea
- Department of Biomedical Sciences, Seoul National University Graduate School, Seoul, South Korea
| | - Han-Kwang Yang
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, South Korea
| | - Hyuk-Joon Lee
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, South Korea
| | - Do Joong Park
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, South Korea
| | - Seong-Ho Kong
- Department of Surgery and Cancer Research Institute, Seoul National University College of Medicine, Seoul, South Korea
| | - Sue K. Park
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul, South Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, South Korea
- Integrated Major in Innovative Medical Science, Seoul National University College of Medicine, Seoul, South Korea
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33
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Cencer MM, Suslick BA, Moore JS. From skeptic to believer: The power of models. Tetrahedron 2022. [DOI: 10.1016/j.tet.2022.132984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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34
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Wang X, You J, Liu A, Qi X, Li D, Zhao Y, Zhang Y, Zhang L, Zhang X, Li P. Variation in Melatonin Contents and Genetic Dissection of Melatonin Biosynthesis in Sesame. PLANTS (BASEL, SWITZERLAND) 2022; 11:2005. [PMID: 35956483 PMCID: PMC9370803 DOI: 10.3390/plants11152005] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 07/22/2022] [Accepted: 07/24/2022] [Indexed: 06/15/2023]
Abstract
In recent years, people have become increasingly interested in bioactive molecules in plants that are beneficial to human health, and melatonin (N-acetyl-5-methoxytryptamine) has attracted research attention due to its excellent performance. In this study, the content of melatonin in oilseeds was investigated. From the results, it was found that sesame is an important natural food source of melatonin intake. Furthermore, the variation in melatonin content was explored in a natural sesame population, and its contents varied from 0.04 to 298.62 ng g-1. Through a genome-wide association study (GWAS), a candidate gene SiWRKY67 was screened that regulates melatonin content in sesame. The sesame hairy root transformation system was developed and used to verify this gene, and it was found that the overexpression of SiWRKY67 could positively promote the melatonin content in the hairy roots. Our results provide not only a foundation for understanding the genetic structure of melatonin content in sesame seeds but also a reference for the marker-assisted breeding of sesame varieties with high melatonin content.
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Affiliation(s)
- Xiao Wang
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (X.W.); (A.L.); (X.Q.); (D.L.); (Y.Z.); (Y.Z.)
| | - Jun You
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (X.W.); (A.L.); (X.Q.); (D.L.); (Y.Z.); (Y.Z.)
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Wuhan 430062, China
| | - Aili Liu
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (X.W.); (A.L.); (X.Q.); (D.L.); (Y.Z.); (Y.Z.)
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Wuhan 430062, China
| | - Xin Qi
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (X.W.); (A.L.); (X.Q.); (D.L.); (Y.Z.); (Y.Z.)
| | - Donghua Li
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (X.W.); (A.L.); (X.Q.); (D.L.); (Y.Z.); (Y.Z.)
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Wuhan 430062, China
| | - Ya Zhao
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (X.W.); (A.L.); (X.Q.); (D.L.); (Y.Z.); (Y.Z.)
| | - Yanxin Zhang
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (X.W.); (A.L.); (X.Q.); (D.L.); (Y.Z.); (Y.Z.)
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Wuhan 430062, China
| | - Liangxiao Zhang
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (X.W.); (A.L.); (X.Q.); (D.L.); (Y.Z.); (Y.Z.)
- Quality Inspection and Test Center for Oilseed Products, Ministry of Agriculture and Rural Affairs, Wuhan 430062, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Xiurong Zhang
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (X.W.); (A.L.); (X.Q.); (D.L.); (Y.Z.); (Y.Z.)
- Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Wuhan 430062, China
| | - Peiwu Li
- Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (X.W.); (A.L.); (X.Q.); (D.L.); (Y.Z.); (Y.Z.)
- Quality Inspection and Test Center for Oilseed Products, Ministry of Agriculture and Rural Affairs, Wuhan 430062, China
- Hubei Hongshan Laboratory, Wuhan 430070, China
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35
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Tang X, Xie L, Liu S, Chen Z, Rao L, Chen L, Li L, Xiao S, Zhang Z, Huang L. Extensive evaluation of prediction performance for 15 pork quality traits using large scale VIS/NIRS data. Meat Sci 2022; 192:108902. [DOI: 10.1016/j.meatsci.2022.108902] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 06/01/2022] [Accepted: 06/30/2022] [Indexed: 01/10/2023]
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36
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Bai Q, Wang M, Xia C, See DR, Chen X. Identification of Secreted Protein Gene-Based SNP Markers Associated with Virulence Phenotypes of Puccinia striiformis f. sp. tritici, the Wheat Stripe Rust Pathogen. Int J Mol Sci 2022; 23:ijms23084114. [PMID: 35456934 PMCID: PMC9033109 DOI: 10.3390/ijms23084114] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 04/07/2022] [Accepted: 04/07/2022] [Indexed: 01/14/2023] Open
Abstract
Stripe rust caused by Puccinia striiformis f. sp. tritici (Pst) is a destructive disease that occurs throughout the major wheat-growing regions of the world. This pathogen is highly variable due to the capacity of virulent races to undergo rapid changes in order to circumvent resistance in wheat cultivars and genotypes and to adapt to different environments. Intensive efforts have been made to study the genetics of wheat resistance to this disease; however, no known avirulence genes have been molecularly identified in Pst so far. To identify molecular markers for avirulence genes, a Pst panel of 157 selected isolates representing 126 races with diverse virulence spectra was genotyped using 209 secreted protein gene-based single nucleotide polymorphism (SP-SNP) markers via association analysis. Nineteen SP-SNP markers were identified for significant associations with 12 avirulence genes: AvYr1, AvYr6, AvYr7, AvYr9, AvYr10, AvYr24, AvYr27, AvYr32, AvYr43, AvYr44, AvYrSP, and AvYr76. Some SP-SNPs were associated with two or more avirulence genes. These results further confirmed that association analysis in combination with SP-SNP markers is a powerful tool for identifying markers for avirulence genes. This study provides genomic resources for further studies on the cloning of avirulence genes, understanding the mechanisms of host–pathogen interactions, and developing functional markers for tagging specific virulence genes and race groups.
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Affiliation(s)
- Qing Bai
- Department of Plant Pathology, Washington State University, Pullman, WA 99164-6430, USA; (Q.B.); (M.W.); (C.X.); (D.R.S.)
| | - Meinan Wang
- Department of Plant Pathology, Washington State University, Pullman, WA 99164-6430, USA; (Q.B.); (M.W.); (C.X.); (D.R.S.)
| | - Chongjing Xia
- Department of Plant Pathology, Washington State University, Pullman, WA 99164-6430, USA; (Q.B.); (M.W.); (C.X.); (D.R.S.)
- Wheat Research Institute, School of Life Sciences and Engineering, Southwest University of Science and Technology, Mianyang 621010, China
| | - Deven R. See
- Department of Plant Pathology, Washington State University, Pullman, WA 99164-6430, USA; (Q.B.); (M.W.); (C.X.); (D.R.S.)
- U.S. Department of Agriculture, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164-6430, USA
| | - Xianming Chen
- Department of Plant Pathology, Washington State University, Pullman, WA 99164-6430, USA; (Q.B.); (M.W.); (C.X.); (D.R.S.)
- U.S. Department of Agriculture, Agricultural Research Service, Wheat Health, Genetics, and Quality Research Unit, Pullman, WA 99164-6430, USA
- Correspondence: ; Tel.: +1-509-335-8086
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Piras D, Lepori N, Cabiddu G, Pani A. How Genetics Can Improve Clinical Practice in Chronic Kidney Disease: From Bench to Bedside. J Pers Med 2022; 12:jpm12020193. [PMID: 35207681 PMCID: PMC8875178 DOI: 10.3390/jpm12020193] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 01/17/2022] [Accepted: 01/24/2022] [Indexed: 01/27/2023] Open
Abstract
Chronic kidney disease (CKD) is considered a major global health problem with high socio-economic costs: the risk of CKD in individuals with an affected first degree relative has been found to be three times higher than in the general population. Genetic factors are known to be involved in CKD pathogenesis, both due to the possible presence of monogenic pathologies as causes of CKD, and to the role of numerous gene variants in determining susceptibility to the development of CKD. The genetic study of CKD patients can represent a useful tool in the hands of the clinician; not only in the diagnostic and prognostic field, but potentially also in guiding therapeutic choices and in designing clinical trials. In this review we discuss the various aspects of the role of genetic analysis on clinical management of patients with CKD with a focus on clinical applications. Several topics are discussed in an effort to provide useful information for daily clinical practice: definition of susceptibility to the development of CKD, identification of unrecognized monogenic diseases, reclassification of the etiological diagnosis, role of pharmacogenetics.
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Affiliation(s)
- Doloretta Piras
- Struttura Complessa di Nefrologia, Dialisi e Trapianto, ARNAS Brotzu, 09134 Cagliari, Italy; (N.L.); (G.C.); (A.P.)
- Correspondence:
| | - Nicola Lepori
- Struttura Complessa di Nefrologia, Dialisi e Trapianto, ARNAS Brotzu, 09134 Cagliari, Italy; (N.L.); (G.C.); (A.P.)
| | - Gianfranca Cabiddu
- Struttura Complessa di Nefrologia, Dialisi e Trapianto, ARNAS Brotzu, 09134 Cagliari, Italy; (N.L.); (G.C.); (A.P.)
- Dipartimento di Scienze Mediche e Sanità Pubblica, Università degli Studi di Cagliari, 09134 Cagliari, Italy
| | - Antonello Pani
- Struttura Complessa di Nefrologia, Dialisi e Trapianto, ARNAS Brotzu, 09134 Cagliari, Italy; (N.L.); (G.C.); (A.P.)
- Dipartimento di Scienze Mediche e Sanità Pubblica, Università degli Studi di Cagliari, 09134 Cagliari, Italy
- Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerce (CNR), 09042 Monserrato, Italy
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Vahedi SM, Salek Ardestani S, Karimi K, Banabazi MH. Weighted single-step GWAS for body mass index and scans for recent signatures of selection in Yorkshire pigs. J Hered 2022; 113:325-335. [DOI: 10.1093/jhered/esac004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 01/24/2022] [Indexed: 11/14/2022] Open
Abstract
Abstract
Controlling extra fat deposition is economically favorable in modern swine industry. Understanding the genetic architecture of fat deposition traits such as body mass index (BMI) can help in improving genomic selection for such traits. We utilized a weighted single-step genome-wide association study (WssGWAS) to detect genetic regions and candidate genes associated with BMI in a Yorkshire pig population. Three extended haplotype homozygosity (EHH)-related statistics were also incorporated within a de-correlated composite of multiple signals (DCMS) framework to detect recent selection signatures signals. Overall, the full pedigree consisted of 7,016 pigs, of which 5,561 had BMI records and 598 pigs were genotyped with an 80 K single nucleotide polymorphism (SNP) array. Results showed that the most significant windows (top 15) explained 9.35% of BMI genetic variance. Several genes were detected in regions previously associated with pig fat deposition traits and treated as potential candidate genes for BMI in Yorkshire pigs: FTMT, SRFBP1, KHDRBS3, FOXG1, SOD3, LRRC32, TSKU, ACER3, B3GNT6, CCDC201, ADCY1, RAMP3, TBRG4, CCM2. Signature of selection analysis revealed multiple candidate genes previously associated with various economic traits. However, BMI genetic variance explained by regions under selection pressure was minimal (1.31%). In conclusion, candidate genes associated with Yorkshire pigs’ BMI trait were identified using WssGWAS. Gene enrichment analysis indicated that the identified candidate genes were enriched in the insulin secretion pathway. We anticipate that these results further advance our understanding of the genetic architecture of BMI in Yorkshire pigs and provide information for genomic selection for fat deposition in this breed.
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Affiliation(s)
- Seyed Milad Vahedi
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, Canada
| | | | - Karim Karimi
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, NS, Canada
| | - Mohammad Hossein Banabazi
- Department of Biotechnology, Animal Science Research Institute of Iran, Agricultural Research, Education & Extension Organization, Karaj, Iran
- Department of animal breeding and genetics (HGEN), Centre for Veterinary Medicine and Animal Science (VHC), Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden
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Sotnikova EA, Kiseleva AV, Meshkov AN, Ershova AI, Ivanova AA, Kolchina MA, Kutsenko VA, Skripnikova IA, Drapkina OM. Biobank data for studying the genetic architecture of osteoporosis and developing genetic risk scores. КАРДИОВАСКУЛЯРНАЯ ТЕРАПИЯ И ПРОФИЛАКТИКА 2022. [DOI: 10.15829/1728-8800-2021-3045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Osteoporosis is a chronic systemic disease of the skeleton, characterized by a decrease in bone mass and an impairment of bone microarchitecture, which can lead to a decrease in bone strength and an increase in the risk of minor trauma fractures. Osteoporosis is diagnosed on the basis of bone mineral density (BMD). BMD is characterized by high heritability that ranges according to various sources from 50 to 85%. As in the case of other complex traits, the most common approach to searching for genetic variants that affect BMD is a genome-wide association study. The lower effect size or frequency of a variant is, the larger the sample size is required to achieve statistically significant data on associations. Therefore, the studies involving hundreds of thousands of participants based on biobank data can identify the largest number of variants associated with BMD. In addition, biobank data are used in the development of genetic risk scores for osteoporosis that can be used both in combination with existing prognosis algorithms and independently of them. The aim of this review was to present the most significant studies of osteoporosis genetics, including those based on biobank data and genome-wide association studies, as well as studies on the genetic risk scores and the contribution of rare variants.
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Affiliation(s)
- E. A. Sotnikova
- National Research Center for Therapy and Preventive Medicine
| | - A. V. Kiseleva
- National Research Center for Therapy and Preventive Medicine
| | - A. N. Meshkov
- National Medical Research Center for Therapy and Preventive Medicine; Russian National Research Medical University
| | - A. I. Ershova
- National Research Center for Therapy and Preventive Medicine
| | - A. A. Ivanova
- National Research Center for Therapy and Preventive Medicine
| | - M. A. Kolchina
- National Research Center for Therapy and Preventive Medicine
| | - V. A. Kutsenko
- National Medical Research Center for Therapy and Preventive Medicine; Lomonosov Moscow State University
| | - I. A. Skripnikova
- National Medical Research Center for Therapy and Preventive Medicine
| | - O. M. Drapkina
- National Medical Research Center for Therapy and Preventive Medicine
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Cairns J, Kalari KR, Ingle JN, Shepherd LE, Ellis MJ, Goss PE, Barman P, Carlson EE, Goodnature B, Goetz MP, Weinshilboum RM, Gao H, Wang L. Interaction Between SNP Genotype and Efficacy of Anastrozole and Exemestane in Early-Stage Breast Cancer. Clin Pharmacol Ther 2021; 110:1038-1049. [PMID: 34048027 PMCID: PMC8449801 DOI: 10.1002/cpt.2311] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 05/08/2021] [Indexed: 12/24/2022]
Abstract
Aromatase inhibitors (AIs) are the treatment of choice for hormone receptor-positive early breast cancer in postmenopausal women. None of the third-generation AIs are superior to the others in terms of efficacy. We attempted to identify genetic factors that could differentiate between the effectiveness of adjuvant anastrozole and exemestane by examining single-nucleotide polymorphism (SNP)-treatment interaction in 4,465 patients. A group of SNPs were found to be differentially associated between anastrozole and exemestane regarding outcomes. However, they showed no association with outcome in the combined analysis. We followed up common SNPs near LY75 and GPR160 that could differentiate anastrozole from exemestane efficacy. LY75 and GPR160 participate in epithelial-to-mesenchymal transition and growth pathways, in both cases with SNP-dependent variation in regulation. Collectively, these studies identified SNPs that differentiate the efficacy of anastrozole and exemestane and they suggest additional genetic biomarkers for possible use in selecting an AI for a given patient.
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Affiliation(s)
- Junmei Cairns
- Division of Clinical PharmacologyDepartment of Molecular Pharmacology and Experimental TherapeuticsMayo ClinicRochesterMinnesotaUSA
| | - Krishna R. Kalari
- Division of Biomedical Statistics and InformaticsDepartment of Health Sciences ResearchMayo ClinicRochesterMinnesotaUSA
| | - James N. Ingle
- Division of Medical OncologyDepartment of OncologyMayo ClinicRochesterMinnesotaUSA
| | | | - Matthew J. Ellis
- Department of MedicineBaylor University College of MedicineHoustonTexasUSA
| | - Paul E. Goss
- Massachusetts General Hospital Cancer CenterHarvard UniversityBostonMassachusettsUSA
| | - Poulami Barman
- Division of Biomedical Statistics and InformaticsDepartment of Health Sciences ResearchMayo ClinicRochesterMinnesotaUSA
| | - Erin E. Carlson
- Division of Biomedical Statistics and InformaticsDepartment of Health Sciences ResearchMayo ClinicRochesterMinnesotaUSA
| | - Barbara Goodnature
- Patient AdvocateMayo Clinic Breast Cancer Specialized Program of Research ExcellenceRochesterMinnesotaUSA
| | - Matthew P. Goetz
- Division of Medical OncologyDepartment of OncologyMayo ClinicRochesterMinnesotaUSA
| | - Richard M. Weinshilboum
- Division of Clinical PharmacologyDepartment of Molecular Pharmacology and Experimental TherapeuticsMayo ClinicRochesterMinnesotaUSA
| | - Huanyao Gao
- Division of Clinical PharmacologyDepartment of Molecular Pharmacology and Experimental TherapeuticsMayo ClinicRochesterMinnesotaUSA
| | - Liewei Wang
- Division of Clinical PharmacologyDepartment of Molecular Pharmacology and Experimental TherapeuticsMayo ClinicRochesterMinnesotaUSA
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41
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Parras D, Solé P, Delong T, Santamaría P, Serra P. Recognition of Multiple Hybrid Insulin Peptides by a Single Highly Diabetogenic T-Cell Receptor. Front Immunol 2021; 12:737428. [PMID: 34527002 PMCID: PMC8435627 DOI: 10.3389/fimmu.2021.737428] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 08/03/2021] [Indexed: 12/11/2022] Open
Abstract
The mechanisms underlying the major histocompatibility complex class II (MHCII) type 1 diabetes (T1D) association remain incompletely understood. We have previously shown that thymocytes expressing the highly diabetogenic, I-Ag7-restricted 4.1-T-cell receptor (TCR) are MHCII-promiscuous, and that, in MHCII-heterozygous mice, they sequentially undergo positive and negative selection/Treg deviation by recognizing pro- and anti-diabetogenic MHCII molecules on cortical thymic epithelial cells and medullary hematopoietic antigen-presenting cells (APCs), respectively. Here, we use a novel autoantigen discovery approach to define the antigenic specificity of this TCR in the context of I-Ag7. This was done by screening the ability of random epitope-GS linker-I- A β g 7 chain fusion pools to form agonistic peptide-MHCII complexes on the surface of I- A α d chain-transgenic artificial APCs. Pool deconvolution, I-Ag7-binding register-fixing, TCR contact residue mapping, and alanine scanning mutagenesis resulted in the identification of a 4.1-TCR recognition motif XL(G/A)XEXE(D/E)X that was shared by seven agonistic hybrid insulin peptides (HIPs) resulting from the fusion of several different chromogranin A and/or insulin C fragments, including post-translationally modified variants. These data validate a novel, highly sensitive MHCII-restricted epitope discovery approach for orphan TCRs and suggest thymic selection of autoantigen-promiscuous TCRs as a mechanism for the murine T1D-I-Ag7-association.
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MESH Headings
- Animals
- Autoantigens/genetics
- Autoantigens/immunology
- Autoantigens/metabolism
- CD4-Positive T-Lymphocytes/immunology
- CD4-Positive T-Lymphocytes/metabolism
- CHO Cells
- Coculture Techniques
- Cricetulus
- DNA-Binding Proteins/genetics
- DNA-Binding Proteins/metabolism
- Diabetes Mellitus, Type 1/genetics
- Diabetes Mellitus, Type 1/immunology
- Diabetes Mellitus, Type 1/metabolism
- Epitopes
- HEK293 Cells
- Histocompatibility Antigens Class II/genetics
- Histocompatibility Antigens Class II/immunology
- Histocompatibility Antigens Class II/metabolism
- Humans
- Insulin/genetics
- Insulin/immunology
- Insulin/metabolism
- Jurkat Cells
- Mice, Inbred NOD
- Mice, Knockout
- Peptide Fragments/genetics
- Peptide Fragments/immunology
- Peptide Fragments/metabolism
- Receptors, Antigen, T-Cell/genetics
- Receptors, Antigen, T-Cell/immunology
- Receptors, Antigen, T-Cell/metabolism
- Mice
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Affiliation(s)
- Daniel Parras
- Institut D’Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
| | - Patricia Solé
- Institut D’Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
| | - Thomas Delong
- Skaggs School of Pharmacy and Pharmaceutical Sciences (SSPPS), Department of Pharmaceutical Sciences, University of Colorado, Aurora, CO, United States
| | - Pere Santamaría
- Institut D’Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
- Julia McFarlane Diabetes Research Centre (JMDRC) and Department of Microbiology, Immunology and Infectious Diseases, Snyder Institute for Chronic Diseases, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Pau Serra
- Institut D’Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
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The scope of liquid biopsy in the clinical management of oral cancer. Int J Oral Maxillofac Surg 2021; 51:591-601. [PMID: 34462176 DOI: 10.1016/j.ijom.2021.08.017] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 06/18/2021] [Accepted: 08/11/2021] [Indexed: 12/24/2022]
Abstract
Oral squamous cell carcinoma (OSCC) is one of the most prevalent forms of head and neck cancer, and it remains a leading cause of death in developing countries. Failure to detect the disease at an early stage is the main reason for the lack of improvement in the overall survival rate over the decades. Even though tissue biopsy is considered as the gold standard for diagnosis and molecular workup, it is an invasive, expensive and time-consuming procedure. Besides, it may not indicate the genetic status of the entire tumour owing to the heterogeneity of the cancer. In this context, liquid biopsy could be quite useful as it provides a more representative picture of the circulating tumour cells, circulating tumour DNA, circulating RNA, and tumour-derived exosomes obtained from all types of body fluids. This technique provides real-time assessment of variations in the molecular profile of the whole tumour and enables the serial monitoring of the disease status. The method has many advantages, such as easy accessibility, reliability, reproducibility and the possibility for early detection of the disease. However, the concept is still in its infancy, and the research on its application in various tumours including OSCC is rapidly progressing.
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Recessive/dominant model: Alternative choice in case-control-based genome-wide association studies. PLoS One 2021; 16:e0254947. [PMID: 34288964 PMCID: PMC8294554 DOI: 10.1371/journal.pone.0254947] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 07/06/2021] [Indexed: 12/14/2022] Open
Abstract
An additive genetic model is usually employed in case-control-based genome-wide association studies. The model usually encodes "AA", "Aa" and "aa" ("a" represents the minor allele) as three different numbers, implying the contribution of genotype "Aa" to the phenotype is different from "AA" and "aa". From the perspective of biological phenomena, the coding is reasonable since the phenotypes of lives are not "black and white". A case-control based study, however, has only two phenotypes, case and control, which means that the phenotypes are "black and white". It suggests that a recessive/dominant model may be an alternative to the additive model. In order to investigate whether the alternative is feasible, we conducted comparative experiments on several models used in those studies through chi-square test and logistic regression. Our simulation experiments demonstrate that a recessive model is better than the additive model. The area under the curve of the former has increased by 5% compared with the latter, the discrimination of identifying risk single nucleotide polymorphisms has been improved by 61%, and the precision has also reached 1.10 times that of the latter. Furthermore, the real data experiments show that the precision and area under the curve of the former are 16% and 20% higher than the latter respectively, and the area under the curve of dominant model of the former is 13% higher than the latter. The results indicate a recessive/dominant model may be an alternative to the additive model and suggest a new route for case-control-based studies.
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44
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Quantitative neurogenetics: applications in understanding disease. Biochem Soc Trans 2021; 49:1621-1631. [PMID: 34282824 DOI: 10.1042/bst20200732] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/11/2021] [Accepted: 06/21/2021] [Indexed: 12/31/2022]
Abstract
Neurodevelopmental and neurodegenerative disorders (NNDs) are a group of conditions with a broad range of core and co-morbidities, associated with dysfunction of the central nervous system. Improvements in high throughput sequencing have led to the detection of putative risk genetic loci for NNDs, however, quantitative neurogenetic approaches need to be further developed in order to establish causality and underlying molecular genetic mechanisms of pathogenesis. Here, we discuss an approach for prioritizing the contribution of genetic risk loci to complex-NND pathogenesis by estimating the possible impacts of these loci on gene regulation. Furthermore, we highlight the use of a tissue-specificity gene expression index and the application of artificial intelligence (AI) to improve the interpretation of the role of genetic risk elements in NND pathogenesis. Given that NND symptoms are associated with brain dysfunction, risk loci with direct, causative actions would comprise genes with essential functions in neural cells that are highly expressed in the brain. Indeed, NND risk genes implicated in brain dysfunction are disproportionately enriched in the brain compared with other tissues, which we refer to as brain-specific expressed genes. In addition, the tissue-specificity gene expression index can be used as a handle to identify non-brain contexts that are involved in NND pathogenesis. Lastly, we discuss how using an AI approach provides the opportunity to integrate the biological impacts of risk loci to identify those putative combinations of causative relationships through which genetic factors contribute to NND pathogenesis.
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Moldovan A, Waldman YY, Brandes N, Linial M. Body Mass Index and Birth Weight Improve Polygenic Risk Score for Type 2 Diabetes. J Pers Med 2021; 11:582. [PMID: 34205563 PMCID: PMC8233887 DOI: 10.3390/jpm11060582] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/10/2021] [Accepted: 06/17/2021] [Indexed: 12/25/2022] Open
Abstract
One of the major challenges in the post-genomic era is elucidating the genetic basis of human diseases. In recent years, studies have shown that polygenic risk scores (PRS), based on aggregated information from millions of variants across the human genome, can estimate individual risk for common diseases. In practice, the current medical practice still predominantly relies on physiological and clinical indicators to assess personal disease risk. For example, caregivers mark individuals with high body mass index (BMI) as having an increased risk to develop type 2 diabetes (T2D). An important question is whether combining PRS with clinical metrics can increase the power of disease prediction in particular from early life. In this work we examined this question, focusing on T2D. We present here a sex-specific integrated approach that combines PRS with additional measurements and age to define a new risk score. We show that such approach combining adult BMI and PRS achieves considerably better prediction than each of the measures on unrelated Caucasians in the UK Biobank (UKB, n = 290,584). Likewise, integrating PRS with self-reports on birth weight (n = 172,239) and comparative body size at age ten (n = 287,203) also substantially enhance prediction as compared to each of its components. While the integration of PRS with BMI achieved better results as compared to the other measurements, the latter are early-life measurements that can be integrated already at childhood, to allow preemptive intervention for those at high risk to develop T2D. Our integrated approach can be easily generalized to other diseases, with the relevant early-life measurements.
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Affiliation(s)
- Avigail Moldovan
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel;
| | | | - Nadav Brandes
- The Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 91904, Israel;
| | - Michal Linial
- Department of Biological Chemistry, Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel;
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Verzegnazzi AL, Dos Santos IG, Krause MD, Hufford M, Frei UK, Campbell J, Almeida VC, Zuffo LT, Boerman N, Lübberstedt T. Major locus for spontaneous haploid genome doubling detected by a case-control GWAS in exotic maize germplasm. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1423-1434. [PMID: 33543310 DOI: 10.1007/s00122-021-03780-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 01/19/2021] [Indexed: 06/12/2023]
Abstract
A major locus for spontaneous haploid genome doubling was detected by a case-control GWAS in an exotic maize germplasm. The combination of double haploid breeding method with this locus leads to segregation distortion on genomic regions of chromosome five. Temperate maize (Zea mays L.) breeding programs often rely on limited genetic diversity, which can be expanded by incorporating exotic germplasm. The aims of this study were to perform characterization of inbred lines derived from the tropical BS39 population using different breeding methods, to identify genomic regions showing segregation distortion in lines derived by the DH process using spontaneous haploid genome doubling (SHGD), and use case-control association mapping to identify loci controlling SHGD. Four different sets were used: BS39_DH and BS39_SSD were derived from the BS39 population by DH and single-seed descendent (SSD) methods, and BS39 × A427_DH and BS39 × A427_SSD from the cross between BS39 and A427. A total of 663 inbred lines were genotyped. The analyses of gene diversity and genetic differentiation for the DH sets provided evidence of the presence of a SHGD locus near the centromere of chromosome 5. The case-control GWAS for the DH set also pinpointed this locus. Haplotype sharing analysis showed almost 100% exclusive contribution of the A427 genome in the same region on chromosome 5 of BS39 × A427_DH, presumably due to an allele in this region affecting SHGD. This locus enables DH line production in exotic populations without colchicine or other artificial haploid genome doubling.
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Affiliation(s)
| | | | | | - Matthew Hufford
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA
| | | | | | - Vinícius Costa Almeida
- Department of General Biology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | - Leandro Tonello Zuffo
- Department of Plant Sciences, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
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Han X, Hewitt AW, MacGregor S. Predicting the Future of Genetic Risk Profiling of Glaucoma: A Narrative Review. JAMA Ophthalmol 2021; 139:224-231. [PMID: 33331888 DOI: 10.1001/jamaophthalmol.2020.5404] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Importance Glaucoma is the world's leading cause of irreversible blindness. Primary open-angle glaucoma (POAG) is typically asymptomatic early in the disease process, and unfortunately, many are diagnosed too late to prevent vision loss. Observations Genome-wide association studies, which evaluate the association between genetic variants and phenotype across the genome, have mapped many genes for POAG. As well as uncovering new biology, genetic information can be combined into a polygenic risk score (PRS), which aggregates an individual's disease risk over many genetic variants. In this nonsystematic review, performed from June 21, 2019, to October 1, 2020, we address a series of questions to explain the challenges and opportunities in translating genetic discoveries in POAG. We summarize what is known about POAG genetics and how its endophenotypes, such as intraocular pressure or cup-disc ratio, can help with prediction. We discuss the sample sizes available and how increases in the future may have an effect on the utility of prediction approaches. We explore particular scenarios, such as the use of PRS in risk stratification, and applications for individuals who are particularly high risk for POAG as a result of them carrying both a high penetrance mutation and an unfavorable PRS. Finally, we discuss the issue of equity in applying these tests and the prospects for prediction for people from various ancestry groups. The cost-effectiveness evaluation of glaucoma PRS in direct-to-consumer genetic testing and across different ancestry groups is warranted in future research. Conclusions and Relevance Advances in glaucoma genetics have opened the door for risk stratification based on genetic risk predictions. The PRS approach has shown good promise in predicting who will be at highest risk of POAG, which could improve outcomes if these predictions can be acted on to result in improved clinical outcomes.
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Affiliation(s)
- Xikun Han
- QIMR Berghofer Medical Research Institute, Brisbane, Australia.,School of Medicine, University of Queensland, St Lucia, Brisbane, Australia
| | - Alex W Hewitt
- Menzies Institute for Medical Research, University of Tasmania, Tasmania, Australia.,Centre for Eye Research Australia, University of Melbourne, Australia
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Lu H, Zhang J, Jiang Z, Zhang M, Wang T, Zhao H, Zeng P. Detection of Genetic Overlap Between Rheumatoid Arthritis and Systemic Lupus Erythematosus Using GWAS Summary Statistics. Front Genet 2021; 12:656545. [PMID: 33815486 PMCID: PMC8012913 DOI: 10.3389/fgene.2021.656545] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 03/01/2021] [Indexed: 01/04/2023] Open
Abstract
Background Clinical and epidemiological studies have suggested systemic lupus erythematosus (SLE) and rheumatoid arthritis (RA) are comorbidities and common genetic etiologies can partly explain such coexistence. However, shared genetic determinations underlying the two diseases remain largely unknown. Methods Our analysis relied on summary statistics available from genome-wide association studies of SLE (N = 23,210) and RA (N = 58,284). We first evaluated the genetic correlation between RA and SLE through the linkage disequilibrium score regression (LDSC). Then, we performed a multiple-tissue eQTL (expression quantitative trait loci) weighted integrative analysis for each of the two diseases and aggregated association evidence across these tissues via the recently proposed harmonic mean P-value (HMP) combination strategy, which can produce a single well-calibrated P-value for correlated test statistics. Afterwards, we conducted the pleiotropy-informed association using conjunction conditional FDR (ccFDR) to identify potential pleiotropic genes associated with both RA and SLE. Results We found there existed a significant positive genetic correlation (rg = 0.404, P = 6.01E-10) via LDSC between RA and SLE. Based on the multiple-tissue eQTL weighted integrative analysis and the HMP combination across various tissues, we discovered 14 potential pleiotropic genes by ccFDR, among which four were likely newly novel genes (i.e., INPP5B, OR5K2, RP11-2C24.5, and CTD-3105H18.4). The SNP effect sizes of these pleiotropic genes were typically positively dependent, with an average correlation of 0.579. Functionally, these genes were implicated in multiple auto-immune relevant pathways such as inositol phosphate metabolic process, membrane and glucagon signaling pathway. Conclusion This study reveals common genetic components between RA and SLE and provides candidate associated loci for understanding of molecular mechanism underlying the comorbidity of the two diseases.
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Affiliation(s)
- Haojie Lu
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Jinhui Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Zhou Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Meng Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Ting Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China.,Center for Medical Statistics and Data Analysis, School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Huashuo Zhao
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China.,Center for Medical Statistics and Data Analysis, School of Public Health, Xuzhou Medical University, Xuzhou, China
| | - Ping Zeng
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, China.,Center for Medical Statistics and Data Analysis, School of Public Health, Xuzhou Medical University, Xuzhou, China
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Saunders EJ, Kote-Jarai Z, Eeles RA. Identification of Germline Genetic Variants that Increase Prostate Cancer Risk and Influence Development of Aggressive Disease. Cancers (Basel) 2021; 13:760. [PMID: 33673083 PMCID: PMC7917798 DOI: 10.3390/cancers13040760] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 02/08/2021] [Accepted: 02/09/2021] [Indexed: 12/15/2022] Open
Abstract
Prostate cancer (PrCa) is a heterogeneous disease, which presents in individual patients across a diverse phenotypic spectrum ranging from indolent to fatal forms. No robust biomarkers are currently available to enable routine screening for PrCa or to distinguish clinically significant forms, therefore late stage identification of advanced disease and overdiagnosis plus overtreatment of insignificant disease both remain areas of concern in healthcare provision. PrCa has a substantial heritable component, and technological advances since the completion of the Human Genome Project have facilitated improved identification of inherited genetic factors influencing susceptibility to development of the disease within families and populations. These genetic markers hold promise to enable improved understanding of the biological mechanisms underpinning PrCa development, facilitate genetically informed PrCa screening programmes and guide appropriate treatment provision. However, insight remains largely lacking regarding many aspects of their manifestation; especially in relation to genes associated with aggressive phenotypes, risk factors in non-European populations and appropriate approaches to enable accurate stratification of higher and lower risk individuals. This review discusses the methodology used in the elucidation of genetic loci, genes and individual causal variants responsible for modulating PrCa susceptibility; the current state of understanding of the allelic spectrum contributing to PrCa risk; and prospective future translational applications of these discoveries in the developing eras of genomics and personalised medicine.
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Affiliation(s)
- Edward J. Saunders
- The Institute of Cancer Research, London SM2 5NG, UK; (Z.K.-J.); (R.A.E.)
| | - Zsofia Kote-Jarai
- The Institute of Cancer Research, London SM2 5NG, UK; (Z.K.-J.); (R.A.E.)
| | - Rosalind A. Eeles
- The Institute of Cancer Research, London SM2 5NG, UK; (Z.K.-J.); (R.A.E.)
- Royal Marsden NHS Foundation Trust, London SW3 6JJ, UK
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