Plants defend themselves against pathogens using either resistance, measured as the host's ability to limit pathogen multiplication, or tolerance, measured as the host's ability to reduce the negative effects of infection. Tolerance is a promising trait for crop breeding, but its genetic basis has rarely been studied and remains poorly understood. Here, we reveal the genetic basis of leaf tolerance to the fungal pathogen Zymoseptoria tritici that causes the globally important septoria tritici blotch (STB) disease on wheat. Leaf tolerance to Z. tritici is a quantitative trait that was recently discovered in wheat by using automated image analyses that quantified the symptomatic leaf area and counted the number of pycnidia found on the same leaf. A genome-wide association study identified four chromosome intervals associated with tolerance and a separate chromosome interval associated with resistance. Within these intervals, we identified candidate genes, including wall-associated kinases similar to Stb6, the first cloned STB resistance gene. Our analysis revealed a strong negative genetic correlation between tolerance and resistance to STB, indicative of a trade-off. Such a trade-off between tolerance and resistance would hinder breeding simultaneously for both traits, but our findings suggest a way forward using marker-assisted breeding. We expect that the methods described here can be used to characterize tolerance to other fungal diseases that produce visible fruiting bodies, such as speckled leaf blotch on barley, potentially unveiling conserved tolerance mechanisms shared among plant species. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Standard measures of linkage disequilibrium (LD) are affected by admixture and population structure, such that loci that are not in LD within each ancestral population appear linked when considered jointly across the populations. The influence of population structure on LD can cause problems for downstream analysis methods, in particular those that rely on LD pruning or clumping. To address this issue, we propose a measure of LD that accommodates population structure using the top inferred principal components. We estimate LD from the correlation of genotype residuals and prove that this LD measure remains unaffected by population structure when analyzing multiple populations jointly, even with admixed individuals. Based on this adjusted measure of LD, we can perform LD pruning to remove the correlation between markers for downstream analysis. Traditional LD pruning is more likely to remove markers with high differences in allele frequencies between populations, which biases measures for genetic differentiation and removes markers that are not in LD in the ancestral populations. Using data from moderately differentiated human populations and highly differentiated giraffe populations we show that traditional LD pruning biases FST and principal component analysis (PCA), which can be alleviated with the adjusted LD measure. In addition, we show that the adjusted LD leads to better PCA when pruning and that LD clumping retains more sites with the retained sites having stronger associations.

An early event in plant organogenesis is establishment of a boundary between the stem cell containing meristem and differentiating lateral organ. In maize (Zea mays), evidence suggests a common gene network functions at boundaries of distinct organs and contributes to pleiotropy between leaf angle and tassel branch number, two agronomic traits. To uncover regulatory variation at the nexus of these two traits, we use regulatory network topologies derived from specific developmental contexts to guide multivariate genome-wide association analyses. In addition to defining network plasticity around core pleiotropic loci, we identify new transcription factors that contribute to phenotypic variation in canopy architecture, and structural variation that contributes to cis-regulatory control of pleiotropy between tassel branching and leaf angle across maize diversity. Results demonstrate the power of informing statistical genetics with context-specific developmental networks to pinpoint pleiotropic loci and their cis-regulatory components, which can be used to fine-tune plant architecture for crop improvement.

Due to the monogamous mating system and late maturity of pigeons, their breeding cycle is longer compared to that of other poultry species, which has hindered the optimization of growth traits and meat quality. While traditional breeding methods are commonly used, they lack precision and are time-consuming. This study integrates phenotypic data from Tarim pigeons and White King pigeons with genomic information, using genome-wide association analysis (GWAS) to identify genetic markers associated with key economic traits, thereby accelerating the breeding process. The results reveal significant correlations between body type characteristics (e.g., live weight and chest depth) and carcass traits, supporting their use as indirect selection criteria. GWAS identified several candidate genes, including PPARGC1A and ADGRA3, linked to muscle development and metabolic regulation. To enhance breeding efficiency, this study developed a Liquid Phase Chip (LPC), designed to use high-throughput technology for identifying genetic markers related to carcass traits. Although the LPC is not yet commercially available, the 50 K pigeon LPC from this study could provide crucial theoretical support for its future application. Ultimately, the LPC will serve as an important tool for precision and efficiency in pigeon breeding, driving the development and optimization of the pigeon industry.

Domestic dogs exhibit significant diversity in both morphology and personality. Recent studies focusing on large-breed dogs reported the contribution of genetic factors to personality. However, the genetic influence in small-breed dogs remains unexplored. In the present study, we investigated the personality of two small-breed dogs using a questionnaire and genome-wide single-nucleotide polymorphism data obtained from 301 Toy Poodles and 183 Miniature Dachshunds using the Illumina CanineHD 230K SNP BeadChip. The factor analysis conducted on a questionnaire consisting of 39 items identified seven factors. Among the seven personality factors, 'activeness' in Toy Poodles and 'human-directed sociability' in Miniature Dachshunds had an estimated heritability of 0.425 (SE = 0.311) and 0.514 (SE = 0.355), respectively. In addition, genome-wide association study suggested that two genomic regions possibly affect personality. The dog breeds focused on in this study are most popular in Japan, thus their information is in high demand.

The rapid growth of modern biobanks is creating new opportunities for large-scale genome-wide association studies (GWASs) and the analysis of complex traits. However, performing GWASs on millions of samples often leads to trade-offs between computational efficiency and statistical power, reducing the benefits of large-scale data collection efforts. We developed Quickdraws, a method that increases association power in quantitative and binary traits without sacrificing computational efficiency, leveraging a spike-and-slab prior on variant effects, stochastic variational inference and graphics processing unit acceleration. We applied Quickdraws to 79 quantitative and 50 binary traits in 405,088 UK Biobank samples, identifying 4.97% and 3.25% more associations than REGENIE and 22.71% and 7.07% more than FastGWA. Quickdraws had costs comparable to REGENIE, FastGWA and SAIGE on the UK Biobank Research Analysis Platform service, while being substantially faster than BOLT-LMM. These results highlight the promise of leveraging machine learning techniques for scalable GWASs without sacrificing power or robustness.

Qingyuan Partridge chickens represent a notable breed of high-quality, slow-growing chickens. The cost of feed constitutes 65-70 % of the total breeding expense for Qingyuan Partridge chickens. Enhancing feed utilization efficiency and reducing feed consumption are crucial for the advancement of Qingyuan Partridge chickens and the broader poultry industry. To investigate the key candidate genes associated with feed efficiency in Qingyuan Partridge chickens for genome selection, the genome-wide association study (GWAS) was performed in this study. Genetic parameters estimation results indiated that the heritability of 12-17 feed conversion ratio was 0.19, with the highest genetic correlation observed with 17 body weight (-0.96). Additionally, the heritability of 12-17 residual feed intake was 0.09, with the highest genetic correlation with 12-14 average daily feed intake (0.93). GWAS results revealed 28 significant SNPs associated with body weight, feed intake, metabolic weight, weight gain, feed conversion ratio, and residual feed intake. The multiple genes are significantly enriched in the aromatic compound biosynthetic process, heterocycle biosynthetic process, and nucleobase-containing compound biosynthetic process. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis showed that the expression levels of four genes-exocyst complex component 4(EXOC4), fibrosin like 1(FBRSL1), methionine adenosyltransferase 2 non-catalytic beta subunit (MAT2B), and cytidine/uridine monophosphate kinase 1(CMPK1)-related to significant SNPs exhibited significant differences in the liver tissues of high residual feed intake group compared with low residual feed intake group. These findings contribute to a better understanding of the molecular mechanisms underlying chicken feed efficiency traits, enabling further genetic improvement of Qingyuan Partridge chickens, and improving industrial efficiency.

Cardiomyopathies are the most common heritable heart diseases in cats and humans. This study aimed to identify novel genetic variants in cats with hypertrophic cardiomyopathy (HCM) and restrictive cardiomyopathy (RCM) using a targeted panel of genes associated with human cardiomyopathy. Cats were phenotyped for HCM/RCM by echocardiography ± necropsy. DNA was extracted from residual blood, and targeted next-generation sequencing was performed on two separate feline cohorts: an across-breed cohort (23 healthy cats and 21 HCM-affected pedigree or Domestic Shorthair cats), and a within-breed cohort of Birman pedigree cats (14 healthy, 8 HCM-affected, and 6 RCM-affected). Genome Analysis Toolkit was used for variant discovery. Genomic association analyses, including the covariates breed, age, and sex, were conducted to identify genetic variants of interest. We identified genetic variants associated with both HCM and RCM susceptibility in the sarcomeric genes ACTC1, ACTN2, MYH7, TNNT2 and the non-sarcomeric gene CSRP3 in the Birman pedigree cats. These findings suggest that, as proposed in humans, there is at least partial overlap in the genetic background between the HCM and RCM phenotypes in cats. These findings offer potential insights for comparative cardiac research and translational medicine.

Genome-wide association studies (GWASs) are often performed on ratios composed of a numerator trait divided by a denominator trait. Examples include body mass index (BMI) and the waist-to-hip ratio, among many others. Explicitly or implicitly, the goal of forming the ratio is typically to adjust for an association between the numerator and denominator. While forming ratios may be clinically expedient, there are several important issues with performing GWAS on ratios. Forming a ratio does not "adjust" for the denominator in the sense of conditioning on it, and it is unclear whether associations with ratios are attributable to the numerator, the denominator, or both. Here we demonstrate that associations arising in ratio GWAS can be entirely denominator driven, implying that at least some associations uncovered by ratio GWAS may be due solely to a putative adjustment variable. In a survey of 10 common ratio traits, we find that the ratio model disagrees with the adjusted model (performing GWAS on the numerator while conditioning on the denominator) at around 1/3 of loci. Using BMI as an example, we show that variants detected by only the ratio model are more strongly associated with the denominator (height), while variants detected by only the adjusted model are more strongly associated with the numerator (weight). Although the adjusted model provides effect sizes with a clearer interpretation, it is susceptible to collider bias. We propose and validate a simple method of correcting for the genetic component of collider bias via leave-one-chromosome-out polygenic scoring.

Deep phenotyping can enhance the power of genetic analysis, including genome-wide association studies (GWAS), but the occurrence of missing phenotypes compromises the potential of such resources. Although many phenotypic imputation methods have been developed, the accurate imputation of millions of individuals remains challenging. In the present study, we have developed a multi-phenotype imputation method based on mixed fast random forest (PIXANT) by leveraging efficient machine learning (ML)-based algorithms. We demonstrate by extensive simulations that PIXANT is reliable, robust and highly resource-efficient. We then apply PIXANT to the UKB data of 277,301 unrelated White British citizens and 425 traits, and GWAS is subsequently performed on the imputed phenotypes, 18.4% more GWAS loci are identified than before imputation (8710 vs 7355). The increased statistical power of GWAS identified some additional candidate genes affecting heart rate, such as RNF220, SCN10A, and RGS6, suggesting that the use of imputed phenotype data from a large cohort may lead to the discovery of additional candidate genes for complex traits.

It is well-established that large and heavy dogs tend to live shorter lives. In this study, we aimed to determine whether traits other than body size are associated with the life expectancy of dogs. We compiled a dataset of 20 phenotypes, including body size, lifespan, snout ratio, and shedding, into a single matrix for 149 dog breeds using data from the American Kennel Club and other peer-reviewed sources. The analysis revealed that drooling might be associated with both the lifespan and body mass index of dogs. Furthermore, a genome-wide association study with adjusted phenotypes and statistical verification methods, such as Mendelian randomization. Additionally, conducting differential gene expression analysis with the salivary gland for the 2 cases, hypersalivation/less drooling vs various body sizes, we could observe the hypersalivation-related proteins. This genetic analysis suggests that body size and drooling might be candidate factors influencing lifespan. Consequently, we identified several candidate genes, including IGSF1, PACSIN2, PIK3R1, and MCCC2, as potential genetic factors influencing longevity-related phenotypes.

Understanding the genetic regulatory mechanisms of gene expression is an ongoing challenge. Genetic variants that are associated with expression levels are readily identified when they are proximal to the gene (i.e., cis-eQTLs), but SNPs distant from the gene whose expression levels they are associated with (i.e., trans-eQTLs) have been much more difficult to discover, even though they account for a majority of the heritability in gene expression levels. A major impediment to the identification of more trans-eQTLs is the lack of statistical methods that are powerful enough to overcome the obstacles of small effect sizes and large multiple testing burden of trans-eQTL mapping. Here, we propose ADELLE, a powerful statistical testing framework that requires only summary statistics and is designed to be most sensitive to SNPs that are associated with multiple gene expression levels, a characteristic of many trans-eQTLs. In simulations, we show that for detecting SNPs that are associated with 0.1%-2% of 10,000 traits, among the 8 methods we consider ADELLE is clearly the most powerful overall, with either the highest power or power not significantly different from the highest for all settings in that range. We apply ADELLE to a mouse advanced intercross line data set and show its ability to find trans-eQTLs that were not significant under a standard analysis. We also apply ADELLE to trans-eQTL mapping in the eQTLGen data, and for 1,451 previously identified trans-eQTLs, we discover trans association with additional expression traits beyond those previously identified. This demonstrates that ADELLE is a powerful tool at uncovering trans regulators of genetic expression.

The body conformation traits of dairy cattle are closely related to their production performance and health. The present study aimed to identify gene variants associated with body conformation traits in Chinese Holstein cattle and provide marker loci for genomic selection in dairy cattle breeding. These findings could offer robust theoretical support for optimizing the health of dairy cattle and enhancing their production performance.

This study involved 586 Chinese Holstein cattle and used the predicted transmitting abilities (PTAs) of 17 body conformation traits evaluated by the Council on Dairy Cattle Breeding in the USA as phenotypic values. These traits were categorized into body size traits, rump traits, feet/legs traits, udder traits, and dairy characteristic traits. On the basis of the genomic profiling results from the Genomic Profiler Bovine 100 K SNP chip, genotype data were quality controlled via PLINK software, and 586 individuals and 80,713 SNPs were retained for further analysis. Genome-wide association studies (GWASs) were conducted via GEMMA software, which employs both univariate linear mixed models (LMMs) and multivariate linear mixed models (mvLMMs). The Bonferroni method was used to determine the significance threshold, identifying gene variants significantly associated with body conformation traits in Chinese Holstein cattle. The single-trait GWAS identified 24 SNPs significantly associated with body conformation traits (P < 0.01), with annotation leading to the identification of 21 candidate genes. The multi-trait GWAS identified 54 SNPs, which were annotated to 57 candidate genes, including 39 new SNPs not identified in the single-trait GWAS. Additionally, 14 SNPs in the 86.84-87.41 Mb region of chromosome 6 were significantly associated with multiple traits, such as body size, udder, and dairy characteristics. Four genes-SLC4A4, GC, NPFFR2, and ADAMTS3-were annotated in this region.

A total of 63 SNPs were identified as significantly associated with 17 body conformation traits in Chinese Holstein cattle through both single-trait and multi-trait GWAS analyses. Sixty-six candidate genes were annotated, with 12 genes identified by both methods, such as SLC4A4, GC, NPFFR2, and ADAMTS3, which are involved in pathways such as growth hormone synthesis and secretion, sphingolipid signaling, and dopaminergic synapse pathways. These findings provide potential genetic marker information related to body conformation traits for the breeding of Chinese Holstein cattle.

Carotenoid pigments produce the yellow and red colors of birds and other vertebrates. Despite their importance in social signaling and sexual selection, our understanding of how carotenoid ornamentation evolves in nature remains limited. Here, we examine the long-tailed finch Poephila acuticauda, an Australian songbird with a yellow-billed western subspecies acuticauda and a red-billed eastern subspecies hecki, which hybridize where their ranges overlap. We found that yellow bills can be explained by the loss of C(4)-oxidation, thus preventing yellow dietary carotenoids from being converted to red. Combining linked-read genomic sequencing and reflectance spectrophotometry measurements of bill color collected from wild-sampled finches and laboratory crosses, we identify four loci that together explain 53% of variance in this trait. The two loci of largest effect contain the genes CYP2J19, an essential enzyme for producing red carotenoids, and TTC39B, an enhancer of carotenoid metabolism. A paucity of protein-coding changes and an enrichment of associated upstream variants suggest that the loss of C(4)-oxidation results from cis-regulatory evolution. Evolutionary genealogy reconstruction indicates that the red-billed phenotype is ancestral and that yellow alleles at CYP2J19 and TTC39B first arose and fixed in acuticauda approximately 100 kya. Yellow alleles subsequently introgressed into hecki less than 5 kya. Across all color loci, acuticauda-derived variants show evidence of selective sweeps, implying that yellow bill coloration has been favored by natural selection. Our study illustrates how evolutionary transitions between yellow and red coloration can be achieved by successive selective events acting on regulatory changes at a few interacting genes.

Differences in interspecific mating traits, such as male sexual signals and female preferences, often evolve quickly as initial barriers to gene flow between nascent lineages, and they may also strengthen such barriers during secondary contact via reinforcement. However, it is an open question whether loci contributing to intraspecific variation in sexual traits are co-opted during the formation and strengthening of mating barriers between species. To test this, we used a population genomics approach in natural populations of Australian cricket sister species that overlap in a contact zone: Teleogryllus oceanicus and Teleogryllus commodus. First, we identified loci associated with intraspecific variation in T. oceanicus mating signals: advertisement song and cuticular hydrocarbon (CHC) pheromones. We then separately identified candidate interspecific barrier loci between the species. Genes showing elevated allelic divergence between species were enriched for neurological functions, indicating potential behavioral rewiring. Only two CHC-associated genes overlapped with these interspecific candidate barrier loci, and intraspecific CHC loci showed signatures of being under strong selective constraints between species. In contrast, 10 intraspecific song-associated genes showed high genetic differentiation between T. commodus and T. oceanicus, and 2 had signals of high genomic divergence. The overall lack of shared loci in intra vs. interspecific comparisons of mating trait and candidate barrier loci is consistent with limited co-option of the genetic architecture of interspecific mating signals during the establishment and maintenance of reproductive isolation.

Chicken body weight (BW) is a critical trait in breeding. Although genetic variants associated with BW have been investigated by genome-wide association studies (GWAS), the contributions of causal variants and their molecular mechanisms remain largely unclear in chickens. In this study, we construct a comprehensive genetic atlas of chicken BW by integrative analysis of 30 age points and 5 quantitative trait loci (QTL) across 27 tissues. We find that chicken growth is a cumulative non-linear process, which can be divided into three distinct stages. Our GWAS analysis reveals that BW-related genetic variations show ordered patterns in these three stages. Genetic variations in chromosome 1 may regulate the overall growth process, likely by modulating the hypothalamus-specific expression of SLC25A30 and retina-specific expression of NEK3. Moreover, genetic variations in chromosome 4 and chromosome 27 may play dominant roles in regulating BW during Stage 2 (8-22 weeks) and Stage 3 (23-72 weeks), respectively. In summary, our study presents a comprehensive genetic atlas regulating developmental stage-specific changes in chicken BW, thus providing important resources for genomic selection in breeding programs.

Macauba (Acrocomia aculeata) is a non-domesticated neotropical palm that has been attracting attention for economic use due to its great potential for oil production comparable to the commercially used oil palm (Elaeis guineensis). The discovery of associations between quantitative trait loci and economically important traits represents an advance toward understanding its genetic architecture and can contribute to accelerating macauba domestication. Pursuing this advance, this study performs single-trait and multi-trait GWAS models to identify candidate genes associated with vegetative and oil production traits in macauba. Eighteen phenotypic traits were evaluated from 201 palms within a native population. Genotyping was performed with SNP markers, following the protocol of genotyping-by-sequencing. Given that macauba lacks a reference genome, SNP calling was performed using three different strategies: using i) de novo sequencing, ii) the Elaeis guineenses Jacq. reference genome and iii) the macauba transcriptome sequences. After quality control, we identified a total of 27,410 SNPs in 153 individuals for the de novo genotypic dataset, 10,444 SNPs in 158 individuals using the oil palm genotypic dataset, and 4,329 SNPs in 167 individuals using the transcriptome genotypic dataset. The GWAS analysis was then performed on these three genotypic datasets.

Statistical phenotypic analyses revealed significant differences across all studied traits, with heritability values ranging from 63 to 95%. This indicates that the population contains promising genotypes for selection and the initiation of breeding programs. Genetic correlations between the 18 traits ranged from -0.47 to 0.99. The total number of significant SNPs in the single-trait and multi-trait GWAS was 92 and 6 using the de novo genotypic dataset, 19 and 11 using the oil palm genotypic dataset, and 1 and 2 using the transcriptome genotypic dataset, respectively. Gene annotation identified 12 candidate genes in the single-trait GWAS and four in the multi-trait GWAS, across the 18 phenotypic traits studied, in the three genotypic datasets. Gene mapping of the macauba candidate genes revealed similarities with Elaeis guineensis and Phoenix dactylifera. The candidate genes detected are responsible for metal ion binding and transport, protein transportation, DNA repair, and other cell regulation biological processes.

We provide new insights into genomic regions that map candidate genes associated with vegetative and oil production traits in macauba. These potential candidate genes require confirmation through targeted functional analyses in the future, and multi-trait associations need to be scrutinized to investigate the presence of pleiotropic or linked genes. Markers linked to traits of interest could serve as valuable resources for the development of marker-assisted selection in macauba for its domestication and pre-breeding.

Large structural variants in the genome, such as inversions, may play an important role in producing population structure and local adaptation to the environment through suppression of recombination. However, relatively few studies have linked inversions to phenotypic traits that are sexually selected and may play a role in reproductive isolation. Here, we found that geographic differences in the sexually selected plumage of a warbler, the common yellowthroat (Geothlypis trichas), are largely due to differences in the Z (sex) chromosome (males are ZZ), which contains at least one putative inversion spanning 40% (31/77 Mb) of its length. The inversions on the Z chromosome vary dramatically east and west of the Appalachian Mountains, which provides evidence of cryptic population structure within the range of the most widespread eastern subspecies (G. t. trichas). In an eastern (New York) and western (Wisconsin) population of this subspecies, female prefer different male ornaments; larger black facial masks are preferred in Wisconsin and larger yellow breasts are preferred in New York. The putative inversion also contains genes related to vision, which could influence mating preferences. Thus, structural variants on the Z chromosome are associated with geographic differences in male ornaments and female choice, which may provide a mechanism for maintaining different patterns of sexual selection in spite of gene flow between populations of the same subspecies.

Although plants harbor a huge phytochemical diversity, only a fraction of plant metabolites is functionally characterized. In this work, we aimed to identify the genetic basis of metabolite functions during harsh environmental conditions in Arabidopsis thaliana. With machine learning algorithms we predicted stress-specific metabolomes for 23 (a)biotic stress phenotypes of 300 natural Arabidopsis accessions. The prediction models identified several aliphatic glucosinolates (GLSs) and their breakdown products to be implicated in responses to heat stress in siliques and herbivory by Western flower thrips, Frankliniella occidentalis. Bivariate GWA mapping of the metabolome predictions and their respective (a)biotic stress phenotype revealed genetic associations with MAM, AOP, and GS-OH, all three involved in aliphatic GSL biosynthesis. We, therefore, investigated thrips herbivory on AOP, MAM, and GS-OH loss-of-function and/or overexpression lines. Arabidopsis accessions with a combination of MAM2 and AOP3, leading to 3-hydroxypropyl dominance, suffered less from thrips feeding damage. The requirement of MAM2 for this effect could, however, not be confirmed with an introgression line of ecotypes Cvi and Ler, most likely due to other, unknown susceptibility factors in the Ler background. However, AOP2 and GS-OH, adding alkenyl or hydroxy-butenyl groups, respectively, did not have major effects on thrips feeding. Overall, this study illustrates the complex implications of aliphatic GSL diversity in plant responses to heat stress and a cell-content-feeding herbivore.

Phenotypic plasticity describes a genotype's ability to produce different phenotypes in response to different environments. Breeding crops that exhibit appropriate levels of plasticity for future climates will be crucial to meeting global demand, but knowledge of the critical environmental factors is limited to a handful of well-studied major crops. Using 727 maize (Zea mays L.) hybrids phenotyped for grain yield in 45 environments, we investigated the ability of a genetic algorithm and two other methods to identify environmental determinants of grain yield from a large set of candidate environmental variables constructed using minimal assumptions. The genetic algorithm identified pre- and postanthesis maximum temperature, mid-season solar radiation, and whole season net evapotranspiration as the four most important variables from a candidate set of 9150. Importantly, these four variables are supported by previous literature. After calculating reaction norms for each environmental variable, candidate genes were identified and gene annotations investigated to demonstrate how this method can generate insights into phenotypic plasticity. The genetic algorithm successfully identified known environmental determinants of hybrid maize grain yield. This demonstrates that the methodology could be applied to other less well-studied phenotypes and crops to improve understanding of phenotypic plasticity and facilitate breeding crops for future climates.

With the development of new technologies in recent years, researchers have made significant progress in crop breeding. Modern breeding differs from traditional breeding because of great changes in technical means and breeding concepts. Whereas traditional breeding initially focused on high yields, modern breeding focuses on breeding orientations based on different crops' audiences or by-products. The process of modern breeding starts from the creation of material populations, which can be constructed by natural mutagenesis, chemical mutagenesis, physical mutagenesis transfer DNA (T-DNA), Tos17 (endogenous retrotransposon), etc. Then, gene function can be mined through QTL mapping, Bulked-segregant analysis (BSA), Genome-wide association studies (GWASs), RNA interference (RNAi), and gene editing. Then, at the transcriptional, post-transcriptional, and translational levels, the functions of genes are described in terms of post-translational aspects. This article mainly discusses the application of the above modern scientific and technological methods of breeding and the advantages and limitations of crop breeding and diversity. In particular, the development of gene editing technology has contributed to modern breeding research.

In the realm of animal breeding for sustainability, domestic camels have traditionally been valued for their milk and meat production. However, key aspects such as zoometrics, biomechanics, and behavior have often been overlooked in terms of their genetic foundations. Recognizing this gap, the present study perfomed genome-wide association analyses to identify genetic markers associated with zoometrics-, biomechanics-, and behavior-related traits in dromedary camels (Camelus dromedarius). 16 and 108 genetic markers were significantly associated (q < 0.05) at genome and chromosome-wide levels of significance, respectively, with zoometrics- (width, length, and perimeter/girth), biomechanics- (acceleration, displacement, spatial position, and velocity), and behavior-related traits (general cognition, intelligence, and Intelligence Quotient (IQ)) in dromedaries. In most association loci, the nearest protein-coding genes are linkedto neurodevelopmental and sensory disorders. This suggests that genetic variations related to neural development and sensory perception play crucial roles in shaping a dromedary camel's physical characteristics and behavior. In summary, this research advances our understanding of the genomic basis of essential traits in dromedary camels. Identifying specific genetic markers associated with zoometrics, biomechanics, and behavior provides valuable insights into camel domestication. Moreover, the links between these traits and genes related to neurodevelopmental and sensory disorders highlight the broader implications of domestication and modern selection on the health and welfare of dromedary camels. This knowledge could guide future breeding strategies, fostering a more holistic approach to camel husbandry and ensuring the sustainability of these animals in diverse agricultural contexts.

Rice is one of the most important food crops in the world and is important for global food security. However, damage caused by striped stem borer (SSB) seriously threatens rice production and can cause significant yield losses. The development and use of resistant rice varieties or genes is currently the most effective strategy for controlling SSB. We genotyped 201 rice samples using 2849855 high-confidence single nucleotide polymorphisms (SNPs). We conducted a genome-wide association study (GWAS) based on observed variation data of 201 rice cultivars resistant to SSB. We obtained a quantitative trait locus (QTL)-qRSSB4 that confers resistance to SSB. Through annotation and analysis of genes within the qRSSB4 locus, as well as qRT-PCR detection in resistant rice cultivars, we ultimately selected the candidate gene LOC_Os04g34140 (named OsRSSB4) for further analysis. Next, we overexpressed the candidate gene OsRSSB4 in Nipponbare through transgenic methods, resulting in OsRSSB4 overexpressing lines (OsRSSB4OE). In addition, we evaluated the insect resistance of OsRSSB4OE lines using wild type (Nipponbare) as a control. The bioassay experiment results of live plants showed that after 20 days of inoculation with SSB, the withering heart rate of OsRSSB4OE-34 and OsRSSB4OE-39 lines was only 8.3% and 0%, with resistance levels of 1 and 0, respectively; however, the withering heart rate of the wild-type reached 100%, with a resistance level of 9. The results of the in vitro stem bioassay showed that, compared with the wild-type, the average corrected mortality rate of the SSB fed on the OsRSSB4OE line reached 94.3%, and the resistance reached a high level. In summary, we preliminarily confirmed that OsRSSB4 positively regulates the defense of rice against SSB. This research findings reveal new SSB resistance gene resources, providing an important genetic basis for SSB resistance breeding in rice crops.

Genome-wide association study (GWAS) by measuring the joint effect of multiple loci on multiple traits, has recently attracted interest, due to the decreased costs of high-throughput genotyping and phenotyping technologies. Previous studies mainly focused on either multilocus models that identify associations with a single trait or multitrait models that scan a single marker at a time. Since these types of models cannot fully utilize the association information, the powers of the tests are usually low. To potentially address this problem, we present here a multitrait multilocus (MTML) modeling framework that implements in three steps: (1) simplify the complex calculation; (2) reduce the model dimension; (3) integrate the joint contribution of single markers to multiple traits by Cauchy combination. The performances of MTML are evaluated and compared with other three published methods by Monte Carlo simulations. Simulation results show that MTML is more powerful for quantitative trait nucleotide detection and robust for various numbers of traits. In the meanwhile, MTML can effectively control type I error rate at a reasonable level. Real data analysis of Arabidopsis thaliana shows that MTML identifies more pleiotropic genetic associations. Therefore, we conclude that MTML is an efficient GWAS method for joint analysis of multiple quantitative traits. The R package MTML, which facilitates the implementation of the proposed method, is publicly available on GitHub https://github.com/Guohongping/MTML.

Alcohol use disorder (AUD) is a prominent contributor to global morbidity and mortality. Its complex etiology involves genetics, epigenetics, and environmental factors. We review progress in understanding the genetics and epigenetics of AUD, summarizing the key findings. Advancements in technology over the decades have elevated research from early candidate gene studies to present-day genome-wide scans, unveiling numerous genetic and epigenetic risk factors for AUD. The latest GWAS on more than one million participants identified more than 100 genetic variants, and the largest epigenome-wide association studies (EWAS) in blood and brain samples have revealed tissue-specific epigenetic changes. Downstream analyses revealed enriched pathways, genetic correlations with other traits, transcriptome-wide association in brain tissues, and drug-gene interactions for AUD. We also discuss limitations and future directions, including increasing the power of GWAS and EWAS studies as well as expanding the diversity of populations included in these analyses. Larger samples, novel technologies, and analytic approaches are essential; these include whole-genome sequencing, multiomics, single-cell sequencing, spatial transcriptomics, deep-learning prediction of variant function, and integrated methods for disease risk prediction.

Immunoglobulin (Ig) glycosylation modulates the immune response and plays a critical role in ageing and diseases. Studies have mainly focused on IgG glycosylation, and little is known about the genetics and epidemiology of IgA glycosylation.

We generated, using a novel liquid chromatography-mass spectrometry method, the first large-scale IgA glycomics dataset in serum from 2423 twins, encompassing 71 N- and O-glycan species.

We showed that, despite the lack of a direct genetic template, glycosylation is highly heritable, and that glycopeptide structures are sex-specific, and undergo substantial changes with ageing. We observe extensive correlations between the IgA and IgG glycomes, and, exploiting the twin design, show that they are predominantly influenced by shared genetic factors. A genome-wide association study identified eight loci associated with both the IgA and IgG glycomes (ST6GAL1, ELL2, B4GALT1, ABCF2, TMEM121, SLC38A10, SMARCB1, and MGAT3) and two novel loci specifically modulating IgA O-glycosylation (C1GALT1 and ST3GAL1). Validation of our findings in an independent cohort of 320 individuals from Qatar showed that the underlying genetic architecture is conserved across ancestries.

Our study delineates the genetic landscape of IgA glycosylation and provides novel potential functional links with the aetiology of complex immune diseases, including genetic factors involved in IgA nephropathy risk.

Joint association analysis of multiple traits with multiple genetic variants can provide insight into genetic architecture and pleiotropy, improve trait prediction, and increase power for detecting association. Furthermore, some traits are naturally high-dimensional, e.g., images, networks, or longitudinally measured traits. Assessing significance for multitrait genetic association can be challenging, especially when the sample has population sub-structure and/or related individuals. Failure to adequately adjust for sample structure can lead to power loss and inflated type 1 error, and commonly used methods for assessing significance can work poorly with a large number of traits or be computationally slow. We developed JASPER, a fast, powerful, robust method for assessing significance of multitrait association with a set of genetic variants, in samples that have population sub-structure, admixture, and/or relatedness. In simulations, JASPER has higher power, better type 1 error control, and faster computation than existing methods, with the power and speed advantage of JASPER increasing with the number of traits. JASPER is potentially applicable to a wide range of association testing applications, including for multiple disease traits, expression traits, image-derived traits, and microbiome abundances. It allows for covariates, ascertainment, and rare variants and is robust to phenotype model misspecification. We apply JASPER to analyze gene expression in the Framingham Heart Study, where, compared to alternative approaches, JASPER finds more significant associations, including several that indicate pleiotropic effects, most of which replicate previous results, while others have not previously been reported. Our results demonstrate the promise of JASPER for powerful multitrait analysis in structured samples.

Transcriptomics and proteomics information collected on a platform can predict additive and non-additive effects for platform traits and additive effects for field traits. The effects of climate change in the form of drought, heat stress, and irregular seasonal changes threaten global crop production. The ability of multi-omics data, such as transcripts and proteins, to reflect a plant's response to such climatic factors can be capitalized in prediction models to maximize crop improvement. Implementing multi-omics characterization in field evaluations is challenging due to high costs. It is, however, possible to do it on reference genotypes in controlled conditions. Using omics measured on a platform, we tested different multi-omics-based prediction approaches, using a high dimensional linear mixed model (MegaLMM) to predict genotypes for platform traits and agronomic field traits in a panel of 244 maize hybrids. We considered two prediction scenarios: in the first one, new hybrids are predicted (CV-NH), and in the second one, partially observed hybrids are predicted (CV-POH). For both scenarios, all hybrids were characterized for omics on the platform. We observed that omics can predict both additive and non-additive genetic effects for the platform traits, resulting in much higher predictive abilities than GBLUP. It highlights their efficiency in capturing regulatory processes in relation to growth conditions. For the field traits, we observed that the additive components of omics only slightly improved predictive abilities for predicting new hybrids (CV-NH, model MegaGAO) and for predicting partially observed hybrids (CV-POH, model GAOxW-BLUP) in comparison to GBLUP. We conclude that measuring the omics in the fields would be of considerable interest in predicting productivity if the costs of omics drop significantly.

Genome-wide association study (GWAS) is a powerful tool to identify genomic loci underlying complex traits. However, the application in natural populations comes with challenges, especially power loss due to population stratification. Here, we introduce a bivariate analysis approach to a GWAS dataset of Arabidopsis thaliana. We demonstrate the efficiency of dual-phenotype analysis to uncover hidden genetic loci masked by population structure via a series of simulations. In real data analysis, a common allele, strongly confounded with population structure, is discovered to be associated with late flowering and slow maturation of the plant. The discovered genetic effect on flowering time is further replicated in independent datasets. Using Mendelian randomization analysis based on summary statistics from our GWAS and expression QTL scans, we predicted and replicated a candidate gene AT1G11560 that potentially causes this association. Further analysis indicates that this locus is co-selected with flowering-time-related genes. The discovered pleiotropic genotype-phenotype map provides new insights into understanding the genetic correlation of complex traits.

Vernicia montana is a dioecious plant widely cultivated for high-quality tung oil production and ornamental purposes in the Euphorbiaceae family. The lack of genomic information has severely hindered molecular breeding for genetic improvement and early sex identification in V. montana. Here, we present a chromosome-level reference genome of a male V. montana with a total size of 1.29 Gb and a contig N50 of 3.69 Mb. Genome analysis revealed that different repeat lineages drove the expansion of genome size. The model of chromosome evolution in the Euphorbiaceae family suggests that polyploidization-induced genomic structural variation reshaped the chromosome structure, giving rise to the diverse modern chromosomes. Based on whole-genome resequencing data and analyses of selective sweep and genetic diversity, several genes associated with stress resistance and flavonoid synthesis such as CYP450 genes and members of the LRR-RLK family, were identified and presumed to have been selected during the evolutionary process. Genome-wide association studies were conducted and a putative sex-linked insertion and deletion (InDel) (Chr 2: 102 799 917-102 799 933 bp) was identified and developed as a polymorphic molecular marker capable of effectively detecting the gender of V. montana. This InDel is located in the second intron of VmBASS4, suggesting a possible role of VmBASS4 in sex determination in V. montana. This study sheds light on the genome evolution and sex identification of V. montana, which will facilitate research on the development of agronomically important traits and genomics-assisted breeding.

The date palm is economically vital in the Middle East and North Africa, providing essential fibres, vitamins, and carbohydrates. Understanding the genetic architecture of its traits remains complex due to the tree's perennial nature and long generation times. This study aims to address these complexities by employing advanced genome-wide association (GWAS) and genomic prediction models using previously published data involving fruit acid content, sugar content, dimension, and colour traits. The multivariate GWAS model identified seven QTL, including five novel associations, that shed light on the genetic control of these traits. Furthermore, the research evaluates different genomic prediction models that considered genotype by environment and genotype by trait interactions. While colour- traits demonstrate strong predictive power, other traits display moderate accuracies across different models and scenarios aligned with the expectations when using small reference populations. When designing the cross-validation to predict new individuals, the accuracy of the best multi-trait model was significantly higher than all single-trait models for dimension traits, but not for the remaining traits, which showed similar performances. However, the cross-validation strategy that masked random phenotypic records (i.e., mimicking the unbalanced phenotypic records) showed significantly higher accuracy for all traits except acid contents. The findings underscore the importance of understanding genetic architecture for informed breeding strategies. The research emphasises the need for larger population sizes and multivariate models to enhance gene tagging power and predictive accuracy to advance date palm breeding programs. These findings support more targeted breeding in date palm, improving productivity and resilience to various environments.

Genome-wide association studies (GWAS) in long-lived human populations have led to identification of variants associated with Alzheimer's disease and cardiovascular disease, the latter being the most common cause of mortality in people worldwide. In contrast, naturally occurring cancer represents the leading cause of death in pet dogs, and specific breeds like the Golden Retriever (GR) carry up to a 65% cancer-related death rate. We hypothesized that GWAS of long-lived GRs might lead to the identification of genetic variants capable of modifying longevity within this cancer-predisposed breed. A GWAS was performed comparing GR dogs ≥ 14 years to dogs dying prior to age 12 which revealed a significant association to ERBB4, the only member of the epidermal growth factor receptor family capable of serving as both a tumor suppressor gene and an oncogene. No coding variants were identified, however, distinct haplotypes in the 5'UTR were associated with reduced lifespan in two separate populations of GR dogs. When all GR dogs were analyzed together (n = 304), the presence of haplotype 3 was associated with shorter survival (11.8 years vs. 12.8 years, p = 0.024). GRs homozygous for haplotype 3 had the shortest survival, and GRs homozygous for haplotype 1 had the longest survival (11.6 years vs. 13.5 years, p = 0.0008). Sub-analyses revealed that the difference in lifespan for GRs carrying at least 1 copy of haplotype 3 was specific to female dogs (p = 0.009), whereas survival remained significantly different in both male and female GRs homozygous for haplotype 1 or haplotype 3 (p = 0.026 and p = 0.009, respectively). Taken together, these findings implicate a potential role for ERBB4 in GR longevity and provide evidence that within-breed canine lifespan studies could serve as a mechanism to identify favorable or disease-modifying variants important to the axis of aging and cancer.

Eggshell and shank color in poultry is an intriguing topic of research due to the roles in selection, breed recognition, and environmental adaptation. This study delves into the genomics foundations of shank and eggshell pigmentation in Italian local chickens through genome-wide association studies analysis to uncover the mechanisms governing these phenotypes. To this purpose, 483 animals from 20 local breeds (n = 466) and 2 commercial lines (n = 17) were considered and evaluated for shank and eggshell color. All animals were genotyped using the Affymetrix Axiom 600 K Chicken Genotyping Array. As regards shank color, the most interesting locus was detected on chromosome Z, close to the TYRP1 gene, known to play a key role in avian pigmentation. Additionally, several novel loci and genes associated with shank pigmentation, skin pigmentation, UV protection, and melanocyte regulation were identified (e.g., MTAP, CDKN2A, CDKN2B). In eggshell, fewer significant loci were identified, including SLC7A11 and MITF on chromosomes 4 and 12, respectively, associated with melanocyte processes and pigment synthesis. This comprehensive study shed light on the genetic architecture underlying shank and eggshell color in Italian native chicken breeds, contributing to a better understanding of this phenomenon which plays a role in breed identification and conservation, and has ecological and economic implications.

Wolfberry is a plant with medicinal and food values. However, its bioactive ingredients and the corresponding genetic bases have not been determined. Here, we de novo generated a chromosome-level genome assembly for wolfberry, yielding a genome sequence of ~1.77 Gb with contig N50 of 50.55 Mb and 39 224 predicted gene models. A variation map, using 307 re-sequenced accessions, was called based on this genome assembly. Furthermore, the fruit metabolome of these accessions was profiled using 563 annotated metabolites, which separated Lycium barbarum L. and non-L. barbarum L. The flavonoids, coumarins, alkaloids and nicotinic acid contents were higher in the former than in the latter. A metabolite-based genome-wide association study mapped 156 164 significant single nucleotide polymorphisms corresponding to 340 metabolites. This included 19 219 unique lead single nucleotide polymorphisms in 1517 significant association loci, of which three metabolites, flavonoids, betaine and spermidine, were highlighted. Two candidate genes, LbUGT (evm.TU.chr07.2692) and LbCHS (evm.TU.chr07.2738), with non-synonymous mutations, were associated with the flavonoids content. LbCHS is a structural gene that interacts with a nearby MYB transcription factor (evm.TU.chr07.2726) both in L. barbarum and L. ruthenicum. Thus, these three genes might be involved in the biosynthesis/metabolism of flavonoids. LbSSADH (evm.TU.chr09.627) was identified as possibly participating in betaine biosynthesis/metabolism. Four lycibarbarspermidines (E-G and O) were identified, and only the lycibarbarspermidines O content was higher in L. barbarum varieties than in non-L. barbarum varieties. The evm.TU.chr07.2680 gene associated with lycibarbarspermidines O was annotated as an acetyl-CoA-benzylalcohol acetyltransferase, suggesting that it is a candidate gene for spermidine biosynthesis. These results provide novel insights into the specific metabolite profile of non-L. barbarum L. and the genetic bases of flavonoids, betaine and spermidine biosynthesis/metabolism.

Drought is one of the major environmental constraints for wheat production world-wide. As the progenitor and genetic reservoir of common wheat, emmer wheat is considered as an invaluable gene pool for breeding drought-tolerant wheat. Combining GWAS and eGWAS analysis of 107 accessions, we identified 86 QTLs, 105 462 eQTLs as well as 68 eQTL hotspots associating with drought tolerance (DT) in emmer wheat. A complex regulatory network composed of 185 upstream regulator and 2432 downstream drought-responsive candidates was developed, of which TtOTS1 was found to play a negative effect in determining DT through affecting root development. This study sheds light on revealing the genetic basis underlying DT, which will provide the indispensable genes and germplasm resources for elite drought tolerance wheat improvement and breeding.

Knowing the genes involved in quantitative traits provides an entry point to understanding the biological bases of behavior, but there are very few examples where the pathway from genetic locus to behavioral change is known. To explore the role of specific genes in fear behavior, we mapped three fear-related traits, tested fourteen genes at six quantitative trait loci (QTLs) by quantitative complementation, and identified six genes. Four genes, Lamp, Ptprd, Nptx2, and Sh3gl, have known roles in synapse function; the fifth, Psip1, was not previously implicated in behavior; and the sixth is a long non-coding RNA, 4933413L06Rik, of unknown function. Variation in transcriptome and epigenetic modalities occurred preferentially in excitatory neurons, suggesting that genetic variation is more permissible in excitatory than inhibitory neuronal circuits. Our results relieve a bottleneck in using genetic mapping of QTLs to uncover biology underlying behavior and prompt a reconsideration of expected relationships between genetic and functional variation.

Weeds pose a major constraint in lentil cultivation, leading to decrease farmers' revenues by reducing the yield and increasing the management costs. The development of herbicide tolerant cultivars is essential to increase lentil yield. Even though herbicide tolerant lines have been identified in lentils, breeding efforts are still limited and lack proper validation. Marker assisted selection (MAS) can increase selection accuracy at early generations. Total 292 lentil accessions were evaluated under different dosages of two herbicides, metribuzin and imazethapyr, during two seasons at Marchouch, Morocco and Terbol, Lebanon. Highly significant differences among accessions were observed for days to flowering (DF) and maturity (DM), plant height (PH), biological yield (BY), seed yield (SY), number of pods per plant (NP), as well as the reduction indices (RI) for PH, BY, SY and NP. A total of 10,271 SNPs markers uniformly distributed along the lentil genome were assayed using Multispecies Pulse SNP chip developed at Agriculture Victoria, Melbourne. Meta-GWAS analysis was used to detect marker-trait associations, which detected 125 SNPs markers associated with different traits and clustered in 85 unique quantitative trait loci. These findings provide valuable insights for initiating MAS programs aiming to enhance herbicide tolerance in lentil crop.

Community genetics seeks to understand the mechanisms by which natural genetic variation in heritable host phenotypes can encompass assemblages of organisms such as bacteria, fungi, and many animals including arthropods. Prior studies that focused on plant genotypes have been unable to identify genes controlling community composition, a necessary step to predict ecosystem structure and function as underlying genes shift within plant populations. We surveyed arthropods within an association population of Populus trichocarpa in three common gardens to discover plant genes that contributed to arthropod community composition. We analyzed our surveys with traditional single-trait genome-wide association analysis (GWAS), multitrait GWAS, and functional networks built from a diverse set of plant phenotypes. Plant genotype was influential in structuring arthropod community composition among several garden sites. Candidate genes important for higher level organization of arthropod communities had broadly applicable functions, such as terpenoid biosynthesis and production of dsRNA binding proteins and protein kinases, which may be capable of targeting multiple arthropod species. We have demonstrated the ability to detect, in an uncontrolled environment, individual genes that are associated with the community assemblage of arthropods on a host plant, further enhancing our understanding of genetic mechanisms that impact ecosystem structure.

The liver is an important metabolic organ that governs glucolipid metabolism, and its dysfunction may cause non-alcoholic fatty liver disease, type 2 diabetes mellitus, dyslipidaemia, etc. We aimed to systematic investigate the key factors related to hepatic glucose metabolism, which may be beneficial for understanding the underlying pathogenic mechanisms for obesity and diabetes mellitus.

Oral glucose tolerance test (OGTT) phenotypes and liver transcriptomes of BXD mice under chow and high-fat diet conditions were collected from GeneNetwork. QTL mapping was conducted to pinpoint genomic regions associated with glucose homeostasis. Candidate genes were further nominated using a multi-criteria approach and validated to confirm their functional relevance in vitro.

Our results demonstrated that plasma glucose levels in OGTT were significantly affected by both diet and genetic background, with six genetic regulating loci were mapped on chromosomes 1, 4, and 7. Moreover, TEAD1, MYO7A and NDUFC2 were identified as the candidate genes. Functionally, siRNA-mediated TEAD1, MYO7A and NDUFC2 knockdown significantly decreased the glucose uptake and inhibited the transcription of genes related to insulin and glucose metabolism pathways.

Our study contributes novel insights to the understanding of hepatic glucose metabolism, demonstrating the impact of TEAD1, MYO7A and NDUFC2 on mitochondrial function in the liver and their regulatory role in maintaining in glucose homeostasis.