• Aicardi–Goutières syndrome
• Kabuki syndrome
• childhood‐onset systemic lupus erythematosus
• dyschromatosis symmetrica hereditaria
• monogenic systemic lupus erythematosus
• whole exome sequencing

• Humans
• Lupus Erythematosus, Systemic/genetics
• Lupus Erythematosus, Systemic/diagnosis
• Exome Sequencing
• Female
• Male
• Child
• Adolescent
• Age of Onset
• Croatia
• Genetic Predisposition to Disease
• Mutation/genetics
• DNA-Binding Proteins/genetics
• Pedigree
• Adult
• Exome/genetics
• src-Family Kinases

• APP1079648 Australian NHMRC Centre of Research Excellence grant (2015-2019)

• Barley
• Biodiversity
• Molecular markers
• PCoA
• STRUCTURE

• Hordeum/genetics
• Genotype
• Genetic Variation
• Genetic Markers/genetics

• Researchers Supporting Project number (RSP2024R390) King Saud University

• gene expression
• human diseases
• linear mixed model
• low-effect genetic variants

• Humans
• Linear Models
• Phenotype
• Multifactorial Inheritance
• Sample Size
• Models, Genetic
• Genome-Wide Association Study
• Polymorphism, Single Nucleotide

• NFRFE-2018-00748 New Frontiers in Research Fund and an HBI pilot
• 222300769 Alberta Innovates LevMax-Health Program Bridge Funds
• 36605 Canada Foundation for Innovation
• RGPIN-2018-04328 NSERC Discovery
• Campbell McLaurin Chair for Hearing Deficiencies
• Alberta Innovates Graduate Student Scholarships

• GWAS
• ecological genomics
• heterogeneous germplasm
• phenomics
• phenotypic characterization

• Humans
• Animals
• Triticum/genetics
• Aphids/genetics
• Zea mays/genetics
• Genetic Variation
• Microsatellite Repeats/genetics

• ICAR-ICAR- Indian Institute of Wheat and Barley Research

• Adiposity
• Circadian rhythm
• Inflammation
• Insulin resistance
• Iron overload
• Single nucleotide polymorphism

• Male
• Adult
• Humans
• Female
• Insulin Resistance/genetics
• Body Mass Index
• Retrospective Studies
• Cross-Sectional Studies
• Polymorphism, Single Nucleotide
• Republic of Korea

• genetic variation
• haplotypes
• genomics
• animal breeding

• Animals
• Cattle/genetics
• Quantitative Trait Loci
• Bayes Theorem
• Polymorphism, Single Nucleotide
• Chromosome Mapping
• Haplotypes

• HCV
• SR-BI
• liver disease
• rs5888
• scavenger receptor class B type 1
• single nucleotide polymorphisms

• Bayesian models
• GBLUP
• beef cattle
• haplotype
• linkage disequilibrium

• Beijing Natural Science Foundation
• National Beef Cattle Industrial Technology System
• National Natural Science Foundation of China

Genetic biofortification is recognized as a cost-effective and sustainable strategy to reduce micronutrient malnutrition. Genomic regions governing grain iron concentration (GFeC), grain zinc concentration (GZnC), and thousand kernel weight (TKW) were investigated in a set of 280 diverse bread wheat genotypes. The genome-wide association (GWAS) panel was genotyped using 35 K Axiom Array and phenotyped in five environments. The GWAS analysis showed a total of 17 Bonferroni-corrected marker-trait associations (MTAs) in nine chromosomes representing all the three wheat subgenomes. The TKW showed the highest MTAs (7), followed by GZnC (5) and GFeC (5). Furthermore, 14 MTAs were identified with more than 10% phenotypic variation. One stable MTA i.e. AX-95025823 was identified for TKW in both E4 and E5 environments along with pooled data, which is located at 68.9 Mb on 6A chromosome. In silico analysis revealed that the SNPs were located on important putative candidate genes such as Multi antimicrobial extrusion protein, F-box domain, Late embryogenesis abundant protein, LEA-18, Leucine-rich repeat domain superfamily, and C3H4 type zinc finger protein, involved in iron translocation, iron and zinc homeostasis, and grain size modifications. The identified novel MTAs will be validated to estimate their effects in different genetic backgrounds for subsequent use in marker-assisted selection. The identified SNPs will be valuable in the rapid development of biofortified wheat varieties to ameliorate the malnutrition problems.

The OCA2-HERC2 locus is responsible for the greatest proportion of eye color variation in humans. Numerous studies extensively described both functional SNPs and associated patterns of variation over this region. The goal of our study is to examine how these haplotype structures and allelic associations vary when highly variable markers such as microsatellites are used. Eleven microsatellites spanning 357 Kb of OCA2-HERC2 genes are analyzed in 3029 individuals from worldwide populations. We found that several markers display large differences in allele frequency (10% to 35% difference) among Europeans, East Asians and Africans. In Europe, the alleles showing increased frequency can also discriminate individuals with (IrisPlex) predicted blue and brown eyes. Distinct haplotypes are identified around the variants C and T of the functional SNP rs12913832 (associated to blue eyes), with linkage disequilibrium r² values significant up to 237 Kb. The haplotype carrying the allele rs12913832 C has high frequency (76%) in blue eye predicted individuals (30% in brown eye predicted individuals), while the haplotype associated to the allele rs12913832 T is restricted to brown eye predicted individuals. Finally, homozygosity values reach levels of 91% near rs12913832. Odds ratios show values of 4.2, 7.4 and 10.4 for four markers around rs12913832 and 7.1 for their core haplotype. Hence, this study provides an example on the informativeness of multiallelic markers that, despite their current limited potential contribution to forensic eye color prediction, supports the use of microsatellites for identifying causing variants showing similar genetic features and history.

A haplotype is defined as a combination of alleles at adjacent loci belonging to the same chromosome that can be transmitted as a unit. In this study, we used both the Illumina BovineHD chip (HD chip) and imputed whole-genome sequence (WGS) data to explore haploblocks and assess haplotype effects, and the haploblocks were defined based on the different LD thresholds. The accuracies of genomic prediction (GP) for dressing percentage (DP), meat percentage (MP), and rib eye roll weight (RERW) based on haplotype were investigated and compared for both data sets in Chinese Simmental beef cattle. The accuracies of GP using the entire imputed WGS data were lower than those using the HD chip data in all cases. For DP and MP, the accuracy of GP using haploblock approaches outperformed the individual single nucleotide polymorphism (SNP) approach (GBLUP_In_Block) at specific LD levels. Hotelling’s test confirmed that GP using LD-based haplotypes from WGS data can significantly increase the accuracies of GP for RERW, compared with the individual SNP approach (∼1.4 and 1.9% for G_HBLUP and G_HBLUP+GBLUP, respectively). We found that the accuracies using haploblock approach varied with different LD thresholds. The LD thresholds (r² ≥ 0.5) were optimal for most scenarios. Our results suggested that LD-based haploblock approach can improve accuracy of genomic prediction for carcass traits using both HD chip and imputed WGS data under the optimal LD thresholds in Chinese Simmental beef cattle.

• Chinese Simmental beef cattle
• LD
• genomic prediction
• haplotype
• prediction accuracy

• biomarker
• cholestasis
• liver
• omics

• Adaptation, Physiological/genetics
• Alleles
• Aluminum
• Brazil
• Chromosome Mapping
• Edible Grain
• Genetic Association Studies
• Genotype
• Linkage Disequilibrium
• Phosphorus
• Plant Breeding
• Quantitative Trait Loci
• Selection, Genetic
• Soil/chemistry
• Sorghum/genetics
• Tropical Climate

• Association mapping
• Common bean
• SSR markers
• Yield

Stripe rust caused by the pathogen Puccinia striiformis f. sp. tritici (Pst) is a major threat for wheat, resulting in low yield and grain quality loss in many countries. Genetic resistance is a prevalent method to combat the disease. Mapping the resistant loci and their association with traits is highly exploited in this era. A panel of 465 Pakistani spring wheat genotypes were evaluated for their phenotypic response to stripe rust at the seedling and adult plant stages. A total of 765 single nucleotide polymorphism (SNP) markers were applied on 465 wheat genotypes to evaluate their stripe rust response against nine races during the seedling test and in three locations for the field test. Currently, twenty SNPs dispersed on twelve chromosomal regions (1A, 1B, 1D, 2A, 2B, 4A, 4B, 5B, 6A, 6B, 6D and 7B) have been identified that were associated with rust race-specific resistance at the seedling stage. Thirty SNPs dispersed on eighteen chromosomal regions (1A, 1B, 1D, 2A, 2B, 2D, 3A, 3B, 3D, 4B, 5A, 5B, 6A, 6B, 6D, 7A, 7B and 7D) are associated with adult plant resistance. SNP loci IWB3662 was linked with all three Pakistani races, and likewise IWA2344 and IWA4096 were found to be linked with three different USA races. The present research findings can be applied by wheat breeders to increase their resistant capability and yield potential of their cultivars, through marker-assisted selection.

• SNP
• association mapping
• genome-wide association studies
• stripe rust
• wheat

• GATA1
• Goats
• IFNGR1
• Molecular markers
• Resistant gene
• Susceptible genotype

• encrypted computing
• genome-wide association studies
• homomorphic encryption

• Computer Security
• Genome-Wide Association Study/methods
• Genomics
• Humans
• Polymorphism, Single Nucleotide/genetics
• Time Factors

• R01 CA227237 NCI NIH HHS
• R43 HG010123 NHGRI NIH HHS

• GWAS
• genetic loci
• superior and inferior alleles
• wheat
• zinc accumulation

• Chromosome Mapping
• Chromosomes, Plant/genetics
• Genome-Wide Association Study
• Genotype
• Plant Breeding
• Polymorphism, Single Nucleotide
• Quantitative Trait Loci
• Triticum/genetics
• Triticum/metabolism
• Zinc/metabolism

• Annotation
• GWAS
• Polygenic risk
• Prediction
• Schizophrenia

• endometriosis
• epigenetics
• genetics
• immunology
• pathogenesis

• Endometriosis/genetics
• Endometriosis/pathology
• Endometrium/metabolism
• Endometrium/pathology
• Epigenesis, Genetic
• Female
• Humans
• Stem Cells/metabolism
• Stem Cells/pathology

• Alleles
• Genotype
• Haplotypes
• Humans
• Polymorphism, Single Nucleotide
• Reproducibility of Results

Improving the gelatinization temperature (GT), gel consistency (GC) and amylose content (AC) for parental grain eating and cooking qualities (ECQs) are key factors for enhancing average grain ECQs for hybrid japonica rice.

In this study, a genome-wide association mapping (GWAS) for ECQs was performed on a selected sample of 462 rice accessions in 5 environments using 262 simple sequence repeat markers. We identified 10 loci and 27 favorable alleles for GT, GC and AC, and some of these loci were overlapped with starch synthesis-related genes. Four SSR loci for the GT trait were distributed on chromosomes 3, 5, 8, and 9, of which two SSR loci were novel. Two SSR loci associated with the GC trait were distributed on chromosomes 3 and 6, although only one SSR locus was novel. Four SSR loci associated with the AC trait were distributed on chromosomes 3, 6, 10, and 11, of which three SSR loci were novel. The novel loci RM6712 and RM6327 were simultaneously identified in more than 2 environments and were potentially reliable QTLs for ECQs, with 15 parental combinations being predicted. These QTLs and parental combinations should be used in molecular breeding to improve japonica rice average ECQs.

Among the 10 SSR loci associated with GT, GC and AC for grain ECQs detected in 27 favorable alleles, the favorable allele RM3600-90bp on chromosome 9 could significantly reduce GT, RM5753-115bp on chromosome 6 could significantly increase GC, and RM6327-230bp on chromosome 11 could significantly reduce AC in hybrid japonica rice mixed rice samples.

The online version of this article (10.1186/s12863-019-0735-y) contains supplementary material, which is available to authorized users.

• Amylose content
• Gel consistency
• Gelatinization temperature
• Linkage disequilibrium,·Genome-wide association mapping
• Oryza sativa
• Phenotypic and genetic diversities

Climatic conditions affect the growth, development and final crop production. As wheat is of paramount importance as a staple crop in the human diet, there is a growing need to study its abiotic stress adaptation through the performance of key breeding traits. New and complementary approaches, such as genome-wide association studies (GWAS) and genomic selection (GS), are used for the dissection of different agronomic traits. The present study focused on the dissection of agronomic and quality traits of interest (initial agronomic score, yield, gluten index, sedimentation index, specific weight, whole grain protein and yellow colour) assessed in a panel of 179 durum wheat lines (Triticum durum Desf.), grown under rainfed conditions in different Mediterranean environments in Southern Spain (Andalusia). The findings show a total of 37 marker-trait associations (MTAs) which affect phenotype expression for three quality traits (specific weight, gluten and sedimentation indexes). MTAs could be mapped on the A and B durum wheat subgenomes (on chromosomes 1A, 1B, 2A, 2B and 3A) through the recently available bread wheat reference assembly (IWGSC RefSeqv1). Two of the MTAs found for quality traits (gluten index and SDS) corresponded to the known Glu-B1 and Glu-A1 loci, for which candidate genes corresponding to high molecular weight glutenin subunits could be located. The GS prediction ability values obtained from the breeding materials analyzed showed promising results for traits as grain protein content, sedimentation and gluten indexes, which can be used in plant breeding programs.

• Genes, Plant/genetics
• Genetic Association Studies
• Genetic Loci/genetics
• Genetic Markers/genetics
• Genome-Wide Association Study
• Plant Breeding
• Quantitative Trait, Heritable
• Spain
• Triticum/genetics
• Triticum/growth & development

• Junta de Andalucía
• Ministerio de Economía, Industria y Competitividad, Gobierno de España

• Genetic association studies
• Meta-analysis
• Multivariate methodology

• association analysis
• core collection
• genetic diversity
• hexaploid wheat
• population structure

• Brucellosis
• Genetic resistance
• IRF3
• KALRN
• Polymorphisms
• TIRAP

Genome-wide prediction approaches represent versatile tools for the analysis and prediction of complex traits. Mostly they rely on marker-based information, but scenarios have been reported in which models capitalizing on closely-linked markers that were combined into haplotypes outperformed marker-based models. Detailed comparisons were undertaken to reveal under which circumstances haplotype-based genome-wide prediction models are superior to marker-based models. Specifically, it was of interest to analyze whether and how haplotype-based models may take local epistatic effects between markers into account. Assuming that populations consisted of fully homozygous individuals, a marker-based model in which local epistatic effects inside haplotype blocks were exploited (LEGBLUP) was linearly transformable into a haplotype-based model (HGBLUP). This theoretical derivation formally revealed that haplotype-based genome-wide prediction models capitalize on local epistatic effects among markers. Simulation studies corroborated this finding. Due to its computational efficiency the HGBLUP model promises to be an interesting tool for studies in which ultra-high-density SNP data sets are studied. Applying the HGBLUP model to empirical data sets revealed higher prediction accuracies than for marker-based models for both traits studied using a mouse panel. In contrast, only a small subset of the traits analyzed in crop populations showed such a benefit. Cases in which higher prediction accuracies are observed for HGBLUP than for marker-based models are expected to be of immediate relevance for breeders, due to the tight linkage a beneficial haplotype will be preserved for many generations. In this respect the inheritance of local epistatic effects very much resembles the one of additive effects.

• GenPred
• Genomic Selection
• Shared Data Resources
• epistasis
• genome-wide prediction
• haplotype
• local epistatic effect

• Association mapping (AM)
• Linkage disequilibrium (LD)
• Marker-assisted selection (MAS)
• Quantitative trait loci (QTLs)

• Chromosome Mapping
• Genotype
• Linkage Disequilibrium
• Phenotype
• Plant Breeding
• Plants/genetics
• Quantitative Trait Loci

• agronomic traits
• climate variables
• genetic plasticity
• genome wide association studies
• oat

The production and cultivation of hybrid wheat is a possible strategy to close the yield gap in wheat. Efficient hybrid wheat seed production largely depends on high rates of cross-pollination which can be ensured through high anther extrusion (AE) by male parental lines. Here, we report the AE capacity and elucidate its genetics in 514 elite European winter wheat varieties via genome-wide association studies (GWAS). We observed a wide range of variation among genotypes and a high heritability (0.80) for AE. The whole panel was genotyped with the 35k Affymetrix and 90k iSELECT single nucleotide polymorphism (SNP) arrays plus Ppd-D1, Rht-B1 and Rht-D1 candidate markers. GWAS revealed 51 marker-trait associations (MTAs) on chromosomes 1A, 1B, 2A, 4D and 5B, with Rht-D1 (4D) being the most significant marker. Division of whole panel according to the Rht-D1 genotype resulted in 212 and 294 varieties harboring Rht-D1a and Rht-D1b allele, respectively. The presence of Rht-D1a compared to Rht-D1b (mutant) allele had a large phenotypic influence on AE resulting in its ~17% increase. GWAS performed on the sub-panels detected novel MTAs on chromosomes 2D, 3B and 6A with increased phenotypic variance imparted by individual markers. Our study shows that AE is a highly quantitative trait and wild type Rht-D1a allele greatly improves AE. Moreover, demarcating the quantitative trait loci regions based on intra-chromosomal linkage disequilibrium revealed AE’s candidate genes/genomic regions. Understanding the genetics of AE in elite European wheat and utilizing the linked markers in breeding programs can help to enhance cross-pollination for better exploitation of heterosis.

• Flowers/genetics
• Flowers/growth & development
• Genome-Wide Association Study
• Genotype
• Phenotype
• Physical Chromosome Mapping
• Quantitative Trait Loci/genetics
• Triticum/genetics
• Triticum/growth & development

• Deutscher Akademischer Austauschdienst
• Bayer AG, Division Crop Science

• Association mapping
• Common bean (Phaseolus vulgaris L.)
• Fe
• Population structure
• Protein
• Zn

• gene–environment interactions
• heritability
• methodology
• monogenic
• obesity
• polygenic

Fitting covariates representing the number of haplotype alleles rather than single nucleotide polymorphism (SNP) alleles may increase genomic prediction accuracy if linkage disequilibrium between quantitative trait loci and SNPs is inadequate. The objectives of this study were to evaluate the accuracy, bias and computation time of Bayesian genomic prediction methods that fit fixed-length haplotypes or SNPs. Genotypes at 37,740 SNPs that were common to Illumina BovineSNP50 and high-density panels were phased for ~58,000 New Zealand dairy cattle. Females born before 1 June 2008 were used for training, and genomic predictions for milk fat yield (n = 24,823), liveweight (n = 13,283) and somatic cell score (n = 24,864) were validated within breed (predominantly Holstein–Friesian, predominantly Jersey, or admixed KiwiCross) in later-born females. Covariates for haplotype alleles of five lengths (125, 250, 500 kb, 1 or 2 Mb) were generated and rare haplotypes were removed at four thresholds (1, 2, 5 or 10%), resulting in 20 scenarios tested. Genomic predictions fitting covariates for either SNPs or haplotypes were calculated by using BayesA, BayesB or BayesN. This is the first study to quantify the accuracy of genomic prediction using haplotypes across the whole genome in an admixed population.

A correlation of 0.349 ± 0.016 between yield deviation and genomic breeding values was obtained for milk fat yield in Holstein–Friesians using BayesA fitting covariates. Genomic predictions were more accurate with short haplotypes than with SNPs but less accurate with longer haplotypes than with SNPs. Fitting only the most frequent haplotype alleles reduced computation time with little decrease in prediction accuracy for short haplotypes. Trends were similar for all traits and breeds and there was little difference between Bayesian methods.

Fitting covariates for haplotype alleles rather than SNPs can increase prediction accuracy, although it decreased drastically for long (>500 kb) haplotypes. In this population, fitting 250 kb haplotypes with a 1% frequency threshold resulted in the highest genomic prediction accuracy and fitting 125 kb haplotypes with a 10% frequency threshold improved genomic prediction accuracy with comparable computation time to fitting SNPs. This increased accuracy is likely to increase genetic gain by changing the ranking of selection candidates.

The online version of this article (doi:10.1186/s12711-017-0329-y) contains supplementary material, which is available to authorized users.

• Connexins/genetics
• Databases, Factual
• Genetic Association Studies
• Humans
• Myoclonic Epilepsy, Juvenile/genetics
• Myoclonic Epilepsy, Juvenile/pathology
• Polymorphism, Single Nucleotide
• Protein Serine-Threonine Kinases/genetics
• Receptors, Metabotropic Glutamate/genetics
• Transcription Factors
• Gap Junction delta-2 Protein

• Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq);
• Fundação de Amparo à Pesquisa do Estado de Alagoas (FAPEAL);
• Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES);

• Brassica napus L.
• LD analysis
• Population structure
• SLAF-seq
• SNP loci

Alcoholism has a strong genetic component. Twin studies have demonstrated the heritability of a large proportion of phenotypic variance of alcoholism ranging from 50–80%. The search for genetic variants associated with this complex behavior has epitomized sequence-based studies for nearly a decade. The limited success of genome-wide association studies (GWAS), possibly precipitated by the polygenic nature of complex traits and behaviors, however, has demonstrated the need for novel, multivariate models capable of quantitatively capturing interactions between a host of genetic variants and their association with non-genetic factors. In this regard, capturing the network of SNP by SNP or SNP by environment interactions has recently gained much interest.

Here, we assessed 3,776 individuals to construct a network capable of detecting and quantifying the interactions within and between plausible genetic and environmental factors of alcoholism. In this regard, we propose the use of first-order dependence tree of maximum weight as a potential statistical learning technique to delineate the pattern of dependencies underpinning such a complex trait. Using a predictive based analysis, we further rank the genes, demographic factors, biological pathways, and the interactions represented by our SNP \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \times $$\end{document}×SNP\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \times $$\end{document}×E network. The proposed framework is quite general and can be potentially applied to the study of other complex traits. 

The online version of this article (doi:10.1186/s12918-017-0403-7) contains supplementary material, which is available to authorized users.

• Alcoholism
• Environment
• GWAS
• Interaction
• Network
• SNP

• Brassica napus L.
• LD analysis
• Population structure
• SLAF-seq
• SNP loci

• Chromosome Mapping
• Chromosomes, Plant/genetics
• Disease Resistance/genetics
• Genetic Markers
• Genome-Wide Association Study
• Kansas
• Mexico
• Seeds/genetics
• Seeds/microbiology
• Triticum/genetics
• Triticum/microbiology

• ADHD
• SNP
• microRNAs
• rs1051312
• rs3746544

• D-serine
• Migraine
• NMDA hypo-/hyperactivation
• Schizophrenia
• Serine-racemase

• endometriosis
• genes
• genome-wide association study (gwas)
• epigenetic
• phenotype
• ephect

• Endometriosis/genetics
• Endometriosis/physiopathology
• Female
• Genetic Heterogeneity
• Genetic Loci
• Genetic Predisposition to Disease
• Genome-Wide Association Study/methods
• Humans
• Polymorphism, Single Nucleotide/genetics
• Risk

• Wellcome Trust
• 098017 Wellcome Trust
• MR/K011480/1 Medical Research Council

Over the last decade, high-throughput genotyping and sequencing technologies have contributed to major advancements in genetics research, as these technologies now facilitate affordable mapping of the entire genome for large sets of individuals. Given this, genome-wide association studies are proving to be powerful tools in identifying genetic variants that have the capacity to modify the probability of developing a disease or trait of interest. However, when the study’s goal is to evaluate the effect of the presence of genetic variants mapping to specific chromosomes regions on a specific phenotype, the candidate loci approach is still preferred. Regardless of which approach is taken, such a large data set calls for the establishment and development of appropriate analytical methods in order to translate such knowledge into biological or clinical findings. Standard univariate tests often fail to identify informative genetic variants, especially when dealing with complex traits, which are more likely to result from a combination of rare and common variants and non-genetic determinants. These limitations can partially be overcome by multivariate methods, which allow for the identification of informative combinations of genetic variants and non-genetic features. Furthermore, such methods can help to generate additive genetic scores and risk stratification algorithms that, once extensively validated in independent cohorts, could serve as useful tools to assist clinicians in decision-making. This review aims to provide readers with an overview of the main multivariate methods for genetic data analysis that could be applied to the analysis of cardiovascular traits.

• SNPs
• cardiovascular diseases
• multivariate methods
• risk scores
• risk stratification