scholarly journals Using prior information from humans to prioritize genes and gene-associated variants for complex traits in livestock

2020 ◽  
Author(s):  
Biaty Raymond ◽  
Loic Yengo ◽  
Roy Costilla ◽  
Chris Schrooten ◽  
Aniek C. Bouwman ◽  
...  
Keyword(s):  
Complex Traits ◽  
Prior Information ◽  
Genetic Variance ◽  
Association Studies ◽  
Mammalian Species ◽  
Significant Proportion ◽  
Significant Snps ◽  
Genome Wide Association Studies ◽  
Randomized Sampling ◽  
Human Height

Genome-Wide Association Studies (GWAS) in large human cohorts have identified thousands of loci associated with complex traits and diseases. For identifying the genes and gene-associated variants that underlie complex traits in livestock, especially where sample sizes are limiting, it may help to integrate the results of GWAS for equivalent traits in humans as prior information. In this study, we sought to investigate the usefulness of results from a GWAS on human height as prior information for identifying the genes and gene-associated variants that affect stature in cattle, using GWAS summary data on samples sizes of 700,000 and 58,265 for humans and cattle, respectively. Using Fisher's exact test, we observed a significant proportion of cattle stature-associated genes (30/77) that are also associated with human height (odds ratio = 5.1, p = 3.1e-10). Result of randomized sampling tests showed that cattle orthologs of human height-associated genes, hereafter referred to as candidate genes (C-genes), were more enriched for cattle stature GWAS signals than random samples of genes in the cattle genome (p=0.01). Randomly sampled SNPs within the C-genes also tend to explain more genetic variance for cattle stature (up to 13.2%) than randomly sampled SNPs within random cattle genes (p=0.09). The most significant SNPs from a cattle GWAS for stature within the C-genes did not explain more genetic variance for cattle stature than the most significant SNPs within random cattle genes (p=0.87). Altogether, our findings support previous studies that suggest a similarity in the genetic regulation of height across mammalian species. However, with the availability of a powerful GWAS for stature that combined data from 8 cattle breeds, prior information from human-height GWAS does not seem to provide any additional benefit with respect to the identification of genes and gene-associated variants that affect stature in cattle.

scholarly journals Exome resequencing and GWAS for growth, ecophysiology, and chemical and metabolomic composition of wood of Populus trichocarpa

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Fernando P. Guerra ◽  
Haktan Suren ◽  
Jason Holliday ◽  
James H. Richards ◽  
Oliver Fiehn ◽  
...  
Keyword(s):  
Biomass Production ◽  
Complex Traits ◽  
Association Studies ◽  
Populus Trichocarpa ◽  
Significant Snps ◽  
Snp Markers ◽  
Exome Capture ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Improvement Programs

Abstract Background Populus trichocarpa is an important forest tree species for the generation of lignocellulosic ethanol. Understanding the genomic basis of biomass production and chemical composition of wood is fundamental in supporting genetic improvement programs. Considerable variation has been observed in this species for complex traits related to growth, phenology, ecophysiology and wood chemistry. Those traits are influenced by both polygenic control and environmental effects, and their genome architecture and regulation are only partially understood. Genome wide association studies (GWAS) represent an approach to advance that aim using thousands of single nucleotide polymorphisms (SNPs). Genotyping using exome capture methodologies represent an efficient approach to identify specific functional regions of genomes underlying phenotypic variation. Results We identified 813 K SNPs, which were utilized for genotyping 461 P. trichocarpa clones, representing 101 provenances collected from Oregon and Washington, and established in California. A GWAS performed on 20 traits, considering single SNP-marker tests identified a variable number of significant SNPs (p-value < 6.1479E-8) in association with diameter, height, leaf carbon and nitrogen contents, and δ15N. The number of significant SNPs ranged from 2 to 220 per trait. Additionally, multiple-marker analyses by sliding-windows tests detected between 6 and 192 significant windows for the analyzed traits. The significant SNPs resided within genes that encode proteins belonging to different functional classes as such protein synthesis, energy/metabolism and DNA/RNA metabolism, among others. Conclusions SNP-markers within genes associated with traits of importance for biomass production were detected. They contribute to characterize the genomic architecture of P. trichocarpa biomass required to support the development and application of marker breeding technologies.

scholarly journals Extensive impact of low-frequency variants on the phenotypic landscape at population-scale

2019 ◽  
Author(s):  
T. Fournier ◽  
O. Abou Saada ◽  
J. Hou ◽  
J. Peter ◽  
E. Caudal ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Complex Traits ◽  
Growth Traits ◽  
Association Studies ◽  
Significant Proportion ◽  
Low Frequency ◽  
Population Level ◽  
Initial Population ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies

AbstractGenome-wide association studies (GWAS) allows to dissect the genetic basis of complex traits at the population level1. However, despite the extensive number of trait-associated loci found, they often fail to explain a large part of the observed phenotypic variance2–4. One potential source of this discrepancy could be the preponderance of undetected low-frequency genetic variants in natural populations5,6. To increase the allele frequency of those variants and assess their phenotypic effects at the population level, we generated a diallel panel consisting of 3,025 hybrids, derived from pairwise crosses between a subset of natural isolates from a completely sequenced 1,011 Saccharomyces cerevisiae population. We examined each hybrid across a large number of growth traits, resulting in a total of 148,225 cross/trait combinations. Parental versus hybrid regression analysis showed that while most phenotypic variance is explained by additivity, a significant proportion (29%) is governed by non-additive effects. This is confirmed by the fact that a majority of complete dominance is observed in 25% of the traits. By performing GWAS on the diallel panel, we detected 1,723 significantly associated genetic variants, with 16.3% of them being low-frequency variants in the initial population. These variants, which would not be detected using classical GWAS, explain 21% of the phenotypic variance on average. Altogether, our results demonstrate that low-frequency variants should be accounted for as they contribute to a large part of the phenotypic variation observed in a population.

scholarly journals Genomic Prediction and Association Analysis with Models Including Dominance Effects for Important Traits in Chinese Simmental Beef Cattle

Animals ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 1055 ◽  
Author(s):  
Ying Liu ◽  
Lei Xu ◽  
Zezhao Wang ◽  
Ling Xu ◽  
Yan Chen ◽  
...  
Keyword(s):  
Beef Cattle ◽  
Genomic Prediction ◽  
Complex Traits ◽  
Genetic Variance ◽  
Association Studies ◽  
Additive Genetic Variance ◽  
Additive Models ◽  
Carcass Weight ◽  
Phenotypic Variance ◽  
Genome Wide Association Studies

Non-additive effects play important roles in determining genetic changes with regard to complex traits; however, such effects are usually ignored in genetic evaluation and quantitative trait locus (QTL) mapping analysis. In this study, a two-component genome-based restricted maximum likelihood (GREML) was applied to obtain the additive genetic variance and dominance variance for carcass weight (CW), dressing percentage (DP), meat percentage (MP), average daily gain (ADG), and chuck roll (CR) in 1233 Simmental beef cattle. We estimated predictive abilities using additive models (genomic best linear unbiased prediction (GBLUP) and BayesA) and dominance models (GBLUP-D and BayesAD). Moreover, genome-wide association studies (GWAS) considering both additive and dominance effects were performed using a multi-locus mixed-model (MLMM) approach. We found that the estimated dominance variances accounted for 15.8%, 16.1%, 5.1%, 4.2%, and 9.7% of the total phenotypic variance for CW, DP, MP, ADG, and CR, respectively. Compared with BayesA and GBLUP, we observed 0.5–1.1% increases in predictive abilities of BayesAD and 0.5–0.9% increases in predictive abilities of GBLUP-D, respectively. Notably, we identified a dominance association signal for carcass weight within RIMS2, a candidate gene that has been associated with carcass weight in beef cattle. Our results suggest that dominance effects yield variable degrees of contribution to the total genetic variance of the studied traits in Simmental beef cattle. BayesAD and GBLUP-D are convenient models for the improvement of genomic prediction, and the detection of QTLs using a dominance model shows promise for use in GWAS in cattle.

scholarly journals Quantifying genetic heterogeneity between continental populations for human height and body mass index

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jing Guo ◽  
Andrew Bakshi ◽  
Ying Wang ◽  
Longda Jiang ◽  
Loic Yengo ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Complex Traits ◽  
Association Studies ◽  
Significant Snps ◽  
Complex Trait ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Population Difference ◽  
Genome Wide

AbstractGenome-wide association studies (GWAS) in samples of European ancestry have identified thousands of genetic variants associated with complex traits in humans. However, it remains largely unclear whether these associations can be used in non-European populations. Here, we seek to quantify the proportion of genetic variation for a complex trait shared between continental populations. We estimated the between-population correlation of genetic effects at all SNPs ($$r_{g}$$ r g ) or genome-wide significant SNPs ($$r_{{g\left( {GWS} \right)}}$$ r g GWS ) for height and body mass index (BMI) in samples of European (EUR; $$n = 49,839$$ n = 49 , 839 ) and African (AFR; $$n = 17,426$$ n = 17 , 426 ) ancestry. The $$\hat{r}_{g}$$ r ^ g between EUR and AFR was 0.75 ($${\text{s}}.{\text{e}}. = 0.035$$ s . e . = 0.035 ) for height and 0.68 ($${\text{s}}.{\text{e}}. = 0.062$$ s . e . = 0.062 ) for BMI, and the corresponding $$\hat{r}_{{g\left( {GWS} \right)}}$$ r ^ g GWS was 0.82 ($${\text{s}}.{\text{e}}. = 0.030$$ s . e . = 0.030 ) for height and 0.87 ($${\text{s}}.{\text{e}}. = 0.064$$ s . e . = 0.064 ) for BMI, suggesting that a large proportion of GWAS findings discovered in Europeans are likely applicable to non-Europeans for height and BMI. There was no evidence that $$\hat{r}_{g}$$ r ^ g differs in SNP groups with different levels of between-population difference in allele frequency or linkage disequilibrium, which, however, can be due to the lack of power.

scholarly journals Sexual differences in genetic architecture in UK Biobank

2020 ◽  
Author(s):  
Elena Bernabeu ◽  
Oriol Canela-Xandri ◽  
Konrad Rawlik ◽  
Andrea Talenti ◽  
James Prendergast ◽  
...  
Keyword(s):  
Sexual Dimorphism ◽  
Complex Traits ◽  
Genetic Architecture ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Mammalian Species ◽  
Genetic Correlations ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Uk Biobank

ABSTRACTSex is arguably the most important differentiating characteristic in most mammalian species, separating populations into different groups, with varying behaviors, morphologies, and physiologies based on their complement of sex chromosomes. In humans, despite males and females sharing nearly identical genomes, there are differences between the sexes in complex traits and in the risk of a wide array of diseases. Gene by sex interactions (GxS) are thought to account for some of this sexual dimorphism. However, the extent and basis of these interactions are poorly understood.Here we provide insights into both the scope and mechanism of GxS across the genome of circa 450,000 individuals of European ancestry and 530 complex traits in the UK Biobank. We found small yet widespread differences in genetic architecture across traits through the calculation of sex-specific heritability, genetic correlations, and sex-stratified genome-wide association studies (GWAS). We also found that, in some cases, sex-agnostic GWAS efforts might be missing loci of interest, and looked into possible improvements in the prediction of high-level phenotypes. Finally, we studied the potential functional role of the dimorphism observed through sex-biased eQTL and gene-level analyses.This study marks a broad examination of the genetics of sexual dimorphism. Our findings parallel previous reports, suggesting the presence of sexual genetic heterogeneity across complex traits of generally modest magnitude. Our results suggest the need to consider sex-stratified analyses for future studies in order to shed light into possible sex-specific molecular mechanisms.

scholarly journals Quantifying genetic heterogeneity between continental populations for human height and body mass index

2019 ◽  
Author(s):  
Jing Guo ◽  
Andrew Bakshi ◽  
Ying Wang ◽  
Longda Jiang ◽  
Loic Yengo ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Complex Traits ◽  
Association Studies ◽  
Significant Snps ◽  
Complex Trait ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Population Difference ◽  
Genome Wide

AbstractGenome-wide association studies (GWAS) in samples of European ancestry have identified thousands of genetic variants associated with complex traits in humans. However, it remains largely unclear whether these associations can be used in non-European populations. Here, we seek to quantify the proportion of genetic variation for a complex trait shared between continental populations. We estimated the between-population correlation of genetic effects at all SNPs (rg) or genome-wide significant SNPs (rg(GWS)) for height and body mass index (BMI) in samples of European (EUR; n = 49,839) and African (AFR; n = 17,426) ancestry. The between EUR and AFR was 0.75 (s. e. = 0.035) for height and 0.68 (s. e. = 0.062) for BMI, and the corresponding was 0.82 (s. e. = 0.030) for height and 0.87 (s. e. = 0.064) for BMI, suggesting that a large proportion of GWAS findings discovered in Europeans are likely applicable to non-Europeans for height and BMI. There was no evidence that differs in SNP groups with different levels of between-population difference in allele frequency or linkage disequilibrium, which, however, can be due to the lack of power.

scholarly journals Quantifying the impact of genetically regulated expression on complex traits and diseases

2019 ◽  
Author(s):  
Mingxuan Cai ◽  
Lin Chen ◽  
Jin Liu ◽  
Can Yang
Keyword(s):  
Complex Traits ◽  
Wide Spectrum ◽  
Association Studies ◽  
Strong Association ◽  
Significant Proportion ◽  
Reference Panel ◽  
Genome Wide Association Studies ◽  
Regulated Expression ◽  
Phenotypic Variations ◽  
The Impact

About 90% of risk variants identified from genome-wide association studies (GWAS) are located in non-coding regions, highlighting the regulatory role of genetic variants. We propose a unified statistical framework, IGREX, for quantifying the impact of genetically regulated expression (GREX). This is achieved by estimating proportion of phenotypic variations that can be explained by the GREX component. IGREX only requires summary-level GWAS data and a gene expression reference panel as input. In real data analysis, using 48 tissues from the GTEx project as the reference panel, we applied IGREX to a wide spectrum of phenotypes in GWAS, and observed a significant proportion of phenotypic variations could be attributed to the GREX component. In particular, the results given by IGREX revealed tissue-across and tissue-specific patterns of the GREX effects. We also observed strong association between GREX effect and immune-related proteins, further supporting the relevance between GREX and the immune processes.

scholarly journals The UCR Minicore: a valuable resource for cowpea research and breeding

2021 ◽  
Author(s):  
María Muñoz-Amatriaín ◽  
Sassoum Lo ◽  
Ira A. Herniter ◽  
Ousmane Boukar ◽  
Christian Fatokun ◽  
...  
Keyword(s):  
Complex Traits ◽  
Agronomic Traits ◽  
Association Studies ◽  
Geographic Origin ◽  
Principal Component ◽  
Significant Snps ◽  
High Density ◽  
Genome Wide Association Studies ◽  
Biotic Stresses ◽  
Cultivar Group

ABSTRACTIncorporation of new sources of genetic diversity into plant breeding programs is crucial for continuing to improve yield and quality, as well as tolerance to abiotic and biotic stresses. A minicore (the “UCR Minicore”) composed of 368 worldwide accessions of cultivated cowpea has been assembled, having been derived from the University of California, Riverside cowpea collection. High-density genotyping with 51,128 SNPs followed by principal component and genetic assignment analyses identified six subpopulations in the UCR Minicore, mainly differentiated by cultivar group and geographic origin. All six subpopulations were present to some extent in West African material, suggesting that West Africa is a center of diversity for cultivated cowpea. Additionally, population structure analyses supported two routes of introduction of cowpea into the U.S.: (1) from Spain to the southwest U.S. through Northern Mexico, and (2) from Africa to the southeast U.S. via the Caribbean. Genome-wide association studies (GWAS) of important agronomic traits including flowering time, resulted in the identification of significant SNPs for all traits and environments. The mapping resolution achieved by high-density genotyping of this diverse minicore collection allowed the identification of strong candidate genes, including orthologs of the Arabidopsis FLOWERING LOCUS T. In summary, this diverse, yet compact cowpea collection constitutes a suitable resource to identify loci controlling complex traits, consequently providing markers to assist with breeding to improve this crop of high relevance to global food and nutritional security.

scholarly journals Family-based gene-environment interaction using sequence kernel association test (FGE-SKAT) for complex quantitative traits

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chao-Yu Guo ◽  
Reng-Hong Wang ◽  
Hsin-Chou Yang
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Association Test ◽  
Whole Genome Sequence ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Sequence Kernel Association Test ◽  
Gene Environment ◽  
Family Based

AbstractAfter the genome-wide association studies (GWAS) era, whole-genome sequencing is highly engaged in identifying the association of complex traits with rare variations. A score-based variance-component test has been proposed to identify common and rare genetic variants associated with complex traits while quickly adjusting for covariates. Such kernel score statistic allows for familial dependencies and adjusts for random confounding effects. However, the etiology of complex traits may involve the effects of genetic and environmental factors and the complex interactions between genes and the environment. Therefore, in this research, a novel method is proposed to detect gene and gene-environment interactions in a complex family-based association study with various correlated structures. We also developed an R function for the Fast Gene-Environment Sequence Kernel Association Test (FGE-SKAT), which is freely available as supplementary material for easy GWAS implementation to unveil such family-based joint effects. Simulation studies confirmed the validity of the new strategy and the superior statistical power. The FGE-SKAT was applied to the whole genome sequence data provided by Genetic Analysis Workshop 18 (GAW18) and discovered concordant and discordant regions compared to the methods without considering gene by environment interactions.

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