Abstract P455: Epistatic Regulation of Hematological-related Traits and Gene Expression in Mice Reveals Additional Heritability Missed by Standard GWAS-type Analyses

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Anna Miller ◽  
Anlu Chen ◽  
David Buchner ◽  
Scott Williams
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Association Studies ◽  
Additive Models ◽  
Blood Analysis ◽  
Gene Interactions ◽  
Genome Wide Association Studies ◽  
Epistatic Qtls ◽  
Distribution Width ◽  
Additive Qtls

The genetic contribution of additive versus non-additive (epistasis) effects in the regulation of hematologic and other complex traits is unclear. Although many variants have been associated with a range of complex traits via genome wide association studies (GWAS), these loci combined in additive models do not account for most of the trait heritability. GWAS-type analyses typically ignore gene-gene interactions, in part because of the difficulty in detecting them in complex multicellular organisms, especially humans. We have previously shown that mouse chromosome substitution strains (CSSs) are a powerful model for detecting epistasis, and that for certain complex traits the relative contribution of epistasis to heritability is as important as additivity. We have now applied the use of these CSSs to identify and map additive and epistatic loci that regulate a range of hematological-related traits and hepatic gene expression levels. A modified backcross was performed with CSS strains carrying the A/J-derived substituted chromosomes 4 and 6 on an otherwise C57BL/6J genetic background. By analyzing the transcriptomes of offspring from this cross, we identified and mapped additive quantitative trait loci (QTLs) that regulated the expression of 770 genes, and epistatic QTLs for 802 genes. Similarly we performed a complete blood analysis of offspring from the cross and identified additive QTLs for platelets and percentage of granulocyte in the blood as well as epistatic QTLs controlling the percentage of lymphocytes in the blood (rs13477644, rs13478739; LOD = 3.4) and red cell distribution width (rs13477864, rs13478802; LOD = 3.7). The variance attributable to the epistatic QTLs was approximately equal to that of the additive QTLs, highlighting the importance of identifying genetic interactions. Of note, even the SNPs associated with the most significant epistatic interactions were undetected in our single loci GWAS-like association analyses, demonstrating the need to specifically test for gene-gene interactions in studies of complex traits. In summary, our studies identified epistatic loci in mice that are important regulators of hematological-related traits and gene expression. Additionally, our studies call attention to the importance of extending single loci GWAS-type analyses to include analyses of gene-gene interactions to improve our ability to identify genetic variants that regulate complex traits.

scholarly journals A novel mapping strategy utilizing mouse chromosome substitution strains identifies multiple epistatic interactions that regulate complex traits

2020 ◽  
Author(s):  
Anna K. Miller ◽  
Anlu Chen ◽  
Jacquelaine Bartlett ◽  
Li Wang ◽  
Scott M. Williams ◽  
...  
Keyword(s):  
Mouse Chromosome ◽  
Complex Traits ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Chromosome Substitution ◽  
Distribution Width ◽  
Epistatic Qtl ◽  
Hepatic Expression ◽  
Chromosome Substitution Strains ◽  
Additive Qtls

AbstractThe genetic contribution of additive versus non-additive (epistatic) effects in the regulation of complex traits is unclear. While genome-wide association studies typically ignore gene-gene interactions, in part because of the lack of statistical power for detecting them, mouse chromosome substitution strains (CSSs) represent an alternate and powerful model for detecting epistasis given their limited allelic variation. Therefore, we utilized CSSs to identify and map both additive and epistatic loci that regulate a range of hematologic- and metabolism-related traits, as well as hepatic gene expression. Quantitative trait loci (QTLs) were identified using a CSS-based backcross strategy involving the segregation of variants on the A/J-derived substituted chromosomes 4 and 6 on an otherwise C57BL/6J genetic background. In the liver transcriptomes of offspring from this cross, we identified and mapped additive QTLs regulating the hepatic expression of 768 genes, and epistatic QTL pairs for 519 genes. Similarly, we identified additive QTLs for fat pad weight, platelets, and the percentage of granulocytes in blood, as well as epistatic QTL pairs controlling the percentage of lymphocytes in blood and red cell distribution width. The variance attributed to the epistatic QTL pairs was approximately equal to that of the additive QTLs; however, the SNPs in the epistatic QTL pairs that accounted for the largest variances were undetected in our single locus association analyses. These findings highlight the need to account for epistasis in association studies, and more broadly demonstrate the importance of identifying genetic interactions to understand the complete genetic architecture of complex traits.

scholarly journals A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome

2019 ◽  
Author(s):  
Tom G Richardson ◽  
Gibran Hemani ◽  
Tom R Gaunt ◽  
Caroline L Relton ◽  
George Davey Smith
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Complex Traits ◽  
Mendelian Randomization ◽  
Drug Repositioning ◽  
Association Studies ◽  
Thyroid Tissue ◽  
Genome Wide Association Studies ◽  
Tissue Specific ◽  
Genome Wide

AbstractBackgroundDeveloping insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. By applying the principles of Mendelian randomization, we have undertaken a systematic analysis to evaluate transcriptome-wide associations between gene expression across 48 different tissue types and 395 complex traits.ResultsOverall, we identified 100,025 gene-trait associations based on conventional genome-wide corrections (P < 5 × 10−08) that also provided evidence of genetic colocalization. These results indicated that genetic variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. We identified many examples of tissue-specific effects, such as genetically-predicted TPO, NR3C2 and SPATA13 expression only associating with thyroid disease in thyroid tissue. Additionally, FBN2 expression was associated with both cardiovascular and lung function traits, but only when analysed in heart and lung tissue respectively.We also demonstrate that conducting phenome-wide evaluations of our results can help flag adverse on-target side effects for therapeutic intervention, as well as propose drug repositioning opportunities. Moreover, we find that exploring the tissue-dependency of associations identified by genome-wide association studies (GWAS) can help elucidate the causal genes and tissues responsible for effects, as well as uncover putative novel associations.ConclusionsThe atlas of tissue-dependent associations we have constructed should prove extremely valuable to future studies investigating the genetic determinants of complex disease. The follow-up analyses we have performed in this study are merely a guide for future research. Conducting similar evaluations can be undertaken systematically at http://mrcieu.mrsoftware.org/Tissue_MR_atlas/.

scholarly journals Trans effects on gene expression can drive omnigenic inheritance

2018 ◽  
Author(s):  
Xuanyao Liu ◽  
Yang I Li ◽  
Jonathan K Pritchard
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Formal Model ◽  
Association Studies ◽  
Direct Consequence ◽  
Large Contribution ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Genetic Contributions ◽  
Core Genes

Early genome-wide association studies (GWAS) led to the surprising discovery that, for typical complex traits, the most significant genetic variants contribute only a small fraction of the estimated heritability. Instead, it has become clear that a huge number of common variants, each with tiny effects, explain most of the heritability. Previously, we argued that these patterns conflict with standard conceptual models, and that new models are needed. Here we provide a formal model in which genetic contributions to complex traits can be partitioned into direct effects from core genes, and indirect effects from peripheral genes acting as trans-regulators. We argue that the central importance of peripheral genes is a direct consequence of the large contribution of trans-acting variation to gene expression variation. In particular, we propose that if the core genes for a trait are co-regulated – as seems likely – then the effects of peripheral variation can be amplified by these co-regulated networks such that nearly all of the genetic variance is driven by peripheral genes. Thus our model proposes a framework for understanding key features of the architecture of complex traits.

scholarly journals Comparison of GWAS models to identify non-additive genetic control of flowering time in sunflower hybrids

2017 ◽  
Author(s):  
Fanny Bonnafous ◽  
Ghislain Fievet ◽  
Nicolas Blanchet ◽  
Marie-Claude Boniface ◽  
Sébastien Carrère ◽  
...  
Keyword(s):  
Quantitative Trait Loci ◽  
Flowering Time ◽  
Genetic Control ◽  
Quantitative Trait ◽  
Complex Traits ◽  
Association Studies ◽  
Additive Models ◽  
Genome Wide Association Studies ◽  
Trait Loci ◽  
Genomic Regions

AbstractGenome-wide association studies are a powerful and widely used tool to decipher the genetic control of complex traits. One of the main challenges for hybrid crops, such as maize or sunflower, is to model the hybrid vigor in the linear mixed models, considering the relatedness between individuals. Here, we compared two additive and three non-additive association models for their ability to identify genomic regions associated with flowering time in sunflower hybrids. A panel of 452 sunflower hybrids, corresponding to incomplete crossing between 36 male lines and 36 female lines, was phenotyped in five environments and genotyped for 2,204,423 SNPs. Intra-locus effects were estimated in multi-locus models to detect genomic regions associated with flowering time using the different models. Thirteen quantitative trait loci were identified in total, two with both model categories and one with only non-additive models. A quantitative trait loci on LG09, detected by both the additive and non-additive models, is located near a GAI homolog and is presented in detail. Overall, this study shows the added value of non-additive modeling of allelic effects for identifying genomic regions that control traits of interest and that could participate in the heterosis observed in hybrids.

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 Effect sizes of causal variants for gene expression and complex traits differ between populations

2021 ◽  
Author(s):  
Roshni A. Patel ◽  
Shaila A. Musharoff ◽  
Jeffrey P. Spence ◽  
Harold Pimentel ◽  
Catherine Tcheandjieu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Association Studies ◽  
Causal Variant ◽  
Effect Sizes ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Polygenic Scores ◽  
Causal Variants ◽  
Variant Effect

Despite the growing number of genome-wide association studies (GWAS) for complex traits, it remains unclear whether effect sizes of causal genetic variants differ between populations. In principle, effect sizes of causal variants could differ between populations due to gene-by-gene or gene-by-environment interactions. However, comparing causal variant effect sizes is challenging: it is difficult to know which variants are causal, and comparisons of variant effect sizes are confounded by differences in linkage disequilibrium (LD) structure between ancestries. Here, we develop a method to assess causal variant effect size differences that overcomes these limitations. Specifically, we leverage the fact that segments of European ancestry shared between European-American and admixed African-American individuals have similar LD structure, allowing for unbiased comparisons of variant effect sizes in European ancestry segments. We apply our method to two types of traits: gene expression and low-density lipoprotein cholesterol (LDL-C). We find that causal variant effect sizes for gene expression are significantly different between European-Americans and African-Americans; for LDL-C, we observe a similar point estimate although this is not significant, likely due to lower statistical power. Cross-population differences in variant effect sizes highlight the role of genetic interactions in trait architecture and will contribute to the poor portability of polygenic scores across populations, reinforcing the importance of conducting GWAS on individuals of diverse ancestries and environments.

scholarly journals Scalable unified framework of total and allele-specific counts for cis-QTL, fine-mapping, and prediction

2020 ◽  
Author(s):  
Yanyu Liang ◽  
François Aguet ◽  
Alvaro Barbeira ◽  
Kristin Ardlie ◽  
Hae Kyung Im
Keyword(s):  
Gene Expression ◽  
Fine Mapping ◽  
Complex Traits ◽  
Association Studies ◽  
Average Power ◽  
Specific Gene ◽  
Genome Wide Association Studies ◽  
Unified Framework ◽  
Causal Genes ◽  
Allele Specific

AbstractGenome-wide association studies (GWAS) have been highly successful in identifying genomic loci associated with complex traits. However, identification of the causal genes that mediate these associations remains challenging, and many approaches integrating transcriptomic data with GWAS have been proposed. However, there currently exist no computationally scalable methods that integrate total and allele-specific gene expression to maximize power to detect genetic effects on gene expression. Here, we describe a unified framework that is scalable to studies with thousands of samples. Using simulations and data from GTEx, we demonstrate an average power gain equivalent to a 29% increase in sample size for genes with sufficient allele-specific read coverage. We provide a suite of freely available tools, mixQTL, mixFine, and mixPred, that apply this framework for mapping of quantitative trait loci, fine-mapping, and prediction.

scholarly journals Prediction of gene expression from regulatory sequence composition enhances transcriptome-wide association studies

2021 ◽  
Author(s):  
Federico Marotta ◽  
Reza Mozafari ◽  
Elena Grassi ◽  
Alessandro Lussana ◽  
Elisa Mariella ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regression Model ◽  
Complex Traits ◽  
Association Studies ◽  
Regulatory Region ◽  
Regulatory Sequence ◽  
Genome Wide Association Studies ◽  
Data Set ◽  
Sequence Composition ◽  
The Uk

Transcriptome-wide association studies (TWAS) can prioritize trait-associated genes by finding correlations between a trait and the genetically regulated component of gene expression. A basic ingredient of a TWAS is a regression model, typically trained in an external reference data set, used to impute the genetically-regulated expression. We devised a model that improves the accuracy of the imputation by using, as predictors, not the genotypes directly but rather the sequence composition of the proximal gene regulatory region, expressed as its profile of affinities for a set of position weight matrices. When trained on 48 tissues from GTEx, the regression model showed improved performance compared with models regressing expression directly on the genotype. We imputed the expression levels in genotyped individuals from the ADNI data set, and used the imputed expression to perform a TWAS. We also developed a method to perform the TWAS based on summary statistics from genome-wide association studies, and applied it to 11 complex traits from the UK Biobank. The greater accuracy in the prediction of gene expression allowed us to report hundreds of new gene-phenotype association candidates.

scholarly journals Transcriptome-wide association supplements genome-wide association in Zea mays

2018 ◽  
Author(s):  
Karl A. G. Kremling ◽  
Christine H. Diepenbrock ◽  
Michael A. Gore ◽  
Edward S. Buckler ◽  
Nonoy B. Bandillo
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Large Scale ◽  
New Technologies ◽  
Seed Quality ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Biological Organization ◽  
Genome Wide

AbstractModern improvement of complex traits in agricultural species relies on successful associations of heritable molecular variation with observable phenotypes. Historically, this pursuit has primarily been based on easily measurable genetic markers. The recent advent of new technologies allows assaying and quantifying biological intermediates (hereafter endophenotypes) which are now readily measurable at a large scale across diverse individuals. The potential of using endophenotypes for dissecting traits of interest remains underexplored in plants. The work presented here illustrated the utility of a large-scale (299 genotype and 7 tissue) gene expression resource to dissect traits across multiple levels of biological organization. Using single-tissue- and multi-tissue-based transcriptome-wide association studies (TWAS), we revealed that about half of the functional variation for agronomic and seed quality (carotenoid, tocochromanol) traits is regulatory. Comparing the efficacy of TWAS with genome-wide association studies (GWAS) and an ensemble approach that combines both GWAS and TWAS, we demonstrated that results of TWAS in combination with GWAS increase the power to detect known genes and aid in prioritizing likely causal genes. Using a variance partitioning approach in the independent maize Nested Association Mapping (NAM) population, we also showed that the most strongly associated genes identified by combining GWAS and TWAS explain more heritable variance for a majority of traits, beating the heritability captured by the random genes and the genes identified by GWAS or TWAS alone. This improves not only the ability to link genes to phenotypes, but also highlights the phenotypic consequences of regulatory variation in plants.Author summaryWe examined the ability to associate variability in gene expression directly with terminal phenotypes of interest, as a supplement linking genotype to phenotype. We found that transcriptome-wide association studies (TWAS) are a useful accessory to genome-wide association studies (GWAS). In a combined test with GWAS results, TWAS improves the capacity to re-detect genes known to underlie quantitative trait loci for kernel and agronomic phenotypes. This improves not only the capacity to link genes to phenotypes, but also illustrates the widespread importance of regulation for phenotype.

scholarly journals Integrating gene expression with summary association statistics to identify susceptibility genes for 30 complex traits

2016 ◽  
Author(s):  
Nicholas Mancuso ◽  
Huwenbo Shi ◽  
Pagé Goddard ◽  
Gleb Kichaev ◽  
Alexander Gusev ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Correlation ◽  
Complex Traits ◽  
Association Studies ◽  
Susceptibility Genes ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Causal Variants

AbstractAlthough genome-wide association studies (GWASs) have identified thousands of risk loci for many complex traits and diseases, the causal variants and genes at these loci remain largely unknown. We leverage recently introduced methods to integrate gene expression measurements from 45 expression panels with summary GWAS data to perform 30 transcriptome-wide association studies (TWASs). We identify 1,196 susceptibility genes whose expression is associated with these traits; of these, 168 reside more than 0.5Mb away from any previously reported GWAS significant variant, thus providing new risk loci. Second, we find 43 pairs of traits with significant genetic correlation at the level of predicted expression; of these, 8 are not found through genetic correlation at the SNP level. Third, we use bi-directional regression to find evidence for BMI causally influencing triglyceride levels, and triglyceride levels causally influencing LDL. Taken together, our results provide insights into the role of expression to susceptibility of complex traits and diseases.

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