A Multiple-Trait Bayesian Variable Selection Regression Method for Integrating Phenotypic Causal Networks in Genome-Wide Association Studies

Wang, Zigui; Chapman, David C.; Morota, Gota; Cheng, Hao

doi:10.1534/g3.120.401618

Cited by 12 publications

(9 citation statements)

References 36 publications

(75 reference statements)

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“…similar to a single-SNP GWA for individual traits), with application to the estimation of the direct and indirect effects of SNPs on traits in broiler chickens. Wang et al [78] implemented multi-marker SEM-GWA BVS models, introducing the SEM Bayesian Alphabet. They showed that SEM-GWA BVS models have similar or greater power to detect QTL than multi-trait BVS, but provide greater insight into biological mechanisms of the effects of QTL on traits through direct and indirect effects.…”

Section: Gwa Using Bayesian Structural Equations Modelsmentioning

confidence: 99%

Application of Bayesian genomic prediction methods to genome-wide association analyses

Wolc

Dekkers

2022

Genet Sel Evol

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Background Bayesian genomic prediction methods were developed to simultaneously fit all genotyped markers to a set of available phenotypes for prediction of breeding values for quantitative traits, allowing for differences in the genetic architecture (distribution of marker effects) of traits. These methods also provide a flexible and reliable framework for genome-wide association (GWA) studies. The objective here was to review developments in Bayesian hierarchical and variable selection models for GWA analyses. Results By fitting all genotyped markers simultaneously, Bayesian GWA methods implicitly account for population structure and the multiple-testing problem of classical single-marker GWA. Implemented using Markov chain Monte Carlo methods, Bayesian GWA methods allow for control of error rates using probabilities obtained from posterior distributions. Power of GWA studies using Bayesian methods can be enhanced by using informative priors based on previous association studies, gene expression analyses, or functional annotation information. Applied to multiple traits, Bayesian GWA analyses can give insight into pleiotropic effects by multi-trait, structural equation, or graphical models. Bayesian methods can also be used to combine genomic, transcriptomic, proteomic, and other -omics data to infer causal genotype to phenotype relationships and to suggest external interventions that can improve performance. Conclusions Bayesian hierarchical and variable selection methods provide a unified and powerful framework for genomic prediction, GWA, integration of prior information, and integration of information from other -omics platforms to identify causal mutations for complex quantitative traits.

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Section: Gwa Using Bayesian Structural Equations Modelsmentioning

confidence: 99%

Application of Bayesian genomic prediction methods to genome-wide association analyses

Wolc

Dekkers

2022

Genet Sel Evol

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“…Genome windows with WPPA ≥0.8 (Wang et al, 2020) were considered as possible QTL regions associated with the studied traits. Candidate genes were searched for 1-Mb window using the Ensembl database and the Map Viewer tool of the bovine genome based on the starting and ending coordinates of significant windows.…”

Section: Identification Of Candidate Genes and Functional Enrichment Analysismentioning

confidence: 99%

Genome-wide Association Study for Carcass Primal Cut Yields Using Single-step Bayesian Approach in Hanwoo Cattle

et al. 2021

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The importance of meat and carcass quality is growing in beef cattle production to meet both producer and consumer demands. Primal cut yields, which reflect the body compositions of carcass, could determine the carcass grade and, consequently, command premium prices. Despite its importance, there have been few genome-wide association studies on these traits. This study aimed to identify genomic regions and putative candidate genes related to 10 primal cut traits, including tenderloin, sirloin, striploin, chuck, brisket, top round, bottom round, shank, flank, and rib in Hanwoo cattle using a single-step Bayesian regression (ssBR) approach. After genomic data quality control, 43,987 SNPs from 3,745 genotyped animals were available, of which 3,467 had phenotypic records for the analyzed traits. A total of 16 significant genomic regions (1-Mb window) were identified, of which five large-effect quantitative trait loci (QTLs) located on chromosomes 6 at 38–39 Mb, 11 at 21–22 Mb, 14 at 6–7 Mb and 26–27 Mb, and 19 at 26–27 Mb were associated with more than one trait, while the remaining 11 QTLs were trait-specific. These significant regions were harbored by 154 genes, among which TOX, FAM184B, SPP1, IBSP, PKD2, SDCBP, PIGY, LCORL, NCAPG, and ABCG2 were noteworthy. Enrichment analysis revealed biological processes and functional terms involved in growth and lipid metabolism, such as growth (GO:0040007), muscle structure development (GO:0061061), skeletal system development (GO:0001501), animal organ development (GO:0048513), lipid metabolic process (GO:0006629), response to lipid (GO:0033993), metabolic pathways (bta01100), focal adhesion (bta04510), ECM–receptor interaction (bta04512), fat digestion and absorption (bta04975), and Rap1 signaling pathway (bta04015) being the most significant for the carcass primal cut traits. Thus, identification of quantitative trait loci regions and plausible candidate genes will aid in a better understanding of the genetic and biological mechanisms regulating carcass primal cut yields.

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“…In this paper, we incorporate whole-genome regression approaches into a Bayesian sparse factor model named MegaBayesianAlphabet to incorporate thousands of traits for genome-wide prediction and association studies. The Bayesian Alphabet methods with mixture priors on marker effects (Kizilkaya et al 2010; Habier et al 2011; Moser et al 2015; Wolc et al 2016; Mehrban et al 2017; Wang et al 2020) are popular genetic models due to their incorporation of biologically meaningful assumptions and the variable selection procedure performed during model fitting. We focus on BayesC as an example of a Bayesian Alphabet method (Kizilkaya et al 2010; Habier et al .…”

Section: Introductionmentioning

confidence: 99%

MegaBayesianAlphabet: Mega-scale Bayesian Regression methods for genome-wide prediction and association studies with thousands of traits

Runcie

Cheng

2022

Preprint

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Large-scale phenotype data are expected to increase the accuracy of genome-wide prediction and the power of genome-wide association analyses. However, genomic analyses of high-dimensional, highly correlated data are challenging. We developed MegaBayesianAlphabet to simultaneously analyze genetic variants underlying thousands of traits using the flexible priors of the Bayesian Alphabet family. As a demonstration, we implemented the BayesC prior in the R package MegaLMM and applied it to both simulated and real data sets. Our analyses show that the resulting model MegaBayesC can effectively use high-dimensional phenotypic data to improve the accuracy of genetic value prediction, the reliability of marker discovery, and the accuracy of marker effect size estimation in genome-wide analyses.

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A Multiple-Trait Bayesian Variable Selection Regression Method for Integrating Phenotypic Causal Networks in Genome-Wide Association Studies

Cited by 12 publications

References 36 publications

Application of Bayesian genomic prediction methods to genome-wide association analyses

Application of Bayesian genomic prediction methods to genome-wide association analyses

Genome-wide Association Study for Carcass Primal Cut Yields Using Single-step Bayesian Approach in Hanwoo Cattle

MegaBayesianAlphabet: Mega-scale Bayesian Regression methods for genome-wide prediction and association studies with thousands of traits

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