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

Qu, J.; Runcie, D. E.; Cheng, H.

doi:10.1101/2022.05.06.490983

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bioRxiv · 10.1101/2022.05.06.490983

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

Qu, J.·Runcie, D. E.·Cheng, H.

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Abstract

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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Qu, J., Runcie, D. E., Cheng, H.. 2022-05-08. MegaBayesianAlphabet: Mega-scale Bayesian Regression methods for genome-wide prediction and association studies with thousands of traits. https://doi.org/10.1101/2022.05.06.490983

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