We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Mega-scale Bayesian regression methods for genome-wide prediction and association studies with thousands of traits.
- Authors
Jiayi Qu; Runcie, Daniel; Hao Cheng
- 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 traits are challenging. We developed a method for implementing high-dimensional Bayesian multivariate regression to simultaneously analyze genetic variants underlying thousands of traits. As a demonstration, we implemented the BayesC prior in the R package MegaLMM. Applied to Genomic Prediction, MegaBayesC effectively integrated hyperspectral reflectance data from 620 hyperspectral wavelengths to improve the accuracy of genetic value prediction on grain yield in a wheat dataset. Applied to Genome-Wide Association Studies, we used simulations to show that MegaBayesC can accurately estimate the effect sizes of QTL across a range of genetic architectures and causes of correlations among traits. To apply MegaBayesC to a realistic scenario involving whole-genome marker data, we developed a 2-stage procedure involving a preliminary step of candidate marker selection prior to multivariate regression. We then used MegaBayesC to identify genetic associations with flowering time in Arabidopsis thaliana, leveraging expression data from 20,843 genes. MegaBayesC selected 15 single nucleotide polymorphisms as important for flowering time, with 13 located within 100 kb of known flowering-time related genes, a higher validation rate than achieved by a single-stage analysis using only the flowering time data itself. These results demonstrate that MegaBayesC can efficiently and effectively leverage high-dimensional phenotypes in genetic analyses.
- Subjects
PERSONALITY; MULTIVARIATE analysis; SIMULATION methods in education; GENOMICS; RESEARCH funding; STATISTICAL models; PREDICTIVE validity; PROBABILITY theory; PHENOTYPES
- Publication
Genetics, 2023, Vol 223, Issue 3, p1
- ISSN
0016-6731
- Publication type
Article
- DOI
10.1093/genetics/iyac183