Abstract
Genomic prediction has been widely used in multiple areas and various genomic prediction methods have been developed. The majority of these methods, however, focus on statistical properties and ignore the abundant useful biological information like genome annotation or previously discovered causal variants. Therefore, to improve prediction performance, several methods have been developed to incorporate biological information into genomic prediction, mostly in single-trait analysis. A commonly used method to incorporate biological information is allocating molecular markers into different classes based on the biological information and assigning separate priors to molecular markers in different classes. It has been shown that such methods can achieve higher prediction accuracy than conventional methods in some circumstances. However, these methods mainly focus on single-trait analysis, and available priors of these methods are limited. Thus, in both single-trait and multiple-trait analysis, we propose the multi-class Bayesian Alphabet methods, in which multiple Bayesian Alphabet priors, including RR-BLUP, BayesA, BayesB, BayesCΠ, and Bayesian LASSO, can be used for markers allocated to different classes. The superior performance of the multi-class Bayesian Alphabet in genomic prediction is demonstrated using both real and simulated data. The software tool JWAS offers open-source routines to perform these analyses.
Highlights
Genomic prediction, proposed by Meuwissen et al (2001), utilizes genomic information, such as single-nucleotide polymorphisms (SNPs), to estimate genotypic values or breeding values of complex traits
Most genomic prediction methods usually assume all marker effects share the same prior distribution. This assumption, is not biologically meaningful and may potentially reduce the prediction performance when genetic architectures vary across different genomic regions (Speed and Balding, 2014)
In this study, we presented the multi-class Bayesian Alphabet methods, which can perform both single-trait and multipletrait analysis and provide multiple Bayesian Alphabet priors for markers allocated to different classes
Summary
Genomic prediction, proposed by Meuwissen et al (2001), utilizes genomic information, such as single-nucleotide polymorphisms (SNPs), to estimate genotypic values or breeding values of complex traits. Accompanied by the high-density data, genomic prediction has been widely used in many areas, including animal breeding (e.g., Hayes et al, 2009a; Erbe et al, 2012), plant breeding (e.g., Wang et al, 2018; Moeinizade et al, 2020), and human disease risk prediction (e.g., Abraham et al, 2014, 2016). A large number of genomic prediction methods with different statistical assumptions have been developed. Among these methods, genomic best linear unbiased prediction (GBLUP) (Habier et al, 2007; VanRaden, 2008; Hayes et al, 2009b), where a genomic relationship matrix is used to accommodate the covariances among breeding values, is widely used.
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