Joint prediction of multiple quantitative traits using a Bayesian multivariate antedependence model

Predicting organismal phenotypes from genotype data is important for preventive and personalized medicine as well as plant and animal breeding. Although genome-wide association studies (GWAS) for complex traits have discovered a large number of trait- and disease-associated variants, phenotype prediction based on associated variants is usually in low accuracy even for a high-heritability trait because these variants can typically account for a limited fraction of total genetic variance. In comparison with GWAS, the whole-genome prediction (WGP) methods can increase prediction accuracy by making use of a huge number of variants simultaneously. Among various statistical methods for WGP, multiple-trait model and antedependence model show their respective advantages. To take advantage of both strategies within a unified framework, we proposed a novel multivariate antedependence-based method for joint prediction of multiple quantitative traits using a Bayesian algorithm via modeling a linear relationship of effect vector between each pair of adjacent markers. Through both simulation and real-data analyses, our studies demonstrated that the proposed antedependence-based multiple-trait WGP method is more accurate and robust than corresponding traditional counterparts (Bayes A and multi-trait Bayes A) under various scenarios. Our method can be readily extended to deal with missing phenotypes and resequence data with rare variants, offering a feasible way to jointly predict phenotypes for multiple complex traits in human genetic epidemiology as well as plant and livestock breeding.

基于基因型数据预测生物体表型，对于预防医学与个性化医疗，以及动植物育种均具有重要意义。尽管针对复杂性状的全基因组关联分析（Genome-Wide Association Studies, GWAS）已发现大量与性状及疾病相关的遗传变异，但基于此类关联变异的表型预测精度通常较低——即便对于高遗传力性状亦是如此，原因在于这些变异仅能解释总遗传方差的有限部分。与GWAS相比，全基因组预测（Whole-Genome Prediction, WGP）方法可通过同时利用海量遗传变异，有效提升表型预测精度。在各类WGP统计方法中，多性状模型与前依懒模型（antedependence model）各有独到优势。为在统一框架下融合两种策略的优势，本研究提出一种基于前依懒模型的新型多变量方法：通过对每对相邻标记间的效应向量线性关系进行建模，结合贝叶斯算法完成多数量性状的联合预测。通过模拟分析与真实数据分析，本研究证实：在所覆盖的各类场景下，所提出的基于前依懒模型的多性状WGP方法，其预测精度与稳健性均优于传统对应方法（贝叶斯A（Bayes A）与多性状贝叶斯A（multi-trait Bayes A））。本方法可便捷拓展至缺失表型与携带稀有变异的重测序数据分析场景，为人类遗传流行病学、动植物育种中的多复杂性状表型联合预测提供了切实可行的解决方案。