Data from: 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. Though 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 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 Study, GWAS）已发现大量与性状及疾病相关的遗传变异，但基于这些关联变异开展的表型预测精度通常较低，即便针对高遗传力性状亦是如此，原因在于此类变异仅能解释总遗传方差中的有限份额。相较于GWAS，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，提升表型预测精度。在各类全基因组预测统计方法中，多性状模型与前依模型（antedependence model）各有优势。为在统一框架下兼顾两种策略的优势，我们提出了一种基于前依模型的新型多变量方法，通过建模相邻标记间效应向量的线性关联，结合贝叶斯算法实现多数量性状的联合预测。通过模拟与真实数据分析，本研究证实：在多种场景下，所提出的基于前依模型的多性状WGP方法，相较于传统对应方法（贝叶斯A与多性状贝叶斯A），具备更高的预测精度与稳健性。该方法可便捷扩展以处理缺失表型数据与携带罕见变异的重测序数据，为人类遗传流行病学、动植物育种中的多复杂性状联合表型预测提供了可行途径。