Jiang J, Zhang Q, Ma L, Li J, Wang Z, Liu J-F
Department of Animal Genetics, Breeding and Reproduction, China Agricultural University, Beijing, China.
Department of Animal and Avian Sciences, University of Maryland, College Park, MD, USA.
Heredity (Edinb). 2015 Jul;115(1):29-36. doi: 10.1038/hdy.2015.9. Epub 2015 Apr 15.
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.
从基因型数据预测生物体表型对于预防医学、个性化医疗以及动植物育种都非常重要。尽管针对复杂性状的全基因组关联研究（GWAS）已经发现了大量与性状和疾病相关的变异，但基于关联变异的表型预测通常准确性较低，即使对于高遗传力性状也是如此，因为这些变异通常仅占总遗传变异的有限部分。与GWAS相比，全基因组预测（WGP）方法可以通过同时利用大量变异来提高预测准确性。在用于WGP的各种统计方法中，多性状模型和前相依模型各有优势。为了在统一框架内利用这两种策略，我们提出了一种基于贝叶斯算法的新型多变量前相依方法，通过对每对相邻标记之间效应向量的线性关系进行建模，联合预测多个数量性状。通过模拟和实际数据分析，我们的研究表明，在各种情况下，所提出的基于前相依的多性状WGP方法比相应的传统方法（贝叶斯A和多性状贝叶斯A）更准确、更稳健。我们的方法可以很容易地扩展以处理缺失表型和带有稀有变异的重测序数据，为人类遗传流行病学以及动植物育种中多个复杂性状的联合表型预测提供了一种可行的方法。
