Simultaneous discovery, estimation and prediction analysis of complex traits using a bayesian mixture model.
Simultaneous discovery, estimation and prediction analysis of complex traits using a bayesian mixture model.
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DOI：
10.1371/journal.pgen.1004969
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发表时间：
2015-04
期刊：
影响因子：
4.5
通讯作者：
Visscher PM
中科院分区：
文献类型：
--
作者：
Moser G;Lee SH;Hayes BJ;Goddard ME;Wray NR;Visscher PM
基因发现、SNP阵列遗传力估计、遗传结构推断和复杂性状的预测分析通常使用不同的统计模型和方法进行，导致效率低下和功率损失。在这里，我们使用贝叶斯混合模型，同时允许变异发现，估计遗传方差解释的所有变异和预测未观察到的表型在新的样品。我们将该方法应用于数量性状的模拟数据和Welcome Trust Case Control Consortium（WTCCC）关于疾病的数据，并表明它提供了基于SNP的遗传力的准确估计，在新样本中产生风险的无偏估计值，并且它可以通过在数百到数千个SNP中划分变异来估计遗传结构。我们估计，根据性状，2，633至9，411个SNP解释了WTCCC疾病中所有基于SNP的遗传力。这些SNP中的大多数（> 96%）具有较小的影响，证实了常见疾病的实质性多基因成分。由大效应解释的基于SNP的方差的比例（每个SNP解释1%的方差）在疾病之间存在显著差异，从双相情感障碍的几乎为零到1型糖尿病的72%。预测分析表明，对于具有主要位点的疾病，如1型糖尿病和类风湿性关节炎，贝叶斯方法优于轮廓评分或混合模型方法。迄今为止，大多数全基因组关联研究都集中在测试个体遗传标记与表型的关联。最近，分析多个标记对遗传变异的联合作用的方法为复杂人类性状的遗传基础提供了进一步的见解。此外，人们对使用基因型数据进行疾病的遗传风险预测越来越感兴趣。通常，这些任务中的每一项都使用不同的分析方法。我们提出了一种灵活的新方法，同时进行识别的易感基因座，推理的遗传结构，并提供多基因的风险预测在同一统计模型。我们通过考虑模拟和真实的数据来说明该方法的广泛适用性。在7种常见疾病的分析中，我们发现由于不同效应大小的基因座的遗传变异比例存在很大差异，复杂性状之间的预测准确性也存在差异。这些发现对于未来的研究和了解常见疾病的复杂遗传结构非常重要。
Gene discovery, estimation of heritability captured by SNP arrays, inference on genetic architecture and prediction analyses of complex traits are usually performed using different statistical models and methods, leading to inefficiency and loss of power. Here we use a Bayesian mixture model that simultaneously allows variant discovery, estimation of genetic variance explained by all variants and prediction of unobserved phenotypes in new samples. We apply the method to simulated data of quantitative traits and Welcome Trust Case Control Consortium (WTCCC) data on disease and show that it provides accurate estimates of SNP-based heritability, produces unbiased estimators of risk in new samples, and that it can estimate genetic architecture by partitioning variation across hundreds to thousands of SNPs. We estimated that, depending on the trait, 2,633 to 9,411 SNPs explain all of the SNP-based heritability in the WTCCC diseases. The majority of those SNPs (>96%) had small effects, confirming a substantial polygenic component to common diseases. The proportion of the SNP-based variance explained by large effects (each SNP explaining 1% of the variance) varied markedly between diseases, ranging from almost zero for bipolar disorder to 72% for type 1 diabetes. Prediction analyses demonstrate that for diseases with major loci, such as type 1 diabetes and rheumatoid arthritis, Bayesian methods outperform profile scoring or mixed model approaches. Most genome-wide association studies performed to date have focused on testing individual genetic markers for associations with phenotype. Recently, methods that analyse the joint effects of multiple markers on genetic variation have provided further insights into the genetic basis of complex human traits. In addition, there is increasing interest in using genotype data for genetic risk prediction of disease. Often disparate analytical methods are used for each of these tasks. We propose a flexible novel approach that simultaneously performs identification of susceptibility loci, inference on the genetic architecture and provides polygenic risk prediction in the same statistical model. We illustrate the broad applicability of the approach by considering both simulated and real data. In the analysis of seven common diseases we show large differences in the proportion of genetic variation due to loci with different effect sizes and differences in prediction accuracy between complex traits. These findings are important for future studies and the understanding of the complex genetic architecture of common diseases.
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