How powerful are summary-based methods for identifying expression-trait associations under different genetic architectures?
How powerful are summary-based methods for identifying expression-trait associations under different genetic architectures?
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DOI：
10.1142/9789813235533_0021
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发表时间：
2018-01
影响因子：
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通讯作者：
Y. Veturi;M. Ritchie
中科院分区：
文献类型：
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作者：
Y. Veturi;M. Ritchie
全转录组关联研究（TWAS）最近被用作一种方法，可以利用全基因组关联研究（GWAS）和基因表达研究的优点来识别与复杂性状相关的基因。与标准GWAS不同，汇总级数据足以满足TWAS的需求，并提供了更高的统计能力。两种流行的TWAS方法包括（a）将来自较小规模研究（使用多SNP预测或MP）的基因表达的顺式遗传组分估算到GWAS提供的更大有效样本量中- TWAS-MP或（B）使用基于汇总的孟德尔随机化- TWAS-SMR。虽然这些方法在检测功能变异方面是有效的，但目前尚不清楚复杂性状和疾病的遗传结构的广泛变异性如何影响TWAS结果。我们的目标是调查不同的情况下，这些方法产生足够的权力，以检测显着的表达性状协会。在这项研究中，我们进行了广泛的模拟的基础上，随机选择的6000名无关的高加索男性从Geisinger的MyCode人口比较的权力，以检测顺式表达性状协会（500 kb内的基因）使用上述方法。为了在不同的遗传背景下测试TWAS，我们使用每个基因的不同数量性状位点和遗传模型下的顺式表达/性状遗传力来模拟基因表达和表型，所述遗传模型将因果关系的影响与多效性的影响区分开。对于每个基因，在100至1000个个体的训练集上，我们（a）使用五种不同的方法估计回归系数，其中基因表达作为响应：LASSO，弹性网，贝叶斯LASSO，贝叶斯钉板和贝叶斯岭回归或（B）进行eQTL分析。然后，我们从剩下的5000个个体的测试集中分别抽取了50，000、150，000和300，000个个体，并对每个集合进行了GWAS。随后，我们将来自测试集的GWAS汇总统计量与来自训练集的权重（或eQTL）进行整合，以使用（a）TWAS-MP（B）TWAS-SMR（c）基于eQTL的GWAS或（d）独立GWAS识别表达-性状关联。最后，我们研究了在所考虑的模拟场景下使用不同方法检测功能相关基因的能力。总的来说，我们观察到TWAS-MP方法之间有很大的相似性，尽管贝叶斯方法与LASSO和弹性网络相比提高了功效，因为特征结构变得更加复杂，而训练样本量和表达遗传力仍然很小。最后，我们观察到因果关系下的高功效，但多效性下的功效非常低至中等。
Transcriptome-wide association studies (TWAS) have recently been employed as an approach that can draw upon the advantages of genome-wide association studies (GWAS) and gene expression studies to identify genes associated with complex traits. Unlike standard GWAS, summary level data suffices for TWAS and offers improved statistical power. Two popular TWAS methods include either (a) imputing the cis genetic component of gene expression from smaller sized studies (using multi-SNP prediction or MP) into much larger effective sample sizes afforded by GWAS –- TWAS-MP or (b) using summary-based Mendelian randomization –- TWAS-SMR. Although these methods have been effective at detecting functional variants, it remains unclear how extensive variability in the genetic architecture of complex traits and diseases impacts TWAS results. Our goal was to investigate the different scenarios under which these methods yielded enough power to detect significant expression-trait associations. In this study, we conducted extensive simulations based on 6000 randomly chosen, unrelated Caucasian males from Geisinger’s MyCode population to compare the power to detect cis expression-trait associations (within 500 kb of a gene) using the above-described approaches. To test TWAS across varying genetic backgrounds we simulated gene expression and phenotype using different quantitative trait loci per gene and cis-expression /trait heritability under genetic models that differentiate the effect of causality from that of pleiotropy. For each gene, on a training set ranging from 100 to 1000 individuals, we either (a) estimated regression coefficients with gene expression as the response using five different methods: LASSO, elastic net, Bayesian LASSO, Bayesian spike-slab, and Bayesian ridge regression or (b) performed eQTL analysis. We then sampled with replacement 50,000, 150,000, and 300,000 individuals respectively from the testing set of the remaining 5000 individuals and conducted GWAS on each set. Subsequently, we integrated the GWAS summary statistics derived from the testing set with the weights (or eQTLs) derived from the training set to identify expression-trait associations using (a) TWAS-MP (b) TWAS-SMR (c) eQTL-based GWAS, or (d) standalone GWAS. Finally, we examined the power to detect functionally relevant genes using the different approaches under the considered simulation scenarios. In general, we observed great similarities among TWAS-MP methods although the Bayesian methods resulted in improved power in comparison to LASSO and elastic net as the trait architecture grew more complex while training sample sizes and expression heritability remained small. Finally, we observed high power under causality but very low to moderate power under pleiotropy.