Transcriptome-wide association studies (TWAS) have been widely used to integrate transcriptomic and genetic data to study complex human diseases. Within a test dataset lacking transcriptomic data, existing TWAS methods first impute gene expression by creating a weighted sum that aggregates SNPs with their corresponding cis-eQTL effects on reference transcriptome. Existing TWAS methods then employ a linear regression model to assess the association between imputed gene expression and test phenotype, thereby assuming the effect of a cis-eQTL SNP on test phenotype is a linear function of the eQTL's estimated effect on reference transcriptome. To increase TWAS robustness to this assumption, we propose a novel Variance-Component TWAS procedure (VC-TWAS) that assumes the effects of cis-eQTL SNPs on phenotype are random (with variance proportional to corresponding reference cis-eQTL effects) rather than fixed. VC-TWAS is applicable to both continuous and dichotomous phenotypes, as well as individual-level and summary-level GWAS data. Using simulated data, we show VC-TWAS is more powerful than traditional TWAS especially when eQTL genetic effects on test phenotype are no longer a linear function of their eQTL genetic effects on reference transcriptome. We further applied VC-TWAS to both individual-level (N=~3.4K) and summary-level (N=~54K) GWAS data to study Alzheimer's dementia (AD). With the individual-level data, we detected 13 significant risk genes including 6 known GWAS risk genes such as TOMM40 that were missed by existing TWAS methods. With the summary-level data, we detected 57 significant risk genes considering only cis-SNPs and 71 significant genes considering both cis- and trans- SNPs; these findings also validated our findings with the individual-level GWAS data. Our VC-TWAS method is implemented in the TIGAR tool for public use.

中文翻译：

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研究复杂人类疾病的新型方差分量TWAS方法在阿尔茨海默氏痴呆症中的应用

转录组范围的关联研究（TWAS）已被广泛用于整合转录组和遗传数据，以研究复杂的人类疾病。在缺乏转录组数据的测试数据集中，现有的TWAS方法首先通过创建加权总和来估算基因表达，该加权总和将SNP及其相应的cis-eQTL效应聚合到参考转录组上。然后，现有的TWAS方法采用线性回归模型来评估估算的基因表达与测试表型之间的关联，从而假设cis-eQTL SNP对测试表型的影响是eQTL对参考转录组估计影响的线性函数。为了将TWAS的鲁棒性提高到这个假设，我们提出了一种新颖的方差分量TWAS方法（VC-TWAS），该方法假设cis-eQTL SNP对表型的影响是随机的（方差与相应的参考cis-eQTL效应成正比），而不是固定的。VC-TWAS适用于连续表型和二分表型，以及个人级别和摘要级别的GWAS数据。使用模拟数据，我们显示VC-TWAS比传统TWAS更强大，尤其是当eQTL遗传对测试表型的影响不再是其eQTL遗传对参考转录组的线性影响时。我们进一步将VC-TWAS应用于个人水平（N =〜3.4K）和摘要水平（N =〜54K）GWAS数据，以研究阿尔茨海默氏痴呆症（AD）。利用个人数据，我们检测到13个重要的风险基因，包括现有的TWAS方法所遗漏的6个已知的GWAS风险基因，例如TOMM40。利用汇总水平的数据，我们检测到57个仅考虑顺式SNP的重要风险基因，以及71个同时考虑顺式和反式SNP的重要基因。这些发现还通过个人级别的GWAS数据验证了我们的发现。TIGAR工具中实现了我们的VC-TWAS方法，供公众使用。