Supplementary Material for: Integrative post-GWAS analyses relevant to psychiatric disorders: Imputing transcriptome and proteome signals

Background The genome-wide association study (GWAS) is a common tool to identify genetic variants associated with complex traits, including psychiatric disorders (PDs). However, post-GWAS analyses are needed to extend the statistical inference to biologically relevant entities, e.g., genes, proteins and pathways. To achieve this goal, researchers developed methods that incorporate biologically relevant intermediate molecular phenotypes, such as gene expression and protein abundance, which are posited to mediate the variant-trait association. Transcriptome-wide association study (TWAS) and proteome-wide association study (PWAS) are commonly used methods to test the association between these molecular mediators and the trait. Summary In this review, we discuss the most recent developments in TWAS and PWAS. These methods integrate existing 'omic' information with the GWAS summary statistics for trait(s) of interest. Specifically, they impute transcript/protein data and test the association between imputed gene expression/protein level with phenotype of interest by using i) GWAS summary statistics and ii) reference transcriptomic/proteomic/genomic data sets. TWAS and PWAS are suitable as analysis tools for i) primary association scan and ii) fine-mapping to identify potentially causal genes for PDs. Key Messages As post-GWAS analyses, TWAS and PWAS have the potential to highlight causal genes in PDs. These prioritized genes could indicate targets for the development of novel drug therapies. For researchers attempting such analyses, we recommend Mendelian randomization (MR) tools that use GWAS statistics for both trait and reference data sets, e.g., SMR. We base our recommendation on i) being able to use the same tool for both TWAS and PWAS, ii) not requiring the pre-computed weights (and thus easier to update for larger reference data sets) and iii) most larger transcriptome reference data sets are publicly available and easy to transform into a compatible format for SMR analysis.

背景：全基因组关联研究（genome-wide association study, GWAS）是识别与复杂性状（包括精神障碍（psychiatric disorders, PDs））相关遗传变异的常用工具。然而，需开展GWAS后分析，以将统计推断拓展至生物学相关实体，例如基因、蛋白质及通路。为达成该目标，研究者开发了整合生物学相关中间分子表型的方法，例如基因表达与蛋白质丰度，这类表型被认为介导了变异-性状关联。转录组全关联研究（transcriptome-wide association study, TWAS）与蛋白质组全关联研究（proteome-wide association study, PWAS）是检验这类分子介导物与性状间关联的常用方法。
概述：本综述探讨了TWAS与PWAS领域的最新研究进展。此类方法将现有组学信息与目标性状的GWAS汇总统计量相结合。具体而言，它们通过以下两类资源实现转录本/蛋白质数据的推断，并检验推断后的基因表达/蛋白质水平与目标表型间的关联：i）GWAS汇总统计量；ii）参考转录组/蛋白质组/基因组数据集。TWAS与PWAS可作为分析工具，用于i）初步关联扫描，以及ii）精细定位以识别与PDs相关的潜在因果基因。
核心要点：作为GWAS后分析手段，TWAS与PWAS有望揭示PDs中的因果基因。这些经优先级排序的基因可作为新型药物疗法的开发靶点。对于开展此类分析的研究者，我们推荐使用同时基于性状与参考数据集的GWAS统计量的孟德尔随机化（Mendelian randomization, MR）工具，例如SMR。我们提出该推荐的依据如下：i）可使用同一工具同时开展TWAS与PWAS分析；ii）无需预先计算权重（因此在面对更大规模的参考数据集时更易更新）；iii）多数大型转录组参考数据集均可公开获取，且易于转换为适配SMR分析的格式。