Opportunities and challenges for transcriptome-wide association studies
Opportunities and challenges for transcriptome-wide association studies
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DOI：
10.1038/s41588-019-0385-z
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发表时间：
2019-04-01
期刊：
影响因子：
30.8
通讯作者：
Kundaje, Anshul
中科院分区：
文献类型：
--
作者：
Wainberg, Michael;Sinnott-Armstrong, Nasa;Kundaje, Anshul
全转录组关联研究（TWAS）整合了全基因组关联研究（GWAS）和基因表达数据集，以确定基因-性状关联。在这个角度来看，我们探索TWAS作为一种潜在的方法，优先考虑在GWAS基因座的因果基因的属性，通过使用模拟和案例研究的文献策划的候选因果基因精神分裂症，低密度脂蛋白胆固醇和克罗恩病。我们探索风险基因座，TWAS准确地优先考虑可能的因果基因，以及基因座TWAS优先考虑多个基因，一些可能是非因果关系，由于共享的表达数量性状基因座（eQTL）。TWAS特别容易对来自非性状相关组织或细胞类型的表达数据进行虚假优先排序，这是由于表达水平和eQTL强度的大量跨细胞类型变化。尽管如此，TWAS比简单的基线更准确地优先考虑候选因果基因。我们提出了TWAS因果基因优先级排序的最佳实践，并讨论了未来的改进机会。我们的研究结果展示了使用eQTL数据集来确定GWAS位点的因果基因的优势和局限性。
Transcriptome-wide association studies (TWAS) integrate genome-wide association studies (GWAS) and gene expression datasets to identify gene-trait associations. In this Perspective, we explore properties of TWAS as a potential approach to prioritize causal genes at GWAS loci, by using simulations and case studies of literature-curated candidate causal genes for schizophrenia, low-density-lipoprotein cholesterol and Crohn's disease. We explore risk loci where TWAS accurately prioritizes the likely causal gene as well as loci where TWAS prioritizes multiple genes, some likely to be non-causal, owing to sharing of expression quantitative trait loci (eQTL). TWAS is especially prone to spurious prioritization with expression data from non-trait-related tissues or cell types, owing to substantial cross-cell-type variation in expression levels and eQTL strengths. Nonetheless, TWAS prioritizes candidate causal genes more accurately than simple baselines. We suggest best practices for causal-gene prioritization with TWAS and discuss future opportunities for improvement. Our results showcase the strengths and limitations of using eQTL datasets to determine causal genes at GWAS loci.