Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets
Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets
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DOI：
10.1038/ng.3538
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发表时间：
2016-05-01
期刊：
影响因子：
30.8
通讯作者：
Yang, Jian
中科院分区：
文献类型：
--
作者：
Zhu, Zhihong;Zhang, Futao;Yang, Jian
全基因组关联研究（GWAS）已经确定了数千种与人类复杂性状相关的遗传变异。然而，这些变异体对性状产生影响的基因或功能性DNA元件往往是未知的。我们提出了一种方法（称为SMR），集成的数据从GWAS与表达数量性状基因座（eQTL）的研究，以确定基因的表达水平与一个复杂的性状，因为多效性的总结水平。我们使用多达339，224个个体的GWAS数据和5，311个个体的eQTL数据将该方法应用于5个人类复杂性状，并优先考虑126个基因（例如，类风湿性关节炎的TRAF 1和ANKRD 55以及精神分裂症的SNX 19和NMRAL 1），其中25个基因是新的候选基因; 77个基因不是最接近的注释基因。这些基因为设计未来的功能研究提供了重要的线索，以了解DNA变异导致复杂性状变异的机制。
Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with human complex traits. However, the genes or functional DNA elements through which these variants exert their effects on the traits are often unknown. We propose a method (called SMR) that integrates summary-level data from GWAS with data from expression quantitative trait locus (eQTL) studies to identify genes whose expression levels are associated with a complex trait because of pleiotropy. We apply the method to five human complex traits using GWAS data on up to 339,224 individuals and eQTL data on 5,311 individuals, and we prioritize 126 genes (for example, TRAF1 and ANKRD55 for rheumatoid arthritis and SNX19 and NMRAL1 for schizophrenia), of which 25 genes are new candidates; 77 genes are not the nearest annotated gene to the top associated GWAS SNP. These genes provide important leads to design future functional studies to understand the mechanism whereby DNA variation leads to complex trait variation.