Integrating Gene Expression with Summary Association Statistics to Identify Genes Associated with 30 Complex Traits
Integrating Gene Expression with Summary Association Statistics to Identify Genes Associated with 30 Complex Traits
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DOI：
10.1016/j.ajhg.2017.01.031
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发表时间：
2017-03-02
影响因子：
9.8
通讯作者：
Pasaniuc, Bogdan
中科院分区：
文献类型：
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作者：
Mancuso, Nicholas;Shi, Huwenbo;Pasaniuc, Bogdan
尽管全基因组关联研究（GWAS）已经确定了许多复杂性状和疾病的数千个风险基因座，但这些基因座上的致病变异和基因在很大程度上仍然未知。在这里，我们介绍了一种方法，用于估计基因表达和一个复杂的性状之间的局部遗传相关性，并利用它来估计遗传相关性，由于对性状之间的预测表达。我们整合了来自45个表达组的基因表达测量结果和总结GWAS数据，进行了30项多组织全转录组关联研究（TWAS）。我们确定了1,196个基因，其表达与这些性状相关;其中，168个基因距离任何先前报道的GWAS显著变异体超过0.5 Mb。然后，我们使用我们的方法找到了43对在预测表达水平上具有显著遗传相关性的性状;其中8对在SNP水平上没有发现遗传相关性。最后，我们使用双向回归来寻找BMI因果影响甘油三酯水平和甘油三酯水平因果影响低密度脂蛋白的证据。总之，我们的研究结果提供了深入了解基因表达在复杂性状和疾病易感性中的作用。
Although genome-wide association studies (GWASs) have identified thousands of risk loci for many complex traits and diseases, the causal variants and genes at these loci remain largely unknown. Here, we introduce a method for estimating the local genetic correlation between gene expression and a complex trait and utilize it to estimate the genetic correlation due to predicted expression between pairs of traits. We integrated gene expression measurements from 45 expression panels with summary GWAS data to perform 30 multi-tissue transcriptome-wide association studies (TWASs). We identified 1,196 genes whose expression is associated with these traits; of these, 168 reside more than 0.5 Mb away from any previously reported GWAS significant variant. We then used our approach to find 43 pairs of traits with significant genetic correlation at the level of predicted expression; of these, eight were not found through genetic correlation at the SNP level. Finally, we used bi-directional regression to find evidence that BMI causally influences triglyceride levels and that triglyceride levels causally influence low-density lipoprotein. Together, our results provide insight into the role of gene expression in the susceptibility of complex traits and diseases.