Integrative approaches for large-scale transcriptome-wide association studies
Integrative approaches for large-scale transcriptome-wide association studies
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DOI：
10.1038/ng.3506
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发表时间：
2016-03-01
期刊：
影响因子：
30.8
通讯作者：
Pasaniuc, Bogdan
中科院分区：
文献类型：
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作者：
Gusev, Alexander;Ko, Arthur;Pasaniuc, Bogdan
许多遗传变异通过调节基因表达来影响复杂的性状，从而改变一种或多种蛋白质的丰度。在这里，我们介绍了一个强大的策略，整合基因表达测量与汇总关联统计从大规模全基因组关联研究（GWAS），以确定基因的顺式调控表达与复杂的性状。我们利用来自遗传数据的表达插补来进行全转录组关联研究（TWAS）以确定显著的表达-性状关联。我们将我们的方法应用于血液和脂肪组织的表达数据，这些数据在大约3,000名个体中进行了测量。我们将来自超过900，000个表型测量的基因表达输入GWAS数据，以确定69个与肥胖相关性状（BMI，血脂和身高）显著相关的新基因。这些基因中的许多与杂交小鼠多样性面板中的相关表型相关。我们的研究结果展示了整合基因型，基因表达和表型的力量，以深入了解复杂性状的遗传基础。
Many genetic variants influence complex traits by modulating gene expression, thus altering the abundance of one or multiple proteins. Here we introduce a powerful strategy that integrates gene expression measurements with summary association statistics from large-scale genome-wide association studies (GWAS) to identify genes whose cis-regulated expression is associated with complex traits. We leverage expression imputation from genetic data to perform a transcriptome-wide association study (TWAS) to identify significant expression-trait associations. We applied our approaches to expression data from blood and adipose tissue measured in similar to 3,000 individuals overall. We imputed gene expression into GWAS data from over 900,000 phenotype measurements to identify 69 new genes significantly associated with obesity-related traits (BMI, lipids and height). Many of these genes are associated with relevant phenotypes in the Hybrid Mouse Diversity Panel. Our results showcase the power of integrating genotype, gene expression and phenotype to gain insights into the genetic basis of complex traits.