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对一个奠基人群体进行基因表达与基因关联研究的综合分析。

Integrated analyses of gene expression and genetic association studies in a founder population.

作者信息

Cusanovich Darren A, Caliskan Minal, Billstrand Christine, Michelini Katelyn, Chavarria Claudia, De Leon Sherryl, Mitrano Amy, Lewellyn Noah, Elias Jack A, Chupp Geoffrey L, Lang Roberto M, Shah Sanjiv J, Decara Jeanne M, Gilad Yoav, Ober Carole

机构信息

Department of Human Genetics and.

Division of Biology and Medicine, Brown University, Providence, RI 02912, USA and.

出版信息

Hum Mol Genet. 2016 May 15;25(10):2104-2112. doi: 10.1093/hmg/ddw061. Epub 2016 Feb 29.

Abstract

Genome-wide association studies (GWASs) have become a standard tool for dissecting genetic contributions to disease risk. However, these studies typically require extraordinarily large sample sizes to be adequately powered. Strategies that incorporate functional information alongside genetic associations have proved successful in increasing GWAS power. Following this paradigm, we present the results of 20 different genetic association studies for quantitative traits related to complex diseases, conducted in the Hutterites of South Dakota. To boost the power of these association studies, we collected RNA-sequencing data from lymphoblastoid cell lines for 431 Hutterite individuals. We then used Sherlock, a tool that integrates GWAS and expression quantitative trait locus (eQTL) data, to identify weak GWAS signals that are also supported by eQTL data. Using this approach, we found novel associations with quantitative phenotypes related to cardiovascular disease, including carotid intima-media thickness, left atrial volume index, monocyte count and serum YKL-40 levels.

摘要

全基因组关联研究(GWAS)已成为剖析基因对疾病风险贡献的标准工具。然而,这些研究通常需要非常大的样本量才能有足够的效力。将功能信息与基因关联相结合的策略已被证明在提高GWAS效力方面是成功的。遵循这一范式,我们展示了在南达科他州的哈特派中进行的20项针对与复杂疾病相关的数量性状的不同基因关联研究的结果。为了提高这些关联研究的效力,我们收集了431名哈特派个体淋巴母细胞系的RNA测序数据。然后,我们使用了Sherlock,一种整合GWAS和表达数量性状基因座(eQTL)数据的工具,来识别也得到eQTL数据支持的微弱GWAS信号。使用这种方法,我们发现了与心血管疾病相关的数量表型的新关联,包括颈动脉内膜中层厚度、左心房容积指数、单核细胞计数和血清YKL-40水平。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44cf/5062579/879edc1abc36/ddw06101.jpg

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