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通过区域和全基因组关联研究绘制人类基因表达的决定因素。

Mapping determinants of human gene expression by regional and genome-wide association.

作者信息

Cheung Vivian G, Spielman Richard S, Ewens Kathryn G, Weber Teresa M, Morley Michael, Burdick Joshua T

机构信息

Department of Pediatrics, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA.

出版信息

Nature. 2005 Oct 27;437(7063):1365-9. doi: 10.1038/nature04244.

Abstract

To study the genetic basis of natural variation in gene expression, we previously carried out genome-wide linkage analysis and mapped the determinants of approximately 1,000 expression phenotypes. In the present study, we carried out association analysis with dense sets of single-nucleotide polymorphism (SNP) markers from the International HapMap Project. For 374 phenotypes, the association study was performed with markers only from regions with strong linkage evidence; these regions all mapped close to the expressed gene. For a subset of 27 phenotypes, analysis of genome-wide association was performed with >770,000 markers. The association analysis with markers under the linkage peaks confirmed the linkage results and narrowed the candidate regulatory regions for many phenotypes with strong linkage evidence. The genome-wide association analysis yielded highly significant results that point to the same locations as the genome scans for about 50% of the phenotypes. For one candidate determinant, we carried out functional analyses and confirmed the variation in cis-acting regulatory activity. Our findings suggest that association studies with dense SNP maps will identify susceptibility loci or other determinants for some complex traits or diseases.

摘要

为了研究基因表达自然变异的遗传基础,我们之前进行了全基因组连锁分析,并对大约1000个表达表型的决定因素进行了定位。在本研究中,我们利用来自国际人类基因组单体型图计划(International HapMap Project)的密集单核苷酸多态性(SNP)标记集进行了关联分析。对于374个表型,仅使用来自具有强连锁证据区域的标记进行关联研究;这些区域均定位在靠近表达基因的位置。对于27个表型的一个子集,使用超过77万个标记进行全基因组关联分析。对连锁峰下标记的关联分析证实了连锁结果,并缩小了许多具有强连锁证据表型的候选调控区域。全基因组关联分析产生了高度显著的结果,约50%的表型指向与基因组扫描相同的位置。对于一个候选决定因素,我们进行了功能分析,并证实了顺式作用调控活性的变异。我们的研究结果表明,利用密集SNP图谱进行关联研究将识别出一些复杂性状或疾病的易感位点或其他决定因素。

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