Nica Alexandra C, Dermitzakis Emmanouil T
The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1HH, UK.
Hum Mol Genet. 2008 Oct 15;17(R2):R129-34. doi: 10.1093/hmg/ddn285.
The identification of complex disease susceptibility loci through genome-wide association studies (GWAS) has recently become possible and is now a method of choice for investigating the genetic basis of complex traits. The number of results from such studies is constantly increasing but the challenge lying forward is to identify the biological context in which these statistically significant candidate variants act. Regulatory variation plays an important role in shaping phenotypic differences among individuals and thus is very likely to also influence disease susceptibility. As such, integrating gene expression data and other disease relevant intermediate phenotypes with GWAS results could potentially help prioritize fine-mapping efforts and provide a shortcut to disease biology. Combining these different levels of information in a meaningful way is however not trivial. In the present review, we outline the several approaches that have been explored so far in this sense and their achievements. We also discuss the limitations of the methods and how upcoming technological developments could help circumvent these limitations. Overall, such efforts will be very helpful in understanding initially regulatory effects on disease and disease etiology in general.
通过全基因组关联研究（GWAS）识别复杂疾病易感位点最近已成为可能，并且现在是研究复杂性状遗传基础的首选方法。此类研究的结果数量在不断增加，但面临的挑战是确定这些具有统计学意义的候选变异起作用的生物学背景。调控变异在塑造个体间的表型差异中起着重要作用，因此很可能也会影响疾病易感性。因此，将基因表达数据和其他与疾病相关的中间表型与GWAS结果相结合，可能有助于优先进行精细定位工作，并为疾病生物学提供一条捷径。然而，以有意义的方式整合这些不同层面的信息并非易事。在本综述中，我们概述了迄今为止在这方面探索的几种方法及其成果。我们还讨论了这些方法的局限性以及即将到来的技术发展如何有助于克服这些局限性。总体而言，此类努力将非常有助于理解最初对疾病的调控作用以及一般疾病病因。
