Spain Sarah L, Barrett Jeffrey C
Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1HH, UK.
Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1HH, UK
Hum Mol Genet. 2015 Oct 15;24(R1):R111-9. doi: 10.1093/hmg/ddv260. Epub 2015 Jul 8.
Genome-wide association studies (GWAS) have identified thousands of robust and replicable genetic associations for complex disease. However, the identification of the causal variants that underlie these associations has been more difficult. This problem of fine-mapping association signals predates GWAS, but the last few years have seen a surge of studies aimed at pinpointing causal variants using both statistical evidence from large association data sets and functional annotations of genetic variants. Combining these two approaches can often determine not only the causal variant but also the target gene. Recent contributions include analyses of custom genotyping arrays, such as the Immunochip, statistical methods to identify credible sets of causal variants and the addition of functional genomic annotations for coding and non-coding variation to help prioritize variants and discern functional consequence and hence the biological basis of disease risk.
全基因组关联研究(GWAS)已经识别出数千种与复杂疾病相关的可靠且可重复的基因关联。然而,确定这些关联背后的因果变异却更加困难。这种对关联信号进行精细定位的问题在GWAS出现之前就已存在,但在过去几年中,出现了大量旨在利用来自大型关联数据集的统计证据和基因变异的功能注释来精确确定因果变异的研究。将这两种方法结合起来,通常不仅可以确定因果变异,还能确定目标基因。近期的成果包括对定制基因分型阵列(如免疫芯片)的分析、识别可信因果变异集的统计方法,以及为编码和非编码变异添加功能基因组注释,以帮助对变异进行优先级排序、辨别功能后果,进而了解疾病风险的生物学基础。