Gorroochurn Prakash
Division of Statistical Genetics, Department of Biostatistics, Mailman School of Public Health, Columbia University, Room 620, 722 West 168th Street, New York, NY 10032, USA.
Cancer Epidemiol Biomarkers Prev. 2008 Dec;17(12):3292-7. doi: 10.1158/1055-9965.EPI-08-0717.
It is generally believed that genome-wide association (GWA) studies stand a good chance for finding susceptibility genes for common complex diseases. Although the results thus far have been somewhat promising, there are still many inherent difficulties and many initial associations do not get replicated. The common strategy in GWA studies has been that of selecting the most statistically significant single nucleotide polymorphisms with the hope that these will be very physically close to causal variants because of strong linkage disequilibrium (LD). Using simple ideas from population genetics, this commentary explains why this strategy can be misleading. It argues that there is an intrinsic problem in the way LD is currently used for fine-mapping. This is because most of the metrics that are currently used to measure LD are inadequate, as they do not take into account evolutionary variables that shape the LD structure of the human genome. Recent research on another metric, based on Malécot's model for isolation by distance, holds considerable promise for GWA studies and merits more serious consideration by geneticists.
人们普遍认为，全基因组关联（GWA）研究很有可能找到常见复杂疾病的易感基因。尽管目前的结果还算比较有希望，但仍存在许多内在困难，而且许多最初发现的关联无法得到重复验证。GWA研究的常见策略是选择统计学上最显著的单核苷酸多态性，希望由于强连锁不平衡（LD），这些多态性在物理位置上非常接近致病变异。本文运用群体遗传学的简单概念，解释了为何这一策略可能会产生误导。文章认为，目前在精细定位中使用LD的方式存在内在问题。这是因为目前用于测量LD的大多数指标都不够充分，因为它们没有考虑到塑造人类基因组LD结构的进化变量。最近基于马勒科特距离隔离模型的另一项指标研究，对GWA研究具有很大的前景，值得遗传学家更认真地考虑。
