Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA.
Brain Res. 2011 Mar 22;1380:78-84. doi: 10.1016/j.brainres.2010.11.026. Epub 2010 Nov 12.
Both rare and common genetic variants underlie risk for almost any complex disease. Over the past few years a common tool for identifying common risk variants is genome-wide association or GWA. Our analyses focus on results from GWA targeting common variants affecting risk for autism spectrum disorders (ASD). Thus far three large GWA studies have been published, each of which highlights a single, non-overlapping risk locus. Evaluation of these studies suggests that combination of their data would diminish evidence for all of these loci, making none of them significant. Despite this paucity of findings, statistical theory can be used to infer a plausible distribution of effect sizes for SNPs affecting risk for ASD. We lay out this theory, calculate plausible distributions, and discuss the results in the context of results from GWA studies for schizophrenia.
几乎任何复杂疾病的风险都源于罕见和常见的遗传变异。在过去的几年中,一种用于识别常见风险变异的常用工具是全基因组关联或 GWA。我们的分析集中在针对影响自闭症谱系障碍(ASD)风险的常见变异的 GWA 结果上。到目前为止,已经发表了三项大型 GWA 研究,每项研究都突出了一个单一的、不重叠的风险位点。对这些研究的评估表明,合并它们的数据将减少所有这些位点的证据,使它们都不显著。尽管发现很少,但统计理论可用于推断影响 ASD 风险的 SNPs 的效应大小的合理分布。我们阐述了这一理论,计算了合理的分布,并在 GWA 研究结果的背景下讨论了结果。