Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA.
Genet Epidemiol. 2011 Jul;35(5):303-9. doi: 10.1002/gepi.20578. Epub 2011 Mar 3.
Even in large-scale genome-wide association studies (GWASs), only a fraction of the true associations are detected at the genome-wide significance level. When few or no associations reach the significance threshold, one strategy is to follow up on the most promising candidates, i.e. the single nucleotide polymorphisms (SNPs) with the smallest association-test P-values, by genotyping them in additional studies. In this communication, we propose an overall test for GWASs that analyzes the SNPs with the most promising P-values simultaneously and therefore allows an early assessment of whether the follow-up of the selected SNPs is likely promising. We theoretically derive the properties of the proposed overall test under the null hypothesis and assess its power based on simulation studies. An application to a GWAS for chronic obstructive pulmonary disease suggests that there are true association signals among the top SNPs and that an additional follow-up study is promising.
即使在大规模全基因组关联研究(GWAS)中,也只有一部分真正的关联在全基因组显著性水平上被检测到。当很少或没有关联达到显著性阈值时,一种策略是通过在其他研究中对最有前途的候选者(即关联检验 P 值最小的单核苷酸多态性(SNP))进行基因分型来跟进。在本通讯中,我们提出了一种用于 GWAS 的综合检验方法,该方法同时分析最有前途的 P 值的 SNP,从而可以早期评估选择的 SNP 进行后续研究是否有希望。我们在零假设下从理论上推导出了所提出的综合检验的性质,并基于模拟研究评估了其功效。对慢性阻塞性肺疾病 GWAS 的应用表明,在顶级 SNP 中存在真正的关联信号,并且进一步的后续研究是有希望的。