A comparison of Cox and logistic regression for use in genome-wide association studies of cohort and case-cohort design.
作者： James R Staley , , Edmund Jones , Stephen Kaptoge , Adam S Butterworth
DOI: 10.1038/EJHG.2017.78
关键词:
摘要: Logistic regression is often used instead of Cox to analyse genome-wide association studies (GWAS) single-nucleotide polymorphisms (SNPs) and disease outcomes with cohort case-cohort designs, as it less computationally expensive. Although logistic models have been compared previously in studies, this work does not completely cover the GWAS setting nor extend study design. Here, we evaluated applied genetic using simulated data from EPIC-CVD study. In setting, there was a modest improvement power detect SNP–disease associations regression, which increased incidence increased. contrast, had more than (Prentice weighted) setting. yielded inflated effect estimates (assuming hazard ratio underlying measure association) for both especially SNPs greater on disease. Given substantially efficient settings, propose two-step approach studies. First all identify associated variants below pre-defined P-value threshold, second fit (appropriately weighted studies) those identified ensure accurate estimation
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