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• W2034220820 abstract "Genome-wide association studies of clinically defined cases against controls have transformed our understanding of the genetic causes of many diseases. However, there are limitations to the simple clinical definitions used in these studies, and GWAS analyses are beginning to explore more refined phenotypes in subgroups of the existing data sets. These analyses are often performed ad hoc without considering the power requirements to justify such analyses. Here we derive expressions for the relative power of such subgroup analyses and determine the genotypic relative risks (GRRs) required to achieve equivalent power to a full analysis for relevant scenarios. We show that only modest increases in GRRs may be required to offset the reduction in power from analysing fewer cases, implying that analyses of more genetically homogenous case subgroups may have the potential to identify further associations. We find that, for lower genotypic relative risks in the full sample, subgroup analyses of more homogeneous cases have relatively more power than for higher index genotypic relative risks and that this effect is stronger for rare as opposed to common variants. As GWA studies are likely to have now identified the majority of SNPs with stronger effects, these results strongly advocate a renewed effort to identify phenotypically homogeneous disease groups, in which power to detect genetic variants with small effects will be greater. These results suggest that analysis of case subsets could be a powerful strategy to uncover some of the hidden heritability for common complex disorders, particularly in identifying rarer variants of modest effect." @default.
• W2034220820 created "2016-06-24" @default.
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• W2034220820 date "2014-10-01" @default.
• W2034220820 modified "2023-10-17" @default.
• W2034220820 title "Homogeneous case subgroups increase power in genetic association studies" @default.
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• W2034220820 doi "https://doi.org/10.1038/ejhg.2014.194" @default.
• W2034220820 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4795054" @default.
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• W2034220820 hasPublicationYear "2014" @default.
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