A powerful statistical framework for generalization testing in GWAS, with application to the HCHS/SOL.

Genet Epidemiol 2017 04 15;41(3):251-258. Epub 2017 Jan 15.

Department of Biostatistics, University of Washington, Seattle, WA, USA.

In genome-wide association studies (GWAS), "generalization" is the replication of genotype-phenotype association in a population with different ancestry than the population in which it was first identified. Current practices for declaring generalizations rely on testing associations while controlling the family-wise error rate (FWER) in the discovery study, then separately controlling error measures in the follow-up study. This approach does not guarantee control over the FWER or false discovery rate (FDR) of the generalization null hypotheses. It also fails to leverage the two-stage design to increase power for detecting generalized associations. We provide a formal statistical framework for quantifying the evidence of generalization that accounts for the (in)consistency between the directions of associations in the discovery and follow-up studies. We develop the directional generalization FWER (FWER ) and FDR (FDR ) controlling r-values, which are used to declare associations as generalized. This framework extends to generalization testing when applied to a published list of Single Nucleotide Polymorphism-(SNP)-trait associations. Our methods control FWER or FDR under various SNP selection rules based on P-values in the discovery study. We find that it is often beneficial to use a more lenient P-value threshold than the genome-wide significance threshold. In a GWAS of total cholesterol in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), when testing all SNPs with P-values <5×10-8 (15 genomic regions) for generalization in a large GWAS of whites, we generalized SNPs from 15 regions. But when testing all SNPs with P-values <6.6×10-5 (89 regions), we generalized SNPs from 27 regions.

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http://dx.doi.org/10.1002/gepi.22029DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5340573PMC
April 2017
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