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A Powerful Statistical Framework for Generalization Testing in GWAS, with Application to the HCHS/SOL


Sofer, Tamar; Heller, Ruth; Bogomolov, Marina; Avery, Christy L.; Graff, Mariaelisa; North, Kari E.; Reiner, Alexander P.; Thornton, Timothy A.; Rice, Kenneth M.; & Benjamini, Yoav, et al. (2017). A Powerful Statistical Framework for Generalization Testing in GWAS, with Application to the HCHS/SOL. Genetic Epidemiology, 41(3), 251-258. PMCID: PMC5340573


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 (FWERg ) and FDR (FDRg) 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 FWERg or FDRg 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 <5x10-8 (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 <6.6x10-5 (89 regions), we generalized SNPs from 27 regions.


Reference Type

Journal Article

Year Published


Journal Title

Genetic Epidemiology


Sofer, Tamar
Heller, Ruth
Bogomolov, Marina
Avery, Christy L.
Graff, Mariaelisa
North, Kari E.
Reiner, Alexander P.
Thornton, Timothy A.
Rice, Kenneth M.
Benjamini, Yoav
Laurie, Cathy C.
Kerr, Kathleen F.