title: A powerful statistical framework for generalization testing in GWAS, with application to the HCHS/SOL.
abstract: 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 FWERgor FDRgunder 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.
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA, USA.
authors.forename: Tamar
authors.initials: T
authors.lastname: Sofer
authors.affiliation: Department of Statistics and Operations Research, Tel-Aviv University, Tel-Aviv, Israel.
authors.forename: Ruth
authors.initials: R
authors.lastname: Heller
authors.affiliation: Faculty of Industrial Engineering and Management, Technion-Israel Institute of Technology, Haifa, Israel.
authors.forename: Marina
authors.initials: M
authors.lastname: Bogomolov
authors.affiliation: Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.
authors.forename: Christy L
authors.initials: CL
authors.lastname: Avery
authors.affiliation: Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.
authors.forename: Mariaelisa
authors.initials: M
authors.lastname: Graff
authors.affiliation: Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA.
authors.forename: Kari E
authors.initials: KE
authors.lastname: North
authors.affiliation: Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
authors.forename: Alex P
authors.initials: AP
authors.lastname: Reiner
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA, USA.
authors.forename: Timothy A
authors.initials: TA
authors.lastname: Thornton
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA, USA.
authors.forename: Kenneth
authors.initials: K
authors.lastname: Rice
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA, USA.
authors.forename: Yoav
authors.initials: Y
authors.lastname: Benjamini
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA, USA.
authors.forename: Cathy C
authors.initials: CC
authors.lastname: Laurie
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA, USA.
authors.forename: Kathleen F
authors.initials: KF
authors.lastname: Kerr
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D005500:Follow-Up Studies; D015894:Genome, Human; D055106:Genome-Wide Association Study / Q000379:methods*; D023281:Genomics; D006630:Hispanic or Latino / Q000235:genetics*; D006801:Humans; D015810:Linkage Disequilibrium; D015233:Models, Statistical; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*
keywords: multiple testing; one-sided P-values; shared genetics