Variant-specific inflation factors for assessing population stratification at the phenotypic variance level. Nat Commun 2021 Jun 09;12(1):3506
Date
06/11/2021Pubmed ID
34108454Pubmed Central ID
PMC8190158DOI
10.1038/s41467-021-23655-2Scopus ID
2-s2.0-85107746519 (requires institutional sign-in at Scopus site) 3 CitationsAbstract
In modern Whole Genome Sequencing (WGS) epidemiological studies, participant-level data from multiple studies are often pooled and results are obtained from a single analysis. We consider the impact of differential phenotype variances by study, which we term 'variance stratification'. Unaccounted for, variance stratification can lead to both decreased statistical power, and increased false positives rates, depending on how allele frequencies, sample sizes, and phenotypic variances vary across the studies that are pooled. We develop a procedure to compute variant-specific inflation factors, and show how it can be used for diagnosis of genetic association analyses on pooled individual level data from multiple studies. We describe a WGS-appropriate analysis approach, implemented in freely-available software, which allows study-specific variances and thereby improves performance in practice. We illustrate the variance stratification problem, its solutions, and the proposed diagnostic procedure, in simulations and in data from the Trans-Omics for Precision Medicine Whole Genome Sequencing Program (TOPMed), used in association tests for hemoglobin concentrations and BMI.
Author List
Sofer T, Zheng X, Laurie CA, Gogarten SM, Brody JA, Conomos MP, Bis JC, Thornton TA, Szpiro A, O'Connell JR, Lange EM, Gao Y, Cupples LA, Psaty BM, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Rice KMAuthor
Ulrich Broeckel MD Chief, Center Associate Director, Professor in the Pediatrics department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
AlgorithmsComputer Simulation
Gene Frequency
Genetic Variation
Genome-Wide Association Study
Humans
Phenotype
Sample Size