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Variant-specific inflation factors for assessing population stratification at the phenotypic variance level. Nat Commun 2021 Jun 09;12(1):3506

Date

06/11/2021

Pubmed ID

34108454

Pubmed Central ID

PMC8190158

DOI

10.1038/s41467-021-23655-2

Scopus ID

2-s2.0-85107746519 (requires institutional sign-in at Scopus site)   3 Citations

Abstract

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 KM

Author

Ulrich Broeckel MD Chief, Center Associate Director, Professor in the Pediatrics department at Medical College of Wisconsin




MESH terms used to index this publication - Major topics in bold

Algorithms
Computer Simulation
Gene Frequency
Genetic Variation
Genome-Wide Association Study
Humans
Phenotype
Sample Size