Variant-Specific inflation Factors For assessing Population Stratification at the Phenotypic Variance Level

Authors

Tamar Sofer
Xiuwen Zheng
Cecelia A Laurie
Stephanie M Gogarten
Jennifer A Brody
Matthew P Conomos
Joshua C Bis
Timothy A Thornton
Adam Szpiro
Jeffrey R O'Connell
Ethan M Lange
Yan Gao
L Adrienne Cupples
Bruce M Psaty
Kenneth M Rice

Publication Date

6-9-2021

Journal

Nature Communications

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.

Keywords

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