School of Medicine Publications and Presentations
Document Type
Article
Publication Date
6-2021
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.
Recommended Citation
Sofer, T., Zheng, X., Laurie, C.A. et al. Variant-specific inflation factors for assessing population stratification at the phenotypic variance level. Nat Commun 12, 3506 (2021). https://doi.org/10.1038/s41467-021-23655-2
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication Title
Nat Commun
DOI
10.1038/s41467-021-23655-2
Academic Level
faculty
Mentor/PI Department
Office of Human Genetics