Variant-specific inflation factors for assessing population stratification at the phenotypic variance level

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; NHLBI TransOmics for Precision Medicine (TOPMed) Consortium; Kenneth M. Rice

doi:10.1038/s41467-021-23655-2

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Variant-specific inflation factors for assessing population stratification at the phenotypic variance level

Journal article

Open access

Peer reviewed

Variant-specific inflation factors for assessing population stratification at the phenotypic variance level

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, …

Nature communications, Vol.12(1), pp.3506-3506

06/09/2021

DOI: 10.1038/s41467-021-23655-2

PMCID: PMC8190158

PMID: 34108454

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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. Pooling participant-level genetic data into a single analysis can result in variance stratification, reducing statistical performance. Here, the authors develop variant-specific inflation factors to assess variance stratification and apply this to pooled individual-level data from whole genome sequencing.

Genetics research

Genome-wide association studies

Next-generation sequencing

Statistical methods

Details

Title: Subtitle: Variant-specific inflation factors for assessing population stratification at the phenotypic variance level
Creators: Tamar Sofer - Harvard University
Xiuwen Zheng - University of Washington
Cecelia A. Laurie - University of Washington
Stephanie M. Gogarten - University of Washington
Jennifer A. Brody - University of Washington
Matthew P. Conomos - University of Washington
Joshua C. Bis - University of Washington
Timothy A. Thornton - University of Washington
Adam Szpiro - University of Washington
Jeffrey R. O’Connell - Baltimore, MD USA
Ethan M. Lange - University of Colorado Anschutz Medical Campus
Yan Gao - University of Mississippi Medical Center
L. Adrienne Cupples - Boston University
Bruce M. Psaty - University of Washington
NHLBI TransOmics for Precision Medicine (TOPMed) Consortium
Kenneth M. Rice - University of Washington
Contributors: Karin Hoth (Contributor) - University of Iowa, Psychiatry
Resource Type: Journal article
Publication Details: Nature communications, Vol.12(1), pp.3506-3506
DOI: 10.1038/s41467-021-23655-2
PMID: 34108454
PMCID: PMC8190158
NLM abbreviation: Nat Commun
ISSN: 2041-1723
eISSN: 2041-1723
Publisher: Nature Publishing Group UK
Grant note: ; R01HL120393‐03S1; 1R35HL135818; 1R21HL145425 / ;
Language: English
Date published: 06/09/2021
Academic Unit: Psychiatry; Iowa Neuroscience Institute
Record Identifier: 9984293756502771

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