Robust, flexible, and scalable tests for Hardy-Weinberg Equilibrium across diverse ancestries
By
Alan M. Kwong,
Thomas W Blackwell,
Jonathon LeFaive,
Mariza de Andrade,
John Barnard,
Kathleen C Barnes,
John Blangero,
Eric Boerwinkle,
Esteban G. Burchard,
Brian E Cade,
Daniel I. Chasman,
Han Chen,
Matthew P. Conomos,
L. Adrienne Cupples,
Patrick T. Ellinor,
Celeste Eng,
Yan Gao,
Xiuqing Guo,
Marguerite Ryan Irvin,
Tanika N Kelly,
Wonji Kim,
Charles Kooperberg,
Steven A. Lubitz,
Angel C. Y. Mak,
Ani W. Manichaikul,
Rasika A. Mathias,
May E Montasser,
Courtney G Montgomery,
Solomon Musani,
Nicholette D Palmer,
Gina M Peloso,
Dandi Qiao,
Alexander P Reiner,
Dan M. Roden,
M. Benjamin Shoemaker,
Jennifer A. Smith,
Jennifer A Smith,
Jessica Lasky Su,
Hemant K. Tiwari,
Daniel E Weeks,
Scott T Weiss,
NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium,
TOPMed Analysis Working Group,
Laura J Scott,
Albert V Smith,
Gonçalo R. Abecasis,
Michael Boehnke,
Hyun Min Kang
Posted 24 Jun 2020
bioRxiv DOI: 10.1101/2020.06.23.167759
Traditional Hardy-Weinberg equilibrium (HWE) tests (the χ2 test and the exact test) have long been used as a metric for evaluating genotype quality, as technical artifacts leading to incorrect genotype calls often can be identified as deviations from HWE. However, in datasets comprised of individuals from diverse ancestries, HWE can be violated even without genotyping error, complicating the use of HWE testing to assess genotype data quality. In this manuscript, we present the Robust Unified Test for HWE (RUTH) to test for HWE while accounting for population structure and genotype uncertainty, and evaluate the impact of population heterogeneity and genotype uncertainty on the standard HWE tests and alternative methods using simulated and real sequence datasets. Our results demonstrate that ignoring population structure or genotype uncertainty in HWE tests can inflate false positive rates by many orders of magnitude. Our evaluations demonstrate different tradeoffs between false positives and statistical power across the methods, with RUTH consistently amongst the best across all evaluations. RUTH is implemented as a practical and scalable software tool to rapidly perform HWE tests across millions of markers and hundreds of thousands of individuals while supporting standard VCF/BCF formats. RUTH is publicly available at https://www.github.com/statgen/ruth. ### Competing Interest Statement The authors have declared no competing interest.
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