Robust, flexible, and scalable tests for Hardy–Weinberg equilibrium across diverse ancestries

• Published in: Genetics (Austin) 2021-05, Vol.218 (1)
• Main Authors: Kwong, Alan M, Blackwell, Thomas W, LeFaive, Jonathon, de Andrade, Mariza, Barnard, John, Barnes, Kathleen C, Blangero, John, Boerwinkle, Eric, Burchard, Esteban G, Cade, Brian E, Chasman, Daniel I, Chen, Han, Conomos, Matthew P, Cupples, L Adrienne, Ellinor, Patrick T, Eng, Celeste, Gao, Yan, Guo, Xiuqing, Irvin, Marguerite Ryan, Kelly, Tanika N, Kim, Wonji, Kooperberg, Charles, Lubitz, Steven A, Mak, Angel C Y, Manichaikul, Ani W, Mathias, Rasika A, Montasser, May E, Montgomery, Courtney G, Musani, Solomon, Palmer, Nicholette D, Peloso, Gina M, Qiao, Dandi, Reiner, Alexander P, Roden, Dan M, Shoemaker, M Benjamin, Smith, Jennifer A, Smith, Nicholas L, Su, Jessica Lasky, Tiwari, Hemant K, Weeks, Daniel E, Weiss, Scott T, Scott, Laura J, Smith, Albert V, Abecasis, Gonçalo R, Boehnke, Michael, Kang, Hyun Min
• Format: Article
• Language: English
• Subjects: Alleles; Chi-square test; Datasets; Evaluation; Gene Frequency - genetics; Genetics; Genetics, Population - methods; Genotype; Genotype & phenotype; Genotypes; Genotyping; Heterogeneity; Humans; Investigation; Linkage Disequilibrium - genetics; Models, Genetic; Models, Statistical; Phenotype; Population structure; Quality assessment; Robustness; Software; Software development tools; Statistical tests; Uncertainty
• ISSN: 1943-2631; 0016-6731; 1943-2631
• DOI: 10.1093/genetics/iyab044

Abstract 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 data sets composed 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 to evaluate the impact of population heterogeneity and genotype uncertainty on the standard HWE tests and alternative methods using simulated and real sequence data sets. 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 among 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.