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Sitlani CM, Baldassari AR, Highland HM, et al. Comparison of adaptive multiple phenotype association tests using summary statistics in genome-wide association studies. Hum Mol Genet. 2021;30(15):1371-1383doi: 10.1093/hmg/ddab126.
Sitlani, C. M., Baldassari, A. R., Highland, H. M., Hodonsky, C. J., McKnight, B., & Avery, C. L. (2021). Comparison of adaptive multiple phenotype association tests using summary statistics in genome-wide association studies. Human molecular genetics, 30(15), 1371-1383. https://doi.org/10.1093/hmg/ddab126
Sitlani, Colleen M, et al. "Comparison of adaptive multiple phenotype association tests using summary statistics in genome-wide association studies." Human molecular genetics vol. 30,15 (2021): 1371-1383. doi: https://doi.org/10.1093/hmg/ddab126
Sitlani CM, Baldassari AR, Highland HM, Hodonsky CJ, McKnight B, Avery CL. Comparison of adaptive multiple phenotype association tests using summary statistics in genome-wide association studies. Hum Mol Genet. 2021 Jul 09;30(15):1371-1383. doi: 10.1093/hmg/ddab126. PMID: 33949650; PMCID: PMC8283209.
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Hum Mol Genet. 2021 Jul 09;30(15):1371-1383. doi: 10.1093/hmg/ddab126.

Comparison of adaptive multiple phenotype association tests using summary statistics in genome-wide association studies.

Human molecular genetics

Colleen M Sitlani, Antoine R Baldassari, Heather M Highland, Chani J Hodonsky, Barbara McKnight, Christy L Avery

Affiliations

  1. Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 98101 USA.
  2. Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516 USA.
  3. Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908 USA.
  4. Department of Biostatistics, University of Washington, Seattle, WA 98195 USA.

PMID: 33949650 PMCID: PMC8283209 DOI: 10.1093/hmg/ddab126

Abstract

Genome-wide association studies have been successful mapping loci for individual phenotypes, but few studies have comprehensively interrogated evidence of shared genetic effects across multiple phenotypes simultaneously. Statistical methods have been proposed for analyzing multiple phenotypes using summary statistics, which enables studies of shared genetic effects while avoiding challenges associated with individual-level data sharing. Adaptive tests have been developed to maintain power against multiple alternative hypotheses because the most powerful single-alternative test depends on the underlying structure of the associations between the multiple phenotypes and a single nucleotide polymorphism (SNP). Here we compare the performance of six such adaptive tests: two adaptive sum of powered scores (aSPU) tests, the unified score association test (metaUSAT), the adaptive test in a mixed-models framework (mixAda) and two principal-component-based adaptive tests (PCAQ and PCO). Our simulations highlight practical challenges that arise when multivariate distributions of phenotypes do not satisfy assumptions of multivariate normality. Previous reports in this context focus on low minor allele count (MAC) and omit the aSPU test, which relies less than other methods on asymptotic and distributional assumptions. When these assumptions are not satisfied, particularly when MAC is low and/or phenotype covariance matrices are singular or nearly singular, aSPU better preserves type I error, sometimes at the cost of decreased power. We illustrate this trade-off with multiple phenotype analyses of six quantitative electrocardiogram traits in the Population Architecture using Genomics and Epidemiology (PAGE) study.

© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected].

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