Analyze multivariate phenotypes in genetic association studies by combining univariate association tests
Open accessAbstract
Multivariate phenotypes are frequently encountered in genome‐wide association studies (GWAS). Such phenotypes contain more information than univariate phenotypes, but how to best exploit the information to increase the chance of detecting genetic variant of pleiotropic effect is not always clear. Moreover, when multivariate phenotypes contain a mixture of quantitative and qualitative measures, limited methods are applicable. In this paper, we first evaluated the approach originally proposed by O'Brien and by Wei and Johnson that combines the univariate test statistics and then we proposed two extensions to that approach. The original and proposed approaches are applicable to a multivariate phenotype containing any type of components including continuous, categorical and survival phenotypes, and applicable to samples consisting of families or unrelated samples. Simulation results suggested that all methods had valid type I error rates. Our extensions had a better power than O'Brien's method with heterogeneous means among univariate test statistics, but were less powerful than O'Brien's with homogeneous means among individual test statistics. All approaches have shown considerable increase in power compared to testing each component of a multivariate phenotype individually in some cases. We apply all the methods to GWAS of serum uric acid levels and gout with 550,000 single nucleotide polymorphisms in the Framingham Heart Study. Genet. Epidemiol. 34:444–454, 2010. © 2010 Wiley‐Liss, Inc.
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Historically, psychiatric research has been underfunded in comparison to public health impact (with the possible exception of AD).
“historically, psychiatric research has ben underfunded in comparison to public health impact (with the possible exception of AD). 141–144”
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- Authors
- Yang, Q., Wu, H., Guo, C., & Fox, C. S.
- Affiliations
- Boston University (US)
- Boston Children's Hospital (US)
- Brigham and Women's Hospital (US)
- Corresponding author
- Yang, Qiong
- Venue
- Genetic Epidemiology
- Publisher
- Wiley
- Published
- 2010-06-25
- Volume
- 34
- Issue
- 5
- Pages
- 444-454
- ISSN
- 0741-0395 (ISSN-L)
- 1098-2272
- PMC
- PMC3090041
- OpenAlex
- W2016122322
- Cited by
- 142
- References
- 38
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 1 · 2023: 3 · 2022: 6 · 2021: 7 · 2020: 1 · 2019: 12 · 2018: 21 · 2017: 11 · 2016: 14 · 2015: 10 · 2014: 13 · 2013: 8 · 2012: 17
- FWCI
- 7.01
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Genetic Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- School of Medicine, Boston University
- National Institutes of Health (n01hc25195-41-0-1, n02hl64278-6-0-0)
- National Heart, Lung, and Blood Institute (N01‐HC25195, N02 HL064278, N02- HL-64278, N01-HC-025195)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- multivariate phenotypes, pleiotropy, statistical power, type I error rate, family-based association, serum uric acid, gout, single nucleotide polymorphisms, Framingham Heart Study
- MeSH terms
- Analysis of Variance, Gout, Humans, Genetic Linkage, Models, Genetic, Monte Carlo Method, Phenotype, Uric Acid, Genetic Variation, Models, Statistical, Genome, Human, Polymorphism, Single Nucleotide, Genome-Wide Association Study
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