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Cited by 109Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsBioinformatics and Genomic Networks

A simulation study investigating power estimates in phenome-wide association studies

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Verma, Anurag, Bradford, Yuki, Dudek, Scott, Lucas, Anastasia M., Verma, Shefali S., Pendergrass, Sarah A., Ritchie, Marylyn D.·2018·BMC Bioinformatics
DOI 10.1186/s12859-018-2135-0PMC PMC5885318

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Abstract

BACKGROUND: Phenome-wide association studies (PheWAS) are a high-throughput approach to evaluate comprehensive associations between genetic variants and a wide range of phenotypic measures. PheWAS has varying sample sizes for quantitative traits, and variable numbers of cases and controls for binary traits across the many phenotypes of interest, which can affect the statistical power to detect associations. The motivation of this study is to investigate the various parameters which affect the estimation of statistical power in PheWAS, including sample size, case-control ratio, minor allele frequency, and disease penetrance. RESULTS: We performed a PheWAS simulation study, where we investigated variations in statistical power based on different parameters, such as overall sample size, number of cases, case-control ratio, minor allele frequency, and disease penetrance. The simulation was performed on both binary and quantitative phenotypic measures. Our simulation on binary traits suggests that the number of cases has more impact on statistical power than the case to control ratio; also, we found that a sample size of 200 cases or more maintains the statistical power to identify associations for common variants. For quantitative traits, a sample size of 1000 or more individuals performed best in the power calculations. We focused on common genetic variants (MAF > 0.01) in this study; however, in future studies, we will be extending this effort to perform similar simulations on rare variants. CONCLUSIONS: This study provides a series of PheWAS simulation analyses that can be used to estimate statistical power for some potential scenarios. These results can be used to provide guidelines for appropriate study design for future PheWAS analyses.

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  • Analysis was limited to phecodes that had 200 or more cases to generate improved statistical power for consequent MR analyses.

    “Analysis was limited to phecodes that had 200 or more cases in order to generate improved statistical power for consequent MR analyses (S1 Text) [22,23].”

    From Associations of genetically determined iron status across the phenome: A mendelian randomization study (2019) · cites this paper as [22]

    The Discussion recommends a threshold of at least 200 cases to provide enough power to detect common-variant associations in PheWAS. It does not assess whether that threshold improves power for subsequent Mendelian randomization (MR) analyses, so the MR-specific rationale is not established by this source. (LLM, conf=0.86)

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Research works printed but not linked: 26

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Authors
Verma, A., Bradford, Y., Dudek, S., Lucas, A. M., Verma, S. S., Pendergrass, S. A., & Ritchie, M. D.
Affiliations
  • Pennsylvania State University (US)
  • Geisinger Health System (US)
Corresponding author
Ritchie, Marylyn D.
ORCID
Venue
BMC Bioinformatics
Publisher
Springer Science and Business Media LLC
Published
2018-04-04
Volume
19
Issue
1
Pages
120-120
ISSN
1471-2105 (ISSN-L)
DOI
10.1186/s12859-018-2135-0
PMC
PMC5885318
OpenAlex
W2950459497
Cited by
109
References
0
Citations by year
2026: 7 · 2025: 17 · 2024: 12 · 2023: 25 · 2022: 21 · 2021: 7 · 2020: 7 · 2019: 8 · 2018: 4 · 2016: 1
FWCI
3.98
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Bioinformatics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Pennsylvania Department of Health
  • National Institutes of Health (GM111913, AI077505)
  • National Human Genome Research Institute (HG008679)
  • SAP (SAP 4100070267)
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 › Molecular Biology › Bioinformatics and Genomic Networks
Keywords
phenome-wide association study, statistical power, sample size, minor allele frequency, disease penetrance, common genetic variants, binary traits, quantitative traits, research design
MeSH terms
Algorithms, Computer Simulation, Disease, Humans, Phenotype, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Genome-Wide Association Study, Genetic Association Studies

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