A comparison of approaches to account for uncertainty in analysis of imputed genotypes
Open accessAbstract
The availability of extensively genotyped reference samples, such as "The HapMap" and 1,000 Genomes Project reference panels, together with advances in statistical methodology, have allowed for the imputation of genotypes at single nucleotide polymorphism (SNP) markers that are untyped in a cohort or case-control study. These imputation procedures facilitate the interpretation and meta-analyses of genome-wide association studies. A natural question when implementing these procedures concerns how best to take into account uncertainty in imputed genotypes. Here we compare the performance of the following three strategies: least-squares regression on the "best-guess" imputed genotype; regression on the expected genotype score or "dosage"; and mixture regression models that more fully incorporate posterior probabilities of genotypes at untyped SNPs. Using simulation, we considered a range of sample sizes, minor allele frequencies, and imputation accuracies to compare the performance of the different methods under various genetic models. The mixture models performed the best in the setting of a large genetic effect and low imputation accuracies. However, for most realistic settings, we find that regressing the phenotype on the estimated allelic or genotypic dosage provides an attractive compromise between accuracy and computational tractability.
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The MR approach enables the approximation of life-long differences in average concentrations, but genetic markers do not examine the influences arising from the extremes of non-linear distributions.
“While the MR approach enables the approximation of life-long differences in average concentrations, genetic markers do not examine the influences arising from the extremes of non-linear distributions [28, 50].”
From Causal relationship between obesity and serum testosterone status in men: A bi-directional mendelian randomization analysis (2017) · cites this paper as [50]
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349. Recent advances and future needs in genotype imputation
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- Authors
- Zheng, J., Li, Y., Abecasis, G. R., & Scheet, P.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- University of Michigan (US)
- Corresponding author
- Li, Yun
- Scheet, Paul
- Venue
- Genetic Epidemiology
- Publisher
- Wiley
- Published
- 2011-01-19
- Volume
- 35
- Issue
- 2
- Pages
- 102-110
- ISSN
- 0741-0395 (ISSN-L)
- 1098-2272
- PMC
- PMC3143715
- OpenAlex
- W2124768193
- Cited by
- 105
- References
- 24
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 5 · 2022: 7 · 2021: 7 · 2020: 3 · 2019: 3 · 2018: 4 · 2017: 7 · 2016: 7 · 2015: 13 · 2014: 13 · 2013: 16 · 2012: 13
- FWCI
- 9.18
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Genetic Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- UNC Libraries
- Funders
- National Institutes of Health (CA082659)
- National Heart, Lung, and Blood Institute (U01HL084729)
- National Human Genome Research Institute
- National Cancer Institute (3-R01-CA082659-11S1, R01CA082659)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- genotype imputation, mixture regression models, imputation accuracy, genome-wide association study, single nucleotide polymorphisms, minor allele frequency
- MeSH terms
- Computer Simulation, Data Interpretation, Statistical, Genotype, Humans, Phenotype, Regression Analysis, Software, Reproducibility of Results, Models, Statistical, Cohort Studies, Molecular Epidemiology, Uncertainty, Genome-Wide Association Study
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