A Unified Approach to Genotype Imputation and Haplotype-Phase Inference for Large Data Sets of Trios and Unrelated Individuals
Open accessAbstract
We present methods for imputing data for ungenotyped markers and for inferring haplotype phase in large data sets of unrelated individuals and parent-offspring trios. Our methods make use of known haplotype phase when it is available, and our methods are computationally efficient so that the full information in large reference panels with thousands of individuals is utilized. We demonstrate that substantial gains in imputation accuracy accrue with increasingly large reference panel sizes, particularly when imputing low-frequency variants, and that unphased reference panels can provide highly accurate genotype imputation. We place our methodology in a unified framework that enables the simultaneous use of unphased and phased data from trios and unrelated individuals in a single analysis. For unrelated individuals, our imputation methods produce well-calibrated posterior genotype probabilities and highly accurate allele-frequency estimates. For trios, our haplotype-inference method is four orders of magnitude faster than the gold-standard PHASE program and has excellent accuracy. Our methods enable genotype imputation to be performed with unphased trio or unrelated reference panels, thus accounting for haplotype-phase uncertainty in the reference panel. We present a useful measure of imputation accuracy, allelic R(2), and show that this measure can be estimated accurately from posterior genotype probabilities. Our methods are implemented in version 3.0 of the BEAGLE software package.
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Each study was subject to GWAS quality control filters customized taking into account study-specific features and standardized to the HapMap reference panel and statistical genotype imputation.
“Each study was first subject to GWAS quality control filters (customized taking into account study specific features21 as detailed in Supplementary Table 2) and standardized to the HapMap reference panel and statistical genotype imputation22–25.”
From Seven new loci associated with age-related macular degeneration (2013) · cites this paper as [24]
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- Authors
- Browning, B. L., & Browning, S. R.
- Affiliations
- University of Auckland (NZ)
- Corresponding author
- Browning, Brian L.
- Venue
- The American Journal of Human Genetics
- Publisher
- Elsevier BV
- Published
- 2009-02
- Volume
- 84
- Issue
- 2
- Pages
- 210-223
- ISSN
- 0002-9297 (ISSN-L)
- PMC
- PMC2668004
- OpenAlex
- W2059542829
- Cited by
- 1,640
- References
- 29
- Citations by year
- 2026: 35 · 2025: 44 · 2024: 62 · 2023: 50 · 2022: 86 · 2021: 99 · 2020: 94 · 2019: 102 · 2018: 98 · 2017: 121 · 2016: 123 · 2015: 161 · 2014: 172 · 2013: 132 · 2012: 120
- FWCI
- 50.70
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- The American Journal of Human Genetics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust (076113)
- National Institutes of Health
- National Institute of General Medical Sciences (R01GM075091)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- genotype imputation, parent-offspring trios, low-frequency variants, allele frequency estimation, imputation accuracy, beagle dogs, phase comparison
- MeSH terms
- Computer Simulation, Female, Gene Frequency, Genotype, Haplotypes, Humans, Male, Markov Chains, Models, Genetic, Nuclear Family, Reproducibility of Results
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