Genotype Imputation
Open accessAbstract
Genotype imputation is now an essential tool in the analysis of genome-wide association scans. This technique allows geneticists to accurately evaluate the evidence for association at genetic markers that are not directly genotyped. Genotype imputation is particularly useful for combining results across studies that rely on different genotyping platforms but also increases the power of individual scans. Here, we review the history and theoretical underpinnings of the technique. To illustrate performance of the approach, we summarize results from several gene mapping studies. Finally, we preview the role of genotype imputation in an era when whole genome resequencing is becoming increasingly common.
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Imputation methods are highly problematic for de novo or even moderately rare variants with frequencies below 1%.
“Although highly problematic in situations involving de novo or even moderately rare variants (<1%), imputation methods involving rare variants have begun to receive attention and could be extremely useful in future association studies.98”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [97]
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 [22]
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- Authors
- Li, Y., Willer, C., Sanna, S., & Abecasis, G.
- Affiliations
- University of Michigan (US)
- National Research Council (IT)
- Venue
- Annual Review of Genomics and Human Genetics
- Publisher
- Annual Reviews
- Published
- 2009-09-01
- Volume
- 10
- Issue
- 1
- Pages
- 387-406
- ISSN
- 1527-8204 (ISSN-L)
- 1545-293X
- PMC
- PMC2925172
- OpenAlex
- W4240204556
- Cited by
- 990
- References
- 120
- Citations by year
- 2026: 14 · 2025: 39 · 2024: 38 · 2023: 39 · 2022: 41 · 2021: 37 · 2020: 31 · 2019: 41 · 2018: 56 · 2017: 66 · 2016: 54 · 2015: 67 · 2014: 78 · 2013: 92 · 2012: 117
- FWCI
- 9.30
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Annual Review of Genomics and Human Genetics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Carolina Digital Repository (University of North Carolina at Chapel Hill) (submittedVersion)
- Funders
- National Heart, Lung, and Blood Institute (U01HL084729, HL-84729)
- National Institute of Mental Health (MH-84698, R01MH084698)
- National Human Genome Research Institute (R01HG002651, HG-2651)
- 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
- genotype imputation, genome-wide association study, genetic markers, genotyping platforms, statistical power, gene mapping, whole-genome resequencing
- MeSH terms
- Base Sequence, Genotype, Humans, Pedigree, Genome, Human, Sequence Alignment, Sequence Analysis, DNA
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