A new multipoint method for genome-wide association studies by imputation of genotypes
Abstract
Genome-wide association studies are set to become the method of choice for uncovering the genetic basis of human diseases. A central challenge in this area is the development of powerful multipoint methods that can detect causal variants that have not been directly genotyped. We propose a coherent analysis framework that treats the problem as one involving missing or uncertain genotypes. Central to our approach is a model-based imputation method for inferring genotypes at observed or unobserved SNPs, leading to improved power over existing methods for multipoint association mapping. Using real genome-wide association study data, we show that our approach (i) is accurate and well calibrated, (ii) provides detailed views of associated regions that facilitate follow-up studies and (iii) can be used to validate and correct data at genotyped markers. A notable future use of our method will be to boost power by combining data from genome-wide scans that use different SNP sets.
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Inferring haplotype phase is similar to other cases in which missing data has to be imputed.
“The emerging view of this problem is that inferring haplotype phase is similar to other cases in which missing data (in this case the haplotype phase of a diploid genotype) has to be imputed127.”
From Linkage disequilibrium--understanding the evolutionary past and mapping the medical future. (2008) · cites this paper as [127]
Phenome-wide association studies were adjusted for age, sex and the first 10 ancestry-based principal components.
“with adjustment for age, sex and the first 10 ancestry-based principal components using SNPTEST v2.5.475.”
From Integrative genomic analyses identify candidate causal genes for calcific aortic valve stenosis involving tissue-specific regulation (2024) · cites this paper as [75]
The likelihood was maximized using an expectation-maximization algorithm, as implemented in SNPTEST version 2.1.1 software, in order to account for genotype uncertainty in the regression analyses of the imputed SNPs.
“The likelihood was maximized using an expectation-maximization algorithm, as implemented in SNPTEST version 2.1.1 software (26), in order to account for genotype uncertainty in the regression analyses of the imputed SNPs.”
From The N342S MYLIP polymorphism is associated with high total cholesterol and increased LDL receptor degradation in humans (2011) · cites this paper as [26]
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 [23]
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References 28 · Cited by 2,609
- Authors
- Marchini, J., Howie, B., Myers, S., McVean, G., & Donnelly, P.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Donnelly, Peter
- Venue
- Nature Genetics
- Published
- 2007-06-17
- Volume
- 39
- Issue
- 7
- Pages
- 906-913
- ISSN
- 1061-4036 (ISSN-L)
- 1546-1718
- OpenAlex
- W2149791927
- Cited by
- 2,609
- References
- 28
- Citations by year
- 2026: 56 · 2025: 62 · 2024: 92 · 2023: 84 · 2022: 97 · 2021: 134 · 2020: 91 · 2019: 149 · 2018: 167 · 2017: 163 · 2016: 142 · 2015: 163 · 2014: 164 · 2013: 164 · 2012: 191
- FWCI
- 90.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Genetics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust
- 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, model-based imputation, missing genotypes, single nucleotide polymorphisms, genome-wide association study, statistical power, data validation, follow-up study
- MeSH terms
- Genetic Markers, Genetics, Population, Genotype, Humans, Models, Genetic, Genome, Human, Case-Control Studies, Polymorphism, Single Nucleotide, Genomics
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