ArticleGenetic epidemiology2010
A generic coalescent-based framework for the selection of a reference panel for imputation.
Article in Genetic epidemiology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- RefRGim: an intelligent reference panel reconstruction method for genotype imputation with convolutional neural networks.Briefings in bioinformatics · 2021Article
- Sequencing and imputation in GWAS: Cost-effective strategies to increase power and genomic coverage across diverse populations.Genetic epidemiology · 2020Article
- Imputation of missing genotypes within LD-blocks relying on the basic coalescent and beyond: consideration of population growth and structure.BMC genomics · 2017Article
- Choosing Subsamples for Sequencing Studies by Minimizing the Average Distance to the Closest Leaf.Genetics · 2015Article
- DISTMIX: direct imputation of summary statistics for unmeasured SNPs from mixed ethnicity cohorts.Bioinformatics (Oxford, England) · 2015Article
- A spatial haplotype copying model with applications to genotype imputation.Journal of computational biology : a journal of computational molecular cell biology · 2015Article
- Article
- Genotype imputation in a coalescent model with infinitely-many-sites mutation.Theoretical population biology · 2013Article
- Article
- MaCH-admix: genotype imputation for admixed populations.Genetic epidemiology · 2013Article
- A coalescent model for genotype imputation.Genetics · 2012Article
- Evaluation of the imputation performance of the program IMPUTE in an admixed sample from Mexico City using several model designs.BMC medical genomics · 2012Article
- Imputation of rare variants in next-generation association studies.Human heredity · 2012Article
- Haplotype variation and genotype imputation in African populations.Genetic epidemiology · 2011Article
- Genotype imputation with thousands of genomes.G3 (Bethesda, Md.) · 2011Article
- Enhanced statistical tests for GWAS in admixed populations: assessment using African Americans from CARe and a Breast Cancer Consortium.PLoS genetics · 2011Article
- Genotyping common and rare variation using overlapping pool sequencing.BMC bioinformatics · 2011Article
- Mapping of disease-associated variants in admixed populations.Genome biology · 2011Review
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Authors and funding
6 authors.
Funding
Abstract
An important component in the analysis of genome-wide association studies involves the imputation of genotypes that have not been measured directly in the studied samples. The imputation procedure uses the linkage disequilibrium (LD) structure in the population to infer the genotype of an unobserved single nucleotide polymorphism. The LD structure is normally learned from a dense genotype map of a reference population that matches the studied population. In many instances there is no reference population that exactly matches the studied population, and a natural question arises as to how to choose the reference population for the imputation. Here we present a Coalescent-based method that addresses this issue. In contrast to the current paradigm of imputation methods, our method assigns a different reference dataset for each sample in the studied population, and for each region in the genome. This allows the flexibility to account for the diversity within populations, as well as across populations. Furthermore, because our approach treats each region in the genome separately, our method is suitable for the imputation of recently admixed populations. We evaluated our method across a large set of populations and found that our choice of reference data set considerably improves the accuracy of imputation, especially for regions with low LD and for populations without a reference population available as well as for admixed populations such as the Hispanic population. Our method is generic and can potentially be incorporated in any of the available imputation methods as an add-on.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.