ArticleHuman immunology2016
HLA imputation in an admixed population: An assessment of the 1000 Genomes data as a training set.
Article in Human immunology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Characterization of a novel MYO3A missense mutation associated with a dominant form of late onset hearing loss.Scientific reports · 2018Trial
- Imputation-Based HLA Typing with GWAS SNPs.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Whole-genome sequencing of 1,171 elderly admixed individuals from São Paulo, Brazil.Nature communications · 2022Article
- Admixture mapping reveals evidence of differential multiple sclerosis risk by genetic ancestry.PLoS genetics · 2019Article
- HLA typing using genome wide data reveals susceptibility types for infections in a psychiatric disease enriched sample.Brain, behavior, and immunity · 2018Article
- Inbreeding estimates in human populations: Applying new approaches to an admixed Brazilian isolate.PloS one · 2018Article
- A genomic perspective on HLA evolution.Immunogenetics · 2018Review
- Comparison of HLA allelic imputation programs.PloS one · 2017Article
- Increasing The Genetic Admixture of Available Lines of Human Pluripotent Stem Cells.Scientific reports · 2016Article
- A web resource for mining HLA associations with adverse drug reactions: HLA-ADR.Database : the journal of biological databases and curation · 2016Article
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Authors and funding
10 authors.
Funding
Abstract
Methods to impute HLA alleles based on dense single nucleotide polymorphism (SNP) data provide a valuable resource to association studies and evolutionary investigation of the MHC region. The availability of appropriate training sets is critical to the accuracy of HLA imputation, and the inclusion of samples with various ancestries is an important pre-requisite in studies of admixed populations. We assess the accuracy of HLA imputation using 1000 Genomes Project data as a training set, applying it to a highly admixed Brazilian population, the Quilombos from the state of São Paulo. To assess accuracy, we compared imputed and experimentally determined genotypes for 146 samples at 4 HLA classical loci. We found imputation accuracies of 82.9%, 81.8%, 94.8% and 86.6% for HLA-A, -B, -C and -DRB1 respectively (two-field resolution). Accuracies were improved when we included a subset of Quilombo individuals in the training set. We conclude that the 1000 Genomes data is a valuable resource for construction of training sets due to the diversity of ancestries and the potential for a large overlap of SNPs with the target population. We also show that tailoring training sets to features of the target population substantially enhances imputation accuracy.
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Registered trials
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