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Using population-specific add-on polymorphisms to improve genotype imputation in underrepresented populations
- Source :
- PLoS Computational Biology, Vol 18, Iss 1, p e1009628 (2022), PLoS computational biology, vol. 18, no. 1, pp. e1009628, PLoS Computational Biology
- Publication Year :
- 2022
- Publisher :
- Public Library of Science (PLoS), 2022.
-
Abstract
- Genome-wide association studies rely on the statistical inference of untyped variants, called imputation, to increase the coverage of genotyping arrays. However, the results are often suboptimal in populations underrepresented in existing reference panels and array designs, since the selected single nucleotide polymorphisms (SNPs) may fail to capture population-specific haplotype structures, hence the full extent of common genetic variation. Here, we propose to sequence the full genomes of a small subset of an underrepresented study cohort to inform the selection of population-specific add-on tag SNPs and to generate an internal population-specific imputation reference panel, such that the remaining array-genotyped cohort could be more accurately imputed. Using a Tanzania-based cohort as a proof-of-concept, we demonstrate the validity of our approach by showing improvements in imputation accuracy after the addition of our designed add-on tags to the base H3Africa array.<br />Author summary Genome-wide association studies, which study the association between genetic variants and various phenotypes, typically rely on genotyping arrays. Only a small proportion of genetic variants within the genome are typed on genotyping arrays. Untyped variants are statistically inferred through a process known as genotype imputation, where correlations between variants (haplotypes) observed in external reference panels are leveraged to infer untyped variants in the study population. However, for study populations that are underrepresented in existing reference panels, the quality of imputation is often sub-optimal. This is because typed variants incorporated on existing genotyping arrays can be unsuitable for the study population, and haplotype structures can be different between the reference and the study population. Here, we illustrate an approach to select a custom set of population-specific typed variants to improve genotype imputation in such underrepresented populations.
- Subjects :
- Male
Genotyping
Heredity
Genotype
QH301-705.5
Single Nucleotide Polymorphisms
Research and Analysis Methods
Polymorphism, Single Nucleotide
Tanzania
Chromosomes
Cellular and Molecular Neuroscience
Genome-Wide Association Studies
Genetics
Humans
Biology (General)
Molecular Biology Techniques
Molecular Biology
Ecology, Evolution, Behavior and Systematics
Evolutionary Biology
Sex Chromosomes
Computational Biology/methods
Genetics, Population/methods
Genetics, Population/standards
Genome-Wide Association Study/methods
Genome-Wide Association Study/standards
Polymorphism, Single Nucleotide/genetics
Population Biology
Ecology
Chromosome Biology
Computational Biology
Biology and Life Sciences
Human Genetics
Genomics
Cell Biology
Genome Analysis
Y Chromosomes
Genetic Mapping
Genetics, Population
Haplotypes
Computational Theory and Mathematics
Modeling and Simulation
Haplogroups
Population Genetics
Genome-Wide Association Study
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 15537358
- Volume :
- 18
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....5aea04383f2ebf6829b5151ecbef9068