PLoS Computational BiologyGeneticsPeer-reviewed journal article
Using population-specific add-on polymorphisms to improve genotype imputation in underrepresented populations
Zhi Ming XuiD, Sina RüegeriD, Michaela ZwyeriD, Daniela BritesiD, Hellen Charles HizaiD, Miriam Reinhard, Liliana K. RutaihwaiD, Sònia BorrelliD, Faima IsihakaiD, Hosiana Temba, Thomas Maroa, Rastard Naftari, Jerry HellaiD, Mohamed SasamaloiD, Klaus ReitheriD, Damien PorteviniD, Sébastien GagneuxiD, Jacques FellayiD
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 genome of a small subset of an underrepresented study cohort to inform the selection of population-specific add-on SNPs, 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 addon SNPs to the base H3Africa array.
- organism: human
- assay: whole-genome sequencing
- research focus: imputation
- research focus: variant calling
- approach: statistical model
- Citations
- 10
- FWCI
- 1.21
- Field percentile
- top 24%
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