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Using population-specific add-on polymorphisms to improve genotype imputation in underrepresented populations.

Zhi Ming XuSina RuegerMichaela ZwyerDaniela BritesHellen HizaMiriam ReinhardLiliana RutaihwaSonia BorrellFaima IsihakaHosiana TembaThomas MaroaRastard NaftariJerry HellaMohamed SasamaloKlaus ReitherDamien PortevinSébastien GagneuxJacques Fellay
Published in: PLoS computational biology (2022)
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.
Keyphrases
  • genome wide association
  • high resolution
  • high throughput
  • genome wide
  • high density
  • single cell