AI Article Synopsis

  • Genome-wide association studies often struggle with imputation of untyped genetic variants in underrepresented populations, leading to suboptimal results.
  • By sequencing full genomes of a small group from these populations, researchers aim to identify specific genetic markers (tag SNPs) to improve imputation accuracy.
  • Using a cohort from Tanzania, the study shows that adding these tailored tag SNPs enhances imputation accuracy compared to the existing genotyping arrays, validating this approach.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791479PMC
http://dx.doi.org/10.1371/journal.pcbi.1009628DOI Listing

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