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

Details

Serval ID
serval:BIB_3994C147BF6F
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Using population-specific add-on polymorphisms to improve genotype imputation in underrepresented populations.
Journal
PLoS computational biology
Author(s)
Xu Z.M., Rüeger S., Zwyer M., Brites D., Hiza H., Reinhard M., Rutaihwa L., Borrell S., Isihaka F., Temba H., Maroa T., Naftari R., Hella J., Sasamalo M., Reither K., Portevin D., Gagneux S., Fellay J.
ISSN
1553-7358 (Electronic)
ISSN-L
1553-734X
Publication state
Published
Issued date
13/01/2022
Peer-reviewed
Oui
Volume
18
Number
1
Pages
e1009628
Language
english
Notes
Publication types: Journal Article
Publication Status: aheadofprint
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.
Pubmed
Open Access
Yes
Create date
25/01/2022 8:27
Last modification date
28/01/2022 7:34
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