Opening the Black Box of Imputation Software to Study the Impact of Reference Panel Composition on Performance
Genotype imputation is widely used to enrich genetic datasets. The operation relies on panels of known reference haplotypes, typically with whole-genome sequencing data. How to choose a reference panel has been widely studied and it is essential to have a panel that is well matched to the individual...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-02-01
|
Series: | Genes |
Subjects: | |
Online Access: | https://www.mdpi.com/2073-4425/14/2/410 |
_version_ | 1797620835448520704 |
---|---|
author | Thibault Dekeyser Emmanuelle Génin Anthony F. Herzig |
author_facet | Thibault Dekeyser Emmanuelle Génin Anthony F. Herzig |
author_sort | Thibault Dekeyser |
collection | DOAJ |
description | Genotype imputation is widely used to enrich genetic datasets. The operation relies on panels of known reference haplotypes, typically with whole-genome sequencing data. How to choose a reference panel has been widely studied and it is essential to have a panel that is well matched to the individuals who require missing genotype imputation. However, it is broadly accepted that such an imputation panel will have an enhanced performance with the inclusion of diversity (haplotypes from many different populations). We investigate this observation by examining, in fine detail, exactly which reference haplotypes are contributing at different regions of the genome. This is achieved using a novel method of inserting synthetic genetic variation into the reference panel in order to track the performance of leading imputation algorithms. We show that while diversity may globally improve imputation accuracy, there can be occasions where incorrect genotypes are imputed following the inclusion of more diverse haplotypes in the reference panel. We, however, demonstrate a technique for retaining and benefitting from the diversity in the reference panel whilst avoiding the occasional adverse effects on imputation accuracy. What is more, our results more clearly elucidate the role of diversity in a reference panel than has been shown in previous studies. |
first_indexed | 2024-03-11T08:47:14Z |
format | Article |
id | doaj.art-c0266717648f48e29257aeb2c615a72c |
institution | Directory Open Access Journal |
issn | 2073-4425 |
language | English |
last_indexed | 2024-03-11T08:47:14Z |
publishDate | 2023-02-01 |
publisher | MDPI AG |
record_format | Article |
series | Genes |
spelling | doaj.art-c0266717648f48e29257aeb2c615a72c2023-11-16T20:42:35ZengMDPI AGGenes2073-44252023-02-0114241010.3390/genes14020410Opening the Black Box of Imputation Software to Study the Impact of Reference Panel Composition on PerformanceThibault Dekeyser0Emmanuelle Génin1Anthony F. Herzig2Inserm, Université de Brest, EFS, UMR 1078, GGB, F-29200 Brest, FranceInserm, Université de Brest, EFS, UMR 1078, GGB, F-29200 Brest, FranceInserm, Université de Brest, EFS, UMR 1078, GGB, F-29200 Brest, FranceGenotype imputation is widely used to enrich genetic datasets. The operation relies on panels of known reference haplotypes, typically with whole-genome sequencing data. How to choose a reference panel has been widely studied and it is essential to have a panel that is well matched to the individuals who require missing genotype imputation. However, it is broadly accepted that such an imputation panel will have an enhanced performance with the inclusion of diversity (haplotypes from many different populations). We investigate this observation by examining, in fine detail, exactly which reference haplotypes are contributing at different regions of the genome. This is achieved using a novel method of inserting synthetic genetic variation into the reference panel in order to track the performance of leading imputation algorithms. We show that while diversity may globally improve imputation accuracy, there can be occasions where incorrect genotypes are imputed following the inclusion of more diverse haplotypes in the reference panel. We, however, demonstrate a technique for retaining and benefitting from the diversity in the reference panel whilst avoiding the occasional adverse effects on imputation accuracy. What is more, our results more clearly elucidate the role of diversity in a reference panel than has been shown in previous studies.https://www.mdpi.com/2073-4425/14/2/410genotype imputationpopulation geneticsrare variantsreference paneladmixture |
spellingShingle | Thibault Dekeyser Emmanuelle Génin Anthony F. Herzig Opening the Black Box of Imputation Software to Study the Impact of Reference Panel Composition on Performance Genes genotype imputation population genetics rare variants reference panel admixture |
title | Opening the Black Box of Imputation Software to Study the Impact of Reference Panel Composition on Performance |
title_full | Opening the Black Box of Imputation Software to Study the Impact of Reference Panel Composition on Performance |
title_fullStr | Opening the Black Box of Imputation Software to Study the Impact of Reference Panel Composition on Performance |
title_full_unstemmed | Opening the Black Box of Imputation Software to Study the Impact of Reference Panel Composition on Performance |
title_short | Opening the Black Box of Imputation Software to Study the Impact of Reference Panel Composition on Performance |
title_sort | opening the black box of imputation software to study the impact of reference panel composition on performance |
topic | genotype imputation population genetics rare variants reference panel admixture |
url | https://www.mdpi.com/2073-4425/14/2/410 |
work_keys_str_mv | AT thibaultdekeyser openingtheblackboxofimputationsoftwaretostudytheimpactofreferencepanelcompositiononperformance AT emmanuellegenin openingtheblackboxofimputationsoftwaretostudytheimpactofreferencepanelcompositiononperformance AT anthonyfherzig openingtheblackboxofimputationsoftwaretostudytheimpactofreferencepanelcompositiononperformance |