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Ultra Low-Coverage Whole-Genome Sequencing as an Alternative to Genotyping Arrays in Genome-Wide Association Studies
An array-based genotyping approach has been the standard practice for genome-wide association studies (GWASs); however, as sequencing costs plummet over the past years, ultra low-coverage whole-genome sequencing (ulcWGS <0.5× coverage) has emerged as a promising alternative that provides superior...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8889143/ https://www.ncbi.nlm.nih.gov/pubmed/35251117 http://dx.doi.org/10.3389/fgene.2021.790445 |
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author | Chat, Vylyny Ferguson, Robert Morales, Leah Kirchhoff, Tomas |
author_facet | Chat, Vylyny Ferguson, Robert Morales, Leah Kirchhoff, Tomas |
author_sort | Chat, Vylyny |
collection | PubMed |
description | An array-based genotyping approach has been the standard practice for genome-wide association studies (GWASs); however, as sequencing costs plummet over the past years, ultra low-coverage whole-genome sequencing (ulcWGS <0.5× coverage) has emerged as a promising alternative that provides superior genomic coverage with substantial reduction of genotyping cost. To evaluate the potential utility of ulcWGS, we performed a whole-genome sequencing (WGS) of 72 European individuals to a target coverage of 0.4× and compared its performance with the widely used Infinium Global Screening Multi-Disease Array (GSA-MD). We showed that the number of variants captured by ulcWGS is comparable with imputed GSA-MD platform, particularly for low-frequency (95.5%) and common variants (99.9%), with high imputation R(2) accuracy (mean 0.93 for SNPs and 0.86 for indels). Using deep-coverage 30× WGS as the “truth” genotypes, we found that ulcWGS has higher overall nonreference genotype concordance compared with imputed GSA-MD for both SNPs (0.90 vs. 0.88) and indels (0.86 vs. 0.83). In addition, ulcWGS proved to be as sensitive as the genotyping-based method in sex imputation and ancestry prediction producing similar principal component (PC) scores. Our findings provide important evidence that the cost efficient ulcWGS of <0.5× generates high genotype accuracy, outperforming the standard genotyping arrays, making it an attractive alternative to the array-based method in next-generation GWAS design. |
format | Online Article Text |
id | pubmed-8889143 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88891432022-03-03 Ultra Low-Coverage Whole-Genome Sequencing as an Alternative to Genotyping Arrays in Genome-Wide Association Studies Chat, Vylyny Ferguson, Robert Morales, Leah Kirchhoff, Tomas Front Genet Genetics An array-based genotyping approach has been the standard practice for genome-wide association studies (GWASs); however, as sequencing costs plummet over the past years, ultra low-coverage whole-genome sequencing (ulcWGS <0.5× coverage) has emerged as a promising alternative that provides superior genomic coverage with substantial reduction of genotyping cost. To evaluate the potential utility of ulcWGS, we performed a whole-genome sequencing (WGS) of 72 European individuals to a target coverage of 0.4× and compared its performance with the widely used Infinium Global Screening Multi-Disease Array (GSA-MD). We showed that the number of variants captured by ulcWGS is comparable with imputed GSA-MD platform, particularly for low-frequency (95.5%) and common variants (99.9%), with high imputation R(2) accuracy (mean 0.93 for SNPs and 0.86 for indels). Using deep-coverage 30× WGS as the “truth” genotypes, we found that ulcWGS has higher overall nonreference genotype concordance compared with imputed GSA-MD for both SNPs (0.90 vs. 0.88) and indels (0.86 vs. 0.83). In addition, ulcWGS proved to be as sensitive as the genotyping-based method in sex imputation and ancestry prediction producing similar principal component (PC) scores. Our findings provide important evidence that the cost efficient ulcWGS of <0.5× generates high genotype accuracy, outperforming the standard genotyping arrays, making it an attractive alternative to the array-based method in next-generation GWAS design. Frontiers Media S.A. 2022-02-15 /pmc/articles/PMC8889143/ /pubmed/35251117 http://dx.doi.org/10.3389/fgene.2021.790445 Text en Copyright © 2022 Chat, Ferguson, Morales and Kirchhoff. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Chat, Vylyny Ferguson, Robert Morales, Leah Kirchhoff, Tomas Ultra Low-Coverage Whole-Genome Sequencing as an Alternative to Genotyping Arrays in Genome-Wide Association Studies |
title | Ultra Low-Coverage Whole-Genome Sequencing as an Alternative to Genotyping Arrays in Genome-Wide Association Studies |
title_full | Ultra Low-Coverage Whole-Genome Sequencing as an Alternative to Genotyping Arrays in Genome-Wide Association Studies |
title_fullStr | Ultra Low-Coverage Whole-Genome Sequencing as an Alternative to Genotyping Arrays in Genome-Wide Association Studies |
title_full_unstemmed | Ultra Low-Coverage Whole-Genome Sequencing as an Alternative to Genotyping Arrays in Genome-Wide Association Studies |
title_short | Ultra Low-Coverage Whole-Genome Sequencing as an Alternative to Genotyping Arrays in Genome-Wide Association Studies |
title_sort | ultra low-coverage whole-genome sequencing as an alternative to genotyping arrays in genome-wide association studies |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8889143/ https://www.ncbi.nlm.nih.gov/pubmed/35251117 http://dx.doi.org/10.3389/fgene.2021.790445 |
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