Very low-depth whole-genome sequencing in complex trait association studies.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
01 08 2019
01 08 2019
Historique:
received:
20
07
2018
revised:
17
11
2018
accepted:
17
12
2018
pubmed:
24
12
2018
medline:
17
6
2020
entrez:
22
12
2018
Statut:
ppublish
Résumé
Very low-depth sequencing has been proposed as a cost-effective approach to capture low-frequency and rare variation in complex trait association studies. However, a full characterization of the genotype quality and association power for very low-depth sequencing designs is still lacking. We perform cohort-wide whole-genome sequencing (WGS) at low depth in 1239 individuals (990 at 1× depth and 249 at 4× depth) from an isolated population, and establish a robust pipeline for calling and imputing very low-depth WGS genotypes from standard bioinformatics tools. Using genotyping chip, whole-exome sequencing (75× depth) and high-depth (22×) WGS data in the same samples, we examine in detail the sensitivity of this approach, and show that imputed 1× WGS recapitulates 95.2% of variants found by imputed GWAS with an average minor allele concordance of 97% for common and low-frequency variants. In our study, 1× further allowed the discovery of 140 844 true low-frequency variants with 73% genotype concordance when compared to high-depth WGS data. Finally, using association results for 57 quantitative traits, we show that very low-depth WGS is an efficient alternative to imputed GWAS chip designs, allowing the discovery of up to twice as many true association signals than the classical imputed GWAS design. The HELIC genotype and WGS datasets have been deposited to the European Genome-phenome Archive (https://www.ebi.ac.uk/ega/home): EGAD00010000518; EGAD00010000522; EGAD00010000610; EGAD00001001636, EGAD00001001637. The peakplotter software is available at https://github.com/wtsi-team144/peakplotter, the transformPhenotype app can be downloaded at https://github.com/wtsi-team144/transformPhenotype. Supplementary data are available at Bioinformatics online.
Identifiants
pubmed: 30576415
pii: 5255875
doi: 10.1093/bioinformatics/bty1032
pmc: PMC6662288
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2555-2561Subventions
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 098051
Pays : United Kingdom
Organisme : Wellcome Trust
ID : WT091310
Pays : United Kingdom
Informations de copyright
© The Author(s) 2018. Published by Oxford University Press.
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