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Efficient approaches for large-scale GWAS with genotype uncertainty

Association studies using genetic data from SNP-chip-based imputation or low-depth sequencing data provide a cost-efficient design for large-scale association studies. We explore methods for performing association studies applicable to such genetic data and investigate how using different priors whe...

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Autores principales: Jørsboe, Emil, Albrechtsen, Anders
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8727990/
https://www.ncbi.nlm.nih.gov/pubmed/34865001
http://dx.doi.org/10.1093/g3journal/jkab385
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author Jørsboe, Emil
Albrechtsen, Anders
author_facet Jørsboe, Emil
Albrechtsen, Anders
author_sort Jørsboe, Emil
collection PubMed
description Association studies using genetic data from SNP-chip-based imputation or low-depth sequencing data provide a cost-efficient design for large-scale association studies. We explore methods for performing association studies applicable to such genetic data and investigate how using different priors when estimating genotype probabilities affects the association results. Our proposed method, ANGSD-asso’s latent model, models the unobserved genotype as a latent variable in a generalized linear model framework. The software is implemented in C/C++ and can be run multi-threaded. ANGSD-asso is based on genotype probabilities, which can be estimated using either the sample allele frequency or the individual allele frequencies as a prior. We explore through simulations how genotype probability-based methods compare with using genetic dosages. Our simulations show that in a structured population using the individual allele frequency prior has better power than the sample allele frequency. In scenarios with sequencing depth and phenotype correlation ANGSD-asso’s latent model has higher statistical power and less bias than using dosages. Adding additional covariates to the linear model of ANGSD-asso’s latent model has higher statistical power and less bias than other methods that accommodate genotype uncertainty, while also being much faster. This is shown with imputed data from UK Biobank and simulations.
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spelling pubmed-87279902022-01-05 Efficient approaches for large-scale GWAS with genotype uncertainty Jørsboe, Emil Albrechtsen, Anders G3 (Bethesda) Software and Data Resources Association studies using genetic data from SNP-chip-based imputation or low-depth sequencing data provide a cost-efficient design for large-scale association studies. We explore methods for performing association studies applicable to such genetic data and investigate how using different priors when estimating genotype probabilities affects the association results. Our proposed method, ANGSD-asso’s latent model, models the unobserved genotype as a latent variable in a generalized linear model framework. The software is implemented in C/C++ and can be run multi-threaded. ANGSD-asso is based on genotype probabilities, which can be estimated using either the sample allele frequency or the individual allele frequencies as a prior. We explore through simulations how genotype probability-based methods compare with using genetic dosages. Our simulations show that in a structured population using the individual allele frequency prior has better power than the sample allele frequency. In scenarios with sequencing depth and phenotype correlation ANGSD-asso’s latent model has higher statistical power and less bias than using dosages. Adding additional covariates to the linear model of ANGSD-asso’s latent model has higher statistical power and less bias than other methods that accommodate genotype uncertainty, while also being much faster. This is shown with imputed data from UK Biobank and simulations. Oxford University Press 2021-12-04 /pmc/articles/PMC8727990/ /pubmed/34865001 http://dx.doi.org/10.1093/g3journal/jkab385 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Genetics Society of America. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Software and Data Resources
Jørsboe, Emil
Albrechtsen, Anders
Efficient approaches for large-scale GWAS with genotype uncertainty
title Efficient approaches for large-scale GWAS with genotype uncertainty
title_full Efficient approaches for large-scale GWAS with genotype uncertainty
title_fullStr Efficient approaches for large-scale GWAS with genotype uncertainty
title_full_unstemmed Efficient approaches for large-scale GWAS with genotype uncertainty
title_short Efficient approaches for large-scale GWAS with genotype uncertainty
title_sort efficient approaches for large-scale gwas with genotype uncertainty
topic Software and Data Resources
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8727990/
https://www.ncbi.nlm.nih.gov/pubmed/34865001
http://dx.doi.org/10.1093/g3journal/jkab385
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