Efficient approaches for large-scale GWAS with genotype uncertainty.

ABSTRACT:

Introduction

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.

Methods

Our proposed method, ANGSD-asso's latent model, models the unobserved genotype as a latent variable in a generalised 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 to using genetic dosages.

Results and discussion

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.

SUBMITTER: Jorsboe E 

PROVIDER: S-EPMC8727990 | biostudies-literature |

REPOSITORIES: biostudies-literature