Efficient approaches for large-scale GWAS with genotype uncertainty.

ABSTRACT: 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

SUBMITTER: Jorsboe E 

PROVIDER: S-EPMC8727990 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature