FINEMAP-miss: fine-mapping genome-wide association studies with missing genotype information.

ABSTRACT:

Motivation

The most informative genome-wide association studies (GWAS) are meta-analyses that have combined multiple studies to increase the GWAS sample size. Statistical fine-mapping is a key downstream analysis of GWAS to jointly evaluate the probability of causality of all variants in a genomic region of interest. Current fine-mapping methods are miscalibrated in the meta-analysis setting due to variation in sample size across the variants.

Results

We introduce FINEMAP-miss, a new fine-mapping method that extends the FINEMAP model to account for variant-specific missingness. We show that FINEMAP-miss is well-calibrated in meta-analysis simulations where the standard fine-mapping fails. Compared to the summary statistics imputation approach, FINEMAP-miss provides clear imp

SUBMITTER: Kartau J 

PROVIDER: S-EPMC12668598 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature