Bioinformatics. 2025 Nov 9:btaf616. doi: 10.1093/bioinformatics/btaf616. Online ahead of print.
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 improvement when the causal variants have low imputation information or when the sample size or complexity of the meta-analysis setting increase. We successfully apply FINEMAP-miss on a breast cancer GWAS meta-analysis where neither the standard fine-mapping nor the summary statistics imputation are applicable.
AVAILABILITY: An open source implementation of FINEMAP-miss as an R package (“finemapmiss”) is available at https://github.com/JoonasKartau/finemapmiss. The archived version of FINEMAP-miss used for this publication can be found on Zenodo at https://doi.org/10.5281/zenodo.17492622.
SUPPLEMENTARY INFORMATION: Supplementary Data is available at the journal’s web site.
PMID:41206934 | DOI:10.1093/bioinformatics/btaf616
