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

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41206934", "kind": "science-record", "revision": 2, "content_hash": "sha256:1f25945f1199854a99da63a730c86ee19013b8911339015eec40ed1aa2327157", "created_at": "2026-11-01T20:31:14Z", "updated_at": "2026-11-01T21:36:00Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "FINEMAP-miss: fine-mapping genome-wide association studies with missing genotype information.", "doi": "10.1093/bioinformatics/btaf616", "pmid": "41206934", "authors": ["Joonas Kartau", "Matti Pirinen"], "journal": "Bioinformatics (Oxford, England)", "year": 2025, "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.\nRESULTS: 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.\nAVAILABILITY: 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.\nSUPPLEMENTARY INFORMATION: is available at the journal's web site.", "claims": [{"id": "claim-method", "statement": "The key novelty of FINEMAP-miss is the inclusion of the covariance matrix RM and sample size matrix N that account for variant-specific differences in GWAS samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41206934", "locator": "sections[6] \"2.4 FINEMAP-miss\" paragraph 6", "quote": "The key novelty of FINEMAP-miss is the inclusion of the covariance matrix RM and sample size matrix N that account for variant-specific differences in GWAS samples."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data for the simulations were generated using the imputed genotype data from UK biobank (UKB) (Bycroft et al. 2018). As the reference LD panel we used the full set of ‘white, British’ individuals from UKB (n=327,307). This research has been conducted using the UK Biobank Resource under Application Number 22627.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41206934", "locator": "sections[13] \"3.1 Data generation\" paragraph 2", "quote": "The data for the simulations were generated using the imputed genotype data from UK biobank (UKB) (Bycroft et al."}, {"asset": "source:pmid:41206934", "locator": "sections[13] \"3.1 Data generation\" paragraph 2", "quote": "2018)."}, {"asset": "source:pmid:41206934", "locator": "sections[17] \"3.5 Breast cancer meta-analysis\" paragraph 3", "quote": "As the reference LD panel we used the full set of ‘white, British’ individuals from UKB (n=327,307)."}, {"asset": "source:pmid:41206934", "locator": "sections[23] \"ack\" paragraph 1", "quote": "This research has been conducted using the UK Biobank Resource under Application Number 22627."}], "state": "complete"}, {"id": "claim-target-result", "statement": "FINEMAP-miss was well-calibrated in meta-analysis simulations where standard fine-mapping failed, and at n=2e+5 the three evaluated approaches each had credible set power of at least 0.907.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41206934", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We show that FINEMAP-miss is well-calibrated in meta-analysis simulations where the standard fine-mapping fails."}, {"asset": "source:pmid:41206934", "locator": "sections[18] \"4. Results\" paragraph 4", "quote": "For the smaller sample size (n=2e+5), FINEMAP-miss, and FINEMAP and SuSiE with scaled imputation performed well, as each method displayed high credible set power (≥0.907), high mean PIPs of the causal variants (C-PIP), while also keeping maximal PIPs of non-causal variants (MNC-PIP) low (Table 1)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "FINEMAP-miss was well-calibrated in meta-analysis simulations where standard fine-mapping failed, and at n=2e+5 the three evaluated approaches each had credible set power of at least 0.907.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:41206934"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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