{"entity": "publication", "iuid": "406a6b57182e4f4993d607f4bc92ff6c", "timestamp": "2026-08-20T20:32:32.911Z", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/406a6b57182e4f4993d607f4bc92ff6c.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/406a6b57182e4f4993d607f4bc92ff6c"}}, "title": "Improving accuracy in genome-wide association studies: a two-step approach for handling below limit of detection biomarker measurements.", "authors": [{"family": "Deng", "given": "Yaqi A", "initials": "YA", "orcid": "0009-0006-2947-2433", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/2f8333c82d644605a42fa231b09f2774.json"}}, {"family": "Karlsson", "given": "Torgny", "initials": "T", "orcid": "0000-0001-8095-6149", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c8b2bb8ecf4545ad946ae2203c870282.json"}}, {"family": "Johansson", "given": "\u00c5sa", "initials": "\u00c5", "orcid": "0000-0002-2915-4498", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a0bac3e1e64949fcbac4b4d839e1144d.json"}}], "type": "journal article", "published": "2025-12-00", "journal": {"title": "NAR Genomics and Bioinformatics", "issn": "2631-9268", "volume": "7", "issue": "4", "pages": "lqaf201", "issn-l": null}, "abstract": "Advances in high-throughput technologies enable large-scale studies on genomics and molecular phenotypes. However, the trade-off between quality and quantity reduces assay sensitivity, and several measurements in large-scale proteomics and metabolomics analytes fall below the limit of detection (LOD). If not properly addressed, this may introduce bias in effect estimates. To address this, we conducted a simulation study to evaluate the performance of linear, Tobit, Cox, and logistic modeling in the presence of below-LOD measurements in genome-wide association studies. We identified the optimal strategy as a two-step Linear-Tobit scheme, including rapid screening with linear regression followed by refinement with Tobit regression to retrieve accurate effect estimates. This higher accuracy helps mitigate a 1.3-fold and 2.7-fold inflation in causal estimates in a Mendelian randomization (MR) study, which would otherwise be present with 50% and 90% values below LOD. Validation through case studies on estradiol and testosterone levels in the UK Biobank confirmed the simulation results across subgroups with varying proportions of below-LOD measurements. The Linear-Tobit scheme offers optimal detection power and efficiency, with a focus on its applicability to biobank-scale datasets and accuracy in effect estimates to mitigate bias in downstream applications such as MR and polygenic risk scores.", "doi": "10.1093/nargab/lqaf201", "pmid": "41480591", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC12754788"}, {"db": "pii", "key": "lqaf201"}], "notes": [], "created": "2026-08-20T09:43:04.586Z", "modified": "2026-08-20T09:43:04.719Z"}