{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T10:42:05Z","timestamp":1775990525553,"version":"3.50.1"},"posted":{"date-parts":[[2025,6,8]]},"group-title":"Genetics","reference-count":31,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,6,8]],"date-time":"2025-06-08T00:00:00Z","timestamp":1749340800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NIH"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,7,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>The standard analysis pipeline for genome-wide association studies (GWAS) is based on marginal tests of association. These are computationally convenient and portable, but the discoveries resulting from their rejections are not immediately interpretable, and require post-processing as \u201cclumping\u201d and \u201cfine mapping.\u201d An interesting alternative is provided by conditional independence hypotheses: their rejections lead to the identification of distinct signals across the genome, accounting for measured confounders, and pointing to separate causal pathways.<\/jats:p>\n                <jats:p>\n                  An obstacle to the wide adoption of this approach has been that it requires access to individual level data. Overcoming this barrier, recent work has shown how summary statistics resulting from the standard marginal GWAS analysis can be used as input of a procedure to test conditional independence hypotheses while controlling the false discovery rate. This secondary analysis requires sampling of synthetic negative controls (knockoffs) from a distribution determined by the linkage disequilibrium patterns in the genome of the population under study. In prior work, we have pre-computed this distribution for European genomes, starting from information derived from the UK Biobank. Thus, researchers working with GWAS in a European population can carry out a knockoff analysis with minimal computational costs, using the distributed routine\n                  <jats:monospace>GhostKnockoffGWAS<\/jats:monospace>\n                  .\n                <\/jats:p>\n                <jats:p>\n                  Here we introduce and release a new software (\n                  <jats:monospace>solveblock<\/jats:monospace>\n                  ) that extends this capability to a much richer collection of studies. Given a set of genotyped samples, or a reference dataset, our pipeline efficiently estimates the high-dimensional correlation matrices that describe dependencies across the genome, making rather common sparsity assumptions. Taking this sample-specific estimate as input, the software identifies groups of genetic variants that are highly correlated, and uses them to define an appropriate resolution for conditional independence hypotheses. Finally, we compute the distribution for the exchangeable negative controls necessary to test these hypotheses. The output of\n                  <jats:monospace>solveblock<\/jats:monospace>\n                  can be passed directly to\n                  <jats:monospace>GhostKnockoffGWAS<\/jats:monospace>\n                  , allowing users to carry out the complete analysis in a two step procedure.\n                <\/jats:p>\n                <jats:p>\n                  Simulations, based on five UK Biobank sub-populations, illustrate the method\u2019s FDR control. The analysis of 26 phenotypes of varying polygenicity in British individuals, results in\n                  <jats:monospace>\u2248<\/jats:monospace>\n                  19 additional discoveries, compared to standard marginal association testing. Our code, precompiled software, and processed files for these five sub-populations are openly shared.\n                <\/jats:p>","DOI":"10.1101\/2025.06.05.658138","type":"posted-content","created":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T03:00:16Z","timestamp":1749438016000},"source":"Crossref","is-referenced-by-count":0,"title":["It\u2019s a wrap: deriving distinct discoveries with FDR control after a GWAS pipeline"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8342-2361","authenticated-orcid":false,"given":"Benjamin B.","family":"Chu","sequence":"first","affiliation":[{"name":"Department of Biomedical Data Science, Stanford School of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zihuai","family":"He","sequence":"additional","affiliation":[{"name":"Department of Biomedical Data Science, Stanford School of Medicine"},{"name":"Department of Neurology and Neurological Sciences, Stanford School of Medicine"},{"name":"Quantitative Sciences Unit, Department of Medicine, Stanford School of Medicine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chiara","family":"Sabatti","sequence":"additional","affiliation":[{"name":"Department of Biomedical Data Science, Stanford School of Medicine"},{"name":"Department of Statistics, Stanford University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2026041202425547000_2025.06.05.658138v2.1","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12265"},{"key":"2026041202425547000_2025.06.05.658138v2.2","doi-asserted-by":"publisher","DOI":"10.2307\/2346101"},{"issue":"1","key":"2026041202425547000_2025.06.05.658138v2.3","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1038\/s41467-020-14791-2","article-title":"Multiresolution localization of causal variants across the genome","volume":"11","year":"2020","journal-title":"Nature communications"},{"key":"2026041202425547000_2025.06.05.658138v2.4","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/asy033"},{"key":"2026041202425547000_2025.06.05.658138v2.5","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2105841118"},{"key":"2026041202425547000_2025.06.05.658138v2.6","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab489"},{"key":"2026041202425547000_2025.06.05.658138v2.7","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-023-01415-w"},{"key":"2026041202425547000_2025.06.05.658138v2.8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2024.02.015"},{"issue":"1","key":"2026041202425547000_2025.06.05.658138v2.9","doi-asserted-by":"crossref","first-page":"7209","DOI":"10.1038\/s41467-022-34932-z","article-title":"GhostKnockoff inference empowers identification of putative causal variants in genome-wide association studies","volume":"13","year":"2022","journal-title":"Nature Communications"},{"key":"2026041202425547000_2025.06.05.658138v2.10","article-title":"Controlled Variable Selection from Summary Statistics Only? A Solution via GhostKnockoffs and Penalized Regression","year":"2024","journal-title":"arXiv preprint"},{"key":"2026041202425547000_2025.06.05.658138v2.11","article-title":"Robust inference with GhostKnockoffs in genome-wide association studies","year":"2023","journal-title":"arXiv preprint"},{"key":"2026041202425547000_2025.06.05.658138v2.12","unstructured":"Zihuai He , Benjamin B Chu , James Yang , Jiaqi Gu , Zhaomeg Chen , Lixin Liu , Tim Morrison , Michael E. Belloy , X. Qi , Guen Yann Le , Stephen Montgomery , Hua M Tang , Trevor M. Grecius Hastie ,, Iuliana Ionita-laza , Chiara Sabatti , and Emmanuel Candes . Beyond guilty by association at scale: searching for causal variants on the basis of genome-wide summary statistics. BioRxiv, pages 2024\u201302, 2024."},{"key":"2026041202425547000_2025.06.05.658138v2.13","article-title":"Second-order group knockoffs with applications to GWAS","year":"2023","journal-title":"arXiv preprint"},{"key":"2026041202425547000_2025.06.05.658138v2.14","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1001779"},{"key":"2026041202425547000_2025.06.05.658138v2.15","unstructured":"Pan-UKB team. Pan-UK Biobank. https:\/\/pan.ukbb.broadinstitute.org, 2020."},{"key":"2026041202425547000_2025.06.05.658138v2.16","unstructured":"European Organization For Nuclear Research and OpenAIRE. Zenodo, 2013."},{"key":"2026041202425547000_2025.06.05.658138v2.17","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btr330"},{"key":"2026041202425547000_2025.06.05.658138v2.18","doi-asserted-by":"publisher","DOI":"10.1086\/519795"},{"key":"2026041202425547000_2025.06.05.658138v2.19","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btv546"},{"key":"2026041202425547000_2025.06.05.658138v2.20","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btac247"},{"key":"2026041202425547000_2025.06.05.658138v2.21","doi-asserted-by":"publisher","DOI":"10.1007\/s00439-019-02001-z"},{"key":"2026041202425547000_2025.06.05.658138v2.22","unstructured":"Jaime Roquero Gimenez and James Zou . Improving the stability of the knockoff procedure: Multiple simultaneous knockoffs and entropy maximization. In The 22nd International Conference on Artificial Intelligence and Statistics, pages 2184\u20132192. 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A blended genome and exome sequencing method captures genetic variation in an unbiased, high-quality, and cost-effective manner. bioRxiv, 2024.","DOI":"10.1101\/2024.09.06.611689"},{"key":"2026041202425547000_2025.06.05.658138v2.31","doi-asserted-by":"publisher","DOI":"10.1137\/141000671. arXiv:1411.1607v4"}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1101\/2025.06.05.658138","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T09:43:05Z","timestamp":1775986985000},"score":1,"resource":{"primary":{"URL":"http:\/\/biorxiv.org\/lookup\/doi\/10.1101\/2025.06.05.658138"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,8]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1101\/2025.06.05.658138","relation":{},"subject":[],"published":{"date-parts":[[2025,6,8]]},"subtype":"preprint"}}