{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T13:33:37Z","timestamp":1768570417451,"version":"3.49.0"},"posted":{"date-parts":[[2024,12,8]]},"group-title":"Genetic and Genomic Medicine","reference-count":65,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2024,12,8]],"date-time":"2024-12-08T00:00:00Z","timestamp":1733616000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,3,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  The increasing availability of whole-genome sequencing (WGS) has begun to elucidate the contribution of rare variants (RVs), both coding and non-coding, to complex disease. Multiple RV association tests are available to study the relationship between genotype and phenotype, but most are restricted to per-gene models and do not fully leverage the availability of variant-level functional annotations. We propose Genome-wide Rare Variant EnRichment Evaluation (\n                  <jats:monospace>gruyere<\/jats:monospace>\n                  ), a Bayesian probabilistic model that complements existing methods by learning global, trait-specific weights for functional annotations to improve variant prioritization. We apply\n                  <jats:monospace>gruyere<\/jats:monospace>\n                  to WGS data from the Alzheimer\u2019s Disease (AD) Sequencing Project, consisting of 7,966 cases and 13,412 controls, to identify AD-associated genes and annotations. Growing evidence suggests that disruption of microglial regulation is a key contributor to AD risk, yet existing methods have not had sufficient power to examine rare non-coding effects that incorporate such cell-type specific information. To address this gap, we 1) use predicted enhancer and promoter regions in microglia and other potentially relevant cell types (oligodendrocytes, astrocytes, and neurons) to define per-gene non-coding RV test sets and 2) include cell-type specific variant effect predictions (VEPs) as functional annotations.\n                  <jats:monospace>gruyere<\/jats:monospace>\n                  identifies 15 significant genetic associations not detected by other RV methods and finds deep learning-based VEPs for splicing, transcription factor binding, and chromatin state are highly predictive of functional non-coding RVs. Our study establishes a novel and robust framework incorporating functional annotations, coding RVs, and cell-type associated non-coding RVs, to perform genome-wide association tests, uncovering AD-relevant genes and annotations.\n                <\/jats:p>","DOI":"10.1101\/2024.12.06.24318577","type":"posted-content","created":{"date-parts":[[2024,12,8]],"date-time":"2024-12-08T14:20:55Z","timestamp":1733667655000},"source":"Crossref","is-referenced-by-count":1,"title":["Leveraging functional annotations to map rare variants associated with Alzheimer\u2019s disease with gruyere"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3885-2594","authenticated-orcid":false,"given":"Anjali","family":"Das","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8431-3690","authenticated-orcid":false,"given":"Chirag","family":"Lakhani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chlo\u00e9","family":"Terwagne","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jui-Shan T.","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tatsuhiko","family":"Naito","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9355-5704","authenticated-orcid":false,"given":"Towfique","family":"Raj","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7408-146X","authenticated-orcid":false,"given":"David A.","family":"Knowles","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2025030704080946000_2024.12.06.24318577v2.1","doi-asserted-by":"publisher","DOI":"10.1038\/s41593-020-0599-5"},{"key":"2025030704080946000_2024.12.06.24318577v2.2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2023.104511"},{"key":"2025030704080946000_2024.12.06.24318577v2.3","doi-asserted-by":"publisher","DOI":"10.1186\/s13073-015-0138-2"},{"key":"2025030704080946000_2024.12.06.24318577v2.4","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-021-00997-7"},{"key":"2025030704080946000_2024.12.06.24318577v2.5","doi-asserted-by":"publisher","DOI":"10.1007\/s00439-016-1679-1"},{"key":"2025030704080946000_2024.12.06.24318577v2.6","doi-asserted-by":"publisher","DOI":"10.1126\/science.abo1131"},{"key":"2025030704080946000_2024.12.06.24318577v2.7","doi-asserted-by":"publisher","DOI":"10.1002\/dad2.12155"},{"key":"2025030704080946000_2024.12.06.24318577v2.8","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-022-01640-x"},{"key":"2025030704080946000_2024.12.06.24318577v2.9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2014.06.009"},{"key":"2025030704080946000_2024.12.06.24318577v2.10","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-020-0676-4"},{"key":"2025030704080946000_2024.12.06.24318577v2.11","doi-asserted-by":"publisher","DOI":"10.1101\/2023.07.12.548506"},{"key":"2025030704080946000_2024.12.06.24318577v2.12","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-023-01373-3"},{"key":"2025030704080946000_2024.12.06.24318577v2.13","doi-asserted-by":"publisher","DOI":"10.1186\/s13059-022-02664-4"},{"key":"2025030704080946000_2024.12.06.24318577v2.14","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-021-01252-x"},{"key":"2025030704080946000_2024.12.06.24318577v2.15","doi-asserted-by":"publisher","DOI":"10.1101\/gr.097857.109"},{"key":"2025030704080946000_2024.12.06.24318577v2.16","doi-asserted-by":"publisher","DOI":"10.1038\/ng.3477"},{"key":"2025030704080946000_2024.12.06.24318577v2.17","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2016.00016"},{"key":"2025030704080946000_2024.12.06.24318577v2.18","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2019.03.002"},{"key":"2025030704080946000_2024.12.06.24318577v2.19","doi-asserted-by":"publisher","DOI":"10.1038\/s41593-020-0603-0"},{"key":"2025030704080946000_2024.12.06.24318577v2.20","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2105191118"},{"key":"2025030704080946000_2024.12.06.24318577v2.21","doi-asserted-by":"publisher","DOI":"10.1101\/2024.03.30.24305115"},{"key":"2025030704080946000_2024.12.06.24318577v2.22","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-019-0538-0"},{"key":"2025030704080946000_2024.12.06.24318577v2.23","unstructured":"Hoffman, M.D. , Blei, D.M. , Wang, C. , Paisley, J. : Stochastic variational inference. 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