{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T20:21:56Z","timestamp":1768508516469,"version":"3.49.0"},"posted":{"date-parts":[[2023,10,20]]},"group-title":"Genetics","reference-count":68,"publisher":"openRxiv","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2023,10,23]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:p>\n                  GWAS methods have identified individual SNPs significantly associated with specific phenotypes. Nonetheless, many complex diseases are polygenic and are controlled by multiple genetic variants that are usually non-linearly dependent. These genetic variants are marginally less effective and remain undetected in GWAS analysis. Kernel-based tests (KBT), which evaluate the joint effect of a group of genetic variants, are therefore critical for complex disease analysis. However, choosing different kernel functions in KBT can significantly influence the type I error control and power, and selecting the optimal kernel remains a statistically challenging task. A few existing methods suffer from inflated type 1 errors, limited scalability, inferior power, or issues of ambiguous conclusions. Here, we present a new Bayesian framework, BayesKAT(\n                  <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/wangjr03\/BayesKAT\">https:\/\/github.com\/wangjr03\/BayesKAT<\/jats:ext-link>\n                  ), which overcomes these kernel specification issues by selecting the optimal composite kernel adaptively from the data while testing genetic associations simultaneously. Furthermore, BayesKAT implements a scalable computational strategy to boost its applicability, especially for high-dimensional cases where other methods become less effective. Based on a series of performance comparisons using both simulated and real large-scale genetics data, BayesKAT outperforms the available methods in detecting complex group-level associations and controlling type I errors simultaneously. Applied on a variety of groups of functionally related genetic variants based on biological pathways, co-expression gene modules, and protein complexes, BayesKAT deciphers the complex genetic basis and provides mechanistic insights into human diseases.\n                <\/jats:p>","DOI":"10.1101\/2023.10.18.562824","type":"posted-content","created":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T13:55:41Z","timestamp":1697810141000},"source":"Crossref","is-referenced-by-count":0,"title":["BayesKAT: Bayesian Optimal Kernel-based Test for genetic association studies reveals joint genetic effects in complex diseases"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-2433-8200","authenticated-orcid":false,"given":"Sikta","family":"Das Adhikari","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8099-1753","authenticated-orcid":false,"given":"Yuehua","family":"Cui","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9290-4888","authenticated-orcid":false,"given":"Jianrong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2023102611000733000_2023.10.18.562824v2.1","doi-asserted-by":"publisher","DOI":"10.1038\/nrn2494"},{"key":"2023102611000733000_2023.10.18.562824v2.2","doi-asserted-by":"publisher","DOI":"10.1016\/S0092-8674(00)81110-1"},{"issue":"1","key":"2023102611000733000_2023.10.18.562824v2.3","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1038\/s43586-021-00056-9","article-title":"Genome-wide association studies","volume":"1","year":"2021","journal-title":"Nature Reviews Methods Primers"},{"key":"2023102611000733000_2023.10.18.562824v2.4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2017.05.038"},{"key":"2023102611000733000_2023.10.18.562824v2.5","doi-asserted-by":"publisher","DOI":"10.1038\/nature09792"},{"key":"2023102611000733000_2023.10.18.562824v2.6","doi-asserted-by":"publisher","DOI":"10.1038\/ng.717"},{"key":"2023102611000733000_2023.10.18.562824v2.7","doi-asserted-by":"publisher","DOI":"10.1038\/ng.609"},{"issue":"3","key":"2023102611000733000_2023.10.18.562824v2.8","first-page":"150","article-title":"Human diseases through the lens of network biology","volume":"29","year":"2012","journal-title":"Trends Genet"},{"key":"2023102611000733000_2023.10.18.562824v2.9","doi-asserted-by":"publisher","DOI":"10.1002\/bies.201500203"},{"key":"2023102611000733000_2023.10.18.562824v2.10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2007.10.010"},{"key":"2023102611000733000_2023.10.18.562824v2.11","doi-asserted-by":"publisher","DOI":"10.1111\/j.1541-0420.2007.00799.x"},{"key":"2023102611000733000_2023.10.18.562824v2.12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajhg.2011.05.029"},{"key":"2023102611000733000_2023.10.18.562824v2.13","unstructured":"Shaoyu Li and Yuehua Cui . 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