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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 (https:\/\/github.com\/wangjr03\/BayesKAT), 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.<\/jats:p>","DOI":"10.1093\/bib\/bbae182","type":"journal-article","created":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T20:05:04Z","timestamp":1712347504000},"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":"10.1093","volume":"25","author":[{"given":"Sikta","family":"Das Adhikari","sequence":"first","affiliation":[{"name":"Department of Statistics and Probability, Michigan State University , East Lansing, MI 48824 , USA"},{"name":"Department of Computational Mathematics, Science and Engineering, Michigan State University , East Lansing, MI 48824 , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuehua","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Statistics and Probability, Michigan State University , East Lansing, MI 48824 , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianrong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computational Mathematics, Science and Engineering, Michigan State University , East Lansing, MI 48824 , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,4,22]]},"reference":[{"issue":"10","key":"2024042309552367400_ref1","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1038\/nrn2494","article-title":"Thirty years of alzheimer\u2019s disease genetics: the implications of systematic meta-analyses","volume":"9","author":"Bertram","year":"2008","journal-title":"Nat Rev 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