{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T19:38:02Z","timestamp":1770665882122,"version":"3.49.0"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":1087,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2014,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Over the past few years several pathway analysis methods have been proposed for exploring and enhancing the analysis of genome-wide association data. Hierarchical models have been advocated as a way to integrate SNP and pathway effects in the same model, but their computational complexity has prevented them being applied on a genome-wide scale to date.<\/jats:p><jats:p>Methods: We present two novel methods for identifying associated pathways. In the proposed hierarchical model, the SNP effects are analytically integrated out of the analysis, allowing computationally tractable model fitting to genome-wide data. The first method uses Bayes factors for calculating the effect of the pathways, whereas the second method uses a machine learning algorithm and adaptive lasso for finding a sparse solution of associated pathways.<\/jats:p><jats:p>Results: The performance of the proposed methods was explored on both simulated and real data. The results of the simulation study showed that the methods outperformed some well-established association methods: the commonly used Fisher\u2019s method for combining P-values and also the recently published BGSA. The methods were applied to two genome-wide association study datasets that aimed to find the genetic structure of platelet function and body mass index, respectively. The results of the analyses replicated the results of previously published pathway analysis of these phenotypes but also identified novel pathways that are potentially involved.<\/jats:p><jats:p>Availability: An R package is under preparation. In the meantime, the scripts of the methods are available on request from the authors.<\/jats:p><jats:p>Contact: marina.evangelou@cimr.cam.ac.uk<\/jats:p><jats:p>Supplementary Information: Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btt583","type":"journal-article","created":{"date-parts":[[2013,10,13]],"date-time":"2013-10-13T00:09:21Z","timestamp":1381622961000},"page":"690-697","source":"Crossref","is-referenced-by-count":12,"title":["Two novel pathway analysis methods based on a hierarchical model"],"prefix":"10.1093","volume":"30","author":[{"given":"Marina","family":"Evangelou","sequence":"first","affiliation":[{"name":"1 Medical Research Council Biostatistics Unit, Institute of Public Health, Cambridge, CB2 0SR, UK, 2JDRF\/Wellcome Trust Diabetes and Inflammation Laboratory, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Addenbrooke\u2019s Hospital, Cambridge, CB2 0XY, UK and 3Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK"},{"name":"1 Medical Research Council Biostatistics Unit, Institute of Public Health, Cambridge, CB2 0SR, UK, 2JDRF\/Wellcome Trust Diabetes and Inflammation Laboratory, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Addenbrooke\u2019s Hospital, Cambridge, CB2 0XY, UK and 3Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Frank","family":"Dudbridge","sequence":"additional","affiliation":[{"name":"1 Medical Research Council Biostatistics Unit, Institute of Public Health, Cambridge, CB2 0SR, UK, 2JDRF\/Wellcome Trust Diabetes and Inflammation Laboratory, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Addenbrooke\u2019s Hospital, Cambridge, CB2 0XY, UK and 3Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lorenz","family":"Wernisch","sequence":"additional","affiliation":[{"name":"1 Medical Research Council Biostatistics Unit, Institute of Public Health, Cambridge, CB2 0SR, UK, 2JDRF\/Wellcome Trust Diabetes and Inflammation Laboratory, NIHR Cambridge Biomedical Research Centre, Cambridge Institute for Medical Research, University of Cambridge, Addenbrooke\u2019s Hospital, Cambridge, CB2 0XY, UK and 3Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, WC1E 7HT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2013,10,11]]},"reference":[{"key":"2023012710440387900_btt583-B1","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1210\/rp.56.1.329","article-title":"The adipocyte as a secretory organ: mechanisms of vesicle transport and secretory pathways","volume":"56","author":"Bradley","year":"2001","journal-title":"Recent Prog. 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