{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T12:36:52Z","timestamp":1788871012254,"version":"build-2803163510"},"reference-count":38,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2014,8,13]],"date-time":"2014-08-13T00:00:00Z","timestamp":1407888000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100000272","name":"National Health Research Institute","doi-asserted-by":"crossref","award":["NHRI-EX101-9918NC"],"award-info":[{"award-number":["NHRI-EX101-9918NC"]}],"id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001868","name":"National Science Council","doi-asserted-by":"crossref","award":["NSC 99-2314-B-002-140-MY3"],"award-info":[{"award-number":["NSC 99-2314-B-002-140-MY3"]}],"id":[{"id":"10.13039\/501100001868","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["American J of Med Genetics Pt B"],"published-print":{"date-parts":[[2014,10]]},"abstract":"<jats:sec>\n                    <jats:label\/>\n                    <jats:p>\n                      Many susceptibility genes for complex traits were identified without conclusive findings. There is a strong need to integrate rapidly accumulated genomic data from multi\u2010dimensional platforms, and to conduct risk evaluation for potential therapeutic and diagnostic usages. We set up an algorithm to computationally search for optimal weight\u2010vector for various data sources, while minimized potential noises. Through gene\u2010prioritization framework, combined scores for the resulting prioritized gene\u2010set were calculated using a genome\u2010wide association (GWA) dataset, following with evaluation using weighted genetic risk score and risk\u2010attributed information using an independent GWA dataset. The significance of association of GWA data was corrected for gene length. Enriched functional pathways were identified for the prioritized gene\u2010set using the Gene Ontology analysis. We illustrated our framework with bipolar disorder. 233 prioritized genes were identified from 10,830 candidates that curated from six platforms. The prioritized genes were significantly enriched (\n                      <jats:italic>P<\/jats:italic>\n                      <jats:sub>adjusted<\/jats:sub>\n                      \u2009&lt;\u20091\u2009\u00d7\u200910\n                      <jats:sup>\u22125<\/jats:sup>\n                      ) in 18 biological functions and molecular mechanisms including membrane, synaptic transmission, transmission of nerve impulse, integral to membrane, and plasma membrane. Our risk evaluation demonstrated higher weighted genetic risk score in bipolar patients than controls (\n                      <jats:italic>P<\/jats:italic>\n                      \u2010values ranged from 0.002 to 3.8\u2009\u00d7\u200910\n                      <jats:sup>\u22126<\/jats:sup>\n                      ). Substantial risk\u2010information (71%) was extracted from prioritized genes for bipolar illness than other candidate\u2010gene sets. Our evidence\u2010based prioritized gene\u2010set provides opportunity to explore the complex network and to conduct follow\u2010up basic and clinical studies for complex traits. \u00a9 2014 Wiley Periodicals, Inc.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ajmg.b.32263","type":"journal-article","created":{"date-parts":[[2014,8,13]],"date-time":"2014-08-13T17:30:42Z","timestamp":1407951042000},"page":"596-606","source":"Crossref","is-referenced-by-count":5,"title":["Risk and information evaluation of prioritized genes for complex traits: Application to bipolar disorder"],"prefix":"10.1002","volume":"165","author":[{"given":"Chung\u2010Feng","family":"Kao","sequence":"first","affiliation":[{"name":"Department of Public Health &amp; Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li\u2010Chung","family":"Chuang","sequence":"additional","affiliation":[{"name":"Department of Nursing Cardinal Tien Junior College of Healthcare &amp; Management Yilan Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Po\u2010Hsiu","family":"Kuo","sequence":"additional","affiliation":[{"name":"Department of Public Health &amp; Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei Taiwan"},{"name":"Research Center for Genes Environment and Human Health National Taiwan University Taipei Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2014,8,13]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btk031"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/mp.2012.37"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s001270050098"},{"key":"e_1_2_6_5_1","first-page":"61","article-title":"Interpretive Risk Assessment on GWA Data with Sparse Linear Regression","author":"Br\u00e6nne I","year":"2010","journal-title":"Machine Learning Reports"},{"key":"e_1_2_6_6_1","doi-asserted-by":"publisher","DOI":"10.1161\/01.CIR.63.4.915"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.209"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng1747"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tig.2007.08.011"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/ajmg.b.31183"},{"issue":"13","key":"e_1_2_6_11_1","first-page":"347","article-title":"Theory of independence","volume":"10","author":"Husik I","year":"1913","journal-title":"J Phil Psychol Sci Methods"},{"key":"e_1_2_6_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/gepi.20346"},{"key":"e_1_2_6_13_1","doi-asserted-by":"publisher","DOI":"10.1186\/1756-6606-2-8"},{"key":"e_1_2_6_14_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0018696"},{"key":"e_1_2_6_15_1","doi-asserted-by":"publisher","DOI":"10.1006\/geno.1995.1023"},{"key":"e_1_2_6_16_1","doi-asserted-by":"publisher","DOI":"10.1001\/archpsyc.62.6.593"},{"key":"e_1_2_6_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpsychires.2012.04.017"},{"key":"e_1_2_6_18_1","doi-asserted-by":"publisher","DOI":"10.1093\/hmg\/2.7.941"},{"key":"e_1_2_6_19_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl569"},{"key":"e_1_2_6_20_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng2127"},{"key":"e_1_2_6_21_1","doi-asserted-by":"publisher","DOI":"10.1001\/archpsyc.60.5.497"},{"key":"e_1_2_6_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.brainres.2005.12.056"},{"key":"e_1_2_6_23_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0049093"},{"key":"e_1_2_6_24_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2164-10-197"},{"key":"e_1_2_6_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.psyneuen.2007.04.015"},{"key":"e_1_2_6_26_1","doi-asserted-by":"publisher","DOI":"10.1038\/ng.943"},{"key":"e_1_2_6_27_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature08185"},{"key":"e_1_2_6_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0006-3223(01)01080-0"},{"issue":"3","key":"e_1_2_6_29_1","first-page":"231","article-title":"Tryptophan and tyrosine catabolic pattern in neuropsychiatric disorders","volume":"48","author":"Ravikumar A","year":"2000","journal-title":"Neurol India"},{"key":"e_1_2_6_30_1","doi-asserted-by":"publisher","DOI":"10.1038\/mp.2008.29"},{"key":"e_1_2_6_31_1","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1948.tb01338.x"},{"key":"e_1_2_6_32_1","doi-asserted-by":"publisher","DOI":"10.1038\/sj.mp.4002151"},{"key":"e_1_2_6_33_1","doi-asserted-by":"publisher","DOI":"10.1001\/archpsyc.56.3.254"},{"key":"e_1_2_6_34_1","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2164-12-579"},{"key":"e_1_2_6_35_1","doi-asserted-by":"publisher","DOI":"10.1001\/archpsyc.63.10.1103"},{"key":"e_1_2_6_36_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1078197"},{"key":"e_1_2_6_37_1","unstructured":"WhittakerJ KaoC\u2010F.2007.A divergence\u2010weighted independence graph for finite population surveys. The Challenges of a Changing World: the Fifth International Conference of the Association for Survey Computing: 247\u2013258. September 2007 University of Southampton. Southampton UK. 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