{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:42Z","timestamp":1772138082124,"version":"3.50.1"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"12","funder":[{"DOI":"10.13039\/100004440","name":"Wellcome Trust","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004440","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,6,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Motivation: Several efficient gene\u2013gene interaction tests have been developed for unrelated case\u2013control samples in genome-wide association studies (GWAS), making it possible to test tens of billions of interaction pairs of single-nucleotide polymorphisms (SNPs) in a reasonable timeframe. However, current family-based gene\u2013gene interaction tests are computationally expensive and are not applicable to genome-wide interaction analysis.<\/jats:p>\n                  <jats:p>Results: We developed an efficient family-based gene\u2013gene interaction test, GCORE, for trios (i.e. two parents and one affected sib). The GCORE compares interlocus correlations at two SNPs between the transmitted and non-transmitted alleles. We used simulation studies to compare the statistical properties such as type I error rates and power for the GCORE with several other family-based interaction tests under various scenarios. We applied the GCORE to a family-based GWAS for autism consisting of approximately 2000 trios. Testing a total of 22 471 383 013 interaction pairs in the GWAS can be finished in 36\u2009h by the GCORE without large-scale computing resources, demonstrating that the test is practical for genome-wide gene\u2013gene interaction analysis in trios.<\/jats:p>\n                  <jats:p>Availability and implementation: GCORE is implemented with C\u2009++\u2009and is available at http:\/\/gscore.sourceforge.net.<\/jats:p>\n                  <jats:p>Contact: \u00a0rchung@nhri.org.tw<\/jats:p>\n                  <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btw077","type":"journal-article","created":{"date-parts":[[2016,2,14]],"date-time":"2016-02-14T20:09:07Z","timestamp":1455480547000},"page":"1848-1855","source":"Crossref","is-referenced-by-count":11,"title":["An efficient gene\u2013gene interaction test for genome-wide association studies in trio families"],"prefix":"10.1093","volume":"32","author":[{"given":"Pei-Yuan","family":"Sung","sequence":"first","affiliation":[{"name":"1 Institute of Statistics, National Tsing Hua University, Hsin-Chu, Taiwan and"}]},{"given":"Yi-Ting","family":"Wang","sequence":"additional","affiliation":[{"name":"1 Institute of Statistics, National Tsing Hua University, Hsin-Chu, Taiwan and"}]},{"given":"Ya-Wen","family":"Yu","sequence":"additional","affiliation":[{"name":"2 Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan"}]},{"given":"Ren-Hua","family":"Chung","sequence":"additional","affiliation":[{"name":"2 Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan"}]}],"member":"286","published-online":{"date-parts":[[2016,2,11]]},"reference":[{"key":"2023020112293819600_btw077-B1","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1038\/ng786","article-title":"Merlin\u2013rapid analysis of dense genetic maps using sparse gene flow trees","volume":"30","author":"Abecasis","year":"2002","journal-title":"Nat. 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