{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T17:41:53Z","timestamp":1775324513408,"version":"3.50.1"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"18","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":1490,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Imaging genetic studies typically focus on identifying single-nucleotide polymorphism (SNP) markers associated with imaging phenotypes. Few studies perform regression of SNP values on phenotypic measures for examining how the SNP values change when phenotypic measures are varied. This alternative approach may have a potential to help us discover important imaging genetic associations from a different perspective. In addition, the imaging markers are often measured over time, and this longitudinal profile may provide increased power for differentiating genotype groups. How to identify the longitudinal phenotypic markers associated to disease sensitive SNPs is an important and challenging research topic.<\/jats:p><jats:p>Results: Taking into account the temporal structure of the longitudinal imaging data and the interrelatedness among the SNPs, we propose a novel \u2018task-correlated longitudinal sparse regression\u2019 model to study the association between the phenotypic imaging markers and the genotypes encoded by SNPs. In our new association model, we extend the widely used \u21132,1-norm for matrices to tensors to jointly select imaging markers that have common effects across all the regression tasks and time points, and meanwhile impose the trace-norm regularization onto the unfolded coefficient tensor to achieve low rank such that the interrelationship among SNPs can be addressed. The effectiveness of our method is demonstrated by both clearly improved prediction performance in empirical evaluations and a compact set of selected imaging predictors relevant to disease sensitive SNPs.<\/jats:p><jats:p>Availability: Software is publicly available at: http:\/\/ranger.uta.edu\/%7eheng\/Longitudinal\/<\/jats:p><jats:p>Contact: \u00a0heng@uta.edu or shenli@inpui.edu<\/jats:p>","DOI":"10.1093\/bioinformatics\/bts411","type":"journal-article","created":{"date-parts":[[2012,9,7]],"date-time":"2012-09-07T20:35:22Z","timestamp":1347050122000},"page":"i619-i625","source":"Crossref","is-referenced-by-count":56,"title":["From phenotype to genotype: an association study of longitudinal phenotypic markers to Alzheimer's disease relevant SNPs"],"prefix":"10.1093","volume":"28","author":[{"given":"Hua","family":"Wang","sequence":"first","affiliation":[{"name":"1 Department of Computer Science and Engineering, University of Texas at Arlington, TX 76019, USA"}]},{"given":"Feiping","family":"Nie","sequence":"additional","affiliation":[{"name":"1 Department of Computer Science and Engineering, University of Texas at Arlington, TX 76019, USA"}]},{"given":"Heng","family":"Huang","sequence":"additional","affiliation":[{"name":"1 Department of Computer Science and Engineering, University of Texas at Arlington, TX 76019, USA"}]},{"given":"Jingwen","family":"Yan","sequence":"additional","affiliation":[{"name":"2 Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA"}]},{"given":"Sungeun","family":"Kim","sequence":"additional","affiliation":[{"name":"2 Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA"}]},{"given":"Kwangsik","family":"Nho","sequence":"additional","affiliation":[{"name":"2 Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA"}]},{"given":"Shannon L.","family":"Risacher","sequence":"additional","affiliation":[{"name":"2 Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA"}]},{"given":"Andrew J.","family":"Saykin","sequence":"additional","affiliation":[{"name":"2 Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA"}]},{"given":"Li","family":"Shen","sequence":"additional","affiliation":[{"name":"2 Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202, USA"}]},{"name":"for the Alzheimer's Disease Neuroimaging Initiative","sequence":"additional","affiliation":[]}],"member":"286","published-online":{"date-parts":[[2012,9,3]]},"reference":[{"key":"2023012513064096100_B1","first-page":"41","article-title":"Multi-task feature learning","author":"Argyriou","year":"2007","journal-title":"NIPS"},{"key":"2023012513064096100_B2","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1006\/nimg.2000.0582","article-title":"Voxel-based morphometry\u2014the methods","volume":"11","author":"Ashburner","year":"2000","journal-title":"Neuroimage"},{"key":"2023012513064096100_B3","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1093\/hmg\/ddn388","article-title":"Genome-wide association analysis of susceptibility and clinical phenotype in multiple sclerosis","volume":"18","author":"Baranzini","year":"2008","journal-title":"Human Mol. 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