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Discovering the bi-multivariate relationship between genetic markers such as single-nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is one major task in imaging genetics. Sparse Canonical Correlation Analysis (SCCA) has been a popular technique in this area for its powerful capability in identifying bi-multivariate relationships coupled with feature selection. The existing SCCA methods impose either the \u21131-norm or its variants to induce sparsity. The \u21130-norm penalty is a perfect sparsity-inducing tool which, however, is an NP-hard problem.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In this paper, we propose the truncated \u21131-norm penalized SCCA to improve the performance and effectiveness of the \u21131-norm based SCCA methods. Besides, we propose an efficient optimization algorithms to solve this novel SCCA problem. The proposed method is an adaptive shrinkage method via tuning \u03c4. It can avoid the time intensive parameter tuning if given a reasonable small \u03c4. Furthermore, we extend it to the truncated group-lasso (TGL), and propose TGL-SCCA model to improve the group-lasso-based SCCA methods. The experimental results, compared with four benchmark methods, show that our SCCA methods identify better or similar correlation coefficients, and better canonical loading profiles than the competing methods. This demonstrates the effectiveness and efficiency of our methods in discovering interesting imaging genetic associations.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The Matlab code and sample data are freely available at http:\/\/www.iu.edu\/\u223cshenlab\/tools\/tlpscca\/.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btx594","type":"journal-article","created":{"date-parts":[[2017,9,15]],"date-time":"2017-09-15T19:13:59Z","timestamp":1505502839000},"page":"278-285","source":"Crossref","is-referenced-by-count":40,"title":["A novel SCCA approach via truncated <i>\n                     <b>\u2113<\/b>\n                  <\/i>1-norm and truncated group lasso for brain imaging genetics"],"prefix":"10.1093","volume":"34","author":[{"given":"Lei","family":"Du","sequence":"first","affiliation":[{"name":"Department of Control and Information, School of Automation, Northwestern Polytechnical University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kefei","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tuo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Control and Information, School of Automation, Northwestern Polytechnical University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohui","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingwen","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shannon L","family":"Risacher","sequence":"additional","affiliation":[{"name":"Department of Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junwei","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Control and Information, School of Automation, Northwestern Polytechnical University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Control and Information, School of Automation, Northwestern Polytechnical University, Xi\u2019an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew J","family":"Saykin","sequence":"additional","affiliation":[{"name":"Department of Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5443-0503","authenticated-orcid":false,"given":"Li","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"for the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2017,9,18]]},"reference":[{"key":"2023012712223137600_btx594-B1","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1186\/1755-8794-1-44","article-title":"A genome-wide association study for late-onset Alzheimer\u2019s disease using dna pooling","volume":"1","author":"Abraham","year":"2008","journal-title":"BMC Med. 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