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Given the high dimensionality in imaging and genetic data, traditional methods based on massive univariate regression entail large computational cost and disregard many-to-many correlations between phenotypes and genetic variants. Several multivariate imaging genetic methods have been proposed to alleviate the above problems. However, most of these methods are based on the l1 penalty, which might cause the over-selection of variables and thus mislead scientists in analyzing data from the field of neuroimaging genetics.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>To address these challenges in both statistics and computation, we propose a novel co-sparse reduced-rank regression model that identifies complex correlations in a dimensional reduction manner. We developed an iterative algorithm based on a group primal dual-active set formulation to detect simultaneously important genetic variants and imaging phenotypes efficiently and precisely via non-convex penalty. The simulation studies showed that our method achieved accurate and stable performance in parameter estimation and variable selection. In real application, the proposed approach successfully detected several novel Alzheimer\u2019s disease-related genetic variants and regions of interest, which indicate that our method may be a valuable statistical toolbox for imaging genetic studies.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The R package csrrr, and the code for experiments in this article is available in Github: https:\/\/github.com\/hailongba\/csrrr.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa650","type":"journal-article","created":{"date-parts":[[2020,7,14]],"date-time":"2020-07-14T19:09:47Z","timestamp":1594753787000},"page":"5214-5222","source":"Crossref","is-referenced-by-count":12,"title":["Co-sparse reduced-rank regression for association analysis between imaging phenotypes and genetic variants"],"prefix":"10.1093","volume":"36","author":[{"given":"Canhong","family":"Wen","sequence":"first","affiliation":[{"name":"International Institute of Finance, School of Management, University of Science and Technology of China , Hefei 230026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hailong","family":"Ba","sequence":"additional","affiliation":[{"name":"International Institute of Finance, School of Management, University of Science and Technology of China , Hefei 230026, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenliang","family":"Pan","sequence":"additional","affiliation":[{"name":"Department of Statistical Science, School of Mathematics, Sun Yat-Sen University , Guangzhou 510275, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2138-9580","authenticated-orcid":false,"given":"Meiyan","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering , Guangzhou 510515, China"},{"name":"Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University , Guangzhou 510515, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"the Alzheimer\u2019s Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,7,19]]},"reference":[{"key":"2023062408064192100_btaa650-B1","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1177\/0271678X16655172","article-title":"Proteomic differences in brain vessels of Alzheimer\u2019s disease mice: normalization by PPAR agonist pioglitazone","volume":"37","author":"Badhwar","year":"2017","journal-title":"J. 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