{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/nonparametric-reduced-rank-regression-for","title":"Nonparametric Reduced-Rank Regression for Multi-SNP, Multi-Trait Association Mapping","arxiv_id":"1512.02306","date":"2015-12-08","proceeding":null,"authors":["Ashlee Valente","Geoffrey Ginsburg","Barbara E. Engelhardt"],"abstract":"Genome-wide association studies have proven to be essential for understanding\nthe genetic basis of disease. However, many complex traits---personality\ntraits, facial features, disease subtyping---are inherently high-dimensional,\nimpeding simple approaches to association mapping. We developed a nonparametric\nBayesian reduced rank regression model for multi-SNP, multi-trait association\nmapping that does not require the rank of the linear subspace to be specified.\nWe show in simulations and real data that our model shares strength over SNPs\nand over correlated traits, improving statistical power to identify genetic\nassociations with an interpretable, SNP-supervised low-dimensional linear\nprojection of the high-dimensional phenotype. On the HapMap phase 3 gene\nexpression QTL study data, we identify pleiotropic expression QTLs that\nclassical univariate tests are underpowered to find and that two step\napproaches cannot recover. Our Python software, BERRRI, is publicly available\nat GitHub: https://github.com/ashlee1031/BERRRI.","url_abs":"http://arxiv.org/abs/1512.02306v1","url_pdf":"http://arxiv.org/pdf/1512.02306v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"nonparametric-reduced-rank-regression-for","repo_url":"https://github.com/ashlee1031/BERRRI","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"regression-1","task_name":"regression"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}