{"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/generalized-similarity-u-a-non-parametric","title":"Generalized Similarity U: A Non-parametric Test of Association Based on Similarity","arxiv_id":"1801.01220","date":"2018-01-04","proceeding":null,"authors":["Changshuai Wei","Qing Lu"],"abstract":"Second generation sequencing technologies are being increasingly used for\ngenetic association studies, where the main research interest is to identify\nsets of genetic variants that contribute to various phenotype. The phenotype\ncan be univariate disease status, multivariate responses and even\nhigh-dimensional outcomes. Considering the genotype and phenotype as two\ncomplex objects, this also poses a general statistical problem of testing\nassociation between complex objects. We here proposed a similarity-based test,\ngeneralized similarity U (GSU), that can test the association between complex\nobjects. We first studied the theoretical properties of the test in a general\nsetting and then focused on the application of the test to sequencing\nassociation studies. Based on theoretical analysis, we proposed to use\nLaplacian kernel based similarity for GSU to boost power and enhance\nrobustness. Through simulation, we found that GSU did have advantages over\nexisting methods in terms of power and robustness. We further performed a whole\ngenome sequencing (WGS) scan for Alzherimer Disease Neuroimaging Initiative\n(ADNI) data, identifying three genes, APOE, APOC1 and TOMM40, associated with\nimaging phenotype. We developed a C++ package for analysis of whole genome\nsequencing data using GSU. The source codes can be downloaded at\nhttps://github.com/changshuaiwei/gsu.","url_abs":"http://arxiv.org/abs/1801.01220v1","url_pdf":"http://arxiv.org/pdf/1801.01220v1.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":"generalized-similarity-u-a-non-parametric","repo_url":"https://github.com/changshuaiwei/gsu","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}