{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,3]],"date-time":"2024-08-03T05:11:24Z","timestamp":1722661884788},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2020,4,4]],"date-time":"2020-04-04T00:00:00Z","timestamp":1585958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R21AI144765","R01CA229542","P50DA039838","R01GM126479","R01HG008983"],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DMS-1820702"],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,6,1]]},"abstract":"Abstract<\/jats:title>Motivation<\/jats:title>Large scale genome-wide association studies (GWAS) have resulted in the identification of a wide range of genetic variants related to a host of complex traits and disorders. Despite their success, the individual single-nucleotide polymorphism (SNP) analysis approach adopted in most current GWAS can be limited in that it is usually biologically simple to elucidate a comprehensive genetic architecture of phenotypes and statistically underpowered due to heavy multiple-testing correction burden. On the other hand, multiple-SNP analyses (e.g. gene-based or region-based SNP-set analysis) are usually more powerful to examine the joint effects of a set of SNPs on the phenotype of interest. However, current multiple-SNP approaches can only draw an overall conclusion at the SNP-set level and does not directly inform which SNPs in the SNP-set are driving the overall genotype\u2013phenotype association.<\/jats:p><\/jats:sec>Results<\/jats:title>In this article, we propose a new permutation-assisted tuning procedure in lasso (plasso) to identify phenotype-associated SNPs in a joint multiple-SNP regression model in GWAS. The tuning parameter of lasso determines the amount of shrinkage and is essential to the performance of variable selection. In the proposed plasso procedure, we first generate permutations as pseudo-SNPs that are not associated with the phenotype. Then, the lasso tuning parameter is delicately chosen to separate true signal SNPs and non-informative pseudo-SNPs. We illustrate plasso using simulations to demonstrate its superior performance over existing methods, and application of plasso to a real GWAS dataset gains new additional insights into the genetic control of complex traits.<\/jats:p><\/jats:sec>Availability and implementation<\/jats:title>R codes to implement the proposed methodology is available at https:\/\/github.com\/xyz5074\/plasso.<\/jats:p><\/jats:sec>Supplementary information<\/jats:title>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa229","type":"journal-article","created":{"date-parts":[[2020,3,31]],"date-time":"2020-03-31T19:21:14Z","timestamp":1585682474000},"page":"3811-3817","source":"Crossref","is-referenced-by-count":15,"title":["Prioritizing genetic variants in GWAS with lasso using permutation-assisted tuning"],"prefix":"10.1093","volume":"36","author":[{"given":"Songshan","family":"Yang","sequence":"first","affiliation":[{"name":"Department of Statistics , Pennsylvania State University, University Park, PA 16802"}]},{"given":"Jiawei","family":"Wen","sequence":"additional","affiliation":[{"name":"Department of Statistics , Pennsylvania State University, University Park, PA 16802"}]},{"given":"Scott T","family":"Eckert","sequence":"additional","affiliation":[{"name":"Department of Public Health Sciences , Pennsylvania State University, Hershey, PA 17033"}]},{"given":"Yaqun","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics , Rutgers University, New Brunswick, NJ 08901, USA"}]},{"given":"Dajiang J","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Public Health Sciences , Pennsylvania State University, Hershey, PA 17033"}]},{"given":"Rongling","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Public Health Sciences , Pennsylvania State University, Hershey, PA 17033"}]},{"given":"Runze","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Statistics , Pennsylvania State University, University Park, PA 16802"}]},{"given":"Xiang","family":"Zhan","sequence":"additional","affiliation":[{"name":"Department of Public Health Sciences , Pennsylvania State University, Hershey, PA 17033"}]}],"member":"286","published-online":{"date-parts":[[2020,4,4]]},"reference":[{"key":"2023070101190512800_btaa229-B1","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1186\/s12863-017-0533-3","article-title":"Resampling-based tests for Lasso in genome-wide association studies","volume":"18","author":"Arbet","year":"2017","journal-title":"BMC Genet"},{"key":"2023070101190512800_btaa229-B2","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1038\/ng.2962","article-title":"Rare and low-frequency coding variants in CXCR2 and other genes are associated with hematological traits","volume":"46","author":"Auer","year":"2014","journal-title":"Nat. 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