{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:19:38Z","timestamp":1760170778109},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"14","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Statistical analyses of genome-wide association studies (GWAS) require fitting large numbers of very similar regression models, each with low statistical power. Taking advantage of repeated observations or correlated phenotypes can increase this statistical power, but fitting the more complicated models required can make computation impractical.<\/jats:p><jats:p>Results: In this article, we present simple methods that capitalize on the structure inherent in GWAS studies to dramatically speed up computation for a wide variety of problems, with a special focus on methods for correlated phenotypes.<\/jats:p><jats:p>Availability: The R package \u2018boss\u2019 is available on the Comprehensive R Archive Network (CRAN) at http:\/\/cran.r-project.org\/web\/packages\/boss\/<\/jats:p><jats:p>Contact: \u00a0voorma@u.washington.edu<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/bts291","type":"journal-article","created":{"date-parts":[[2012,5,17]],"date-time":"2012-05-17T15:46:05Z","timestamp":1337269565000},"page":"1818-1822","source":"Crossref","is-referenced-by-count":12,"title":["Fast computation for genome-wide association studies using boosted one-step statistics"],"prefix":"10.1093","volume":"28","author":[{"given":"Arend","family":"Voorman","sequence":"first","affiliation":[{"name":"1 Department of Biostatistics, University of Washington, Seattle, WA 98195, USA and 2Department of Statistics, University of Auckland, Auckland 1142, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ken","family":"Rice","sequence":"additional","affiliation":[{"name":"1 Department of Biostatistics, University of Washington, Seattle, WA 98195, USA and 2Department of Statistics, University of Auckland, Auckland 1142, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Lumley","sequence":"additional","affiliation":[{"name":"1 Department of Biostatistics, University of Washington, Seattle, WA 98195, USA and 2Department of Statistics, University of Auckland, Auckland 1142, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2012,5,15]]},"reference":[{"key":"2023012512434970800_B1","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1534\/genetics.107.075614","article-title":"Genomewide rapid association using mixed model and regression: a fast and simple method for genomewide pedigree-based quantitative trait loci association analysis","volume":"177","author":"Aulchenko","year":"2007","journal-title":"Genetics"},{"key":"2023012512434970800_B2","author":"Bates","year":"2009","journal-title":"lme4: Linear Mixed-effects Models using S4 Classes."},{"key":"2023012512434970800_B3","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.ajhg.2009.01.005","article-title":"A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals","volume":"84","author":"Browning","year":"2009","journal-title":"Am. 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