{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T09:49:02Z","timestamp":1758707342728},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2001,11,1]],"date-time":"2001-11-01T00:00:00Z","timestamp":1004572800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2001,11,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>We describe an approximate method for the analysis of quantitative trait loci (QTL) based on model selection from multiple regression models with trait values regressed on marker genotypes, using a modification of the easily calculated Bayesian information criterion to estimate the posterior probability of models with various subsets of markers as variables. The BIC-\u03b4 criterion, with the parameter \u03b4 increasing the penalty for additional variables in a model, is further modified to incorporate prior information, and missing values are handled by multiple imputation. Marginal probabilities for model sizes are calculated, and the posterior probability of nonzero model size is interpreted as the posterior probability of existence of a QTL linked to one or more markers. The method is demonstrated on analysis of associations between wood density and markers on two linkage groups in Pinus radiata. Selection bias, which is the bias that results from using the same data to both select the variables in a model and estimate the coefficients, is shown to be a problem for commonly used non-Bayesian methods for QTL mapping, which do not average over alternative possible models that are consistent with the data.<\/jats:p>","DOI":"10.1093\/genetics\/159.3.1351","type":"journal-article","created":{"date-parts":[[2021,4,28]],"date-time":"2021-04-28T04:26:19Z","timestamp":1619583979000},"page":"1351-1364","source":"Crossref","is-referenced-by-count":79,"title":["Bayesian Methods for Quantitative Trait Loci Mapping Based on Model Selection: Approximate Analysis Using the Bayesian Information Criterion"],"prefix":"10.1093","volume":"159","author":[{"given":"Roderick D","family":"Ball","sequence":"first","affiliation":[{"name":"New Zealand Forest Research Institute, Rotorua 3201, New Zealand"}]}],"member":"286","published-online":{"date-parts":[[2001,11,1]]},"reference":[{"issue":"4","key":"2022010507171331200_R1","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/S0950-3293(98)00005-6","article-title":"Statistical analysis relating analytical and consumer panel assessments of kiwifruit flavour compounds in a model juice base","volume":"9","author":"Ball","year":"1998","journal-title":"Food Quality Pref."},{"key":"2022010507171331200_R2","first-page":"250","article-title":"The power and deceit of QTL experiments: lessons from comparative QTL studies","volume-title":"Proceedings of the 49th Annual Corn and Sorghum Industry Research Conference","author":"Beavis","year":"1994"},{"key":"2022010507171331200_R3","volume-title":"The New S Language, a Programming Environment for Data Analysis and Graphics","author":"Becker","year":"1988"},{"key":"2022010507171331200_R4","first-page":"159","article-title":"Statistical analysis and the illusion of objectivity","volume":"76","author":"Berger","year":"1988","journal-title":"Am. 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Genet."},{"key":"2022010507171331200_R21","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1093\/genetics\/121.1.185","article-title":"Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps","volume":"121","author":"Lander","year":"1989","journal-title":"Genetics"},{"key":"2022010507171331200_R22","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1093\/genetics\/149.1.383","article-title":"Quantitative trait locus (QTL) mapping using different testers and independent population samples in Maize reveals low power of QTL detection and large bias in estimates of QTL effects","volume":"149","author":"Melchinger","year":"1998","journal-title":"Genetics"},{"key":"2022010507171331200_R23","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4899-2939-6","volume-title":"Subset Selection in Regression (Monographs on Statistics and Applied Probability 40)","author":"Miller","year":"1990"},{"key":"2022010507171331200_R24","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1101\/gr.5.4.321","article-title":"Molecular dissection of quantitative traits: progress and prospects","volume":"5","author":"Paterson","year":"1995","journal-title":"Genome Res."},{"key":"2022010507171331200_R25","first-page":"111","article-title":"Bayesian model selection in social research (with discussion)","volume-title":"Sociological Methodology","author":"Raftery","year":"1995"},{"issue":"437","key":"2022010507171331200_R26","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1080\/01621459.1997.10473615","article-title":"Bayesian model averaging for linear regression models","volume":"92","author":"Raftery","year":"1997","journal-title":"J. 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