Bayesian QTL mapping using skewed Student-tdistributions

von Rohr, Peter; Hoeschele, Ina

Bayesian QTL mapping using skewed Student-tdistributions

dc.contributor.author	von Rohr, Peter	en
dc.contributor.author	Hoeschele, Ina	en
dc.contributor.department	Dairy Science	en
dc.contributor.department	Statistics	en
dc.date.accessioned	2012-08-24T12:21:47Z	en
dc.date.available	2012-08-24T12:21:47Z	en
dc.date.issued	2002-01-15	en
dc.date.updated	2012-08-24T12:21:47Z	en
dc.description.abstract	In most QTL mapping studies, phenotypes are assumed to follow normal distributions. Deviations from this assumption may lead to detection of false positive QTL. To improve the robustness of Bayesian QTL mapping methods, the normal distribution for residuals is replaced with a skewed Student-t distribution. The latter distribution is able to account for both heavy tails and skewness, and both components are each controlled by a single parameter. The Bayesian QTL mapping method using a skewed Student-t distribution is evaluated with simulated data sets under five different scenarios of residual error distributions and QTL effects.	en
dc.description.version	Published version	en
dc.format.mimetype	application/pdf	en
dc.identifier.citation	Genetics Selection Evolution. 2002 Jan 15;34(1):1-21	en
dc.identifier.doi	https://doi.org/10.1186/1297-9686-34-1-1	en
dc.identifier.uri	http://hdl.handle.net/10919/18982	en
dc.language.iso	en	en
dc.rights	Creative Commons Attribution 4.0 International	en
dc.rights.holder	Peter von Rohr et al.; licensee BioMed Central Ltd.	en
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	en
dc.title	Bayesian QTL mapping using skewed Student-tdistributions	en
dc.title.serial	Genetics Selection Evolution	en
dc.type	Article - Refereed	en
dc.type.dcmitype	Text	en