Please use this identifier to cite or link to this item:
|Title:||Use of the EM algorithm to detect QTL affecting multiple-traits in an across half-sib family analysis||Contributor(s):||Kerr, Richard John (author); McLachlan, G. M. (author); Henshall, John Mckeown (author)||Publication Date:||2005||DOI:||10.1186/1297-9686-37-1-83||Handle Link:||https://hdl.handle.net/1959.11/3073||Abstract:||QTL detection experiments in livestock species commonly use the half-sib design. Each male is mated to a number of females, each female producing a limited number of progeny. Analysis consists of attempting to detect associations between phenotype and genotype measured on the progeny. When family sizes are limiting experimenters may wish to incorporate as much information as possible into a single analysis. However, combining information across sires is problematic because of incomplete linkage disequilibrium between the markers and the QTL in the population. This study describes formulae for obtaining MLEs via the expectation maximization (EM) algorithm for use in a multiple-trait, multiple-family analysis. A model specifying a QTL with only two alleles, and a common within sire error variance is assumed. Compared to single-family analyses, power can be improved up to fourfold with multi-family analyses. The accuracy and precision of QTL location estimates are also substantially improved. With small family sizes, the multi-family, multi-trait analyses reduce substantially, but not totally remove, biases in QTL effect estimates. In situations where multiple QTL alleles are segregating the multi-family analysis will average out the effects of the different QTL alleles.||Publication Type:||Journal Article||Source of Publication:||Genetics Selection Evolution, 37(1), p. 83-103||Publisher:||E. D. P. Sciences||Place of Publication:||France||ISSN:||0999-193X||Field of Research (FOR):||070201 Animal Breeding||Socio-Economic Outcome Codes:||830301 Beef Cattle||Peer Reviewed:||Yes||HERDC Category Description:||C1 Refereed Article in a Scholarly Journal||Statistics to Oct 2018:||Visitors: 156
|Appears in Collections:||Animal Genetics and Breeding Unit (AGBU)|
Files in This Item:
checked on Feb 8, 2019
Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.