Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/3073
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
Views: 157
Downloads: 0
Appears in Collections:Animal Genetics and Breeding Unit (AGBU)
Journal Article

Files in This Item:
2 files
File Description SizeFormat 
Show full item record

Page view(s)

66
checked on Feb 8, 2019
Google Media

Google ScholarTM

Check

Altmetric


Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.