HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Liu, Y. Articles by Lin, C. Y. Search for Related Content PubMed PubMed Citation Articles by Liu, Y. Articles by Lin, C. Y.

Quantitative Trait Loci Mapping for Dairy Cattle Production Traits Using a Maximum Likelihood Method

¹ CGIL, Dept. of Animal & Poultry Science, University of Guelph, Ontario, Canada N1G 2W1
² Dairy and Swine Research and Development Centre, Contribution #754, Agriculture and Agri-Food Canada, Lennoxville, Quebec J1M 1Z3

Corresponding author: Y. Liu; e-mail: yuefu{at}ccsi.ca.

A maximum likelihood method was developed for QTL mapping in half-sib designs and compared to the regression method in analyses of both field and simulated data. The field data consisted of milk production evaluations of 433 progeny tested sons of 6 sires and 64 microsatellite markers distributed over 12 chromosomes. Based on permutation tests, 5 significant QTL were detected in the field data by the regression method compared with 10 by the maximum likelihood method (P < 0.05). In field data analysis, the maximum likelihood method detected more significant QTL and had a smaller residual variance than the regression method. The simulation included 9 scenarios differing in number of families, family size, QTL variance, and marker density, each replicated 100 times. The simulation results suggested that, as for the regression method, the precision of estimating QTL from the maximum likelihood method improves with increasing number of sons per sire, increasing the ratio of QTL to phenotypic variance, and decreasing marker interval. The maximum likelihood method had a smaller dispersion of estimated QTL positions than the regression method in 6 of 9 scenarios simulated. Overall, the maximum likelihood method shows potential advantage in QTL detection over the regression method, especially in the situations with less favorable conditions for QTL detection.

Key Words: QTL mapping • dairy production trait • mixture model • maximum likelihood

Abbreviation key: DYD = daughter yield deviation, EM = expectation-maximization, LOD = logarithm of odds, ML = maximum likelihood

This article has been cited by other articles:

				P. J. Boettcher, P. Moroni, G. Pisoni, and D. Gianola Application of a Finite Mixture Model to Somatic Cell Scores of Italian Goats J Dairy Sci, June 1, 2005; 88(6): 2209 - 2216. [Abstract] [Full Text] [PDF]