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) All Versions of this Article: jas.2007-0291v1 85/12/3148    most recent 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 Google Scholar Google Scholar Articles by Sellner, E. M. Articles by Taylor, J. F. Search for Related Content PubMed PubMed Citation Articles by Sellner, E. M. Articles by Taylor, J. F.

BOARD-INVITED REVIEW: Applications of genomic information in livestock^1,2

E. M. Sellner^*, J. W. Kim^*, M. C. McClure^*, K. H. Taylor, R. D. Schnabel^* and J. F. Taylor^*,3

^* Division of Animal Sciences, University of Missouri, Columbia 65211; and Pathology and Anatomical Sciences, University of Missouri, Columbia 65212

³ Corresponding author: taylorjerr{at}missouri.edu

The availability of whole genome sequences for individual species will change the landscape for livestock genomic research. Animal scientists will have access to whole-genome sequence-based technologies such as high-throughput SNP genotyping assays, gene expression profiling, methylation profiling, RNA interference, and genome resequencing that will revolutionize the scale upon which research will be conducted. These technologies will also alter the ways we think about addressing industry and scientific problems. In this review, we discuss the scientific bases for these emerging technologies and present recent highlights of their application in human, model species, and livestock as well as their potential for future applications in livestock. Additionally, we discuss strategies for their use in the genetic improvement and management of livestock. In particular, we present a strategy for the simultaneous identification of causal mutations underlying phenotypic traits in livestock and discuss issues that will arise in the application of whole genome selection for the prediction of genetic merit in livestock. We also point out that the statistical analysis that underlies the whole genome selection methodology is a sophisticated enhancement of single marker association mapping analysis to allow the entire genome to be simultaneously analyzed.

Key Words: quantitative trait loci • expression quantitative trait loci • whole genome selection • RNA interference • epigenetics • marker assisted selection