RESOURCE DEVELOPMENT FACILITATING BOVINE GENOME SEQUENCE USE TO IMPROVE CATTLE PRODUCTION EFFICIENCY, PRODUCT QUALITY & ENVIRONMENTAL IMPACT
Project Number: 5438-31000-085-00
Start Date: Jul 23, 2007
End Date: Jul 22, 2012
Objective 1. Investigate the basic biology of the bovine genome and support annotation of the genome sequence. Functional annotation of the genome will assist in understanding the relationships between the genetics of individual animals and interaction of genetics with the production environment, and will leverage the investment in collection of the complete cattle genome sequence.
Objective 2. Develop approaches, methods, and software to support efficient conversion of QTL data into DNA markers with predictive merit. It has been established that DNA sequence variation among cattle contributes to differences in economically important traits between individual animals. Our overall hypothesis is that we can successfully identify specific DNA markers that track functional variation, and develop new methods to make the process of marker identification and testing more efficient.
Objective 3. Develop DNA marker systems with predictive merit for phenotypes important to cattle production in the United States, and characterize their effects on a wide range of economically important traits. The hypothesis is that single nucleotide polymorphisms (SNP) can be identified that are in linkage disequilibrium (LD) with functional DNA sequence variation causing effects on production traits. The corollary hypothesis is that an SNP or set of SNP can have sufficient predictive merit in a wide range of production settings and breed backgrounds, permitting Marker Assisted Selection (MAS) to improve herd genetics and Marker Assisted Management (MAM) to classify animals by genetic potential.
The starting point for marker development is initial discovery of QTL positions in the genome. For the current CRIS cycle, three sources of QTL will be utilized. First, published bovine QTL developed at USMARC or at other institutions provide QTL targets. More than 15 QTL for just the two traits of meat tenderness and marbling have been reported. Second, development of comparative QTL database in Objective 2a is expected to identify additional target regions. Finally, we propose to use WGA approach to identify QTL using the SNP developed in Objective 2b. Whichever method is used to provide initial focus on genomic segments, we will develop an additional set of targeted SNP markers for testing to identify marker systems with the most consistent and widely applicable predictive merit. Below we describe the approaches that will be used, the details of proposed WGA studies, and marker system testing. As in the expiring CRIS projects, our focus will be on phenotypes of meat quality (tenderness and marbling), yield, and feed efficiency, however in this CRIS cycle we propose to incorporate the phenotypes available on cows to examine reproductive longevity and success.
Objective 4. Evaluate DNA marker systems in use by industry for their effectiveness in a variety of genetic backgrounds and potential impact on a range of production traits. The hypothesis is that utility of marker systems may depend on genetic background, or have unintentional effects on other traits than that for which the marker was developed. In many cases, data supporting the assertion of association.
The primary objective of the genome project at USMARC, since its inception in 1992, has been development of genetic markers that the cattle industry can use to identify animals with higher genetic merit for production traits not amenable to traditional phenotypic selection. While the original effort to create genome maps was successful, modern industry practices would benefit most from markers that can be cheaply genotyped and possess reliable predictive merit. Two complementary initiatives will be used to identify markers with predictive merit. A major component will be creation of an annotated genome sequence to put functional context to genomic positions discovered in QTL experiments both within cattle, and through application of comparative approaches exploiting QTL information from model species, including humans (described in Objective 1). The second initiative strives to detect existing, naturally occurring variation in DNA sequence affecting performance via genome scans or whole genome association experiments (described in Objectives 2 and 3). Once markers with predictive merit for a given trait have been identified and tested, they will be examined for potential effects on a wide range of cattle production traits to infer the likely effects of marker-assisted selection (described in Objective 4).