1a. Objectives (from AD-416):
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.
1b. Approach (from AD-416):
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).
3. Progress Report:
Final report. (New 5438-31000-088-00D) There was excellent progress on the overall aims to enhance the utility of the bovine genome sequence, develop DNA marker-based systems to improve cattle genetics for target traits, and evaluate the effects of using DNA marker-based selection. The main effort was devoted to exploring application of high density (735,000 markers) genotyping arrays (HD), which were developed in part through efforts of the first two years of the project, to identify genomic regions harboring DNA sequence variation affecting production traits. An approach developed last year to make use of data from HD arrays on relatively few animals, and apply them to a broader population with lower density (48,000 markers) genotyping information, by imputing genotypes was used for rapid estimation of marker effects. It was determined that predicted breeding values using the more expensive, HD arrays were not significantly more informative than the less expensive, lower density arrays, an important result for the industry. The imputation approach identified markers associated with fertility in cows, a major issue for cattle producers in the U.S., providing an opportunity to use marker assisted management and marker assisted selection to improve overall herd fertility and avoid economic losses due to supporting cows that fail to conceive each year. In addition, a promising approach to identifying causative variation (which provides the most robust and predictive markers) by incorporation of low coverage whole genome sequencing of key animals in the population was developed and is being tested. Regardless of whether a marker is predictive for causative variation or is itself the cause, it may have simple or complicated association to the traits that are the target of selection that might influence how the marker(s) are best used. To demonstrate this concept and develop appropriate strategies, two markers shown to influence carcass fatness were examined in depth through a multi-year selection process and were shown to have complex relation to fat and meat tenderness traits. These complex associations may be important for marker-predicted performance schemes. Another effort was continued annotation of the bovine genome sequence, including a collaborative effort to examine microRNA abundance in a wide array of over 90 bovine tissues. MicroRNA are a class of small non-protein coding RNAs that regulate gene translation and subsequent developmental decisions, and the data help to evaluate the role microRNA may play in development and growth of skeletal muscle and adipose tissue in cattle. This data is complete and analyzed, and a manuscript is being drafted. An international collaborative effort to improve the draft genome was launched, and the current Project led the effort to incorporate optical mapping into the new assembly. This ongoing effort is expected to culminate in FY2013 with a new, higher quality assembly and annotation of the cattle genome. Part of the improvement will come from comparative genomics with Bos indicus cattle, Water Buffalo, and Goat genomes, for which we provided substantial support and effort during FY2012.
1. Identification of copy number variation (CNV) associated with reproductive efficiency in cattle. To identify regions of the genome harboring variation affecting reproductive efficiency, we applied a genome-wide association approach based on the >700,000 markers genotyped by a high-density array. Cows from several populations including U.S. Meat Animal Research Center and commercial ranches were assigned to extremes for reproductive efficiency. We identified multiple regions across the genome associated with reproductive efficiency and identified a DNA region specific to cattle that do not get pregnant. These data will enhance the ability to develop DNA markers that can be used by the industry to select for female cattle that have a great propensity for reproductive success.
Casas, E., Garcia, M.D., Wells, J., Smith, T.P.L. 2011. Association of single nucleotide polymorphisms in the ANKRA2 and CD180 genes with bovine respiratory disease and presence of Mycobacterium avium subsp. paratuberculosis. Animal Genetics. 42:571-577.