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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Research Project #416902


Location: Animal Genomics and Improvement Laboratory

2013 Annual Report

1a. Objectives (from AD-416):
Use the BovineSNP50 assay to provide high-accuracy predictions of genetic merit to U.S. beef breeds; 2) Enable the adoption of whole genome enabled animal selection (WGEAS) by developing low-density and low-cost SNP assays for: a) intermediate-accuracy genetic prediction, b) mate selection, and c) parentage verification and traceability; 3) Develop, adapt and optimize statistical methodologies to: a) fully integrate SNP genotype or haplotype effects into existing genetic evaluation technologies, and b) supplement or replace pedigree data; and 4) Collaborate and coordinate U.S. and European Union WGEAS activities.

1b. Approach (from AD-416):
Genetic prediction using high-density SNP data will be implemented using MTDFREML. Implementation of more sophisticated strategies will follow using the MTGSAM programs that will be modified to accommodate extensions to the prediction model. Collaboration with a biotechnology company to develop a 384-SNP assay that is expected to dramatically decrease genotyping costs and increase sample throughput. A machine learning approach using a two-step feature subset selection algorithm will be evaluated for SNP selection for this assay. Develop BLUP approaches for the prediction of genetic merit in non-pedigreed populations using molecular relationship matrices. We shall manage this coordination and collaboration via e-mail and teleconference calls, however, we shall also meet at least annually in conjunction with the PAG or ISAG meetings alternating between the US and Europe to coordinate activities.

3. Progress Report:
In this project year, the genomes of 11 registered Angus bulls were sequenced to an average depth of coverage of 30X and analyzed to detect variants. While the analysis of these data are continuing, millions of variants were identified including single nucleotide polymorphisms (SNP), large and small insertion/deletions (indels), as well as copy number variants (CNV). We expect that these variants will be exceedingly valuable for the identification of variants underlying quantitative trait loci (QTL) within the Angus breed. In particular, 176 predicted loss-of-function alleles in genes were shown to be early developmental lethals in mouse knockouts, and these are almost certainly causal variants for fertility loci in Angus cattle. Data were analyzed and published in four papers. BovineSNP50 data for over 12,000 Angus, Hereford, Simmental, Limousin and Charolais cattle and molecular breeding value (MBV) prediction equations were developed for production, reproduction, and carcass and meat quality traits for these breeds. Results show empirically, and theoretically, that prediction equations trained for one breed do not work in other breeds. Finally, we examined whether common QTL underlie variation in traits across breeds and found that QTL tend to be conserved across many breeds; but when they are not found, it is not clear whether they are fixed or simply not in strong linkage disequilibrium with flanking markers in these breeds. We found that the different phase relationships of markers surrounding QTL alleles in different breeds add considerable value for the identification of candidate mutations underlying QTL and postulate that it will be necessary to identify the largest effect QTL in order to develop genotyping assays and MBV prediction equations with utility across breeds. Work on identifying haplotypes associated with fertility is currently underway and is based on the imputation of 50K genotypes to 778K genotypes to allow potentially increased resolution of haplotype detection in this population. This research supports two objectives of its related in-house project: 1) to use genotypic data and resulting bovine haplotype map to enhance genetic improvement in dairy cattle through development and implementation of whole genome selection and enhanced parentage verification approaches (Objective 2) and 2) to characterize conserved genome elements and identify functional genetic variation (Objective 3).

4. Accomplishments