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United States Department of Agriculture

Agricultural Research Service

Research Project: DEVELOPMENT OF STATISTICAL METHODS FOR THE ANALYSIS OF GENETIC (CO)VARIANCE MATRICES CALCULATED USING SINGLE NUCLEOTIDE POLYMORPHISMS
2011 Annual Report


1a.Objectives (from AD-416)
ARS Project Plan 1265-31000-096-00D has two objectives that directly relate to this agreement. The first is to develop methods to incorporate high-density genomic data in predictions of genetic merit, and the second is to investigate correlations among traits to efficiently combine evaluations to select for healthy dairy animals capable of producing quality milk at a low cost in many environments. The Cooperator has the expertise and infrastructure to develop statistical methods for the analysis of genome- and chromosome-wide (co)variance matrices calculated from genetic marker effects. Such methods may be useful for understanding the biology of economically important traits in dairy cattle, as well as identifying important nodes in gene networks.


1b.Approach (from AD-416)
Single nucleotide polymorphism (SNP) effects estimated independently in the Italian and U.S. Brown Swiss cattle populations by the Cooperator and ARS, respectively, will be used to develop methods for comparing genetic (co)variance matrices for various traits of economic importance in those breeds. Methods developed will be reported in the scientific literature. ARS and the Cooperator will jointly develop the statistical methods using a shared set of simulated data. The methods will be applied by AIPL to U.S. data and by the Cooperator to Italian data. Results will be used to compare the two populations.


3.Progress Report

This project is related to in-house subobjective 3a (develop methodology for calculation of genome-enhanced breeding values using SNP genotypes). Genomic (co)variance matrices were provided to the Cooperator. Genomewide correlation matrices as well as chromosomal correlation matrices for Bos taurus autosomes (BTA) 6, 14, and 18 were analyzed using multivariate factor models for three U.S. dairy cattle breeds (63,615 Holsteins, 8,084 Jerseys, and 2,038 Brown Swiss). A total of 23 productive and functional traits were considered. About 80% of (co)variance was explained by six or seven latent factors. Comparison of correlations between factors and traits highlighted some similarities between breeds at the genomewide level. Latent factors associated with yield traits, milk composition, udder morphology, strength, and functional traits (productive life, somatic cell score, and daughter pregnancy rate) were extracted. Some differences were observed at chromosomal level. On BTA 6, yield and milk composition overlapped with a single factor for Brown Swiss but tended to remain distinct for Holsteins and Jerseys. However, Holsteins and Jerseys had two latent factors associated with functional traits on BTA 6, which has genes that affect milk production and reproduction. Analysis of BTA 14 disclosed a factor that was associated with both Jersey yield and milk composition traits (except for protein percentage) and differences between genomewide and chromosomal analyses were noted, which is consistent with the presence of genes known to affect selected traits (for example, DGAT1). Results for genomewide and chromosomal analyses were similar for BTA 18, which is known to have a quantitative trait locus that affects Holstein calving traits and conformation. Multivariate factor analysis was capable of identifying differences in genetic correlations among traits across the genome and on individual chromosomes and may be a useful tool to identify regions of the genome that affect multiple traits for further study. A scientific abstract was published. Monitoring activities included e-mail correspondence and a meeting with the Cooperator’s principal investigator at a national scientific meeting.


Last Modified: 9/1/2014
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