6/22/2023 0 Comments Mitbbs quantGenetic evaluation of growth in a multibreed beef cattle population using random regression linear spline models. Use of a test day model for dairy goat milk yield across lactations in Germany. Models for evaluation of growth of performance tested bulls. Heat Stress as a Factor in Genotype x Environment Interaction in U.S. Computing strategies in genome-wide selection. Environmental Effects on Conception Rate of Holsteins in New York and Georgia. The method of Gauss-Seidel with residuals update is by far the fastest for genomic selection utilizing SNP DNA data. Impacts Computing times were in the order of a few minutes for Gauss-Seidel with residuals update and preconditioned conjugate gradients, more than 1 h for half-stored Gauss-Seidel, 2 h for Cholesky decomposition, and 4 d for matrix-free Gauss-Seidel. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period. TARGET AUDIENCES: The work is relevant to anyone working in genomic selection involving a large number of SNP markers. PARTICIPANTS: The collaboration was by Andres Legarra from INRA, Tolouse, France. This avoids adjusting the left-hand side of the equations by all other effects at every step of the algorithm and saves considerable computing time.Algorithms were tested on a real mouse data set, which included 1,928 records and 10,946 single-nucleotide polymorphism markers. Matrix-free Gauss-Seidel with residuals update adjusts the residuals after computing the solution for each effect. This work evaluated several computing options, including half-stored Cholesky decomposition, Gauss-Seidel, and 3 matrix-free strategies: Gauss-Seidel, Gauss-Seidel with residuals update, and preconditioned conjugate gradients. Possible computing options include the type of storage and the solving algorithm. This implies dense Henderson's mixed-model equations and considerable computing resources in time and storage, even for a few thousand records. Progress 10/01/03 to 09/30/07 Outputs OUTPUTS: Genome-wide genetic evaluation might involve the computation of BLUP-like estimations, potentially including thousands of covariates (i.e., single-nucleotide polymorphism markers) for each record. How can genomic information from completely sequenced model organisms (fruit fly, human, mouse, zebrafish, and arabadopsis) be used to infer gene structure, regulation and function in agriculturally or ecologically important species, whose genomes may never be completely sequenced? What types of traits benefit most by including molecular information in selection programs? 5. How much additional improvement can a breeder expect to make by including such information in breeding programs and at what cost? 4. What is the optimum method of incorporating such genomic information into breeding programs toĮnhance rates of genetic improvement? 3. What are the optimum strategies to map genes for quantitative traits, so-called quantitative trait loci or QTL, in outbred populations, with and without pedigrees, such as those used in tree breeding? 2. Issues that must be addressed for implementation of genomics in plant and animal breeding programs include: 1. The issues and questions detailed above are best addressed by scientists working in diverse fields of genetics, from gene mapping to bioinformatics, and with a diverse range of species, both plant and animal. With the advent of molecular genetics, those limitations no longer need apply. Those advancements were limited by the relatively simplistic assumptions of the models used for inheritance of quantitative traits and the tools available for estimating genetic worth. Non Technical Summary Population and quantitative genetics have had remarkable success in both plant and animal breeding.
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