Dimensionality of genomic information and performance of the Algorithm for Proven and Young for different livestock species

• Published in: Genetics selection evolution (Paris) 2016-10, Vol.48 (1), p.82-82, Article 82
• Main Authors: Pocrnic, Ivan, Lourenco, Daniela A L, Masuda, Yutaka, Misztal, Ignacy
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Animals; Beef cattle; Breeding; Cattle; Cattle - genetics; Chickens; Chickens - genetics; Computer Simulation; Datasets; Decomposition; Eigenvalues; Genetic algorithms; Genetic aspects; Genome; Genomics; Genotype; Genotypes; Genotyping; Health aspects; Hogs; Life Sciences; Livestock; Livestock - genetics; Models, Genetic; Nucleotides; Phenotype; Phenotypes; Polymorphism, Single Nucleotide; Population; Population Density; Population number; Poultry; Simulation; Single nucleotide polymorphisms; Single-nucleotide polymorphism; Species; Swine - genetics; Variation; Weaning
• ISSN: 1297-9686; 0999-193X; 1297-9686
• DOI: 10.1186/s12711-016-0261-6

A genomic relationship matrix (GRM) can be inverted efficiently with the Algorithm for Proven and Young (APY) through recursion on a small number of core animals. The number of core animals is theoretically linked to effective population size (N ). In a simulation study, the optimal number of core animals was equal to the number of largest eigenvalues of GRM that explained 98% of its variation. The purpose of this study was to find the optimal number of core animals and estimate N for different species. Datasets included phenotypes, pedigrees, and genotypes for populations of Holstein, Jersey, and Angus cattle, pigs, and broiler chickens. The number of genotyped animals varied from 15,000 for broiler chickens to 77,000 for Holsteins, and the number of single-nucleotide polymorphisms used for genomic prediction varied from 37,000 to 61,000. Eigenvalue decomposition of the GRM for each population determined numbers of largest eigenvalues corresponding to 90, 95, 98, and 99% of variation. The number of eigenvalues corresponding to 90% (98%) of variation was 4527 (14,026) for Holstein, 3325 (11,500) for Jersey, 3654 (10,605) for Angus, 1239 (4103) for pig, and 1655 (4171) for broiler chicken. Each trait in each species was analyzed using the APY inverse of the GRM with randomly selected core animals, and their number was equal to the number of largest eigenvalues. Realized accuracies peaked with the number of core animals corresponding to 98% of variation for Holstein and Jersey and closer to 99% for other breed/species. N was estimated based on comparisons of eigenvalue decomposition in a simulation study. Assuming a genome length of 30 Morgan, N was equal to 149 for Holsteins, 101 for Jerseys, 113 for Angus, 32 for pigs, and 44 for broilers. Eigenvalue profiles of GRM for common species are similar to those in simulation studies although they are affected by number of genotyped animals and genotyping quality. For all investigated species, the APY required less than 15,000 core animals. Realized accuracies were equal or greater with the APY inverse than with regular inversion. Eigenvalue analysis of GRM can provide a realistic estimate of N .