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Genomic selection (GS) is routinelyapplied to many purebreds and lines of farm species.However, this method can be extended to predictionsacross purebreds as well as for crossbreds.This is useful for swine and poultry, for whichselection in nucleus herds is typically performedon purebred animals, whereas the commercialproduct is the crossbred animal. Single-stepgenomic BLUP (ssGBLUP) is a widely appliedmethod that can explore the recently developedalgorithm for proven and young (APY). The APYallows for greater efficiency in computing BLUPsolutions by exploiting the theory of limiteddimensionality of genomic information and chromosomesegments (Me). This study investigatesthe predictivity as a proxy for accuracy acrossand within 2 purebred pig lines and their crosses,under the application of APY in ssGBLUP setup,and different levels of Me overlapping acrosspopulations. The data consisted of approximately210k phenotypic records for 2 traits (T1 and T2)with moderate heritability. Genotypes for 43kSNP markers were available for approximately46k animals, from which 26k and 16k belongto 2 pure lines (L1 and L2), and approximately4k are crossbreds. The complete pedigree hadmore than 720k animals. Different multivariatessGBLUP models were applied, either with theregular or APY inverse of the genomic relationshipmatrix (G). The models included a standardbivariate animal model with 3 lines evaluated as 1joint line, and for each trait individually, a 3-traitanimal model with each line treated as a separatetrait. Both models provided the same predictivityacross and within the lines. Using either of thepure lines data as a training set resulted in a similarpredictivity for the crossbreed animals (0.18to 0.21). Across-line predictive ability was limitedto less than half of the maximum predictivityfor each line (L1T1 0.33, L1T2 0.25, L2T1 0.35,L2T2 0.36). For crossbred predictions, APY performedequivalently to regular G inverse when thenumber of core animals was equal to the numberof eigenvalues explaining between 98% and 99%of the variance of G (4k to 8k) including all lines.Predictivity across the lines is achievable becauseof the shared Me between them. The number ofthose shared segments can be obtained via eigenvaluedecomposition of genomic informationavailable for each line.

Es conocido en los sistemas de producción o conservación con reducido número de animales, la ausencia de suficiente información productiva para una evaluación genética confiable. Con el objetivo de evaluar la variabilidad productiva de cabras Blanca Celtibérica mediante un análisis de componentes principales, se utilizaron 38 ejemplares pertenecientes al rebaño de conservación del Instituto Nacional de Investigaciones, Forestales, Agrícolas y Pecuarias (INIFAP), Campo Experimental Zacatecas, México. En las hembras adultas se midió peso, altura y número de crías. En los animales jóvenes se registró el peso al nacimiento y destete, altura, longitud corporal y ganancia diaria de peso. Dos componentes explicaron el 67 % de la variación observada. El análisis de componentes principales permitió evaluar e identificar de manera integral a cabras y progenie superiores, por lo que su uso en explotaciones con reducido número de animales podría ayudar a establecer una base de selección y a su vez, conservar poblaciones locales., {"references":["Agudelo-Gómez, DA; Pelicioni, R; Buzanskas, ME; Ferraudo, AS; Prado, D; Cerón-Muñoz, MF. 2015. Genetic principal components for reproductive and productive traits in dual-purpose buffaloes in Colombia. Journal of Animal Science 93(8):3801-3809.","Assan, N. 2015. Prospects for utilization of the relationship between zoometrical measurements and performance traits for poultry and livestock genetic improvement in developing countries (en línea). Scientific Journal of Animal Science 4(11):124-132. Consultado 1 sep. 2020. Disponible en https://bit.ly/39fo78Q","Bedada, ZE; Gilo, BN; Debela, GT. 2019. 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Tendencia en el crecimiento de cabritos criollos en sistemas extensivos. Archivos de Zootecnia 54(206-207):429-436.","La O Arias, MA; Guevara, F; Fonseca, N; Rodríguez, L; Pinto, R; Gómez, H; Medina, FJ; Hernández, A. 2013. Aplicación de modelos logísticos y Gompertz al análisis de curvas de peso vivo en cabritos criollos cubanos. Revista Cubana de Ciencia Agrícola 47(1):1-5.","Oliveira, DP; Barros, CC; Araujo Neto, FR; Lourenco, DAL; Hurtado-Lugo, NA; Tonhati, H. 2014. Principal components for reproductive and productive traits in Buffaloes from Brazil. In World Congress of Genetics Applied to Livestock production (10, 2014, Vancouver, Canadá) Proceedings, Vancouver, Canadá.","Olivier, J; Cloete, S; Schoeman, S; Muller, C. 2005. Performance testing and recording in meat and dairy goats. Small Ruminant Research 60(1-2):83–93.","Parés-Casanova, PM. 2015. Body weight is an important trait form comparisons of goat breeds. 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The Algorithm for Proven and Young (APY) enables the implementation of single-step genomic BLUP (ssGBLUP) in large, genotyped populations by separatinggenotyped animals into core and non-core subsets and creating a computationallyefficient inverse for the genomic relationship matrix (G). As APY became thechoice for large-scale genomic evaluations in BLUP-based methods, a commonquestion is how to choose the animals in the core subset. We compared severalcore definitions to answer this question. Simulations comprised a moderately heritabletrait for 95,010 animals and 50,000 genotypes for animals across five generations.Genotypes consisted of 25,500 SNP distributed across 15 chromosomes.Genotyping errors and missing pedigree were also mimicked. Core animals weredefined based on individual generations, equal representation across generations,and at random. For a sufficiently large core size, core definitions had the sameaccuracies and biases, even if the core animals had imperfect genotypes. Whengenotyped animals had unknown parents, accuracy and bias were significantlybetter (p ≤ .05) for random and across generation core definitions.

BackgroundThe dimensionality of genomic information is limited by the number of independent chromosome segments (Me), which is a function of the effective population size. This dimensionality can be determined approximately by singular value decomposition of the gene content matrix, by eigenvalue decomposition of the genomic relationship matrix (GRM), or by the number of core animals in the algorithm for proven and young (APY) that maximizes the accuracy of genomic prediction. In the latter, core animals act as proxies to linear combinations ofMe. Field studies indicate that a moderate accuracy of genomic selection is achieved with a small dataset, but that further improvement of the accuracy requires much more data. When only one quarter of the optimal number of core animals are used in the APY algorithm, the accuracy of genomic selection is only slightly below the optimal value. This suggests that genomic selection works on clusters ofMe.ResultsThe simulation included datasets with different population sizes and amounts of phenotypic information. Computations were done by genomic best linear unbiased prediction (GBLUP) with selected eigenvalues and corresponding eigenvectors of the GRM set to zero. About four eigenvalues in the GRM explained 10% of the genomic variation, and less than 2% of the total eigenvalues explained 50% of the genomic variation. With limited phenotypic information, the accuracy of GBLUP was close to the peak where most of the smallest eigenvalues were set to zero. With a large amount of phenotypic information, accuracy increased as smaller eigenvalues were added.ConclusionsA small amount of phenotypic data is sufficient to estimate only the effects of the largest eigenvalues and the associated eigenvectors that contain a large fraction of the genomic information, and a very large amount of data is required to estimate the remaining eigenvalues that account for a limited amount of genomic information. Core animals in the APY algorithm act as proxies of almost the same number of eigenvalues. By using an eigenvalues-based approach, it was possible to explain why the moderate accuracy of genomic selection based on small datasets only increases slowly as more data are added.