Accelerating Improvement of Livestock with Genomic Selection

Abstract

Three recent breakthroughs have resulted in the current widespread use of DNA information: the genomic selection (GS) methodology, which is a form of marker-assisted selection on a genome-wide scale, and the discovery of large numbers of single-nucleotide markers and cost effective methods to genotype them. GS estimates the effect of thousands of DNA markers simultaneously. Nonlinear estimation methods yield higher accuracy, especially for traits with major genes. The marker effects are estimated in a genotyped and phenotyped training population and are used for the estimation of breeding values of selection candidates by combining their genotypes with the estimated marker effects. The benefits of GS are greatest when selection is for traits that are not themselves recorded on the selection candidates before they can be selected. In the future, genome sequence data may replace SNP genotypes as markers. This could increase GS accuracy because the causative mutations should be included in the data.

Effect of cow reference group on validation reliability of genomic evaluation

“…In our case, cow genotypes lessened particularly the bias. The expected increase in validation reliability due to increased reference population can be estimated by non-linear equations suggested by Daetwyler et al (2010) or Meuwissen et al (2013). In these, the information content of reference population is a product of number of animals phenotyped and genotyped and their corresponding evaluation accuracy.…”

Estimation and comparison of conventional and genomic breeding values in Holstein cattle of Antioquia, Colombia

“…Bayes C asume una fracción (π) de SNPs sin efecto ni varianza y la fracción restante (1-π) con efectos, los cuales se distribuyen como una normal, mientras que en Bayes R, aunque es una extensión de bayes C; los efectos de los SNPs se asumen con múltiples distribuciones normales (12).…”

“…Los valores de correlación para PL, PPRO, PGRA y PL fueron: 0.991, 0.983, 0.989 y 0.986 y se consideran altos, lo que indica que no hay cambios significativos en la clasificación o ranking de los animales. Las correlaciones por rango de Spearman entre el effect or variance and the remaining fraction (1-π) with effects, which are distributed as a normal fraction; while in Bayes R, although an extension of bayes C, the effects of SNP's are assumed as multiple normal distributions (12).…”

“…Considering these arguments, several genetic value estimation methods that include molecular markers have been propose, such as bayes A, bayes B, bayes C, bayes Cpi, bayes lasso, bayes R, GBLUP. The main difference between these methods is in the a priori information included in the genomic model with respect to the distribution of the marker's effects (6,(10)(11)(12).…”

“…Estos efectos, son estimados en poblaciones de referencia que portan información fenotípica y genealógica y que luego son utilizados en la estimación de GEBVs en animales jóvenes (8). Considerando estos argumentos, varios métodos de estimación de valores genéticos que incluyen marcadores moleculares han sido propuestos, como bayes A, bayes B, bayes C, bayes Cpi, bayes lasso, bayes R, GBLUP, entre otros y la principal diferencia entre estos métodos se da en la información a priori incluida en el modelo genómico, con respecto la distribución de los efectos de los marcadores (6,(10)(11)(12).…”