Rapid and stable animal individual genome breeding value evaluation method

Abstract

The invention provides a rapid and stable animal individual genome breeding value evaluation method, and relates to the technical field of animal breeding. The rapid and stable animal individual genome breeding value evaluation method comprises the steps that an HIBLUP is adopted and utilizes phenotype information, genotype information and spectrum information to predict genome breeding values, and the final output genome breeding values comprise estimated individual genetic value, additive and dominant effect values of each individual, and a reverse analytic value of each genetic marker effect in a genotyping chip. According to the rapid and stable animal individual genome breeding value evaluation method, the spectrum information, the phenotype information and the genotype information are fully utilized to predict the genetic value (the additive and dominant effects) of each animal and the effective value of each SNP marker site, the most advanced genome breeding value prediction andvariance component estimation algorithm is achieved, and thus genome selection is achieved.

Description

technical field [0001] The invention relates to the technical field of animal breeding, in particular to a rapid and stable method for evaluating the breeding value of individual animal genomes. Background technique [0002] With the development of high-density single nucleotide polymorphism (SNP) genotyping technology covering the entire genome, genomic selection (prediction), as a powerful tool for statistical analysis of genomes, is widely used in the inheritance of complex traits in plant and animal breeding Value (species value) prediction and evaluation, as well as the application in human genetics research are also increasing. Estimation of variance components is probably the most time-consuming part of the genome selection process. Variance component estimation algorithms popular in genome selection, such as EMAI, require iterative calculations, and the computational complexity of each iteration is very high. Previous genomic selection procedures required computing...

AI Technical Summary

Problems solved by technology

Estimation of variance components can be the most time-consuming part of the genome selection process

Variance component estimation algorithms popular in genome selection, such as EMAI, require iterative calculations, and the computational complexity of each iteration is very high

Previous genomic selection procedures required computing the inverse of the genomic kinship matrix, and the computation time increased rapidly as the genotyping sample size increased

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