We established a genomic model of quantitative trait with genomic additive and dominance relationships that parallels the traditional quantitative genetics model, which partitions a genotypic value as breeding value plus dominance deviation and calculates additive and dominance relationships using pedigree information. Based on this genomic model, two sets of computationally complementary but m...

Genotyping-by-sequencing (GBS) technologies have proven capacity for delivering large numbers of marker genotypes with potentially less ascertainment bias than standard single nucleotide polymorphism (SNP) arrays. Therefore, GBS has become an attractive alternative technology for genomic selection. However, the use of GBS data poses important challenges, and the accuracy of genomic prediction u...

Newcastle disease (ND) and avian influenza (AI) are the most feared diseases in the poultry industry worldwide. They can cause flock mortality up to 100%, resulting in a catastrophic economic loss. This is the first study to investigate the feasibility of genomic selection for antibody response to Newcastle disease virus (Ab-NDV) and antibody response to Avian Influenza virus (Ab-AIV) in chicke...

Predicting organismal phenotypes from genotype data is important for plant and animal breeding, medicine, and evolutionary biology. Genomic-based phenotype prediction has been applied for single-nucleotide polymorphism (SNP) genotyping platforms, but not using complete genome sequences. Here, we report genomic prediction for starvation stress resistance and startle response in Drosophila melano...

The phenomenon of genotype × environment (G × E) interaction in plant breeding decreases selection accuracy, thereby negatively affecting genetic gains. Several genomic prediction models incorporating G × E have been recently developed and used in genomic selection of plant breeding programs. Genomic prediction models for assessing multi-environment G × E interaction are extensions of a single-...

Abstract Genetic improvement of quality traits in tea ( Camellia sinensis (L.) O. Kuntze) through conventional breeding methods has been limited, because is a difficult and expensive trait to measure. Genomic selection (GS) suitable for predicting such complex traits, as it uses genome wide markers estimate the genetic values individuals. We compared prediction accuracies six genomic models inc...

Introduction Genomic selection (GS) has gained global importance due to its potential accelerate genetic progress and improve the efficiency of breeding programs. Objectives research In this we proposed a method prediction accuracy tested lines in new (untested) environments. Method-1 The trained model with modified response variable (a difference variables) that decreases lack non-stationary d...

Abstract Plant breeders need efficient systems to identify which inbreds combine create new hybrid cultivars. The North Carolina Design II (NC DII) is a useful mating design evaluate the potential of varieties and their inbred parents. Genomic best linear unbiased prediction (GBLUP) models, either with or without inclusion dominance term in model, have been found be an method for using rich mar...

We examined whether or not the predictive ability of genomic best linear unbiased prediction (GBLUP) could be improved via a resampling method used in machine learning: bootstrap aggregating sampling ("bagging"). In theory, bagging can be useful when the predictor has large variance or when the number of markers is much larger than sample size, preventing effective regularization. After present...

Abstract Background Streptococcosis is a major bacterial disease in Nile tilapia that caused by Streptococcus agalactiae infection, and development of resistant strains represents sustainable approach towards combating this disease. In study, we performed controlled trial on 120 full-sib families to (i) quantify characterize the potential genomic selection for survival S. infection tilapia, (ii...