Further research is needed to permit cost-effective selection decisions using genomic information.
The technical feasibility of applying genomic prediction in a Bos indicus (Nellore) population was demonstrated, although further research on its implementation in breeding schemes is necessary to enable more cost-effective selection decisions using genomic information.
While estimates of protection against exposure are no substitute for careful prospective morbidity and mortality studies, they do allow informed evaluation of transmission control methods and cost-effective selection of those most likely to justify investment in large scale demographic and clinical surveillance systems.
This is the most cost effective option.
Imputation of marker genotypes was seen as a tool to make genomic selection cost effective by genotyping selection candidates with a panel of evenly spaced low-density SNPs instead of the high-density panel used in the training population.
The methodology termed automated sensor and signal processing selection (ASPS), is based on Taguchi's orthogonal arrays in order to provide cost effective and speedy selection of sensors and signal processing methods that are ultimately used for monitoring process conditions.
Trait-specific haplotypes defined by SNP markers discovered using the C6AIR can also be readily converted to other genotyping platforms, such as KASP, Taqman, or Fluidigm, which may be more cost effective for forward selection in breeding programs that target a few, large effect loci.
The prospect of identifying markers very tightly linked to genes controlling traits of interest would also facilitate routine cost effective marker-assisted selection [ 19, 20].
The implementation of biocatalysts in industrial processes relies strongly on fast and cost effective process development, including selection of a biocatalyst form and the strategy for its immobilization.
Efficient, robust, and accurate genotype imputation algorithms make large-scale application of genomic selection cost effective.
Our bespoke algorithms are robust for handling noisy data and cost effective for candidate marker selection.
