BREEDING AND GENETICS SYMPOSIUM: Networks and pathways to guide genomic selection1–3

Abstract

Many traits affecting profi tability and sustainability of meat, milk, and fi ber production are polygenic, with no single gene having an overwhelming infl uence on observed variation. No knowledge of the specifi c genes controlling these traits has been needed to make substantial improvement through selection. Signifi cant gains have been made through phenotypic selection enhanced by pedigree relationships and continually improving statistical methodology. Genomic selection, recently enabled by assays for dense SNP located throughout the genome, promises to increase selection accuracy and accelerate genetic improvement by emphasizing the SNP most strongly correlated to phenotype although the genes and sequence variants affecting phenotype remain largely unknown. These genomic predictions theoretically rely on linkage disequilibrium (LD) between genotyped SNP and unknown functional variants, but familial linkage may increase effectiveness when predicting individuals related to those in the training data. Genomic selection with functional SNP genotypes should be less reliant on LD patterns shared by training and target populations, possibly allowing robust prediction across unrelated populations.

Aflatoxin Resistance in Maize: What Have We Learned Lately?

“…While the possibilities of these SNP associations being nothing more than false positives, having nothing to do with resistance, are fairly high in each case, as a group, many of them will increase resistance of lines containing the resistant form or the sequence polymorphism. Although MAS with each would be a poor use of resources, using these SNPs in an index selection or to guide genomic selection is a real possibility [100]. Furthermore, a recurrent reciprocal selection scheme via MARS may allow for the efficient creation of new, stably resistant inbred lines.…”

Complicated Relationships: A Review of Biological Interaction Networks and Pathways in Animal Science

“…Although this strategy has had several successes, the advent of genetic markers for marker-assisted selection has the potential to increase the accuracy of selection and reduce generation interval resulting in an increase in the response to selection [73]. Genetic markers are especially important when it is expensive to collect the required phenotypic data on the traits of interest, or in traits, such as carcass traits, which cannot be measured until the animal has been sacrificed making it unavailable for breeding.…”

“…This can be partly attributed to the nature of association between the DNA markers and the trait, which may either be statistical or functional. Statistical association is based on linkage disequilibrium (LD) between the marker and the causal DNA variant [73]. Statistical association indicates that the marker associated with the variation in the trait of interest may not be causing the phenotypic variation; however, it is inherited together with the unknown functional DNA variant more frequently than by random chance; that is, the marker is in LD with the functional DNA variant.…”

“…Statistical association indicates that the marker associated with the variation in the trait of interest may not be causing the phenotypic variation; however, it is inherited together with the unknown functional DNA variant more frequently than by random chance; that is, the marker is in LD with the functional DNA variant. Statistical associations are relatively more difficult to replicate across different populations [73] possibly due to decay of LD resulting in different LD patterns between different populations especially populations separated by geographical isolation [19]. Functional association analysis, on the other hand, utilizes DNA markers with functional relevance to the biological mechanisms regulating a trait.…”

SNP detection using RNA-sequences of candidate genes associated with puberty in cattle

“…Since publication of the bovine reference genome in 2009 (Elsik et al, 2009), development and increased knowledge of the -"omics" technologies have accelerated the investigation of the genetic regulation of bovine fertility traits (Fortes et al, 2012;Snelling et al, 2013;Cánovas et al, 2014, Nascimento et al, 2016. Moreover, it is important to delineate candidate genes and to determine whether polymorphisms exist within genes that influence these traits.…”