Single nucleotide polymorphism analysis in meat-production related genes in broiler chickens79-82Views:148
In broiler chickens, the intensive selection for growth rate, feed efficiency, body composition (breast muscle weight) traits in the last decades was successful. To improve economically important characteristics, it is possible to use molecular markers associated with meat production traits. The aim of this study was to examine genotype polymorphisms in ROSS 308 broilers for thyroid hormone responsive Spot14α, insulinlike growth factor 1 (IGF1), IGF-binding protein 2 (IGFBP2), somatostatin (SST) and prolactin (PRL) genes. A further goal of this investigation was to study the relationship between the polymorphisms and phenotypic characteristics.
In the investigated broiler population, the frequency for CC homozygous genotype was 0.77 in Spot14α (AY568628), AA homozygous genotype was 0.80 in IGF1 (M74176), GG homozygous genotype was 0.85 in IGFBP2 (U15086), DD homozygous genotype was 0.60 in PRL (FJ663023 or FJ434669). Only the AA homozygous genotype was found in SST (X60191). Chickens with AC genotype in Spot14α, and with GG genotype in IGFBP2 had higher body weight (BW) and carcass weight (CW), compared to CC and GT genotypes. However, the differences were not significant (P>0.05). There was significant association (P<0.05) between PRL genotypes and body and carcass weight, where chicken with homozygous DD surpassed individuals with homozygous II genotypes.
Upgrading breeding value estimation in beef cattle451-458Views:173
This paper gives a summary of the possibility for applying genomic information for breeding value estimation in beef cattle breeding. This process is called genomic prediction and is now widely used in dairy cattle globally as well as in some beef and sheep populations. The advantage of genomic prediction is a more accurate estimate of the genetic merit of an individual at a young age thereby facilitating greater annual genetic gain, predominantly through shorter generation intervals. Genomic predictions are more advantageous for sex-linked (e.g., milk yield), low heritability (e.g., fertility) and difficult-to-measure (e.g., feed intake) traits. The larger the reference population, on average, the more accurate the genomic predictions; additionally, the closer genetically the reference population is to the candidate population, the greater the accuracy of genomic predictions. Research is continuing on strategies to generate accurate genomic predictions using a reference population consisting of multiple breeds (and crossbred). Retrospective analysis of real-life data where genomic predictions have been operation for several years clearly shows a benefit of this technology.