BackgroundGenomic selection has been successfully implemented in plant and animal breeding programs to shorten generation intervals and accelerate genetic progress per unit of time. In practice, genomic selection can be used to improve several correlated traits simultaneously via multiple-trait prediction, which exploits correlations between traits. However, few studies have explored multiple-trait genomic selection. Our aim was to infer genetic correlations between three traits measured in broiler chickens by exploring kinship matrices based on a linear combination of measures of pedigree and marker-based relatedness. A predictive assessment was used to gauge genetic correlations.MethodsA multivariate genomic best linear unbiased prediction model was designed to combine information from pedigree and genome-wide markers in order to assess genetic correlations between three complex traits in chickens, i.e. body weight at 35 days of age (BW), ultrasound area of breast meat (BM) and hen-house egg production (HHP). A dataset with 1351 birds that were genotyped with the 600 K Affymetrix platform was used. A kinship kernel (K) was constructed as K = λ G + (1 − λ)A, where A is the numerator relationship matrix, measuring pedigree-based relatedness, and G is a genomic relationship matrix. The weight (λ) assigned to each source of information varied over the grid λ = (0, 0.2, 0.4, 0.6, 0.8, 1). Maximum likelihood estimates of heritability and genetic correlations were obtained at each λ, and the “optimum” λ was determined using cross-validation.ResultsEstimates of genetic correlations were affected by the weight placed on the source of information used to build K. For example, the genetic correlation between BW–HHP and BM–HHP changed markedly when λ varied from 0 (only A used for measuring relatedness) to 1 (only genomic information used). As λ increased, predictive correlations (correlation between observed phenotypes and predicted breeding values) increased and mean-squared predictive error decreased. However, the improvement in predictive ability was not monotonic, with an optimum found at some 0 < λ < 1, i.e., when both sources of information were used together.ConclusionsOur findings indicate that multiple-trait prediction may benefit from combining pedigree and marker information. Also, it appeared that expected correlated responses to selection computed from standard theory may differ from realized responses. The predictive assessment provided a metric for performance evaluation as well as a means for expressing uncertainty of outcomes of multiple-trait selection.

BackgroundA lack of neurotrophic support is believed to contribute to the development of diabetic neuropathy. On the other hand, neurotrophins have consistently been shown to increase in the central and peripheral nervous system following exercise, but the effects of exercise intervention on brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF) in diabetic neuropathy are not understood.ObjectivesThis experimental study was designed and carried out at the Tarbiat Modares university (TMU) in Tehran, Iran, to investigate the hypothesis that increased activity as endurance training can help to increase the endogenous expression of neurotrophins in diabetic rats.MethodsThis was an experimental study with 2 × 2 factorial plans performed at TMU in Iran. Sampling was accidental and 28 adult male Wistar rats in the body mass range of 326.3 ± 8.4 g comprised the sample, with each rat randomly assigned to four groups: diabetic control (DC), diabetic training (DT), healthy control (HC), and healthy training (HT). To induce diabetic neuropathy, after 12 hours of food deprivation, an intraperitoneal injection of streptozotocin (STZ) solution (45 mg/Kg) method was used. Two weeks after STZ injection, the endurance training protocol was performed for 6 weeks; 24 hours after the last training session, the rats were sacrificed. Real-time PCR was used for BDNF and NGF expression.ResultsThe data indicate that diabetes decreases BDNF and NGF expression in sensory (92%, P = 0.01; 90%, P = 0.038, respectively) and motor (93%, P = 0.05; 60%, P = 0.029, respectively) roots. However, NGF mRNA levels in the DT group were significantly higher than in the HC group ((7.1-fold), P = 0.01; (2.2-fold), P = 0.001, respectively, for sensory and motor roots), but this was not shown for BDNF. In addition, endurance training can increase NGF expression in healthy rats ((7.4-fold), P = 0.01; (3.8-fold), P = 0.001, respectively, for sensory and motor roots).ConclusionsThis study shows that BDNF and NGF expression decreases in diabetic neuropathy. However, this decrease can be reversed through endurance training. These results also indicate that endurance training may have a potential role in compensating for neurotrophin deficiency following diabetic neuropathy.

No abstract

No abstract

No abstract