A predictive assessment of genetic correlations between traits in chickens using markers

International audienceAbstractBackgroundGenomic 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 = λG + (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

Public Service Motivation and Organizational and non-Organizational Factors

The concept of motivation of employees and attention to factors which creates incentives is one of the most important issues in labor productivity. The motivation of government employees has long been fascinating. The present study aimed to explain a model of organizational and non-organizational factors related to public service motivation. the present study is applied in terms of practical purpose and in terms of descriptive - correlation. The sample size includes 324 employees of the municipalities of Khorasan Razavi province selected by multistage cluster sampling. The tools used in this study are the Combined General Service Motivation Questionnaire, the Combined Questionnaire of Organizational Factors and the Non-Organizational Factors Questionnaire, which reliability was obtained through Cronbach's alpha of  0.822, 0.815 and 0.795, respectively. In order to make sure that the indices are proportional to the components (validity check), expert examination and exploratory factor analysis were used. Data analysis was performed using structural equation modeling using SPSS and Amos softwares. The results showed that there is a significant relationship between the indicators and the motivation of public service staff. Finally, based on the findings of the research, some suggestions have been made