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The article considers econometric ridge regression models of the risk-sensitive sunflower yield on the example of an export-oriented agricultural crop. In particular, we have proved that despite the functional mulcollinearity of the predictors in the sunflower yield model with respect to risk caused by the algorithm peculiarities of the hierarchy analysis methods, the ridge regression procedure makes it possible to obtain its complete specification and provide biased but stable estimates of the forecast parameters in the case of uncertain input variables. It has been substantiated that the rational value of the displacement parameters is expedient to be established using a graphical interpretation of the ridge wake as the border of fast and slow fluctuations in the estimates of the ridge regression coefficients. Econometric models were calculated using SPSS Statistics, Mathcad and FAR-AREA 4.0 software. The empirical basis for forecast calculations was the assessment of trends in sunflower production in all categories of farms in the Rostov region of Russia for the period of 2008-2018. The calculation results of econometric models made it possible to develop three author's scenarios for the sunflower production in the region, namely, inertial, moderate, and optimistic ones that consider the export-oriented strategy of the agro-industrial complex.(AU)

O artigo considera modelos econométricos de regressão de rendimento de girassol sensível ao risco sobre o exemplo de uma cultura agrícola orientada para a exportação. Em particular, provamos que apesar da multicolinearidade funcional dos preditores no modelo de rendimento de girassol com relação ao risco causado pelas peculiaridades dos algoritmos dos métodos de análise hierárquica, o procedimento de regressão de cristas permite obter sua especificação completa e fornecer estimativas tendenciosas, mas estáveis dos parâmetros de previsão no caso de variáveis de entrada incertas. Foi comprovado que o valor racional dos parâmetros de deslocamento é conveniente de ser estabelecido usando uma interpretação gráfica da esteira da crista como fronteira das flutuações rápidas e lentas nas estimativas dos coeficientes de regressão da crista. Os modelos econométricos foram calculados usando o software SPSS Statistics, Mathcad e FAR-AREA 4.0. A base empírica para os cálculos de previsão foi a avaliação das tendências da produção de girassol em todas as categorias de fazendas na região de Rostov na Rússia para o período de 2008-2018. Os resultados dos cálculos dos modelos econométricos permitiram desenvolver três cenários de autor para a produção de girassol na região, a saber, os cenários inercial, moderado e otimista que consideram a estratégia orientada à exportação do complexo agroindustrial.(AU)

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In this study, the influence of carcass parts weights (thigh, breast, wing, back weight, gizzard, heart, and feet) on whole carcass weight of white turkeys (Big-6) was analyzed by regression analysis based on ridge regression and factor analysis scores. For this purpose, a total of 30 turkey carcasses of 15 males and 15 females with 17 weeks of age, were used. To determine the carcass weight (CW), thigh weight (TW), breast weight (BRW), wing weight (WW), back weight (BW), gizzard weight (GW), heart weight (HW), and feet weight (FW) were used. In the ridge regression model, since the Variance Inflation Factor (VIF) values of the variables were less than 10, the multicollinearity problem was eliminated. Furthermore, R2=0.988 was obtained in the ridge regression model. Since the eigenvalues of the two variables predicted by factor analysis scores were greater than 1, the model can be explained by two factors. The variance explained by two factors constitutes 88.80% of the total variance. The regression equation was statistically significant (p<0.01). In the regression equation, two factors obtained by using factor analysis scores were independent variables and standardized carcass weight was considered as dependent variable. In the regression model created by factor analysis scores, the Variance Inflation Factor values were 1 and R2=0.966. Both regression models were found to be suitable for predicting carcass weight of turkeys. However, the ridge regression method, which presented higher R2 value, has been shown to better explain the carcass weight.

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In this study, the influence of carcass parts weights (thigh, breast, wing, back weight, gizzard, heart, and feet) on whole carcass weight of white turkeys (Big-6) was analyzed by regression analysis based on ridge regression and factor analysis scores. For this purpose, a total of 30 turkey carcasses of 15 males and 15 females with 17 weeks of age, were used. To determine the carcass weight (CW), thigh weight (TW), breast weight (BRW), wing weight (WW), back weight (BW), gizzard weight (GW), heart weight (HW), and feet weight (FW) were used. In the ridge regression model, since the Variance Inflation Factor (VIF) values of the variables were less than 10, the multicollinearity problem was eliminated. Furthermore, R2=0.988 was obtained in the ridge regression model. Since the eigenvalues of the two variables predicted by factor analysis scores were greater than 1, the model can be explained by two factors. The variance explained by two factors constitutes 88.80% of the total variance. The regression equation was statistically significant (p<0.01). In the regression equation, two factors obtained by using factor analysis scores were independent variables and standardized carcass weight was considered as dependent variable. In the regression model created by factor analysis scores, the Variance Inflation Factor values were 1 and R2=0.966. Both regression models were found to be suitable for predicting carcass weight of turkeys. However, the ridge regression method, which presented higher R2 value, has been shown to better explain the carcass weight.(AU)

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Gene-environment (GE) interaction has important implications in the etiology of complex diseases that are caused by a combination of genetic factors and environment variables. Several authors have developed GE analysis in the context of independent subjects or longitudinal data using a gene-set. In this paper, we propose to analyze GE interaction for discrete and continuous phenotypes in family studies by incorporating the relatedness among the relatives for each family into a generalized linear mixed model (GLMM) and by using a gene-based variance component test. In addition, we deal with collinearity problems arising from linkage disequilibrium among single nucleotide polymorphisms (SNPs) by considering their coefficients as random effects under the null model estimation. We show that the best linear unbiased predictor (BLUP) of such random effects in the GLMM is equivalent to the ridge regression estimator. This equivalence provides a simple method to estimate the ridge penalty parameter in comparison to other computationally-demanding estimation approaches based on cross-validation schemes. We evaluated the proposed test using simulation studies and applied it to real data from the Baependi Heart Study consisting of 76 families. Using our approach, we identified an interaction between BMI and the Peroxisome Proliferator Activated Receptor Gamma (PPARG) gene associated with diabetes.

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BACKGROUND: The identification of lines resistant to ear diseases is of great importance in maize breeding because such diseases directly interfere with kernel quality and yield. Among these diseases, ear rot disease is widely relevant due to significant decrease in grain yield. Ear rot may be caused by the fungus Stenocarpella maydi; however, little information about genetic resistance to this pathogen is available in maize, mainly related to candidate genes in genome. In order to exploit this genome information we used 23.154 Dart-seq markers in 238 lines and apply genome-wide selection to select resistance genotypes. We divide the lines into clusters to identify groups related to resistance to Stenocarpella maydi and use Bayesian stochastic search variable approach and rr-BLUP methods to comparate their selection results. RESULTS: Through a principal component analysis (PCA) and hierarchical clustering, it was observed that the three main genetic groups (Stiff Stalk Synthetic, Non-Stiff Stalk Synthetic and Tropical) were clustered in a consistent manner, and information on the resistance sources could be obtained according to the line of origin where populations derived from genetic subgroup Suwan presenting higher levels of resistance. The ridge regression best linear unbiased prediction (rr-BLUP) and Bayesian stochastic search variable (BSSV) models presented equivalent abilities regarding predictive processes. CONCLUSION: Our work showed that is possible to select maize lines presenting a high resistance to Stenocarpella maydis. This claim is based on the acceptable level of predictive accuracy obtained by Genome-wide Selection (GWS) using different models. Furthermore, the lines related to background Suwan present a higher level of resistance than lines related to other groups.

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