Nonparametric analysis of casein complex genes' epistasis and their effects on phenotypic expression of milk yield and composition in Murciano-Granadina goats

Improving knowledge on the causative polymorphisms or genes regulating the expression of milk quantitative and qualitative traits and their interconnections plays a major role in dairy goat breeding programs and genomic research. This information enables optimization of predictive and selective tools, to obtain better-performing animals to help satisfy market demands more efficiently. Goat milk casein proteins (αS1, αS2, β, and κ) are encoded by 4 loci (CSN1S1, CSN1S2, CSN2, and CSN3) clustered within 250 kb on chromosome 6. Among the statistical methods used to identify epistatic interactions in genome-wide qualitative association studies (GWAS), gene-based methods have recently grown in popularity due to their better statistical power and biological interpretability. However, most of these methods make strong assumptions about the magnitude of the relationships between SNP and phenotype, limiting statistical power. Thus, the aims of this study were to quantify the epistatic relationships among 48 SNP in the casein complex on the expression of milk yield and components (fat, protein, dry matter, lactose, and somatic cells) in MurcianoGranadina goats, to explain the qualitative nature of the SNP used to quantify the genotypes produced as a result. Categorical principal component analysis (CATPCA) was used to delimit and group the number of SNP studied depending on their implications in the explanation of milk yield and components variability. Afterward, nonlinear canonical correlation analysis was used to identify relationships among and within the SNP groups detected by CATPCA. Our results suggest that 79.65% of variability in the traits evaluated may be ascribed to the epistatic relationships across and within 7 SNP groups. Two partially overlapping groups of epistatically interrelated SNP were detected: one group of 21 SNP, explaining 57.56% of variability, and another group of 20 SNP, explaining 42.43% (multiple fit ≥ 0.1). Additionally, SNP18, 32, and 36 (CSN1S2, CSN1S1, and CSN2 loci, respectively) were the most significant SNP to explain intragroup epistatic variability (component loading > |0.5|). Conclusively, milk yield and quality may not only depend on the specific casein gene pool of individuals, but may also be relevantly conditioned by the relationships set across and within such genes. Hence, studying epistasis in isolation may be crucial to optimize selective practices for economically important dairy traits

Bayesian analysis of the association between casein complex haplotype variants and milk yield, composition, and curve shape parameters in murciano-granadina goats

Considering casein haplotype variants rather than SNPs may maximize the understanding of heritable mechanisms and their implication on the expression of functional traits related to milk production. Effects of casein complex haplotypes on milk yield, milk composition, and curve shape parameters were used using a Bayesian inference for ANOVA. We identified 48 single nucleotide polymorphisms (SNPs) present in the casein complex of 159 unrelated individuals of diverse ancestry, which were organized into 86 haplotypes. The Ali and Schaeffer model was chosen as the best fitting model for milk yield (Kg), protein, fat, dry matter, and lactose (%), while parabolic yield-density was chosen as the best fitting model for somatic cells count (SCC × 103 sc/mL). Peak and persistence for all traits were computed respectively. Statistically significant differences (p < 0.05) were found for milk yield and components. However, no significant difference was found for any curve shape parameter except for protein percentage peak. Those haplotypes for which higher milk yields were reported were the ones that had higher percentages for protein, fat, dry matter, and lactose, while the opposite trend was described by somatic cells counts. Conclusively, casein complex haplotypes can be considered in selection strategies for economically important traits in dairy goats

Immunological impact of induced acute physical activity reduction

[resumo][abstract

Software-automatized individual lactation model fitting, peak and persistence and Bayesian criteria comparison for milk yield genetic studies in Murciano-Granadina goats

SPSS model syntax was defined and used to evaluate the individual performance of 49 linear and non-linear models to fit the lactation curve of 159 Murciano-Granadina goats selected for genotyping analyses. Lactation curve shape, peak and persistence were evaluated for each model using 3107 milk yield controls with an average of 3.78 ± 2.05 lactations per goat. Best fit (Adjusted R2) values (0.47) were reached by the five-parameter logarithmic model of Ali and Schaeffer. Three main possibilities were detected: non-fitting (did not converge), standard (Adjusted R2 over 75%) and atypical curves (Adjusted R2 below 75%). All the goats fitted for 38 models. The ability to fit different possible functional forms for each goat, which progressively increased with the number of parameters comprised in each model, translated into a higher sensitivity to explaining curve shape individual variability. However, for models for which all goats fitted, only moderate increases in explanatory and predictive potential (AIC, AICc or BIC) were found. The Ali and Schaeffer model reported the best fitting results to study the genetic variability behind goat milk yield and perhaps enhance the evaluation of curve parameters as trustable future selection criteria to face the future challenges offered by the goat dairy industry

Software-Automatized Individual Lactation Model Fitting, Peak and Persistence and Bayesian Criteria Comparison for Milk Yield Genetic Studies in Murciano-Granadina Goats

SPSS model syntax was defined and used to evaluate the individual performance of 49 linear and non-linear models to fit the lactation curve of 159 Murciano-Granadina goats selected for genotyping analyses. Lactation curve shape, peak and persistence were evaluated for each model using 3107 milk yield controls with an average of 3.78 ± 2.05 lactations per goat. Best fit (Adjusted R2) values (0.47) were reached by the five-parameter logarithmic model of Ali and Schaeffer. Three main possibilities were detected: non-fitting (did not converge), standard (Adjusted R2 over 75%) and atypical curves (Adjusted R2 below 75%). All the goats fitted for 38 models. The ability to fit different possible functional forms for each goat, which progressively increased with the number of parameters comprised in each model, translated into a higher sensitivity to explaining curve shape individual variability. However, for models for which all goats fitted, only moderate increases in explanatory and predictive potential (AIC, AICc or BIC) were found. The Ali and Schaeffer model reported the best fitting results to study the genetic variability behind goat milk yield and perhaps enhance the evaluation of curve parameters as trustable future selection criteria to face the future challenges offered by the goat dairy industry

Bayesian Analysis of the Association between Casein Complex Haplotype Variants and Milk Yield, Composition, and Curve Shape Parameters in Murciano-Granadina Goats

Considering casein haplotype variants rather than SNPs may maximize the understanding of heritable mechanisms and their implication on the expression of functional traits related to milk production. Effects of casein complex haplotypes on milk yield, milk composition, and curve shape parameters were used using a Bayesian inference for ANOVA. We identified 48 single nucleotide polymorphisms (SNPs) present in the casein complex of 159 unrelated individuals of diverse ancestry, which were organized into 86 haplotypes. The Ali and Schaeffer model was chosen as the best fitting model for milk yield (Kg), protein, fat, dry matter, and lactose (%), while parabolic yield-density was chosen as the best fitting model for somatic cells count (SCC × 103 sc/mL). Peak and persistence for all traits were computed respectively. Statistically significant differences (p < 0.05) were found for milk yield and components. However, no significant difference was found for any curve shape parameter except for protein percentage peak. Those haplotypes for which higher milk yields were reported were the ones that had higher percentages for protein, fat, dry matter, and lactose, while the opposite trend was described by somatic cells counts. Conclusively, casein complex haplotypes can be considered in selection strategies for economically important traits in dairy goats

Goat Milk Nutritional Quality Software-Automatized Individual Curve Model Fitting, Shape Parameters Calculation and Bayesian Flexibility Criteria Comparison

SPSS syntax was described to evaluate the individual performance of 49 linear and non-linear models to fit the milk component evolution curve of 159 Murciano-Granadina does selected for genotyping analyses. Peak and persistence for protein, fat, dry matter, lactose, and somatic cell counts were evaluated using 3107 controls (3.91 ± 2.01 average lactations/goat). Best-fit (adjusted R2) values (0.548, 0.374, 0.429, and 0.624 for protein, fat, dry matter, and lactose content, respectively) were reached by the five-parameter logarithmic model of Ali and Schaeffer (ALISCH), and for the three-parameter model of parabolic yield-density (PARYLDENS) for somatic cell counts (0.481). Cross-validation was performed using the Minimum Mean-Square Error (MMSE). Model comparison was performed using Residual Sum of Squares (RSS), Mean-Squared Prediction Error (MSPE), adjusted R2 and its standard deviation (SD), Akaike (AIC), corrected Akaike (AICc), and Bayesian information criteria (BIC). The adjusted R2 SD across individuals was around 0.2 for all models. Thirty-nine models successfully fitted the individual lactation curve for all components. Parametric and computational complexity promote variability-capturing properties, while model flexibility does not significantly (p > 0.05) improve the predictive and explanatory potential. Conclusively, ALISCH and PARYLDENS can be used to study goat milk composition genetic variability as trustable evaluation models to face future challenges of the goat dairy industry

Does the Acknowledgement of αS1-Casein Genotype Affect the Estimation of Genetic Parameters and Prediction of Breeding Values for Milk Yield and Composition Quality-Related Traits in Murciano-Granadina?

A total of 2090 lactation records for 710 Murciano-Granadina goats were collected during the years 2005–2016 and analyzed to investigate the influence of the αS1-CN genotype on milk yield and components (protein, fat, and dry matter). Goats were genetically evaluated, including and excluding the αS1-CN genotype, in order to assess its repercussion on the efficiency of breeding models. Despite no significant differences being found for milk yield, fat and dry matter heritabilities, protein production heritability considerably increased after aS1-CN genotype was included in the breeding model (+0.23). Standard errors suggest that the consideration of genotype may improve the model’s efficiency, translating into more accurate genetic parameters and breeding values (PBV). Genetic correlations ranged from −0.15 to −0.01 between protein/dry matter and milk yield/protein and fat content, while phenotypic correlations were −0.02 for milk/protein and −0.01 for milk/fat or protein content. For males, the broadest range for reliability (RAP) (0.45–0.71) was similar to that of females (0.37–0.86) when the genotype was included. PBV ranges broadened while the maximum remained similar (0.61–0.77) for males and females (0.62–0.81) when the genotype was excluded, respectively. Including the αS1-CN genotype can increase production efficiency, milk profitability, milk yield, fat, protein and dry matter contents in Murciano-Granadina dairy breeding programs

Structural characterization of InAlAsSb/InGaAs/InP heterostructures for solar cells

In this work, we have characterized by transmission electron microscopy techniques the structural properties of InAlAsSb/InGaAs/InP heterostructures, with target applications in high efficiency solar cells. Previous photoluminescence (PL)1 analysis suggested the existence of compositional fluctuations in the active layer of these heterostructures. 220 bright field (BF)2 diffraction contrast micrographs have revealed strong strain contrast in the InGaAs buffer layer, related to the existence of these compositional fluctuations. The effect of a decomposed buffer on the growth of the InAlAsSb layer has been analyzed through the simulation of the strain fields in the heterostructure using the finite elements method (FEM).3 These simulations have shown that the strain in the buffer layer due to the compositional fluctuations only affects the first few nm of the InAlAsSb layer. The analysis by aberration corrected high angle annular dark field scanning transmission electron microscopy (HAADF-STEM)4and electron energy loss spectroscopy (EELS)5of the composition of the InAlAsSb layer reveals that any compositional fluctuation is only observed as an average effect, rather than in the form of clustering or atomically sharp transitions. The limitations of these techniques for the detection of small 3D compositional fluctuations are discussed

Late Holocene sea-surface temperature and precipitation variability in northern Patagonia, Chile (Jacaf Fjord, 44°S)

Author Posting. © The Author(s), 2009. This is the author's version of the work. It is posted here by permission of Elsevier B.V. for personal use, not for redistribution. The definitive version was published in Quaternary Research 72 (2009): 400-409, doi:10.1016/j.yqres.2009.06.010.A high-resolution multi-proxy study including the elemental and isotopic composition of bulk organic matter, land plant-derived biomarkers, and alkenone-based sea surface temperature (SST) from a marine sedimentary record obtained from the Jacaf Fjord in northern Chilean Patagonia (~ 44°20'S) provided a detailed reconstruction of continental runoff, precipitation, and summer SST spanning the last 1750 years. We observed two different regimes of climate variability in our record: a relatively dry/warm period before 900 cal yr BP (lower runoff and average SST 1°C warmer than present-day) and a wet/cold period after 750 cal yr BP (higher runoff and average SST 1°C colder than present-day). Relatively colder SSTs were found during 750-600 and 450-250 cal yr BP, where the latter period roughly corresponds to the interval defined for the Little Ice Age (LIA). Similar climatic swings have been observed previously in continental and marine archives of the last two millennia from central and southern Chile, suggesting a strong latitudinal sensitivity to changes in the Southern Westerly Winds, the main source of precipitation in southern Chile, and validating the regional nature of the LIA. Our results reveal the importance of the Chilean fjord system for recording climate changes of regional and global significance.The preparation of this article was made possible by the support of the Comité Oceanográfico Nacional Chile through the Special Fund to Promote Interdisciplinary Publications of the CIMAR Program. Sampling was funded by the CIMAR FIORDO-7 Program (Grant CPF 01-10)