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      Meta-analysis of the relationship between milk trans-10 C18:1, milk fatty acids <16 C, and milk fat production

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      Journal of Dairy Science
      American Dairy Science Association

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          Abstract

          The economic value of milk fat and its responsiveness to management strategies provides strong interest in maximizing milk fat production by minimizing occurrence of biohydrogenation-induced milk fat depression (BH-MFD) and maximizing de novo synthesized fatty acids (FA). Tools that allow a timely diagnosis of BH-MFD would improve nutritional management. Specific milk FA or FA categories correlate to milk fat concentration and are of interest for diagnosing the cause of changes in milk fat concentration. The objective of the current study was to characterize the relationship between milk fat concentration and trans -10 C18:1, a proxy for BH-MFD, and FA <16 carbons that originate solely from de novo lipogenesis using a meta-analysis approach that used data from the literature and unpublished Penn State experiments. Prior to the meta-analysis, the effect of FA methylation method on milk FA profile was tested to determine potential bias between papers. There was no difference between sodium methoxide, acid, and acid-base methylation methods on trans -10 C18:1 concentration, but acid methods resulted in loss of short-chain FA. The relationship between trans -10 C18:1 and milk fat percentage was investigated using a 2-component model, where one component described the fraction unresponsive to BH-MFD and the other described a responsive fraction that is exponentially related to trans -10 C18:1. The 2 fractions where characterized utilizing a Bayesian hierarchical model accounting for between-study variability. The model was defined by the function f ( x , θ 1 , θ 2 , θ 3 ) = θ 1 + θ 2 exp (θ 3 ), where the unresponsive θ 1 fraction was 2.15 ± 0.09%, the responsive θ 2 fraction was 1.55 ± 0.08%, and the exponential term θ 3 was −0.503 ± 0.07 (posterior mean ± posterior standard deviation from the Bayesian hierarchical model). A Lin’s concordance correlation coefficient of 0.67 suggested good agreement between observations and predictions from the Bayesian hierarchical model, computed only with the model’s mean population parameters. There was a linear relationship between milk fat concentration and FA <16 C as a percentage of total FA (intercept = 2.68 ± 0.237 and slope = 0.043 ± 0.011; coefficient of determination = 0.31). The relationship between milk FA <16 C and milk fat concentration is weaker than what has been published, likely because multiple factors can reduce de novo FA without reducing milk fat and the broad range of diets present in the literature.

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          Inference from Iterative Simulation Using Multiple Sequences

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            Stan: A Probabilistic Programming Language

            Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.
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              Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition.

              We recently mapped a quantitative trait locus (QTL) with a major effect on milk composition--particularly fat content--to the centromeric end of bovine chromosome 14. We subsequently exploited linkage disequilibrium to refine the map position of this QTL to a 3-cM chromosome interval bounded by microsatellite markers BULGE13 and BULGE09. We herein report the positional candidate cloning of this QTL, involving (1) the construction of a BAC contig spanning the corresponding marker interval, (2) the demonstration that a very strong candidate gene, acylCoA:diacylglycerol acyltransferase (DGAT1), maps to that contig, and (3) the identification of a nonconservative K232A substitution in the DGAT1 gene with a major effect on milk fat content and other milk characteristics.
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                Author and article information

                Contributors
                Journal
                Journal of Dairy Science
                Journal of Dairy Science
                American Dairy Science Association
                00220302
                November 2020
                November 2020
                : 103
                : 11
                : 10195-10206
                Article
                10.3168/jds.2019-18129
                7885267
                32921467
                b54aead1-5fa2-41c1-b80d-2fa95eb6bad2
                © 2020

                https://www.elsevier.com/tdm/userlicense/1.0/

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