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Dataset on growth curves of Boer goats fitted by ten non-linear functions.

ABSTRACT: Data on the description of growth of female Boer goats from the Mexican national breeding flock are presented. Goat meat is highly appreciated for the preparation of traditional dishes of Mexican cuisine, and its demand is on the rise. Boer goats are of relatively recent arrival in Mexico and the size of the performance-recorded flock has been increasing steadily in the last ten years. Repeated measures of body weight at different ages from birth to adulthood of Boer goats are scarce. When available, such data can be used to describe the growth pattern and the meat production potential of goat meat breeds such as the Boer. This paper presents data on estimators of growth curve parameters, plots of average predicted growth curves, plots of residuals on age, and data on goodness of fit statistics of ten non-linear functions fitted to describe the growth curve of Boer goats.

SUBMITTER: Garcia-Muniz JG

PROVIDER: S-EPMC6373209 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Dataset on growth curves of Boer goats fitted by ten non-linear functions.

García-Muñiz J G JG Ramírez-Valverde R R Núñez-Domínguez R R Hidalgo-Moreno J A JA

Data in brief 20190115

Data on the description of growth of female Boer goats from the Mexican national breeding flock are presented. Goat meat is highly appreciated for the preparation of traditional dishes of Mexican cuisine, and its demand is on the rise. Boer goats are of relatively recent arrival in Mexico and the size of the performance-recorded flock has been increasing steadily in the last ten years. Repeated measures of body weight at different ages from birth to adulthood of Boer goats are scarce. When avail ...[more]

PMID: 30805424

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Project description:BACKGROUND:Identification of molecular characterization of genes underlying livestock productive traits may allow applying advanced biotechnology techniques to improve animal productivity. Growth hormone (GH) controls body growth rate, milk production, reproduction as well as carbohydrate, lipid, and protein metabolism. Therefore, the present study aims to investigate the genetic variations of growth hormone cDNA sequences between Assaf sheep (As_GH) and Boer goat (Bo_GH) that mainly used for genetic improvement in Egypt using bioinformatics analysis. Growth hormone cDNA was isolated from the pituitary gland tissue of Assaf sheep Boer goat and subcloned into pTZ57R/T cloning vector for sequencing. RESULTS:Molecular weight of As_GH cDNA was 665?bp and was 774?bp for Bo_GH cDNA. The complete coding sequences (CDS) of As_GH and Bo_GH were registered in the GenBank database under accession number (AC: MH128986 and AC: MG744290, respectively). High homology percentage was observed (99.5%) between AS_GH and Bo_GH protein sequences with one different amino acid in the As_GH protein sequence (Arg194). The protein sequence of As_GH has only one motif signature; Somatotropin_1 from 79 to 112 aa compared to Bo_GH protein sequences and GenBank database that had two motifs signature. The growth hormone cDNA sequence of Assaf sheep has a unique three single nucleotide polymorphisms (SNPs) (A637A638G639) that encodes for arginine (Arg194); this insertion mutation (AAG) was not found in the growth hormone cDNA sequences of Boer goat in the present study and GenBank database breeds. This mutation can be used to develop SNPs markers for Assaf sheep. CONCLUSIONS:GH sequence of Assaf and Boer goat is highly conserved and the homogeny in the codon region (99.5%). The Assaf sheep GH sequence has a unique three SNPs that may be used to develop SNPs markers for such breed. Further studies are needed to investigate the genetic variations of growth hormone gene in different sheep and goat breeds in Egypt and document the relationship between these variations and the productive performance of animals.

Project description:The "binormal" model is the most frequently used tool for parametric receiver operating characteristic (ROC) analysis. The binormal ROC curves can have "improper" (non-concave) shapes that are unrealistic in many practical applications, and several tools (eg, PROPROC) have been developed to address this problem. However, due to the general robustness of binormal ROCs, the improperness of the fitted curves might carry little consequence for inferences about global summary indices, such as the area under the ROC curve (AUC). In this work, we investigate the effect of severe improperness of fitted binormal ROC curves on the reliability of AUC estimates when the data arise from an actually proper curve.We designed theoretically proper ROC scenarios that induce severely improper shape of fitted binormal curves in the presence of well-distributed empirical ROC points. The binormal curves were fitted using maximum likelihood approach. Using simulations, we estimated the frequency of severely improper fitted curves, bias of the estimated AUC, and coverage of 95% confidence intervals (CIs). In Appendix?S1, we provide additional information on percentiles of the distribution of AUC estimates and bias when estimating partial AUCs. We also compared the results to a reference standard provided by empirical estimates obtained from continuous data.We observed up to 96% of severely improper curves depending on the scenario in question. The bias in the binormal AUC estimates was very small and the coverage of the CIs was close to nominal, whereas the estimates of partial AUC were biased upward in the high specificity range and downward in the low specificity range. Compared to a non-parametric approach, the binormal model led to slightly more variable AUC estimates, but at the same time to CIs with more appropriate coverage.The improper shape of the fitted binormal curve, by itself, ie, in the presence of a sufficient number of well-distributed points, does not imply unreliable AUC-based inferences.

Dataset Information

Dataset on growth curves of Boer goats fitted by ten non-linear functions.

Publications

Dataset on growth curves of Boer goats fitted by ten non-linear functions.

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