Special Issue "Solutions for Breeding Healthy and Efficient Animals: Novel Phenotypes and Omics Approaches for Improving Yield and Quality of Animal Products"

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Animal Reproduction".

Deadline for manuscript submissions: 30 April 2022.

Special Issue Editors

Prof. Dr. Giustino Gaspa
E-Mail Website
Guest Editor
Department of Agricultural, Forest and Food Sciences, University of Torino, Largo Baccini 2, 10095 Grugliasco (TO), Italy
Interests: breeding scheme; whole genome sequencing; SNP data; sensor data; welfare
Dr. Fabio Correddu
E-Mail Website
Guest Editor
Dipartimento di Agraria, Sezione di Scienze Zootecniche, University of Sassari, viale Italia, 39, 07100 Sassari, Italy
Interests: sheep; fatty acids; gaschromatography; polyphenols; milk coagulation properties; by-products; antioxidants;conjugated linoleic acid; biohydrogenation; enteric methane
Dr. Alberto Cesarani
E-Mail
Guest Editor
Animal and Dairy Science Department, University of Georgia, Athens, GA 30602, USA
Interests: animal breeding; genomic; biodiversity; statistics; pedigree; multivariate

Special Issue Information

Dear Colleagues,

Modern animal breeding schemes should be designed to improve functional traits and increase animal efficiency in order to reduce the environmental footprint of animal production. Traits such as health, welfare, longevity, feed efficiency and methane emission are some examples. Often, these traits are difficult to measure or present complex genetic background. In many cases, they may exhibit low heritabilities, which further hamper the achievable selection response. However, the recent availability of the whole genome sequencing and SNP data has opened new scenarios and expedited their genetic progress. Moreover, the availability of portable devices and sensors allows one access to automatic data recordings but requires ad hoc methodologies to analyze them (such as multivariate statistics, machine learning approach, etc.). These new records have made registering and improving complex phenotypes, i.e., resilience and environmental footprint, possible. The latter is of growing interest, as can be seen from the increased attention of consumers to these aspects. This Special Issue aims to collect contributions that use “omic” data, innovative phenotypes and their proxies or new methodologies to face the old and new challenges of animal breeding.

Prof. Dr. Giustino Gaspa
Dr. Fabio Correddu
Dr. Alberto Cesarani
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Animals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • breeding scheme
  • environmental footprint
  • whole genome sequencing
  • SNP data
  • multivariate statistics
  • machine learning approaches
  • high throughput phenotype
  • automatic data recording
  • sensor data
  • functional traits
  • welfare and resilience
  • emissions and excretion
  • longevity

Published Papers (1 paper)

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Research

Article
Comparison of Single-Breed and Multi-Breed Training Populations for Infrared Predictions of Novel Phenotypes in Holstein Cows
Animals 2021, 11(7), 1993; https://doi.org/10.3390/ani11071993 - 02 Jul 2021
Viewed by 1125
Abstract
In general, Fourier-transform infrared (FTIR) predictions are developed using a single-breed population split into a training and a validation set. However, using populations formed of different breeds is an attractive way to design cross-validation scenarios aimed at increasing prediction for difficult-to-measure traits in [...] Read more.
In general, Fourier-transform infrared (FTIR) predictions are developed using a single-breed population split into a training and a validation set. However, using populations formed of different breeds is an attractive way to design cross-validation scenarios aimed at increasing prediction for difficult-to-measure traits in the dairy industry. This study aimed to evaluate the potential of FTIR prediction using training set combining specialized and dual-purpose dairy breeds to predict different phenotypes divergent in terms of biological meaning, variability, and heritability, such as body condition score (BCS), serum β-hydroxybutyrate (BHB), and kappa casein (k-CN) in the major cattle breed, i.e., Holstein-Friesian. Data were obtained from specialized dairy breeds: Holstein (468 cows) and Brown Swiss (657 cows), and dual-purpose breeds: Simmental (157 cows), Alpine Grey (75 cows), and Rendena (104 cows), giving a total of 1461 cows from 41 multi-breed dairy herds. The FTIR prediction model was developed using a gradient boosting machine (GBM), and predictive ability for the target phenotype in Holstein cows was assessed using different cross-validation (CV) strategies: a within-breed scenario using 10-fold cross-validation, for which the Holstein population was randomly split into 10 folds, one for validation and the remaining nine for training (10-fold_HO); an across-breed scenario (BS_HO) where the Brown Swiss cows were used as the training set and the Holstein cows as the validation set; a specialized multi-breed scenario (BS+HO_10-fold), where the entire Brown Swiss and Holstein populations were combined then split into 10 folds, and a multi-breed scenario (Multi-breed), where the training set comprised specialized (Holstein and Brown Swiss) and dual-purpose (Simmental, Alpine Grey, and Rendena) dairy cows, combined with nine folds of the Holstein cows. Lastly a Multi-breed CV2 scenario was implemented, assuming the same number of records as the reference scenario and using the same proportions as the multi-breed. Within-Holstein, FTIR predictions had a predictive ability of 0.63 for BCS, 0.81 for BHB, and 0.80 for k-CN. Using a specific breed (Brown Swiss) as the training set for prediction in the Holstein population reduced the prediction accuracy by 10% for BCS, 7% for BHB, and 11% for k-CN. Notably, the combination of Holstein and Brown Swiss cows in the training set increased the predictive ability of the model by 6%, which was 0.66 for BCS, 0.85 for BHB, and 0.87 for k-CN. Using multiple specialized and dual-purpose animals in the training set outperforms the 10-fold_HO (standard) approach, with an increase in predictive ability of 8% for BCS, 7% for BHB, and 10% for k-CN. When the Multi-breed CV2 was implemented, no improvement was observed. Our findings suggest that FTIR prediction of different phenotypes in the Holstein breed can be improved by including different specialized and dual-purpose breeds in the training population. Our study also shows that predictive ability is enhanced when the size of the training population and the phenotypic variability are increased. Full article
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