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Advances in the Genetic Improvement of Farm Animals Using Genomic...
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Advances in the Genetic Improvement of Farm Animals Using Genomic Tools

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Farm Animal Production".

Deadline for manuscript submissions: closed (25 April 2025) | Viewed by 3375

Special Issue Editor

Dr. Panagiota Koutsouli

E-Mail Website
Guest Editor
Laboratory of Animal Breeding and Husbandry, Department of Animal Science, Agricultural University of Athens, 75 Iera Odos, GR11855 Athens, Greece
Interests: population; biochemical and molecular genetics; protein and DNA polymorphisms; genetic diversity of domestic animal breeds; horse genetics; genetic improvement; conservation of endangered breeds; genetic analyses on domestic animals; indigenous breeds; sheep milk quality; carcass quality of cattle; sheep behaviour

Special Issue Information

Dear Colleagues,

The genetic improvement of farm animals is a branch of applied genetics. With the advent of genomic technologies such as genotyping and sequencing, the field has undergone a significant transition towards more precise and efficient genetic improvement strategies in farm animals.

Authors are invited to contribute to this forthcoming Special Issue, which will explore the diverse aspects of genetic improvement in farm animals in the context of genomic technologies.

The articles should be original research, reviews or perspectives that contribute to the advancement of knowledge in the field and address a range of topics, including but not limited to the following:

  1. Novel approaches and methodologies for implementing genomic selection in breeding programs in farm animals and applications of genomic prediction in animal husbandry.
  2. Selective breeding objectives to simultaneously improve desirable traits such as productivity, milk and meat quality, disease resistance and adaptability.
  3. The identification and management of genetic diseases within animal populations, preventing economic losses and ensuring animal welfare.
  4. Exploration of cutting-edge genomic technologies, e.g. gene editing and genetically modified animals.
  5. Maintaining genetic diversity within animal populations, prevention of inbreeding depression, genetic management and conservation of breeds/ populations.
  6. The contribution of genetic improvement to address the challenges to animal production posed by climate change.
  7. The impact of genomic animal selection practices on the environment and the evaluation of the role of genetic improvement in promoting sustainable animal production.
  8. Ethical issues related to the genetic manipulation of farm animals to ensure that genetic improvement is in accordance with animal welfare standards, consumer values and societal norms.

Dr. Panagiota Koutsouli
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Agriculture is an international peer-reviewed open access semimonthly 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 2600 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

  • genomic selection
  • genomic prediction
  • breeding programs
  • genomic technologies
  • genetic diseases
  • genetic diversity
  • genetic variation
  • conservation
  • farm animals

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

13 pages, 4580 KiB  
Article
Analysis of Genetic Diversity and Population Structure of Liangshan Black Pigs, a New Indigenous Pig Breed in Shandong Province
by Jingxuan Li, Xin Zhang, Kaifeng Zhou, Jiying Wang, Yanping Wang, Xingyan Zhao and Xueyan Zhao
Agriculture 2025, 15(9), 952; https://doi.org/10.3390/agriculture15090952 (registering DOI) - 27 Apr 2025
Viewed by 89
Abstract
Liangshan Black pigs are a new Chinese indigenous breed discovered during the Third National Survey of Livestock and Plant Genetic Resources. To uncover genetic diversity, population structure, and potential exotic introgression in this breed, we sampled 191 Liangshan Black pigs from the conservation [...] Read more.
Liangshan Black pigs are a new Chinese indigenous breed discovered during the Third National Survey of Livestock and Plant Genetic Resources. To uncover genetic diversity, population structure, and potential exotic introgression in this breed, we sampled 191 Liangshan Black pigs from the conservation population and genotyped these individuals using the “Zhongxin-I” porcine chip, then conducted in-depth population genetic analyses in the context of pigs from five introduced breeds. The results revealed that the tested individuals exhibited significant genetic diversity, displayed uneven kinship relationships, and were assigned to nine families according to their clustering patterns in the phylogenetic tree. Further relationship analyses with the five introduced breeds demonstrated that Liangshan Black pigs were clustered separately from the introduced breeds, had larger evolutionary distances with the introduced breeds, and possessed certain genetic components of the introduced breeds, especially those of Duroc. These findings demonstrate that Liangshan Black pigs are generally an indigenous breed independent of the introduced breeds but are slightly affected by the introduced breeds. In summary, the results of our study not only contribute to an in-depth understanding of the population genetic characteristics of Liangshan Black pigs but also provide the necessary data for the implementation of conservation programs. Full article
(This article belongs to the Special Issue Advances in the Genetic Improvement of Farm Animals Using Genomic Tools)
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19 pages, 2209 KiB  
Article
Optimizing the Genomic Evaluation Model in Crossbred Cattle for Smallholder Production Systems in India
by Kashif Dawood Khan, Rani Alex, Ashish Yadav, Varadanayakanahalli N. Sahana, Amritanshu Upadhyay, Rajesh V. Mani, Thankappan Sajeev Kumar, Rajeev Raghavan Pillai, Vikas Vohra and Gopal Ramdasji Gowane
Agriculture 2025, 15(9), 945; https://doi.org/10.3390/agriculture15090945 (registering DOI) - 27 Apr 2025
Viewed by 121
Abstract
Implementing genomic selection in smallholder dairy systems is challenging due to limited genetic connectedness and diverse management practices. This study aimed to optimize genomic evaluation models for crossbred cattle in South India. Data included 305-day first lactation milk yield (FLMY) records from 17,650 [...] Read more.
Implementing genomic selection in smallholder dairy systems is challenging due to limited genetic connectedness and diverse management practices. This study aimed to optimize genomic evaluation models for crossbred cattle in South India. Data included 305-day first lactation milk yield (FLMY) records from 17,650 cows (1984–2021), with partial pedigree and genotypes for 1004 bulls and 1568 cows. Non-genetic factors such as geography, season and period of calving, and age at first calving were significant sources of variation. The average milk yield was 2875 ± 123.54 kg. Genetic evaluation models used a female-only reference. Heritability estimates using different approaches were 0.32 ± 0.03 (REML), 0.40 ± 0.03 (ssGREML), and 0.25 ± 0.08 (GREML). Bayesian estimates (Bayes A, B, C, Cπ, and ssBR) ranged from 0.20 ± 0.02 to 0.43 ± 0.04. Genomic-only models showed reduced variance due to the Bulmer effect, as genomic data belonged to recent generations. Breeding value prediction accuracies were 0.60 (PBLUP), 0.45 (GBLUP), and 0.65 (ssGBLUP). Using the LR method, the estimates of bias, dispersion, and ratio of accuracies for ssGBLUP were −39.83, 1.09, and 0.69; for ssBR, they were 71.83, 0.83, and 0.76. ssGBLUP resulted in more accurate and less biased GEBVs than ssBR. We recommend ssGBLUP for genomic evaluation of crossbred cattle for milk production under smallholder systems. Full article
(This article belongs to the Special Issue Advances in the Genetic Improvement of Farm Animals Using Genomic Tools)
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21 pages, 3888 KiB  
Article
Indigenous Greek Horse Breeds: Genetic Structure and the Influence of Foreign Breeds
by Myrina Emilio Katsoulakou, Nikolaos Kostaras, H. Josefina Kjöllerström, George P. Laliotis, Iosif Bizelis, E. Gus Cothran, Rytis Juras and Panagiota Koutsouli
Agriculture 2025, 15(5), 540; https://doi.org/10.3390/agriculture15050540 - 1 Mar 2025
Viewed by 2102
Abstract
This study aims to examine the genetic structure and diversity levels of seven indigenous Greek horse breeds: Andravida, Pindos, Thessaly, Skyros, Penia, Messara and Rodos, using 15 microsatellites. Phenotypic traits were combined with factorial correspondence analyses to create two datasets: one “Baseline” containing [...] Read more.
This study aims to examine the genetic structure and diversity levels of seven indigenous Greek horse breeds: Andravida, Pindos, Thessaly, Skyros, Penia, Messara and Rodos, using 15 microsatellites. Phenotypic traits were combined with factorial correspondence analyses to create two datasets: one “Baseline” containing typical samples, and one “Unknown” with non-typical or of disputed origin samples. In the Greek “Baseline” horses, 142 alleles were found. The mean observed and effective number of alleles, the polymorphism information content and the allelic richness were 6.75, 4.14, 0.63 and 5.12, respectively. The expected and observed heterozygosity and inbreeding coefficient varied between 0.81 and 0.29 and 0.79 and 0.24. The above dataset was enriched with data from 41 foreign horse breeds and 40 Przewalski samples to perform a breed assignment. The highest percentage of successfully assigned samples was for Skyros, Messara and Rodos, with rates of 93%, 89% and 100%, respectively, suggesting their considerable homogeneity, while Andravida, Pindos, Thessaly and Penia scored 32.5, 34.1, 44.0 and 45.7%, respectively. Structural analysis confirmed the results of FCA and showed the genetic similarities of the above breeds. The results revealed the influence of foreign breeds (mainly Garrano, Turkoman, Irish Draft, Hanoverian and Belgian Draft). There is an urgent need to implement management measures for the pure homogeneous breeds and selection strategies for the remaining breeds which are genetically similar. Full article
(This article belongs to the Special Issue Advances in the Genetic Improvement of Farm Animals Using Genomic Tools)
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12 pages, 5164 KiB  
Article
Comparative Analysis of Genomic Prediction for Production Traits Using Genomic Annotation and a Genome-Wide Association Study at Sequencing Levels in Beef Cattle
by Zhida Zhao, Qunhao Niu, Tianyi Wu, Feng Liu, Zezhao Wang, Huijiang Gao, Junya Li, Bo Zhu and Lingyang Xu
Agriculture 2024, 14(12), 2255; https://doi.org/10.3390/agriculture14122255 - 10 Dec 2024
Viewed by 866
Abstract
Leveraging whole-genome sequencing (WGS) that includes the full spectrum of genetic variation provides a better understanding of the biological mechanisms involved in the economically important traits of farm animals. However, the effectiveness of WGS in improving the accuracy of genomic prediction (GP) is [...] Read more.
Leveraging whole-genome sequencing (WGS) that includes the full spectrum of genetic variation provides a better understanding of the biological mechanisms involved in the economically important traits of farm animals. However, the effectiveness of WGS in improving the accuracy of genomic prediction (GP) is limited. Recent genetic analyses of complex traits, such as genome-wide association study (GWAS), have identified numerous genomic regions and potential genes, which can provide valuable prior information for the improvement of genomic selection (GS). In this study, we applied different genome prediction methods to integrate GWAS results and gene feature annotations, which significantly improved the accuracy of GS for beef production traits. The Bayesian models incorporating genomic features showed the highest prediction accuracy, particularly for average daily gain (ADG) and bone weight (BW). Compared to prediction models based on WGS data, GP including biological prior can optimize the prediction accuracy by up to 11.56% for ADG and 14.60% for BW. Also, GP using GBLUP and Bayesian methods integrating biological priors for single-trait GWAS can significantly increase the prediction accuracy. Bayesian methods generally outperformed GBLUP models, with average improvements of 2.25% for ADG, 5.04% for BW, and 3.44% for live weight (LW). Our results indicate that leveraging biological prior knowledge can significantly refine GS models and underline the potential of combining WGS data with biological prior knowledge to further enhance the breeding process. Full article
(This article belongs to the Special Issue Advances in the Genetic Improvement of Farm Animals Using Genomic Tools)
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