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Advancing Cattle Breeding and Production Through Genomics, Phenotyping, Bioinformatics, and Sustainable Practices

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 4389

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Special Issue Editors

Dr. Vinicius Silva Junqueira

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Guest Editor

Bayer Crop Science, Minas Gerais, Uberlândia 38057-049, Brazil
Interests: animal breeding; genomics; bioinformatics; statistics; big data

Dr. Jorge Hidalgo

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Guest Editor

Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602, USA
Interests: genetics; genomics; livestock; statistical genetics
Special Issues, Collections and Topics in MDPI journals

Dr. Gabriel Soares Campos

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Guest Editor

Interbull Centre, Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences (SLU), P.O. Box 7023, 750 07 Uppsala, Sweden
Interests: cattle breeding; genomics; genetic evaluation; statistics

Dr. Delvan Alves da Silva

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Guest Editor

Department of Animal Science, Federal University of Viçosa, Viçosa, MG, Brazil
Interests: cattle breeding; genomics; genetic evaluation; genotype by environment interaction

Special Issue Information

Dear Colleagues,

Recent advances in genomics, phenotyping technologies, and bioinformatics have opened new possibilities for improving cattle breeding and production systems. These approaches enable more accurate selection, accelerated genetic gain, and a deeper understanding of the complex traits related to growth, fertility, disease resistance, environmental resilience, and robustness. At the same time, sustainable practices in livestock management are becoming increasingly important to address global challenges such as climate change, resource efficiency, and animal welfare.

This Special Issue aims to provide a platform for the latest research and innovations that integrate genomic technologies, advanced phenotyping, and data-driven tools with sustainable cattle production systems. We welcome studies that demonstrate how these combined approaches can enhance productivity, genetic diversity, and long-term viability of beef and dairy production.

The Special Issue welcomes original research articles, reviews, and case studies covering, but not limited to, the following topics:

-Genomic selection, genome-wide association studies (GWAS), and gene editing in cattle;
-High-throughput phenotyping and precision livestock technologies;
-Applications of bioinformatics and big data in cattle breeding;
-Multi-omics approaches (e.g., genomics, phenomics, transcriptomics, metabolomics);
-Integration of genetics and nutrition for improved feed efficiency;
-Breeding strategies for climate resilience, robustness, and disease resistance;
-Sustainability and environmental impact of modern breeding programmes;
-Genetic diversity conservation in cattle populations;
-Ethical, economic, and policy considerations in genomic-based breeding.

Dr. Vinicius Silva Junqueira
Dr. Jorge Hidalgo
Dr. Gabriel Soares Campos
Dr. Delvan Alves da Silva
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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 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 2400 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
cattle breeding
high-throughput phenotyping
bioinformatics
feed efficiency
genetic improvement

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Published Papers (5 papers)

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Research

16 pages, 474 KB

Open AccessArticle

Structural Equation Modeling of Genetic and Residual Covariance Matrices for Multiple-Trait Evaluation in Beef Cattle

by Marcos Jun-Iti Yokoo, Gustavo de los Campos, Vinícius Silva Junqueira, Fernando Flores Cardoso, Guilherme Jordão Magalhães Rosa and Lucia Galvão Albuquerque

Animals 2026, 16(5), 817; https://doi.org/10.3390/ani16050817 - 5 Mar 2026

Viewed by 429

Abstract

The continuous growth in both the number of phenotypic records and the range of traits included in beef cattle genetic evaluations poses substantial statistical and computational challenges for the estimation of genetic and residual (co)variance matrices required for breeding value estimation. Structural equation models (SEM), implemented using either factor analysis (FA) or recursive model (REC) structures, provide a flexible framework to model genetic and residual (co)variance matrices while yielding more parsimonious and computationally efficient parameterizations. Here, SEM was applied to estimate parameters for growth and ultrasound-measured carcass traits in beef cattle. The dataset comprised 2942 animals, and six traits were evaluated using standard multiple-trait mixed models (SMTM) and SEM. We considered FA and REC models implemented with six alternative parameterizations, in which random effects were represented as linear combinations of fewer unobservable random variables. Relative to the SMTM, both the model with two factors in the genetic covariance matrix (FA2G) and the model in which six recursive effects were constrained to zero in the residual covariance matrix (REC1) demonstrated a strong ability to capture genetic variability, as reflected by comparable heritability estimates. Correlations between estimated breeding values (EBV) for the same traits across models were consistently high, ranging from 0.94 to 1.00, indicating strong agreement among model estimates. The FA2G model was the most parsimonious in terms of the effective number of parameters (

p_{D}

), with 431.2

p_{D}

, corresponding to a reduction of 25.3 parameters relative to the SMTM. The REC1 model also emerged as a competitive alternative for this dataset, exhibiting a lower

p_{D}

(443.6) than the SMTM (456.5) and the most favorable deviance information criterion among all models evaluated (e.g., 37,868.6 for REC1 versus 37,874.7 for SMTM). Overall, these results demonstrate that mixed-effects multi-trait models for beef cattle genetic evaluation can be effectively implemented using FA or REC structures, which provide parsimonious representations of the underlying covariance patterns while maintaining high agreement in EBV. Full article

(This article belongs to the Special Issue Advancing Cattle Breeding and Production Through Genomics, Phenotyping, Bioinformatics, and Sustainable Practices)

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10 pages, 552 KB

Open AccessArticle

Serial Correlations of Partial Body Weight and Feed Intake in Crossbred Cattle

by Georgette Pyoos, Michiel Scholtz, Michael MacNeil, Mokgadi Seshoka and Frederick Neser

Animals 2026, 16(3), 402; https://doi.org/10.3390/ani16030402 - 28 Jan 2026

Viewed by 376

Abstract

Feeding behavior in cattle affects feed efficiency, which is important for increasing the profitability of production while simultaneously reducing the environmental impact. Over a six-year period, indigenous beef cows (Afrikaner, Bonsmara, Nguni) were crossed with indigenous and exotic (Angus, Simmental) sires in a hot and arid area, to produce 15 breed groups. After weaning, the bull calves were fed in a feedlot setting wherein daily feed intake and partial body weight were measured. The serial correlations of daily feed intake and partial body weight on consecutive days were estimated for each animal. Analyses of variance for the z-transformed serial correlations of daily feed intake and partial body weight were conducted. The linear model included the fixed effect of test group comprising pen and date at the beginning of the test and a fixed breed group effect. The average serial correlation of daily feed intake (r = 0.10) was interpreted to suggest that a test period of 36 days was sufficient to achieve 80% average accuracy for the animals being tested. The average serial correlation of partial body weight was very high (r = 0.94). Thus, there seems little need to average values over days to achieve an accurate estimate of the weight of an animal at any specific point in time. Variation among animals in the serial correlation of daily feed intake indicates differences in feeding behavior over time, but this variability was not related to breed composition. The results indicate that a test period of 36 days is sufficient to achieve 80% accuracy of the mean for daily feed intake of the animals being tested. Full article

(This article belongs to the Special Issue Advancing Cattle Breeding and Production Through Genomics, Phenotyping, Bioinformatics, and Sustainable Practices)

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15 pages, 763 KB

Open AccessArticle

Verification of Accuracy of Genomically Enhanced Predicted Transmitting Ability Techniques in Predicting Milk and Fat Production in Holstein Cattle in Taiwan

by Chun-Hsuan Chao and Jen-Wen Shiau

Animals 2025, 15(22), 3334; https://doi.org/10.3390/ani15223334 - 19 Nov 2025

Viewed by 723

Abstract

This study evaluated the predictive performance of genomically enhanced predicted transmitting abilities for milk (gPTAM) and fat yield (gPTAF) in 986 first-lactation Holstein cows from 25 herds in Taiwan. Ordinary least squares and linear mixed models revealed significant positive associations between genomic predictions and observed yields (milk: β = 1.201, R² = 0.469; fat: β = 1.444, R² = 0.507). Incorporating herd and birth-year effects improved model fit and reduced residual variability. Five-fold cross-validation confirmed the robustness of the full mixed model, with predictive R² increasing to 0.293 for milk and 0.363 for fat, distinct from the OLS R² (0.469 and 0.507) representing phenotypic variation explained, indicating moderate predictive ability of genomic PTA values under subtropical production conditions. Model adequacy checks supported appropriate model specification, with only a mild outlier signal in the milk model. Regional analysis revealed a significant genotype-by-environment interaction for PTAF (p = 0.018) but not for PTAM, indicating reduced prediction accuracy in environmentally variable regions and highlighting trait-specific environmental sensitivity. Quartile stratification by gPTA and Net Merit Score demonstrated clear yield gradients, confirming both the predictive and economic value of genomic evaluations under subtropical dairy production systems. Full article

(This article belongs to the Special Issue Advancing Cattle Breeding and Production Through Genomics, Phenotyping, Bioinformatics, and Sustainable Practices)

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18 pages, 273 KB

Open AccessArticle

Assessment of Longevity and Lifetime Productivity of Local Cattle Breeds in Relation to International Breeds

by Wioletta Sawicka-Zugaj, Witold Chabuz, Joanna Barłowska, Sebastian Mucha and Andrzej Bochniak

Animals 2025, 15(22), 3312; https://doi.org/10.3390/ani15223312 - 17 Nov 2025

Viewed by 1276

Abstract

This study was based on data obtained from a total of 9518 cows belonging to seven cattle breeds: local breeds—Polish White-Backed (249), Polish Red (269), Polish Black-and-White (255), and Polish Red-and-White (290)—and international breeds—Polish Holstein-Friesian (5917), Jersey (940), and Simmental (1598). The breeds were characterised in terms of the following parameters: length of life, length of productive life, milking life, longevity index, percentage share of yield in the first 305-day lactation and first complete lactation in the lifetime yield, and functional longevity. Reasons for culling and the relationship between the length of life/productive life and milk performance parameters were determined as well. The analysis of the length of life and length of productive life in seven different cattle breeds kept in Poland unequivocally demonstrates that local breeds clearly stand out in this regard in comparison to international breeds. They may in the future constitute a valuable gene reservoir for improving longevity in other breeds. The length of life of Polish White-Backed and Polish Red cows was 2817 days and 3607 days, respectively, while that of Polish Holstein-Friesian and Jersey cows was only 2131 and 1956 days, respectively, and the most common cause of culling of cows in Poland (39.07%), irrespective of breed, was reproductive problems. The favourable results of parameters related to the longevity of the local breeds of Polish Red and White-Backed show that they can become a tool for improving the longevity of international breeds. Full article

(This article belongs to the Special Issue Advancing Cattle Breeding and Production Through Genomics, Phenotyping, Bioinformatics, and Sustainable Practices)

15 pages, 4511 KB

Open AccessArticle

Development of a 5K Liquid-Phase Genome-Wide Breeding Chip for Xinglong Buffalo

by Yuqing Jiao, Junming Jiang, Shiyuan Li, Taoyu Chen, Xinjun Qiu, Ke Cui, Boling Li, Si Chen, Qiaoling Chen, Li Du, Churiga Man, Lianbin Li, Fengyang Wang and Hongyan Gao

Animals 2025, 15(18), 2702; https://doi.org/10.3390/ani15182702 - 15 Sep 2025

Viewed by 917

Abstract

The Xinglong buffalo is a local swamp buffalo breed adapted to tropical regions in China. To facilitate the protection and utilization of valuable genetic resources, we first developed the breed-specific single nucleotide polymorphism (SNP) liquid-phase chip based on genotyping-by-target-sequencing (GBTS) technology. Whole-genome resequencing data from 143 buffaloes, resulting in 34,757,694 SNPs, were used to identify 1208 breed-specific and 2889 background sites. This chip also incorporates 965 functional SNP sites derived from literature, including SNPs significantly associated with immunity, reproduction, growth, and production. A total of 5062 SNP sites were successfully identified for the development of a 5K liquid-phase genome-wide breeding chip for the Xinglong buffalo. The validation of the chip using 93 samples showed a high detection rate with good repeatability and consistency. In addition, the chip exhibits strong capabilities in clustering and kinship analysis. Results of kinship analysis underscored the importance of a breed-specific chip for the Xinglong buffalo. These results highlight the advantages of a low-density, cost-effective, and breed-specific SNP chip for accurate genotyping. This chip will support future endeavors in molecular breeding, conservation, and genetic evaluation of Xinglong buffalo, thereby facilitating the sustainable utilization of this valuable indigenous germplasm resource. Full article

(This article belongs to the Special Issue Advancing Cattle Breeding and Production Through Genomics, Phenotyping, Bioinformatics, and Sustainable Practices)

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