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Quantitative Genetics of Livestock Populations

A special issue of Animals (ISSN 2076-2615).

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 3199

Editors


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Guest Editor
Institute of Animal Sciences, Institute of Computer Systems and Data Science, Latvia University of Life Sciences and Technologies (LBTU), Liela Str. 2, LV-3001 Jelgava, Latvia
Interests: multivariate statistics; linear models; estimation of genetic parameters; animal sciences
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Guest Editor
Department of Animal Breeding and Reproduction, Animal Science Institute, Lithuanian University of Health Sciences, R. Žebenkos 12, LT-82317 Baisogala, Lithuania
Interests: farm animals; breeding; small populations; inbreeding; genetics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Quantitative genetic methods investigate the effect of genetic and environmental factors simultaneously, and help improve the efficiency and sustainability of livestock breeding by focusing on traits that are influenced by multiple genes. The aims are to increase the frequency of favourable alleles in the population, which is achieved through animal selection.

Statistical models and breeding values help predict the genetic potential of an animal. By understanding the genetic correlation between different traits, breeders can optimise their selection strategies. Genetic diversity is vital to prevent inbreeding in the population and to a successful breeding plan to obtain healthy and productive animals in the herd or population.

The purpose of this Special Issue is to provide a premier platform for publishing high-quality, peer-reviewed articles that advance the field of quantitative genetics in livestock populations, improving our understanding of quantitative genetics, and implementing scientific findings in practice. We believe that your work, with its novel insights and significant contributions to the field, would be a valuable addition to our publication.

We welcome original research papers that address various aspects of quantitative genetics, including, but not limited to, the following:

  • Quantitative genetic approaches to improve productivity and fertility in livestock;
  • The estimation of genetic parameters and the prediction of animal breeding values using different tools and information;
  • Genetic diversity and inbreeding in animal populations;
  • Impact of crossbreeding on quantitative traits in animal populations;
  • Design of breeding programmes and key issues in breeding programme design.

You may choose our Joint Special Issue in Agriculture.

Prof. Dr. Liga Paura
Dr. Rūta Šveistiene
Guest Editors

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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-anonymized 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

  • heritability
  • variance components
  • genetic correlation
  • breeding value estimation
  • pure breeding and crossbreeding
  • inbreeding
  • selection response
  • animal breeding programmes

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Published Papers (2 papers)

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Research

36 pages, 2189 KB  
Article
SNPs with High Linkage Disequilibrium Increase the Explained Genetic Variance and the Reliability of Genomic Predictions
by José Guadalupe Cortes-Hernández, Felipe de Jesús Ruiz-López, Francisco Peñagaricano, Hugo H. Montaldo and Adriana García-Ruiz
Animals 2026, 16(2), 337; https://doi.org/10.3390/ani16020337 - 22 Jan 2026
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Abstract
The objective of this study was to compare the proportion of explained genetic variance (EXGV) and the reliability of genomic breeding values (GBVs) predictions for milk yield (MY), fat yield (FY), protein yield (PY) fat percentage (FP), protein percentage (PP), and somatic cell [...] Read more.
The objective of this study was to compare the proportion of explained genetic variance (EXGV) and the reliability of genomic breeding values (GBVs) predictions for milk yield (MY), fat yield (FY), protein yield (PY) fat percentage (FP), protein percentage (PP), and somatic cell score (SCS) in Holstein cattle. Three types of genomic information were evaluated. (a) SNP-ALL: this analysis included 88,911 single nucleotide polymorphisms (SNP) from 8290 animals. (b) HAP-PSEUDOSNP: haplotypes, defined based on high linkage disequilibrium (LD, r2 ≥ 0.80) between SNPs, which were encoded as pseudo-SNPs, with a total of 35,552 pseudo-SNPs and 8331 animals included. (c) SNP-HAP: analysis using only individual SNPs included in the haplotypes (without recoding); for this analysis, 33,010 SNPs and 8192 individuals were retained. All analyses were conducted using the single-step genome-wide association study method implemented in the BLUPF90 software package. The results showed that the inclusion of SNPs with high LD (SNP-HAP) increases the reliability of GBVs’ predictions compared to the SNP-ALL analysis; average reliability increased between 0.05 and 0.11. Moreover, the SNP-HAP analysis resulted in a twofold increase in the EXGV for all traits, likely due to increased estimates of individual marker effects compared to the SNP-ALL analysis. Full article
(This article belongs to the Special Issue Quantitative Genetics of Livestock Populations)
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15 pages, 860 KB  
Article
Genomic Analysis of Latvian Brown Old Type and Latvian Blue Local Dairy Cattle Breeds Using SNP Data
by Daina Jonkus, Lasma Cielava, Didzis Dreimanis, Viktorija Nikonova and Liga Paura
Animals 2026, 16(1), 20; https://doi.org/10.3390/ani16010020 - 20 Dec 2025
Viewed by 967
Abstract
Conservation programmes for two local dairy cattle breeds—Latvian Brown old type (BV) and Latvian Blue (LZ)—commenced in 2004. The aim of this study was to evaluate genetic diversity in the BV and LZ local cattle populations using SNP data. This study was based [...] Read more.
Conservation programmes for two local dairy cattle breeds—Latvian Brown old type (BV) and Latvian Blue (LZ)—commenced in 2004. The aim of this study was to evaluate genetic diversity in the BV and LZ local cattle populations using SNP data. This study was based on genotype data from 96 BV and 75 LZ cows and 20 BV and 18 LZ bulls. The SNPs were determined using the GGP 100K bovine SNP BeadChip. Quality control (QC) and genotype data analysis were performed using PLINK v1.9. The observed heterozygosity was moderate, at around 0.4, for both breeds. Inbreeding coefficients were estimated based on homozygosity runs (FROH) to compare recent and ancient inbreeding in the BV and LZ populations. Therefore, the ROH segments were divided into segments with the four classes (1–4 Mb, 4–8 Mb, 8–16 Mb, and above 16 Mb). Shorter ROH regions (ROH < 4 Mb) predominated in the genome. ROH regions with lengths above 16 Mb covers 4–6% of the genome in BV and 11% in LZ population. The average inbreeding coefficient for approximately three generations (FROH>16) was 2.30% and 4.87% for BV and LZ cows (p < 0.05), respectively, and 2.59% and 3.85% for BV and LZ bulls, respectively. This study demonstrates that inbreeding has increased from generation to generation (FROH>16 is higher compared with FROH<16) in both populations. The level of current inbreeding in LZ is higher compared with that in the BV breed. The overall level of inbreeding in the BV and LZ populations is low, but there is a high level of inbreeding among a few animals. The impact of inbreeding on cow productivity has been observed in the LZ and BV cow populations. As a result, breeding organisations need to monitor and control the level of inbreeding and prevent the loss of genetic diversity in these animal populations. Breeders should minimize mating among close relatives; introduce genetically unrelated animals, use pedigree, and genomic information in controlling rates of inbreeding. Full article
(This article belongs to the Special Issue Quantitative Genetics of Livestock Populations)
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