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Animals
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The Impact of Genetic Parameters on Complex Traits of Livestock
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The Impact of Genetic Parameters on Complex Traits of Livestock

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 17587

Special Issue Editor

Dr. Esmaeil Ebrahimie

E-Mail Website
Guest Editor
Genomics Research Platform, School of Life Sciences, College of Science, Health and Engineering, La Trobe University, Melbourne, VIC, Australia
Interests: genome analysis; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Genetic architecture of complex traits such as feed efficiency, longevity and disease resistance in livestock consists of numerous loci. These traits are deemed complex because they are controlled by several genes and environmental factors.

Selection is an effective way to alter the genetic structure of animals. When selection has been carried out in a population, it is essential to investigate the changes of the average phenotypic and genotypic variations and breeding values for determining the efficiency of breeding programs.

In the breeder’s equation, genetic progress is made by enhancing genetic variance and the selection accuracy, which depends on genetic correlations. Powerful selection can result in changing the genetic parameters, and therefore the predicted genetic gain using previous parameters may not be understood. Intensity of selection and initial genetic variance can affect the number of changes of genetic parameters.

The estimation genetic parameter is crucial in obtaining indexes for an optimized response to genetic selection. Heritability, genetic diversity and genetic correlation, average breeding value, and the genome equivalent are the most important genetic parameters for quantitative traits and can be implemented to predict the selection response in breeding programs. Genetic covariance changes are more unpredictable compared to genetic variance changes. Estimation of genetic parameters during the time is complicated.

The methods of genetic parameter estimation have improved over the last 40 years. The usage of animal models accompanied by residual maximum likelihood (REML) methods for mixed models has revolutionized the genetic parameter estimation. Genomic selection was effectively initiated in 2014 and reduces generation intervals and enhances accuracy. Genomic prediction via single-nucleotide polymorphisms (SNPs) and phenotypes is an emerging field. Today, different methods such as the Bayesian method, GBLUP and ssBLUP are used for estimation of genetic parameters. Investigating the effect of genetic parameters of complex traits for breeding objectives will help in planning a total optimized index for accurate evaluation.

Machine learning is a scientific field that confers learning ability to machines without being highly programmed. Machine learning has provided high-performance computing and technologies of big data. It consists of a wide range of modeling tools and algorithms that are applied for different tasks in data processing. Estimating of farming parameters and accurate prediction of production traits in livestock is essential for optimizing the production economic efficiency. Therefore, one of the main aims of machine learning applications in livestock production is to accurately estimate economic balances according to production line monitoring. Additionally, machine learning tools and techniques are applied to investigate the behavior and characteristics of livestock, diagnosis of disease and prediction of genetic and phenotypic parameters.

Meta-analysis applied statistical methods for combining the outcomes of independent experimental research studies. Meta-analysis is used to survey the statistical significance of combined outcomes and investigate methods for combining estimates of different studies. During recent decades, a lot of genetic and phenotypic parameters have been estimated for different traits in various animals. A range of approaches are employed for estimating these parameters. Using meta-analysis can help to combine the results of these studies to reach a final conclusion.

Dr. Esmaeil Ebrahimie
Guest Editor

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Keywords

  • animal breeding
  • genetic parameters
  • heritability
  • genetic variation
  • genetic diversity
  • genetic correlation
  • breeding value
  • genetic covariance
  • machine learning
  • meta‐analysis

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Published Papers (6 papers)

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Research

21 pages, 388 KiB  
Article
Genetic Parameters for Functional Longevity, Type Traits, and Production in the Serbian Holstein
by Radica Djedović, Natascha Vukasinovic, Dragan Stanojević, Vladan Bogdanović, Hasan Ismael, Dobrila Janković, Nikolija Gligović, Muhamed Brka and Ljuba Štrbac
Animals 2023, 13(3), 534; https://doi.org/10.3390/ani13030534 - 2 Feb 2023
Cited by 6 | Viewed by 2331
Abstract
In this study, the authors focused on the evaluation of the genetic parameters of longevity, milk yield traits, and type traits in dairy cattle populations in the Republic of Serbia. The total dataset used consisted of production records and pedigree data for 32,512 [...] Read more.
In this study, the authors focused on the evaluation of the genetic parameters of longevity, milk yield traits, and type traits in dairy cattle populations in the Republic of Serbia. The total dataset used consisted of production records and pedigree data for 32,512 Holstein cows that calved from 1981 to 2015. The animal model was applied to determine the variance and covariance components and genetic parameters of the analyzed traits by applying the restricted maximum likelihood (REML) approach and using the programs VCE6 and PEST. The heritability of longevity traits was estimated using the Survival Kit V6.0 software package. Variance and covariance were estimated for five production traits: milk yield (MY), fat yield (FY), protein yield (PY), milk fat content (MC), and protein content (PC), and three longevity traits: length of productive life (LPL), lifetime milk yield (LMY), and the number of lactations achieved (NL) as well as for 18 standard type traits. Heritabilities for the milk, fat, and protein yield traits were 0.20 (MY), 0.15 (FY), and 0.19 (PY), respectively. The estimated coefficients of heritability for the longevity traits were higher when using the Weibull proportional hazards model compared to the traditional linear methods and ranged from 0.08 for NL to 0.10 for LPL. Heritability values for the type traits varied from a low of 0.10 (RLSsv—rear legs set–side view) to medium values of 0.32 (ST-stature). Genetic correlations were found between MY and the following longevity traits: LPL, LMY, and NL with values of −0.18, −0.11, and −0.09, respectively. Genetic correlations were found between MY and a number of linear type traits and varied from 0.02 (between MY and RUH-rear udder height) to 0.28 (between MY and FUA-fore udder attachment). Genetic correlations between the 18 investigated type traits ranged from −0.33 between TL (top line) and RTP (rear teats position) to 0.71 between AN (angularity) and RUH (rear udder height). Genetic correlations between most linear type traits and longevity traits (LPL, LMY, and NL) were generally negative and very low. The highest positive genetic correlation was found between UD and LPL (rxy = 0.38). Full article
(This article belongs to the Special Issue The Impact of Genetic Parameters on Complex Traits of Livestock)
11 pages, 1505 KiB  
Article
Sequence Variation in Caprine KRTAP6-2 Affects Cashmere Fiber Diameter
by Jian Cao, Jiqing Wang, Huitong Zhou, Jiang Hu, Xiu Liu, Shaobin Li, Yuzhu Luo and Jon G. H. Hickford
Animals 2022, 12(16), 2040; https://doi.org/10.3390/ani12162040 - 11 Aug 2022
Cited by 3 | Viewed by 1534
Abstract
Keratin-associated proteins (KAPs) are a structural component of cashmere fibers and in part determine fiber attributes. The gene encoding the high-glycine/tyrosine KAP6-2 (called KRTAP6-2) has been described in sheep, but it has not been identified goats. In this study, a 252-bp open [...] Read more.
Keratin-associated proteins (KAPs) are a structural component of cashmere fibers and in part determine fiber attributes. The gene encoding the high-glycine/tyrosine KAP6-2 (called KRTAP6-2) has been described in sheep, but it has not been identified goats. In this study, a 252-bp open reading frame with similarity to ovine KRTAP6-2 was found on goat chromosome 1, with its upstream and downstream flanking sequences are closely related with ovine KRTAP6-2 but are clearly distinct from other ovine KRTAP6-n sequences. Polymerase chain reaction amplification followed by single strand conformation polymorphism analysis of this region revealed five distinct banding patterns representing five different sequences (A to E) in 230 Longdong cashmere goats. Eleven diallelic single nucleotide polymorphisms (SNPs), a three-nucleotide sequence variation, and a 12-bp insertion/deletion were found among these five sequences, with most SNPs being either outside the coding region or synonymous. The presence of variant D was found to be associated with decreased mean fiber diameter (MFD; present: 13.26 ± 0.07 µm; absent: 13.55 ± 0.04 µm; p < 0.001), suggesting that variation in KRTAP6-2 may affect fiber diameter and have value as a molecular marker for improving the cashmere fiber diameter trait. Full article
(This article belongs to the Special Issue The Impact of Genetic Parameters on Complex Traits of Livestock)
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11 pages, 686 KiB  
Article
Systems Biology–Derived Genetic Signatures of Mastitis in Dairy Cattle: A New Avenue for Drug Repurposing
by Somayeh Sharifi, Maryam Lotfi Shahreza, Abbas Pakdel, James M. Reecy, Nasser Ghadiri, Hadi Atashi, Mahmood Motamedi and Esmaeil Ebrahimie
Animals 2022, 12(1), 29; https://doi.org/10.3390/ani12010029 - 23 Dec 2021
Cited by 5 | Viewed by 3458
Abstract
Mastitis, a disease with high incidence worldwide, is the most prevalent and costly disease in the dairy industry. Gram-negative bacteria such as Escherichia coli (E. coli) are assumed to be among the leading agents causing acute severe infection with clinical signs. [...] Read more.
Mastitis, a disease with high incidence worldwide, is the most prevalent and costly disease in the dairy industry. Gram-negative bacteria such as Escherichia coli (E. coli) are assumed to be among the leading agents causing acute severe infection with clinical signs. E. Coli, environmental mastitis pathogens, are the primary etiological agents of bovine mastitis in well-managed dairy farms. Response to E. Coli infection has a complex pattern affected by genetic and environmental parameters. On the other hand, the efficacy of antibiotics and/or anti-inflammatory treatment in E. coli mastitis is still a topic of scientific debate, and studies on the treatment of clinical cases show conflicting results. Unraveling the bio-signature of mastitis in dairy cattle can open new avenues for drug repurposing. In the current research, a novel, semi-supervised heterogeneous label propagation algorithm named Heter-LP, which applies both local and global network features for data integration, was used to potentially identify novel therapeutic avenues for the treatment of E. coli mastitis. Online data repositories relevant to known diseases, drugs, and gene targets, along with other specialized biological information for E. coli mastitis, including critical genes with robust bio-signatures, drugs, and related disorders, were used as input data for analysis with the Heter-LP algorithm. Our research identified novel drugs such as Glibenclamide, Ipratropium, Salbutamol, and Carbidopa as possible therapeutics that could be used against E. coli mastitis. Predicted relationships can be used by pharmaceutical scientists or veterinarians to find commercially efficacious medicines or a combination of two or more active compounds to treat this infectious disease. Full article
(This article belongs to the Special Issue The Impact of Genetic Parameters on Complex Traits of Livestock)
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8 pages, 1221 KiB  
Article
Ovine Toll-like Receptor 9 (TLR9) Gene Variation and Its Association with Flystrike Susceptibility
by Xiu Liu, Huitong Zhou, Hua Gong, Wenting Liu, Qian Fang, Yuzhu Luo, Jiqing Wang, Shaobin Li, Jiang Hu and Jonathan G. H. Hickford
Animals 2021, 11(12), 3549; https://doi.org/10.3390/ani11123549 - 14 Dec 2021
Cited by 1 | Viewed by 2340
Abstract
Toll-like receptors (TLRs) are a family of proteins that play a role in innate immune responses by recognising pathogen-associated molecular patterns derived from various microbes. Of these receptors, TLR9 recognises bacterial and viral DNA containing unmethylated cytosine-phosphate-guanine (CpG) motifs, and variation in TLR9 [...] Read more.
Toll-like receptors (TLRs) are a family of proteins that play a role in innate immune responses by recognising pathogen-associated molecular patterns derived from various microbes. Of these receptors, TLR9 recognises bacterial and viral DNA containing unmethylated cytosine-phosphate-guanine (CpG) motifs, and variation in TLR9 has been associated with resistance to various infectious diseases. Flystrike is a problem affecting the sheep industry globally and the immune response of the sheep has been suggested as one factor that influences the response to the disease. In this study, variation in ovine TLR9 from 178 sheep with flystrike and 134 sheep without flystrike was investigated using a polymerase chain reaction-single strand conformation polymorphism (PCR-SSCP) approach. These sheep were collected from both commercial and stud farms throughout New Zealand and they were of 13 different breeds, cross-breds and composites. Four alleles of TLR9 were detected, including three previously identified alleles (*01, *02 and *03) and a new allele (*04). In total six single nucleotide polymorphisms (SNPs) were found. Of the three common alleles in the sheep studied, the presence of *03 was found to be associated with a reduced likelihood of flystrike being present (OR = 0.499, p = 0.024). This suggests that variation in ovine TLR9 may affect a sheep’s response to flystrike, and thus the gene may have value as a genetic marker for improving resistance to the disease. Full article
(This article belongs to the Special Issue The Impact of Genetic Parameters on Complex Traits of Livestock)
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9 pages, 906 KiB  
Article
Sequence Variation in the Bovine Lipin-1 Gene (LPIN1) and Its Association with Milk Fat and Protein Contents in New Zealand Holstein-Friesian × Jersey (HF × J)-cross Dairy Cows
by Xiaohua Du, Huitong Zhou, Xia Liu, Yunhai Li and Jonathan G. H. Hickford
Animals 2021, 11(11), 3223; https://doi.org/10.3390/ani11113223 - 11 Nov 2021
Cited by 5 | Viewed by 1937
Abstract
Lipin-1 is known to play a regulatory role in tissues that function in lipid metabolism. In dairy cows, the lipin-1 gene (LPIN1) is highly expressed in the mammary gland, but its function in milk production is less understood. In this study, [...] Read more.
Lipin-1 is known to play a regulatory role in tissues that function in lipid metabolism. In dairy cows, the lipin-1 gene (LPIN1) is highly expressed in the mammary gland, but its function in milk production is less understood. In this study, we used PCR-single strand conformation polymorphism analysis to investigate sequence variation in three regions of bovine LPIN1 in New Zealand Holstein-Friesian × Jersey (HF × J)-cross dairy cows, including part of the 5′ non-coding region, the region containing the LPIN1β-spliced exon, and the sixth coding exon that encodes the putative transcriptional activating domain of the protein. No variation was found in the LPIN1β-spliced exon, but two sequence variants containing one single nucleotide polymorphism (SNP) were identified in the 5′ non-coding region and four sequence variants containing four non-synonymous SNPs were identified in the sixth coding exon. Among the three common variants of the sixth coding exon, variant C was found to be associated with an increase in milk fat percentage (presence 4.96 ± 0.034% vs. absence 4.81 ± 0.050%; p = 0.006) and milk protein percentage (presence 4.09 ± 0.017% vs. absence 3.99 ± 0.025%; p = 0.001), but no associations (p > 0.01) were detected for milk yield. These results suggest that variation in LPIN1 affect the synthesis of fat and proteins in milk and has potential as a gene-marker to improve milk production traits. Full article
(This article belongs to the Special Issue The Impact of Genetic Parameters on Complex Traits of Livestock)
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11 pages, 1389 KiB  
Article
Exploration of Genetic Variants within the Goat A-Kinase Anchoring Protein 12 (AKAP12) Gene and Their Effects on Growth Traits
by Yangyang Bai, Rongrong Yuan, Yunyun Luo, Zihong Kang, Haijing Zhu, Lei Qu, Xianyong Lan and Xiaoyue Song
Animals 2021, 11(7), 2090; https://doi.org/10.3390/ani11072090 - 14 Jul 2021
Cited by 8 | Viewed by 3236
Abstract
The A-kinase anchoring protein 12 gene (AKAP12) is a scaffold protein, which can target multiple signal transduction effectors, can promote mitosis and cytokinesis and plays an important role in the regulation of growth and development. In our previous study, P1–7 bp [...] Read more.
The A-kinase anchoring protein 12 gene (AKAP12) is a scaffold protein, which can target multiple signal transduction effectors, can promote mitosis and cytokinesis and plays an important role in the regulation of growth and development. In our previous study, P1–7 bp (intron 3) and P2–13 bp (3′UTR) indels within the AKAP12 gene significantly influenced AKAP12 gene expression. Therefore, this study aimed to identify the association between these two genetic variations and growth-related traits in Shaanbei white cashmere goats (SBWC) (n = 1405). Herein, we identified two non-linkage insertions/deletions (indels). Notably, we found that the P1–7 bp indel mutation was related to the height at hip cross (HHC; p < 0.05) and the P2–13 bp indel was associated with body weight, body length, chest depth, chest width, hip width, chest circumference and cannon (bone) circumference in SBWC goats (p < 0.05). Overall, the two indels’ mutations of AKAP12 affected growth traits in goats. Compared to the P1–7 bp indel, the P2–13 bp indel is more suitable for the breeding of goat growth traits. Full article
(This article belongs to the Special Issue The Impact of Genetic Parameters on Complex Traits of Livestock)
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