Special Issue "Farm Animal Gene Exploration"

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

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editor

Prof. Jun-Mo Kim
E-Mail Website
Guest Editor
Animal Functional Genomics & Bioinformatics Lab., Department of Animal Science and Technology, Chung-Ang University, Anseong, Gyeonggi-do, Korea
Interests: quantitative genetics; genomics; system biology

Special Issue Information

Dear Colleagues,

To assign the function of genes before the genomic era, the target genes were individually explored through molecular biological techniques and populational genetics. Moreover, the quantitative trait loci (QTLs) identification regarding economic traits using the selective useful markers was one of the most important jobs for breeding and genetic achievements in livestock. After the development of DNA chips whole genomes of species, it has been more effective and feasible to find major candidate genes or loci using the genome-wide association study, even some traits have very low heritabilities. More recently, after the development of next-generation sequencing (NGS) technology, studies have not only focused on genomes, but also transcriptomes to identify the functional genes for target traits including the conventional economic traits and the brand new issues like disease resistance and immune response. This Special Issue “Farm Animal Genes” invites your manuscripts that discuss the function of genes in any kind of livestock animals using the recent advanced molecular genetic technologies, and/or using the whole genomic and transcriptomic NGS data. We also welcome submissions of prospective novel approaches to identify functional genes.

Prof. Jun-Mo Kim
Guest Editor

Manuscript Submission Information

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Keywords

  • pig
  • cattle
  • chicken
  • livestock
  • gene
  • function
  • GWAS
  • QTL
  • genome
  • transcriptome

Published Papers (8 papers)

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Research

Open AccessArticle
Proteomic Analysis Identifies Potential Markers for Chicken Primary Follicle Development
Animals 2021, 11(4), 1108; https://doi.org/10.3390/ani11041108 - 13 Apr 2021
Viewed by 220
Abstract
Follicles’ development in chicken imparts a major impact on egg production. To enhance the egg-laying efficiency, comprehensive knowledge of different phases of follicular development is a prerequisite. Therefore, we used the tandem mass tag (TMT) based proteomic approach to find the genes involved [...] Read more.
Follicles’ development in chicken imparts a major impact on egg production. To enhance the egg-laying efficiency, comprehensive knowledge of different phases of follicular development is a prerequisite. Therefore, we used the tandem mass tag (TMT) based proteomic approach to find the genes involved in the primary follicular development of chicken. The primary follicles were divided into two groups—small primary follicles (81–150 μm) and developed primary follicles (300–500 μm). Differential expression analysis (fold change > 1.2, p-value < 0.05) revealed a total of 70 differentially expressed proteins (DEPs), of which 38 were upregulated and 32 were downregulated. Gene ontology (GO) enrichment analysis disclosed that DEPs were intricate with cellular protein localization, the establishment of protein localization, and nucleoside phosphate-binding activities. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway indicated the involvement of DEPs in different metabolic pathways such as glycolysis, pyruvate metabolism, galactose metabolism, and fructose and mannose metabolism. The current proteomic analysis suggested suitable markers such as Anxa2, Pdia3, and Capzb, which may serve as a potential role for primary follicle development. The present study provides the first insight into the proteome dynamics of primary follicle development and would play a potential role for further studies in chicken to improve egg productivity. Full article
(This article belongs to the Special Issue Farm Animal Gene Exploration)
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Open AccessArticle
GWAS-Based Identification of New Loci for Milk Yield, Fat, and Protein in Holstein Cattle
Animals 2020, 10(11), 2048; https://doi.org/10.3390/ani10112048 - 05 Nov 2020
Cited by 1 | Viewed by 561
Abstract
High-yield and high-quality of milk are the primary goals of dairy production. Understanding the genetic architecture underlying these milk-related traits is beneficial so that genetic variants can be targeted toward the genetic improvement. In this study, we measured five milk production and quality [...] Read more.
High-yield and high-quality of milk are the primary goals of dairy production. Understanding the genetic architecture underlying these milk-related traits is beneficial so that genetic variants can be targeted toward the genetic improvement. In this study, we measured five milk production and quality traits in Holstein cattle population from China. These traits included milk yield, fat, and protein. We used the estimated breeding values as dependent variables to conduct the genome-wide association studies (GWAS). Breeding values were estimated through pedigree relationships by using a linear mixed model. Genotyping was carried out on the individuals with phenotypes by using the Illumina BovineSNP150 BeadChip. The association analyses were conducted by using the fixed and random model Circulating Probability Unification (FarmCPU) method. A total of ten single-nucleotide polymorphisms (SNPs) were detected above the genome-wide significant threshold (p < 4.0 × 10−7), including six located in previously reported quantitative traits locus (QTL) regions. We found eight candidate genes within distances of 120 kb upstream or downstream to the associated SNPs. The study not only identified the effect of DGAT1 gene on milk fat and protein, but also discovered novel genetic loci and candidate genes related to milk traits. These novel genetic loci would be an important basis for molecular breeding in dairy cattle. Full article
(This article belongs to the Special Issue Farm Animal Gene Exploration)
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Open AccessArticle
Ovarian Transcriptomic Analysis Reveals Differential Expression Genes Associated with Cell Death Process after Selection for Ovulation Rate in Rabbits
Animals 2020, 10(10), 1924; https://doi.org/10.3390/ani10101924 - 20 Oct 2020
Viewed by 459
Abstract
Litter size is an essential trait in rabbit meat production but with low heritability. A selection experiment for ovulation rate has been performed for 10 generations to improve litter size in rabbits. The selected line increased two ova more than the control line [...] Read more.
Litter size is an essential trait in rabbit meat production but with low heritability. A selection experiment for ovulation rate has been performed for 10 generations to improve litter size in rabbits. The selected line increased two ova more than the control line but nevertheless a negative correlation was observed with prenatal survival. A transcriptomic study was performed, using microarrays, in ovarian tissue from females belonging to the selected line and the control line. Our results showed 1357 differential expressed genes and nineteen potential biomarkers associated with prenatal mortality, which could explain differences between litter size in rabbits. Cell death was the most relevant process. Full article
(This article belongs to the Special Issue Farm Animal Gene Exploration)
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Open AccessArticle
Using High-Density SNP Array to Reveal Selection Signatures Related to Prolificacy in Chinese and Kazakhstan Sheep Breeds
Animals 2020, 10(9), 1633; https://doi.org/10.3390/ani10091633 - 11 Sep 2020
Viewed by 534
Abstract
Selection signature provides an efficient tool to explore genes related to traits of interest. In this study, 176 ewes from one Chinese uniparous breed and three Kazakhstan multiparous breeds are genotyped using Affymetrix 600K HD single nucleotide polymorphism (SNP) arrays, F-statistics (Fst), and [...] Read more.
Selection signature provides an efficient tool to explore genes related to traits of interest. In this study, 176 ewes from one Chinese uniparous breed and three Kazakhstan multiparous breeds are genotyped using Affymetrix 600K HD single nucleotide polymorphism (SNP) arrays, F-statistics (Fst), and a Cross Population Extend Haplotype Homozygosity Test (XPEHH). These are conducted to identify genomic regions that might be under selection in three population pairs comprised the one multiparous breed and the uniparous breed. A total of 177 and 3072 common selective signatures were identified by Fst and XPEHH test, respectively. Nearly half of the common signatures detected by Fst were also captured by XPEHH test. In addition, 1337 positive and 1735 common negative signatures were observed by XPEHH in three Kazakhstan multiparous breeds. In total, 242 and 798 genes were identified in selective regions and positive selective regions identified by Fst and XPEHH, respectively. These genes were further clustered in 50 gene ontology (GO) functional terms and 66 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in enrichment analysis. The GO terms and pathways were relevant with reproductive processes, e.g., oxytocin signaling pathway, thyroid hormone synthesis and GnRH signaling pathway, vascular smooth muscle contraction and lipid metabolism (alpha-Linolenic acid metabolism and Linoleic acid metabolism), etc. Based on the findings, six potential candidate genes ESR1, OXTR, MAPK1, RYR1, PDIA4, and CYP19A1, under positive selection related to characteristics of multiparous sheep breeds were revealed. Our results improve our understanding of the mechanisms of selection that underlies the prolificacy trait in sheep, and provide essential references for future sheep breeding. Full article
(This article belongs to the Special Issue Farm Animal Gene Exploration)
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Open AccessArticle
Genomic Analysis Using Bayesian Methods under Different Genotyping Platforms in Korean Duroc Pigs
Animals 2020, 10(5), 752; https://doi.org/10.3390/ani10050752 - 25 Apr 2020
Cited by 2 | Viewed by 686
Abstract
Genomic evaluation has been widely applied to several species using commercial single nucleotide polymorphism (SNP) genotyping platforms. This study investigated the informative genomic regions and the efficiency of genomic prediction by using two Bayesian approaches (BayesB and BayesC) under two moderate-density SNP genotyping [...] Read more.
Genomic evaluation has been widely applied to several species using commercial single nucleotide polymorphism (SNP) genotyping platforms. This study investigated the informative genomic regions and the efficiency of genomic prediction by using two Bayesian approaches (BayesB and BayesC) under two moderate-density SNP genotyping panels in Korean Duroc pigs. Growth and production records of 1026 individuals were genotyped using two medium-density, SNP genotyping platforms: Illumina60K and GeneSeek80K. These platforms consisted of 61,565 and 68,528 SNP markers, respectively. The deregressed estimated breeding values (DEBVs) derived from estimated breeding values (EBVs) and their reliabilities were taken as response variables. Two Bayesian approaches were implemented to perform the genome-wide association study (GWAS) and genomic prediction. Multiple significant regions for days to 90 kg (DAYS), lean muscle area (LMA), and lean percent (PCL) were detected. The most significant SNP marker, located near the MC4R gene, was detected using GeneSeek80K. Accuracy of genomic predictions was higher using the GeneSeek80K SNP panel for DAYS (Δ2%) and LMA (Δ2–3%) with two response variables, with no gains in accuracy by the Bayesian approaches in four growth and production-related traits. Genomic prediction is best derived from DEBVs including parental information as a response variable between two DEBVs regardless of the genotyping platform and the Bayesian method for genomic prediction accuracy in Korean Duroc pig breeding. Full article
(This article belongs to the Special Issue Farm Animal Gene Exploration)
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Open AccessArticle
Copy Number Variation of the PIGY Gene in Sheep and Its Association Analysis with Growth Traits
Animals 2020, 10(4), 688; https://doi.org/10.3390/ani10040688 - 15 Apr 2020
Cited by 3 | Viewed by 688
Abstract
Copy number variation (CNV) is a type of genomic variation with an important effect on animal phenotype. We found that the PIGY gene contains a 3600 bp copy number variation (CNV) region located in chromosome 6 of sheep (Oar_v4.0 36,121,601–36,125,200 bp). This region [...] Read more.
Copy number variation (CNV) is a type of genomic variation with an important effect on animal phenotype. We found that the PIGY gene contains a 3600 bp copy number variation (CNV) region located in chromosome 6 of sheep (Oar_v4.0 36,121,601–36,125,200 bp). This region overlaps with multiple quantitative trait loci related to phenotypes like muscle density and carcass weight. Therefore, in this study, the copy number variation of the PIGY gene was screened in three Chinese sheep breeds, namely, Chaka sheep (CKS, May of 2018, Wulan County, Qinghai Province, China), Hu sheep (HS, May of 2015, Mengjin County, Henan Province, China), and small-tailed Han sheep (STHS, May of 2016, Yongjing, Gansu Province, China). Association analyses were performed on the presence of CNV and sheep body size traits. We used real-time quantitative PCR (qPCR) to detect the CNV for association analysis. According to the results, the loss-type CNV was more common than other types in the three breeds (global average: loss = 61.5%, normal = 17.5%, and gain = 21.0%). The association analysis also showed significant effects of the PIGY gene CNV on body weight, chest circumference, and circumference of the cannon bone of sheep. Sheep with gain-type CNV had better growth traits than those with other types. The results indicate a clear relationship between the PIGY gene CNV and growth traits of sheep, suggesting the use of CNV as a new molecular breeding marker. Full article
(This article belongs to the Special Issue Farm Animal Gene Exploration)
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Open AccessArticle
A Genome-Wide Association Study for Calving Interval in Holstein Dairy Cows Using Weighted Single-Step Genomic BLUP Approach
Animals 2020, 10(3), 500; https://doi.org/10.3390/ani10030500 - 17 Mar 2020
Viewed by 997
Abstract
The aim of the present study was to identify genomic region(s) associated with the length of the calving interval in primiparous (n = 6866) and multiparous (n = 5071) Holstein cows. The single nucleotide polymorphism (SNP) solutions were estimated using a weighted single-step [...] Read more.
The aim of the present study was to identify genomic region(s) associated with the length of the calving interval in primiparous (n = 6866) and multiparous (n = 5071) Holstein cows. The single nucleotide polymorphism (SNP) solutions were estimated using a weighted single-step genomic best linear unbiased prediction (WssGBLUP) approach and imputed high-density panel (777 k) genotypes. The effects of markers and the genomic estimated breeding values (GEBV) of the animals were obtained by five iterations of WssGBLUP. The results showed that the accuracies of GEBVs with WssGBLUP improved by +5.4 to +5.7, (primiparous cows) and +9.4 to +9.7 (multiparous cows) percent points over accuracies from the pedigree-based BLUP. The most accurate genomic evaluation was provided at the second iteration of WssGBLUP, which was used to identify associated genomic regions using a windows-based GWAS procedure. The proportion of additive genetic variance explained by windows of 50 consecutive SNPs (with an average of 165 Kb) was calculated and the region(s) that accounted for equal to or more than 0.20% of the total additive genetic variance were used to search for candidate genes. Three windows of 50 consecutive SNPs (BTA3, BTA6, and BTA7) were identified to be associated with the length of the calving interval in primi- and multiparous cows, while the window with the highest percentage of explained genetic variance was located on BTA3 position 49.42 to 49.52 Mb. There were five genes including ARHGAP29, SEC24D, METTL14, SLC36A2, and SLC36A3 inside the windows associated with the length of the calving interval. The biological process terms including alanine transport, L-alanine transport, proline transport, and glycine transport were identified as the most important terms enriched by the genes inside the identified windows. Full article
(This article belongs to the Special Issue Farm Animal Gene Exploration)
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Open AccessArticle
Reconstruction and Analysis of Cattle Metabolic Networks in Normal and Acidosis Rumen Tissue
Animals 2020, 10(3), 469; https://doi.org/10.3390/ani10030469 - 11 Mar 2020
Cited by 1 | Viewed by 926
Abstract
The objective of this study was to develop a system-level understanding of acidosis biology. Therefore, the genes expression differences between the normal and acidosis rumen epithelial tissues were first examined using the RNA-seq data in order to understand the molecular mechanisms involved in [...] Read more.
The objective of this study was to develop a system-level understanding of acidosis biology. Therefore, the genes expression differences between the normal and acidosis rumen epithelial tissues were first examined using the RNA-seq data in order to understand the molecular mechanisms involved in the disease and then their corresponding metabolic networks constructed. A number of 1074 genes, 978 isoforms, 1049 transcription start sites (TSS), 998 coding DNA sequence (CDS) and 2 promoters were identified being differentially expressed in the rumen tissue between the normal and acidosis samples (p < 0.05). The functional analysis of 627 up-regulated genes revealed their involvement in ion transmembrane transport, filament organization, regulation of cell adhesion, regulation of the actin cytoskeleton, ATP binding, glucose transmembrane transporter activity, carbohydrate binding, growth factor binding and cAMP metabolic process. Additionally, 111 differentially expressed enzymes were identified between the rumen epithelial tissue of the normal and acidosis steers with 46 up-regulated and 65 down-regulated ones in the acidosis group. The pathways and reactions analyses associated with the up-regulated enzymes indicate that most of these enzymes are involved in the fatty acid metabolism, biosynthesis of amino acids, pyruvate and carbon metabolism while most of the down-regulated ones are involved in purine and pyrimidine, vitamin B6 and antibiotics metabolisms. The degree distribution of both metabolic networks follows a power-law one, hence displaying a scale-free property. The top 15 hub metabolites were determined in the acidosis metabolic network with most of them involved in the fatty acid oxidation, VFA biosynthesis, amino acid biogenesis and glutathione metabolism which plays an important role in the stress condition. The limitations of this study were low number of animals and using only epithelial tissue (ventral sac) for RNA-seq. Full article
(This article belongs to the Special Issue Farm Animal Gene Exploration)
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