The objective of this study was to explore the genetic structure of Belgian and Qinchuan cattle, with a particular emphasis on identifying genes associated with reproductive functions.A total of 270 Belgian and 286 Qinchuan cattle were genotyped using the Illumina Bovine SNP 50K microarray. Data processing was conducted using PLINK and Beagle 5.1 to calculate linkage disequilibrium (LD) and effective population size (Ne).Candidate SNP loci were identified by selecting the top 5% based on the fixation index (Fst) and nucleotide diversity (PI), followed by gene annotation.The analysis revealed 160 candidate genes in Qinchuan cattle and 98 candidate genes in Belgian Red and Belgian Red and White cattle. Key genes associated with reproductive function, including NFKBIA, PTHLH, UGT2B10, TRPC4, and ALOX5AP, were identified.This study provides valuable molecular markers for the genetic improvement and selective breeding of Belgian cattle, particularly in enhancing their reproductive efficiency.
Article
Genetic analysis and characterization of reproductive control genes in Belgian and Qinchuan cattle population structure
https://doi.org/10.21203/rs.3.rs-4597076/v1
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Linkage disequilibrium (LD)
Effective population size (Ne)
Fixation index (Fst)
Nucleotide diversity
Enrichment analysis
Belgian Red Bulls are considered a valuable Belgian regional b...reed of cattle, however their reproductive efficiency has led to this population currently facing extinction [1].Reproductive traits are critical for the sustainability and profitability of cattle breeding programs, and enhancing these traits through genetic improvement is imperative [2].Given the current status of Belgian Red Bulls, issues such as genetic bottlenecks and inbreeding have led to breeding challenges [3].Qinchuan cattle are an excellent local breed in China, known for their high disease resistance and adaptability [4].
Currently Belgian red cattle are endangered due to low reproductive efficiency, and comparative study of Qinchuan and Belgian cattle can help to provide an effective strategy to improve the reproductive performance of Belgian cattle, so this study investigates the genetic structure of Belgian red cattle and identifies key genes related to reproductive traits, which will provide a scientific basis for the development of a targeted breeding strategy.
Linkage disequilibrium (LD) improves the accuracy of genomic association analysis and prediction of marker regions[5]. LD decay patterns also provide information about the evolutionary history of populations and can be used to estimate ancestral effective population size (Ne)[6]. Ne and other genetic events also influence the extent of LD in a population[7]. Ne and other genetic events also influence the extent of LD in a population. Thus, LD helps to understand the patterns of selection experienced by individual breeds.The Fst statistic is a statistical measure of the degree of genetic variation between populations. It is commonly used to assess the degree of genetic structure and differentiation between populations. the calculation of Fst is usually based on differences in gene frequencies, which can be estimated by a variety of statistical methods, such as AMOVA (analysis of molecular variance) or calculations based on SNP data. pi (nucleotide diversity) is a measure of the degree of variability in nucleotide sequences in the genome, or within a specific region. It reflects the level of genetic diversity within a population or species.Zhang CL et al[8]. Using sheep Illumina Ovine SNP50 chip data to calculate chain disequilibrium r2 for 73 sheep in Hetian area to infer the effective population size of sheep, we found that LD was highly specific to sheep breeds, and that Ne values were similar to those of other sheep breeds at certain time points of decline.Wang S et al[9]. utilized the whole genome sequencing data of Qinchuan cattle to investigate their selection signals. Fst-based selection in Qinchuan, Red Angus and Japanese Black cattle detected genes related to meat quality: ARIH2, DACT1 and DNM2.
Test animals
In this experiment, 286 Qinchuan cattle and 270 Belgian Blue cattle were analyzed, and their genotype data were obtained from NCBI's GEO dataset (PRJNA390539) and figshare (https://figshare.com/) (10.6084/m9.figshare.17025086), respectively.
SNP chip quality control and imputing
We extracted the GEO dataset Forward strand converted to a format recognizable by the PLINK software, and then merged the files and performed quality control of the SNP data using the PLINK (version number 1.90) software. The following filters were applied to the data of samples and loci: (1) loci with SNP detection rate less than 90% were excluded (-geno 0.1); (2) individual samples with sample detection rate less than 90% were excluded (-mind 0.1); and (3) minimum allele frequency greater than 5% (-maf 0.05). Beagle 5.1 (https://faculty.washington.edu/browning/beagle) was used to populate the genome with fixed phases.
LD calculation method
Studying linkage disequilibrium (LD) decline can reveal the history of population recombination, and by analyzing LD between loci, it can help understand LD levels in the genomes of three beef cattle populations. In this study, we used PopLDdecay (V3.41) software to[10] to calculate the LD of SNP loci on the genome, the value of LD ranges from 0 to 1, and the degree of chaining increases as the value of r2 increases. r2 calculation process[11] Finally, perl scripts were used to visualize the results and plot the distribution of Mean_r2 in different groups.
Estimation of effective group size
Effective population size (Ne) is a key population genetics parameter that measures the rate of genetic drift and inbreeding, and influences the efficiency of systematic evolutionary forces (e.g., mutation, selection, and migration). We used the SMC++ method[12] to estimate Ne. SMC++ predicts the population size history and timing of splits. This method employs a new spline regularization scheme that greatly reduces estimation error. We used the vcf2smc script distributed with SMC++ to convert each VCF file into an input file in SMC++ format. All simulations were performed under initial conditions with a mutation rate of 1.26e-8[13] .
Diversity of population structure
Principal component analysis(PCA) based on variance normalized relationship matrix was performed on the data after quality control using Plink (V1.90) software, NJ matrix was calculated using VCF2Dis (https://github.com/BGI-shenzhen/VCF2Dis) and evolutionary trees were generated using FastME (http://www.atgc- montpellier.fr/fastme/) to generate evolutionary trees.
Fixation Index
Fst is a statistical test measure of the degree of differentiation between populations, which is mainly used to study the degree of genetic variation between different populations as well as population structure and population genetic diversity. It assesses genetic differences between populations by calculating the mean square of error (MSG) for loci within a population and the mean square difference (MSP) for loci between populations.The formula for Fst is given below:
where MSG denotes the mean square of the error of the detected intra-population loci, MSP denotes the mean square of the detected inter-population loci, and N is the corrected inter-population mean sample size. In this study, the vcftools software (version 1.90) was used to calculate the SNP loci left by the Fst statistic calculation, which is an estimate based on Weir and Cockerham's 1984 paper[14] .
Nucleotide diversity (PI)
Nucleotide diversity (PI) is the value obtained by randomly selecting a segment of DNA sequences from multiple samples in a given population and then averaging the bases of these sequences at the same site. It is an important measure of nucleotide polymorphism within a population. The formula for calculating nucleotide diversity is given below:
where S denotes the number of segregating sites and hj denotes the heterozygosity of the jth segregating site. The PI values can be obtained by calculating the nucleotide diversity of the populations using vcftools software. In this study, the degree of selection of this SNP locus in genomic perspective was calculated by counting the ratio of PI on the same SNP locus in Qinchuan cattle and Belgian Red and Belgian Red and White cattle. When the ratio is smaller, it indicates that the nucleotide diversity of Qinchuan cattle population is higher compared with that of Belgian cattle, which indicates that the locus is subjected to a smaller and larger degree of selection on the genome of Qinchuan cattle.
Enrichment analysis of candidate genes
The top 5% of the screened loci were annotated as selected loci with reference to bovine genome UMD_3.1.1. Referring to the NCBI database (http://www.ncbi.nlm.nih.gov/gene), the R language clusterProfiler package was used[15] For autosomal enrichment of candidate genes for GO enrichment analysis:Biological process, Cellular component, Molecular function and kegg pathway enrichment analysis.
Population structure
After quality control of the SNP microarray, 35078 high-quality SNP loci were retained, covering the genotype data of 556 cattle (Figure 1A). Linkage disequilibrium analysis showed that LD decay was slower in Qinchuan cattle, indicating a higher degree of domestication (Figure 1D). Effective population size (Ne) estimation showed that populations reached a maximum at about 100-250 generations, with a significant decline in Ne values within the last 10 generations (Figure 1C).
Selective sweeps
Fixation Index
We set the sliding window size to 500kb and the step size to 50kb,we arranged the Fst values of QinC and BR,BWR in descending order and selected the highest 5% as the selection region respectively. After Fst screening, 4131 versus 4355 genes were identified, respectively (Figure 2:D, Figure 3:D, see Supplementary Data 2 for details).
Nucleotide diversity
Nucleotide diversity P values of snp loci of the QinC population were divided with the P values of the BR population, and were sorted in ascending order by taking e as the base number, selecting two different populations subject to the selection of loci, and selecting the high and low values of 2.5% each as the selection regions, and 4006 and 2660 genes(Figure 2A, Supplementary Data 3) were found, respectively. Nucleotide diversity P-values of snp loci in the QinC population were divided by P-values of the BWR population, and the logarithms were sorted in ascending order, and the 2.5% higher and lower values were selected as the selection regions, and 2,459 versus 2,783 genes were found, respectively (Figure 3A, Supplementary Data 4).
Enrichment Analysis of Candidate Genes
For the QinC and BR populations, 160 intersecting candidate genes were obtained after intersection analysis of the genes screened by Fst and PI (Figure 2B,2C, Supplementary Material 5), for enrichment analysis we found pathways related to growth and development as well as pathways related to reproduction (Table 1).
Table 1, QinC and BR intersection candidate genes enriched for pathways associated with growth, development and reproduction
|
Function |
Term |
Pvalue |
Gene |
|
Growth and development |
GO:0061035~regulation of cartilage development |
0.021454098 |
PTHLH |
|
GO:0002062~chondrocyte differentiation |
0.044240533 |
PTHLH |
|
|
GO:0030282~bone mineralization |
0.056316761 |
PTHLH |
|
|
GO:0061448~connective tissue development |
0.091748173 |
PTHLH |
|
|
bta00040~Pentose and glucuronate interconversions |
9.53677E-05 |
UGT2B10 |
|
|
Reproductive correlation |
GO:0045596~negative regulation of cell differentiation |
0.163777049 |
PTHLH |
|
bta04929~GnRH secretion |
0.159818885 |
TRPC4 |
|
|
bta04917 ~ Prolactin signaling pathway |
0.203839237 |
GCK |
|
|
bta04926~Relaxin signaling pathway |
0.298730333 |
NFKBIA |
For the QinC and BWR populations, after intersection analysis of the genes screened by Fst and PI, we obtained 98 intersection candidate genes, ( Figure 3B, Figure 3C, Supplementary Material 5) for enrichment analysis we found some pathways related to growth and developmental pathways (Figure 4), for example: (GO:0031406~carboxylic acid binding, GO:0043177~organic acid binding).
3.1 Analysis of population structure
Principal Component Analysis (PCA) was performed on 556 cattle to cluster the three populations clearly separated (Fig. 1A), using PC1, PC2 it can be seen that the three groups possess a clear differentiation. Qinchuan cattle were clearly separated compared to the other groups, while Belgian Red and Belgian Red and White cattle were clustered into one group on PC1 which is consistent with the genetic recognition, the PCA results were consistent with the results of the evolutionary tree, the two groups (BR,BWR) of Qinchuan cattle and Belgian cattle possessed completely different evolutionary branches, starting from the root node and later on in different directions (Fig. 1B).
Ne values were estimated for the QinC,BR and BWR populations based on a mutation rate of 1.26e-8. All populations experienced a rise in Ne in the 104th generation, when domestication began and population expansion indicated the expansion of agriculture in the society[16]. In the last 100 generations Ne values started to undergo a scale of decline, probably due to the impact of industrialization on agricultural production, which declined more rapidly compared to QinC,BR & BRW. The results of the analysis showed that the effective population size (Ne) values of all the populations reached the highest in about 100–250 generations due to the demand of agriculture (BWR:767.4344571, BR:738.710378202, QinC:481.511816264). In the recent period of about 10 generations, the Ne of the populations of BWR,BR stayed at a minimum of about 4, and that of QinC stayed at a minimum of about 6 (BWR:767.710378202,BWR:768.511816264). QinC stays around 6 (Fig. 1C, Supporting Material 1).
The distance between LD markers was set between 0 and 300 kb. The average r2 decreased with increasing physical distance (Fig. 1D). From the decay rate, it can be seen that BR decayed the fastest when the distance between markers was 0–10 kb, proving that the genetic diversity of the population was high, while BWR was close to BR proving the fact that these two populations were genetically closer together, compared to the BR and BWR populations QinC decayed slower proving a high degree of domestication.
3.2 Reproductive performance related genes
Reproduction rates of large beef cattle have been an important issue of great concern in agricultural production[17] Belgian cattle are favored by farmers and ranchers for their high meat yield and excellent body condition. Belgian cattle are favored by farmers and ranchers for their high meat yield and excellent body quality[18]. However, the current endangered situation of Belgian Red Cattle has raised concerns. Lucas Marian et al. showed that the Belgian Red Cattle suffers from Neisseria canis infection which may be the cause of reproductive disorders that lead to abortion in the herd[19]. The study was conducted by Lucas Marian et al. We therefore focused on the signaling pathway concerning reproduction in Belgian cattle. We found that the NFKBIA gene is associated with the KEGG pathway bta04926 relaxin signaling pathway (Table 1).The NFKBIA gene encodes a member of the NF-κB inhibitor family, which contains multiple anchor protein repeat structural domains. Inhibition of IKBA phosphorylation significantly enhances sperm motility[20]. Mutations in the NFKBIA gene result in bracketed ectodermal dysplasia[21] So the expression of NFKBIA gene may affect the reproductive performance of the population (Fig. 5).
The PTHLH gene encodes a neuroendocrine peptide that regulates cell and organ growth, development, migration, differentiation and survival[22]. PTHLH also plays a key regulatory role in mammary gland and bone development, contributing to its influence on the reproductive function of the cow. In beef cattle, PTHLH influences bone formation and homeostasis by regulating endochondral osteogenesis and epithelial-mesenchymal interactions[23]. UGT2B10 (Uridine 5'-diphospho-glucuronosyltransferase 2B10) is a UDP-glucuronosyltransferase primarily involved in the N-glycosylation of drugs and xenobiotics.UGT2B10 influences reproductive function of female animals by regulating the metabolism and clearance of estrogens during the reproductive cycle[24]. UGT2B10 affects reproductive hormone levels in female animals, thereby affecting their fertility and the normal progression of the reproductive cycle[25]. The protein encoded by the TRPC4 gene was found to be expressed in gonadotropin-releasing hormone (GnRH) neurons[26, 27] The protein encoded by the TRPC4 gene has been found to be expressed in gonadotropin-releasing hormone (GnRH) neurons and its function has been linked to the regulation of the excitability of GnRH neurons, which controls the secretion of gonadotropic hormones from the pituitary[28] GnRH neurons regulate the secretion of gonadotropins from the pituitary gland, thereby influencing the function of the reproductive system. Through the formation of calcium-permeable cation channels[29, 30], the TRPC4 protein regulates the membrane potential of GnRH neurons, thereby affecting their excitability[31] The TRPC4 protein regulates the membrane potential of GnRH neurons, thereby affecting their excitability. Therefore, changes in TRPC4 gene expression or mutations may interfere with the normal function of GnRH neurons, thereby affecting the release of reproductive hormones and the normal functioning of the reproductive system. ALOX5AP is a gene that encodes a 5-lipoxygenase-activating protein, which plays a role in the prostaglandin synthesis pathway. It plays a role in the prostaglandin synthesis pathway. This protein is involved in the regulation of arachidonic acid metabolism and catalyzes the conversion of arachidonic acid (AA) to leukotriene A4 (LTA4), which is a key enzyme in the prostaglandin synthesis pathway.Overexpression of ALOX5AP is associated with inflammatory and immune response processes[32].
In this study, we investigated the genetic patterns of growth and development of large beef cattle and screened key genes affecting growth and reproductive functions by genotyping Belgian and Qinchuan cattle. The results showed that Qinchuan cattle and Belgian Red Bull and Belgian Red and White cattle had different numbers of candidate genes, respectively, which included several genes related to growth and development and reproduction, such as PTHLH, UGTB210, TRPC4 and ALOX5AP. Through genetic analysis, this study provides an important reference for molecular selection for improving Qinchuan beef cattle.
Declaration of Interest
The authors declare that there are no competing interests associated with the manuscript.
Author Contribution
X.-P. L. and Q.-H. G. designed the experiment. X. -P. L. and X. -Y. W. conducted the experiments and analyzed the results. L. -L. Z. F. H. and P. N. assisted in the analyses of the results and helped in manuscript revision. Other authors havereviewed the manuscript.
Data Availability
The original contributions presented in the study are included in the article; Genotype data for Qinchuan cattle were shared by Northwest Agriculture and Forestry University and can be accessed via https://www.ncbi.nlm.nih.gov/bioproject/PRJNA390539. Genotype data for Belgian Red and Belgian White Red cattle were provided by Meyermans, Roel; Gorssen, Wim; Buys, Nadine; Janssens, Steven (2021) and can be accessed via https://doi.org/10.6084/m9.figshare.17025086.v1.
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No competing interests reported.