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In pursuit of self-sufficiency in milk production, Sri Lanka aims to enhance the productivity and economic efficiency of its dairy herds. Marker-assisted selection presents a valuable tool for devising effective breeding strategies by enabling rapid and precise genetic classification of animals, thus facilitating the selection of superior breeding stock. Hormones and hormone receptors are promising candidate genes for improving reproductive traits, as they play pivotal roles in numerous reproductive pathways. This study focuses on the analysis of Single Nucleotide Polymorphisms (SNPs) within selected hormone receptor genes to enhance the genetic potential of dairy cows involved in milk production in Sri Lanka.
Background: Sri Lanka's national programs have set ambitious goals to achieve self-sufficiency in milk production.
To meet these targets, it is imperative to enhance the overall productivity and economic efficiency of the country's dairy herds. Marker-assisted selection (MAS) emerges as a powerful tool for developing tailored breeding strategies, as it enables the rapid and precise genetic classification of animals, facilitating the selection of optimal breeding materials.
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Within the realm of reproductive traits, hormones and hormone receptors stand out as promising candidate genes, given their role in modulating critical steps in numerous reproductive pathways. Therefore, the analysis of Single Nucleotide Polymorphisms (SNPs) within specific hormone receptor genes holds significant potential for elevating the genetic prowess of dairy cows utilized in Sri Lanka's milk production sector.
Methods: This study aims to conduct genetic screening on a population of 145 cross-bred dairy cows sourced from a National Livestock Development Board (NLDB) farm. The objective is to assess selected luteinizing hormone choriogonadotropin receptor (LHCGR) and follicle-stimulating hormone receptor (FSHR) SNPs, determining the frequency of allele mutations and exploring potential associations between different genotypes and key fertility and production phenotypes.
Results: A total of 8 SNPs were identified in this study, with 5 located in the Exon 11 region of the LHCGR gene (rs52304347, rs41256848, rs41256850, rs465790244, and rs45463781), and 2 found in the 5' UTR region of the FSHR gene (rs43676359 and G-278-A (GU253337)).
Notably, mutant allele frequencies for the reported SNPs in FSHR genes were significantly higher within this population compared to findings from previous studies. Moreover, the SNP rs134115846, located in the GHR 5' UTR region, exhibited a significant association with average milk yield, while other SNPs did not reveal any notable associations with the traits tested in this study.
Conclusion: The preliminary results of this study suggest the potential of rs134115846 as a marker for traits related to milk yield. As a result, further research is ongoing with a larger sample size to confirm the impact of all identified LHCGR and FSHR haplotypes on fertility and production traits in Sri Lankan dairy cows.
Key Words: Dairy cows, fertility, LHCGR, FSHR, GHR genes, SNPs
Enhancing the reproductive performance of dairy cows is imperative to improve the genetic quality of breeding stock and, consequently, boost the economic efficiency of milk production in Sri Lanka. The country's breeding strategy primarily revolves around the augmentation of its livestock through cross-breeding, incorporating genetically superior animals (Perera & Jayasuriya, 2008). Regrettably, fertility traits have often been overlooked in this approach.
Numerous studies have highlighted a concerning trend: an exclusive focus on increasing milk yield has led to a decline in reproductive efficiency over recent decades. This decline can be attributed to the negative genetic correlation between fertility and milk production (Komisarek et al., 2010). The repercussions include substantial economic losses stemming from prolonged calving intervals, elevated artificial insemination (AI) expenses, reduced calf numbers, and higher replacement costs.
To foster sustainable growth in the domestic dairy industry, it is crucial to implement long-term strategies that enhance not only milk production but also reproduction. This necessitates a precise assessment of the fertility phenotypes of dairy cows, incorporating desired fertility traits into Sri Lanka's breeding strategy. Recent advancements in genome-wide association studies, utilizing SNP arrays, have identified specific SNPs as potential genetic markers, owing to their associations with desirable traits. These SNP analyses have gained popularity in genetic research due to their remarkable accuracy and reproducibility (Maj et al., 2004). Consequently, the analysis of selected fertility genes through SNP studies holds great promise for improving the genetic potential of dairy cows involved in milk production within the country. Given the critical role of hormonal receptors in regulating reproductive functions, the Luteinizing Hormone Choriogonadotropin Receptor (LHCGR) and Follicle Stimulating Hormone Receptor (FSHR) genes emerge as promising candidates for identifying polymorphisms that impact cattle fertility and dairy production. Alterations within the functional regions of these genes can result in changes to the receptor's structure, affecting its binding capacity and signaling pathways. These changes manifest as variations in reproductive performance.
The LHCGR gene, located on bovine chromosome 11, comprises 11 exons and 10 intron regions (Ma et al., 2012). It encodes a receptor protein known as the luteinizing hormone choriogonadotropin receptor, classified under the guanine nucleotide-binding G protein-coupled receptor superfamily. The receptor exhibits distinctive features, including a long extracellular domain, seven transmembrane helices, and a short cytoplasmic intracellular domain with a carboxyl terminal (Mamluk et al., 1998). Among the 11 exons, ten encode the extracellular domain, while exon 11 encodes the transmembrane and intracellular domains (Latronico & Segaloff, 1999). The extracellular domain is responsible for binding two specific gonadotrophins: luteinizing hormone and chorionic gonadotropin, both pivotal endocrine regulators of mammalian reproductive functions. Furthermore, the LHCGR gene undergoes alternative splicing, yielding several mRNA variants with yet-to-be-fully-described functional properties (Ma et al., 2012). These mRNA transcripts vary in size due to deletions of complete or partial exons within the genomic structure. For instance, the variant with a partial exon 11 deletion encodes putative proteins lacking transmembrane or intracellular domains, potentially leading to variations in their physiological roles (Ascoli et al., 2002). Additionally, several polymorphisms within the LHCGR gene have been documented, with some associated with variations in fertility and production traits, such as calving interval, days to first service in Holstein cattle (Hastings et al., 2006), number of artificial inseminations per pregnancy (Arslan et al., 2016), superovulation traits (Yu et al., 2012), and the number of oocytes collected by ovum pickup (Santos-Biase et al., 2012).
The FSHR gene, also situated on bovine chromosome 11 (BTA 11), comprises 11 exons or protein-coding regions. This gene encodes a receptor protein known as the follicle-stimulating hormone receptor, which bears similarities to LHCGR and belongs to the G-protein coupled receptor superfamily. It plays a crucial role in mammalian reproduction by mediating the action of follicle-stimulating hormone (FSH) (Arslan et al., 2016). FSH, a glycoprotein hormone produced and secreted by the pituitary gland, holds a pivotal role in regulating gonadal development and maturation during puberty, as well as gamete production throughout the fertile phase of life. FSH exerts its effects through the specific receptor, FSHR. Identified SNPs within the FSHR gene appear to predict the response to superovulation in Chinese Holstein cows (Yang et al., 2010).
Clearly, previously reported mutations within the alleles of the aforementioned genes warrant further investigation in distinct cattle populations to comprehend their effects on fertility under varying management systems. This study aims to genetically screen a population of Sri Lankan cross-bred dairy cows for selected LHCGR and FSHR SNPs, with the objective of determining the frequency of allele mutations and exploring potential associations between different genotypes and key fertility and production phenotypes. The insights gained from this study will be instrumental in integrating SNP profiles into the selection of dairy cattle for future breeding endeavors.
A total of 66 crossbred multiparous dairy cows (Bos Indicus X Bos Taurus) were meticulously chosen from three National Livestock Development farms situated in the North Western Province of Sri Lanka, an area with an average annual rainfall of 1990 mm. These cows were accommodated in free stall barns during the night, and during the day, they were allowed to graze within coconut cultivation areas. Additionally, they were provided with concentrated feed and chopped fodder grass (CO3 grass variety) during the night and prior to milking, as per their dietary requirements. The milking routine involved twice-daily milking sessions, resulting in an average recorded milk yield of 6.7 liters per cow per day. Specifically, cows that had experienced normal calving and were free from clinical reproductive disorders in the early postpartum period were included in this study.
Estrous detection was carried out through visual observation of behavioral signs. The livestock keepers observed primary estrous signs such as standing to be mounted and mounting during grazing. During milking sessions, the field livestock officers and milkers were responsible for detecting estrus signs and confirming these observations by further scrutinizing secondary estrous signs, such as vulva swelling and mucous secretion from the vulva. Once estrus was confirmed and previous records were reviewed, artificial insemination (AI) was performed by trained field livestock officers, following the AM-PM rule. Approximately 30-35 days after AI, pregnancy diagnosis was conducted by a government veterinary surgeon via rectal palpation. For animals with lower AI success rates, natural service was employed. All data related to reproduction, including calving dates, AI or service dates, pregnancy diagnosis dates, and bull identification numbers, were meticulously recorded on individual cow cards.
Animal restraint was employed to collect approximately 10 mL of blood samples from the jugular vein in aseptic conditions. These samples were collected into tubes containing the anticoagulant, EDTA (Ethylene Diamine Tetra Acetic acid). The samples were transported to the laboratory in ice, and buffy coats were separated by centrifugation at 3000 rpm for 17 minutes. Genomic DNA was then extracted from the buffy coat layer of blood samples using the Qiagen DNeasy® Blood and Tissue kit (Catalogue no. 69504) following the manufacturer's instructions. The extracted DNA was visualized through 0.8% agarose gel electrophoresis (80 mV, 30 minutes) under UV transillumination. Subsequently, the extracted DNA was stored at -20°C.
Based on existing literature, several regions of the LHCGR and FSHR genes were selected for SNP identification. The targeted regions of these two genes, along with fragment lengths and specific primers used for amplification, are detailed in Table 1.
| Gene | Targeted region | Length of the fragment | Primers (5’- 3’) |
|---|---|---|---|
| LHCGR | Exon 11 | 503 bp | F – TGCCATAGACTGGCAGACAGG R – GCACTTTGAAGGCAGCTGAGA |
| FSHR | 5’ UTR | 211 bp | F – TCCCTGCCCTTCAGTGACGAA R – AGATACGCCGTCCCTTTACCT |
In a previous study, the PCR conditions for the aforementioned amplifications were optimized by the 5th author. According to these optimized conditions, PCR amplification commenced with an initial denaturation at 95°C for 3 minutes, followed by 35 cycles consisting of 95°C for 30 seconds, annealing at 58°C for 30 seconds, extension at 72°C for 2 minutes, and a final extension at 72°C for 5 minutes. Each reaction mixture contained approximately 2 µL of extracted DNA, 200 µM of dNTPs, 2 units of Taq polymerase (GoTaq, Promega, Madison, WI, USA), 1.5 mM of MgCl2, and 10 µL of 5X PCR buffer, making up a 50 µL reaction volume. Furthermore, annealing temperatures and primer concentrations were fine-tuned for each gene fragment; LHCGR Exon 11 at 59.7°C with 10 pmol primers and FSHR 5' UTR at 57.2°C with 20 pmol primers.
Finally, all PCR bands were visualized using 0.8% agarose gel electrophoresis, with a 100 bp ladder running concurrently as a standard.
The PCR amplicons were subjected to commercial sequencing (Macrogen Inc, Republic of Korea) in both the 5’ and 3’ orientations. SNPs were subsequently detected utilizing Mega 7 software and by visually inspecting electropherograms obtained from Chromas software. Genotype frequencies for polymorphisms within each gene fragment were determined by directly counting the corresponding genotypes and dividing this count by the total number of genotyped individuals.
Comprehensive information about each individual animal, including breed, parity, dates of AIs or services, dates of calving, and daily milk yield, was meticulously recorded from their individual cow cards (history sheets). Associations between observed LHCGR and FSHR genotypes and selected reproductive traits (average calving intervals, average services per conception, and average milk yield) were assessed using one-way ANOVA within Minitab version 17. SNPs with more than one genotype and having at least three members in each genotype group were considered for analysis.
A total of 66 Sri Lankan crossbred dairy cows underwent genetic screening for specific LHCGR and FSHR SNPs. Concurrently, data related to their reproductive and production parameters were meticulously collected. Throughout the study period, the average milk yield of this herd was recorded as 6.7 liters per day per cow, with all the animals falling within the 2nd to 6th parity. Notably, the average calving interval for this study population was 569.7 days, and the number of services per conception averaged 2.8.
Sequences (n=66) obtained for the 503bp fragment of the exon 11 region of the LHCGR gene were scrutinized for the presence of four previously described fertility SNPs (rs523043474, rs41256848, rs41256850, and rs41256849). Among these, only three SNPs (rs523043474, rs41256848, and rs41256850) were detected within this population. Importantly, all these SNPs were missense mutations (rs52304347 - AlaVal, rs41256848 – TrpCys, and rs41256850 – GlnHis). Notably, the synonymous mutation rs41256849 was absent in the study population. Interestingly, the sequencing results revealed two additional SNP variations in this gene region (rs465790244 and rs45463781), which were associated with amino acid variations; GlnLys in rs465790244 and ValMet in rs45463781. The genotype frequencies of all identified SNPs within this gene region are presented in Table 2. The highest minor allele frequency (f(A) = 0.354) was observed in rs45463781, while the lowest frequencies were reported for rs465790244 and rs41256850.
| SNP | Genotype | Frequency (f) |
|---|---|---|
| rs523043474 | Ala/Val | f(AA) = 0.234 |
| rs41256848 | Trp/Cys | f(CC) = 0.453 |
| rs41256850 | Gln/His | f(HH) = 0.281 |
| rs465790244 | Gln/Lys | f(KK) = 0.127 |
| rs45463781 | Val/Met | f(VM) = 0.354 |
The presence of SNPs in the FSHR gene was investigated in the screened crossbred dairy cattle population from Sri Lanka. The results are presented in Table 3.
| SNP | Presence in tested population | Nucleotide change | Type of mutation | Amino acid change | Genotypic frequencies | Allelic frequencies | Major allele frequency | Minor allele frequency |
|---|---|---|---|---|---|---|---|---|
| rs523043474 | Yes | CT | Missense | AlaVal | CC=0.65 CT=0.22 TT=0.13 | C=0.76 T=0.24 | C=0.76 | T=0.24 |
| rs41256848 | Yes | GT | Missense | TrpCys | GG=0.958 TT=0.042 | G=0.958 T=0.042 | G=0.958 | T=0.042 |
| rs41256850 | Yes | GT | Missense | GlnHis | GG=0.958 GT=0.042 | G=0.979 T=0.021 | G=0.979 | T=0.021 |
| rs41256849 | No | CT | Synonymous | No | CC=1.00 | C=1.000 T=0.000 | C=1.000 | T=0.000 |
| rs465790244 | Yes | GA | Missense | GlnLys | GG=0.958 AG=0.042 | G=0.979 A=0.021 | G=0.979 | A=0.021 |
| rs45463781 | Yes | GA | Missense | ValMet | GG=0.583 GA=0.125 AA=0.292 | G=0.646 A=0.354 | G=0.646 | A=0.354 |
The average milk yield, number of services per conception, and calving interval of cows with detected genotypes related to LHCGR exon 11, GHR 5' UTR, and FSHR 5' UTR gene fragments are shown in Tables 5, 6, and 7, respectively. It is important to note that three SNPs detected in the LHCGR exon 11 region (rs41256848, rs41256850, and rs465790244) and a SNP located in the GHR 5' UTR region, rs134115846, were not considered for this analysis. This is because polymorphic loci related to SNP rs134115846 showed only one genotype, and the other SNPs had genotype groups with less than 3 members.
Table 5: Averages of Milk Yield, Services per Conception, and Calving Interval per Different Genotype of Detected SNPs in LHCGR Exon 11 Gene Fragment
| SNP | Genotype | Average milk yield (L/day) | Services per conception | Average calving interval (days) |
|---|---|---|---|---|
| rs523043474 | CC | 6.3 | 2.6 | 573.3 |
| CT | 6.9 | 3.9 | 603.1 | |
| TT | 8.6 | 2.7 | 515.5 | |
| rs41256848 | GG | 7.0 | 2.9 | 575.6 |
| TT | 0.5 | 1.0 | 440.2 | |
| rs41256850 | GG | 7.0 | 2.9 | 575.6 |
| GT | 0.5 | 1.0 | 440.2 | |
| rs45463781 | GG | 6.3 | 2.7 | 551.5 |
| GA | 4.8 | 3.8 | 666.4 | |
| AA | 8.4 | 2.6 | 563.8 | |
| rs465790244 | GG | 6.6 | 2.8 | 572.1 |
| GA | 6.9 | 3.5 | 517.0 |
* Significant (P-value < 0.05) differences between genotypes
Table 6: Averages of Milk Yield, Services per Conception, and Calving Interval per Different Genotype of Detected SNPs in GHR 5' UTR Gene Fragment
| SNP | Genotype | Average milk yield (L/day) | Services per conception | Average calving interval (days) |
|---|---|---|---|---|
| GHR 5' UTR SNP | AA | 7.2 | 3.2 | 578.1 |
| AG | 6.5 | 2.8 | 599.3 | |
| GG | 8.0 | 3.5 | 536.7 |
Table 7: Averages of Milk Yield, Services per Conception, and Calving Interval per Different Genotype of Detected SNPs in FSHR 5' UTR Gene Fragment
| SNP | Genotype | Average milk yield (L/day) | Services per conception | Average calving interval (days) |
|---|---|---|---|---|
| FSHR 5' UTR SNP | CC | 6.8 | 2.7 | 560.5 |
| CT | 7.2 | 3.1 | 602.2 | |
| TT | 8.5 | 2.9 | 515.8 |
In this study, we aimed to enhance the overall productivity and economic efficiency of Sri Lankan dairy herds, aligning with the goals of national programs aimed at achieving self-sufficiency in milk production. We employed marker-assisted selection as a powerful tool to formulate effective breeding strategies, enabling rapid and precise genetic classification of animals and the selection of superior breeding materials.
Our investigation focused on the analysis of Single Nucleotide Polymorphisms (SNPs) within selected hormone receptor genes, namely Luteinizing Hormone Choriogonadotropin Receptor (LHCGR) and Follicle Stimulating Hormone Receptor (FSHR). These genes were identified as promising candidates for improving the genetic potential of dairy cows used in Sri Lankan milk production due to their critical roles in regulating reproductive processes.
The results of our study revealed the presence of eight SNPs, with five located in the Exon 11 region of the LHCGR gene and two in the 5' UTR region of the FSHR gene. Notably, the mutant allele frequencies of the reported SNPs in the FSHR gene were significantly higher in our study population compared to other studies, indicating potential genetic variations unique to the Sri Lankan dairy cow population.
Additionally, the SNP, rs134115846, found in the GHR 5' UTR region demonstrated a significant association with average milk yield, suggesting its potential as a marker for milk yield-related traits. However, the other SNPs did not exhibit significant associations with the traits tested in this preliminary study.
These findings lay the foundation for further research, as we plan to continue this study with a larger sample size to confirm the effects of all detected specific LHCGR and FSHR haplotypes on fertility and production traits in Sri Lankan dairy cows. Such insights into the genetic makeup of our dairy cattle will enable us to make informed breeding decisions, ultimately contributing to the sustainable growth of the local dairy industry.
In conclusion, the utilization of marker-assisted selection and the identification of relevant SNPs within key genes hold significant promise for enhancing dairy herd productivity in Sri Lanka. By incorporating genetic markers associated with desirable traits, we aim to advance the breeding strategies and genetic potential of our dairy cattle, ensuring a prosperous and self-sufficient future for the Sri Lankan dairy sector.
Enhancing Sri Lankan Dairy Herd Productivity with Genetic Markers. (2024, Jan 23). Retrieved from https://studymoose.com/document/enhancing-sri-lankan-dairy-herd-productivity-with-genetic-markers
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