Research Article

Association Between BMP15 and Gdf9 Gene Polymorphisms and Litter Size in Dorper and Local Sheep

Asrullah As1, Chairdin Dwi Nugraha2, Rafika Febriani Putri1, Ari Ardiantoro1, Tri Eko Susilorini1, Wike Andre Septian1, Kuswati Kuswati1, Suyadi Suyadi1*

1Faculty of Animal Science, Brawijaya University, Malang 65145, Indonesia; 2Research Center for Animal Husbandry, National Research and Innovation Agency Republic of Indonesia (BRIN), Bogor, 16911, Indonesia.

Received | May 08, 2025; Accepted | August 04, 2025; Published | August 26, 2025

*Correspondence | Suyadi Suyadi, Faculty of Animal Science, Brawijaya University, Malang 65145, Indonesia; Email: [email protected]

Citation | As A, Nugraha CD, Putri RF, Ardiantoro A, Susilorini TE, Septian WA, Kuswati K, Suyadi S (2025). Association between BMP15 and Gdf9 gene polymorphisms and litter size in dorper and local sheep. Adv. Anim. Vet. Sci., 13(9):1970-1982.

DOI | https://dx.doi.org/10.17582/journal.aavs/2025/13.9.1970.1982

ISSN (Online) | 2307-8316

Copyright: 2025 by the authors. Licensee ResearchersLinks Ltd, England, UK.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

INTRODUCTION

Sheep are small ruminant livestock with significant development potential in Indonesia’s smallholder farming systems due to their easy maintenance, adaptability to various environmental conditions, and role as a source of animal protein. However, according to BPS-Statistics Indonesia (2023), the national sheep population declined from 17.83 million in 2019 to 15.62 million in 2022, primarily due to inadequate genetic improvement programs, limited breeding technology adoption, and conventional management systems (Tienamurti et al., 2020). To address these challenges, genetic improvement through crossbreeding has emerged as an important strategy to increase productivity. Crossbreeding between local sheep and exotic breeds like Dorper is done to combine Dorper’s genetic superiority with Indonesian local sheep’s adaptability. Local sheep are considered superior due to their year-round breeding ability, prolific nature, and resistance to endoparasite attacks (Mangun et al., 2024).

Twin births significantly enhance maternal productivity indices over single births in sheep (Lahir et al., 2015). Litter size is an important reproductive trait for increasing livestock productivity and profitability (Maquivar et al., 2021). Despite its low heritability, this can be improved through selection and genetic quality enhancement (Putra et al., 2021). The ability to give birth to multiple offspring can rapidly increase population growthc even though pre-weaning weights may not be optimal (Sholikah et al., 2021). High litter size not only contributes to population growth but also accelerates the availability of quality breeding stock.

Traditional selection of prospective breeding ewes still relies on conventional methods through phenotypic characteristic assessment. Although long-used, its efficiency is limited due to the relatively long time required. Modern approaches such as Marker-Assisted Selection (MAS) offer potential to accelerate the selection process and improve accuracy in identifying individuals with desired genetic traits. MAS can reduce maintenance costs and enable earlier selection (Abd El-Hack et al., 2018).

Litter size and other reproductive traits results from interactions between various environmental and genetic factors. According to Scaramuzzi et al. (1993), ovulation rate, which is the number of eggs released in one estrus cycle, is a key factor affecting litter size. Ovulation rate is the result of complex processes of follicular development and growth in the ovary involving interactions between gonadotropin hormones, steroid hormones, and growth factors.

There are three main genes controlling prolific traits in sheep: BMP15, BMPR-1B, and GDF9 (Davis, 2005). These genes have mutations related to high prolific traits that affect the bone morphogenetic signaling system in the ovary (Drouilhet et al., 2009). BMP15 and GDF9 act as ligands, while BMPR1B functions as a receptor (Di et al., 2021). BMP proteins play a role in regulating gonadotropin production in the anterior pituitary (Regan et al., 2018).

Various mutations in the BMP15 gene have been reported, such as FecXI, FecXL, FecXH, FecXB, FecXBar, FecXR, FecXG, FecXO, and FecXGR (Davis et al., 2001; Hanrahan et al., 2004; Davis, 2005; Chu et al., 2007; Drouilhet et al., 2009; Lassoed et al., 2017). The GDF9 gene also plays a crucial role in ovarian follicle development and ovulation rate (Elieser et al., 2018). Mutations in the GDF9 gene such as FecGH, FecGT, FecGE, FecGF, and FecGV results in hyperprolificacy in heterozygous sheep and sterility in homozygous sheep (Melo et al., 2008; Mullen et al., 2013; Fiky et al., 2017).

Mutations in BMP15 and GDF9 genes show varying results regarding increased litter size. The G1 mutation in GDF9 increases litter size in heterozygotes but causes infertility in homozygotes (Hanrahan et al., 2004; Abdelgadir et al., 2021). Kirikçi et al. (2021) found similar results in Turkey. In Chios sheep, Liandris et al. (2012) reported that G1 and G8 mutations significantly increased ovulation rates. However, the G8 mutation was not detected in Kazakh sheep (Amandykova et al., 2023), Kermani sheep (Khodabakhshzadeh et al., 2016), Iranian Shal sheep (Ghaffari et al., 2009), and Chinese Merino sheep (Guan et al., 2005).

Polymorphisms of genes controlling reproductive characteristics have been the focus of various studies on different sheep breeds in Indonesia (Maskur et al., 2016; Rahmawati et al., 2019; Abuzahra et al., 2024). However, information about genetic diversity of prolific traits in Local and Dorper sheep raised in Indonesia remains limited. Therefore, this research aims to identify polymorphism patterns in BMP15 and GDF9 genes in Local and Dorper sheep populations, and to understand the factors influencing genetic variation at these loci.

MATERIALS AND METHODS

Ethical approval

This study was approved by the ethical clearance committee of Brawijaya University, Indonesia (Ethical Clearance No.192-KEP-UB-2024). All procedures were conducted in accordance with institutional guidelines for the care and use of animals in research.

Management and feeding practices

All sheep were managed under standardized husbandry practices to ensure uniform management conditions throughout the study period. They were housed in elevated wooden-floored sheds to ensure proper ventilation and sanitation. The diet consisted of fresh Pakchong grass (Pennisetum purpureum cv. Thailand), provided at 10% of the ewe’s body weight daily, supplemented with a formulated concentrate feed containing 14% crude protein. The ingredients used for making concentrate feed include cassava cobs, coffee husks, corn, kapok seeds, cassava peels, coconut meal, CGF (Corn Gluten Feed), SBM (Soybean Meal) or soybean meal, DDGS (Distillers Dried Grains), soy sauce residue, iodised salt, lime, sheep premix, molasses, FML (Fermented Mother Liquor), Soby (Sodium Bicarbonate), and pollard. The selection and usage of these ingredients are tailored based on the sheep’s gender and age phase. Clean drinking water was provided ad libitum. Health checks were conducted biweekly to monitor the general condition of the flock.

 

Laboratory analysis

Genetic analysis was conducted through several sequential steps, including phenotypic data collection (litter size) and blood sample collection from ewes, DNA extraction and quality assessment, polymerase chain reaction (PCR) analysis, restriction fragment length polymorphism (RFLP) analysis, electrophoresis, gel documentation, sequencing analysis, and data analysis. The workflow of genetic analysis in this study is presented in Figure 1.

Sample collection

The study was conducted at CV Kambing Burja, located in Bedali Village, Lawang Subdistrict, Malang Regency, East Java, Indonesia (7°50’20” S, 112°37’50” E). The farm is situated at an altitude of approximately 550 meters above sea level, characterized by a tropical wet climate with an average temperature of 25–30°C and rainfall of 100–200 mm per month. The subjects of the research consisted of 40 ewes (17 Dorper full blood and 23 Local breeds, aged 1.5-3 years) that had given birth and whose litter sizes were recorded. The Dorper sheep used in this study were imported from Australia, characterized by standardized breeding systems and uniform genetic backgrounds, while the local sheep originated from smallholder farmers without structured breeding programs, resulting in greater genetic diversity and variable performance traits. The sheep’s peripheral blood was used as biological material. Blood samples were collected in EDTA vacuum tubes via the jugular vein. Subsequently, the blood samples were delivered to containers with refrigerant and stored in a freezer (at -25°C) until they were used for deoxyribonucleic acid (DNA) analysis.

DNA extraction and quality assessment

DNA isolation was performed using the Geneaid DNA Gysinc Geneaid extraction kit (Taiwan) according to the manufacturer’s protocol. The determination of quantitative indicators was carried out on the nanodrop spectrophotometer while the concentration of DNA. DNA qualitative characteristics were checked through agarose gel electrophoresis. Purified DNA was stored in a freezer at -25 oC before being used for PCR-RFLP (Polymerase Chain Reaction–Restriction Fragment Length Polymorphism) analysis.

PCR amplification and detection

The PCR reaction process was carried out with a total volume for each sample, consisting of 0.4 µl each of forward and reverse primers, 6.2 µl of Nuclease-Free Water (NFW), 7 µl of GoTaq Green, and 1 µl of DNA sample. This mixture was incubated in a PCR Thermocycler-Biorad machine with an initial DNA denaturation stage at 95°C for 5 minutes. The second stage consisted of 35 cycles, each comprising a denaturation process at 95°C for 10 seconds, primer annealing at 60°C for 20 seconds, and DNA extension at 72°C for 30 seconds. The final stage was primer extension (final extension) at 72°C for 5 minutes, ending with a cooling down process at 12°C for 2 minutes.

The DNA amplification results were visualized using 1.5% agarose gel through electrophoresis. Primers for the BMP15 Gene with genbank accession number (ENSOARG00000009372) and the GDF9 Gene with accession number (ENSOARG00000013229) were designed by ourself using the Ensemble genbank website (https://asia.ensembl.org/). The primer designs were subsequently validated using Primer Stats software to assess primer length, melting temperature (Tm), GC content, hairpin structure formation, dimerization potential (self-dimer or cross-dimer), GC clamp positioning, and to avoid repetitive sequences or homopolymers. Following design validation, PCR amplification was performed and products were verified through electrophoresis and gel documentation. Results confirmed that all PCR products matched the expected target fragment lengths (Table 1 and Figure 2).

RFLP and sequencing analysis

RFLP analysis of the BMP15 and GDF9 genes was conducted by digesting PCR-amplified DNA fragments with specific restriction enzymes. The digestion reaction mixture consisted of 5 µL of PCR product, 0.9 µL of nuclease-free water, 0.7 µL of Tango buffer, and 0.4 µL of restriction enzyme (Table 1). The mixture was incubated at 37°C for 16 hours. Digested DNA fragments were separated on a 2% agarose gel, visualized using a Gel Documentation Blue Light System (Gite 965 GW), and fragment sizes were determined using a 100-bp DNA ladder. PCR products of both genes were subsequently subjected to Sanger sequencing. For sequencing, 20 µL of each product was aliquoted into individual tubes, sealed with aluminum foil and plastic wrap, and shipped to PT Genetika Science, Jakarta, for further analysis. PCR-RFLP analysis was performed once for each sample. However, if ambiguous or unclear restriction patterns were observed, the analysis was repeated to ensure accuracy. No discordant genotypes were detected between the PCR-RFLP and sequencing results. All genotypes identified by RFLP analysis were fully consistent with the corresponding DNA sequencing data, confirming the reliability of the genotyping results.

Data analysis

The data were analyzed using Microsoft Excel and SPSS version 26 to calculate genotype frequency, allele frequency, Hardy–Weinberg equilibrium, heterozygosity levels, polymorphic information content (PIC), and to perform gene–trait association analysis with litter size. Sequencing results were analyzed using BioEdit software to examine chromatogram peaks and identify the obtained DNA bases, and MEGA11 was used for DNA base alignment, sequencing, and detection of mutation variants.

RESULTS AND DISCUSSION

Birth types of dorper and local sheep

Reproductive traits such as birth type and litter size reflect the genetic potential and biological capacity of livestock to produce offspring. These traits are directly linked to reproductive efficiency and population development. Observations of reproductive performance in 17 Dorper sheep and 23 Local sheep are presented in Table 2.

 

Table 1: Primer sequences and PCR-RFLP analysis conditions for GDF9 and BMP15 genotyping.

Gene	SNP	Primer sequences (5’-3’)	Fragment length (bp)	Annealing temperatur	Enzyme
BMP15	g.6393 C>G	F:GCATAGCTTCTCTGAGCTTC R:GGTCTTCTGAACACTCTGAG	665 Exon 2)	60oC	HaeIII (GG/CC)
	g.6637 C>T	F:CGCTTTGCTCTTGTTCCC R:CTTTCAGGCCCATCATGC	573 (Exon 2)	60oC	HinfI (G/ANTC)
	g.6790 C>T	F:CTCAGAGTGTTCAGAAGACC R:CTCAAGTTGCTGTCTTCACC	735 (Exon 2)	60oC	SpeI (A/CTAGT)
GDF9	g.792 G>A	F:GGAGAAGCTCAGATTGTAGC R:GACAAGATGCTAACCTCCAG	568 (Exon 1)	60oC	HhaI (GCG/C)
	g.3010 G>A	F:CTGCCAAGTATAGCCCTTTG R:CCAGTGGTTGAACCTACATC	744 (Exon 2)	60oC	HpaI (GTT/AAC)
	g.913 T>C	F:GACTGGTATGGGGAAATGTG R:CAGTAAGATCAAGCACCAGG	591 (Exon 1)	60oC	MspI (C/CGG)

 

Table 2: Birth type and litter size dorper and local sheep.

Breed	Sample size	Litter size (x̅ ± SD, Lambs)	Single birth (%)	Twins birth (%)	Triplet birth (%)	CV (%)1
Dorper	17	1.37 ± 0.58	67.50	27.50	5.00	42.59
Local	23	1.29 ± 0.61	61.70	31.92	6.38	42.77

 

1CV: Coefficient of variation, expressing the relative variability of litter size (%) in relation to its mean

 

 

The average litter size of Dorper sheep observed in this study shows variability compared to previous findings on Dorper crosses sheep in various countries. Some studies report both lower values (1.05–1.10) (Abebe et al., 2023; Tesema et al., 2020) and higher values (1.41–1.65) (Chen et al., 2015; Ji et al., 2023). The average litter size of Local sheep also varies. Purebred Texel sheep in Europe show values of 1.18–1.48 (Wolf et al., 2014; Schmidová et al., 2014), while in China and the Czech Republic, it reaches 1.56–1.89 (Tao et al., 2021; Štolc et al., 2011; Schmidová et al., 2014). Among Indonesian local sheep, values range from 1.35 to 2.1 (Sodiq et al., 2011; Lusi et al., 2022; Hudori et al., 2022; Hakim et al., 2019), whereas studies in Iran and New Zealand report values of 1.42–2.2 (Talebi et al., 2023; Najafabadi et al., 2020).

Regarding the litter size values we reported (1.29–1.37), these figures fall within the global average range for the respective breeds under semi-intensive to intensive management systems and align well with recent findings from similar tropical conditions. Litter sizes in local ewes crossed with Dorper rams ranged from 1.31–1.76, and with Awassi rams from 1.25–1.53, under tropical Indonesian conditions, with these values influenced by maternal age and parity (As et al., 2025). Similarly, purebred Indonesian local sheep have shown a litter size range of 1.35–2.1, with variation influenced by dam age, parity, nutrition, and management (Sodiq et al., 2011; Lusi et al., 2022).

The higher of CV values (42%) in Table 2 showed substantial variation in litter size and further indicates that environmental and management factors strongly affect reproductive performance, which may obscure the detection of genetic effects in a limited population. The frequency differences in litter size and birth type are influenced by genetic, physiological, and environmental factors, including gene mutations, genetic selection, management practices, and molecular mechanisms that regulate ovulation rates and follicular development. Montgomery (2024) highlights the complex interplay between genetic mutations and molecular mechanisms that influence ovulation rates and follicular growth in the ovaries, leading to variations in birth types. The BMPR1B, BMP15, and GDF9 genes play a critical role in intra-ovarian signaling pathways, contributing signals from oocytes as the main driver of follicular regulation rather than circulating gonadotropin concentrations.

The diversity in age and parity of the sheep used in this study may also contribute to variations in litter size. Hudori et al. (2022) and Govur et al. (2015) note that factors such as maternal age, parity, body weight, genetics, nutrition, and management influence litter size. Breeding methods and genetic selection are additional factors affecting outcomes. Tao et al. (2021) state that inbreeding or outbreeding, selection of sires with superior genetic traits, and reproductive management interventions can lead to differences in reproductive performance. The complexity of these factors results in significant variation in litter size across populations and regions.

Although all sheep were reared under uniform management practices at the same commercial farm, including standardized housing, feed (Pakchong grass and 14% CP concentrate), and health monitoring, the high CV suggests substantial environmental or management influences on reproductive outcomes. Natural variation in age, parity, and physiological status likely contributed to the high coefficient of variation observed among individuals. Kandiwa et al. (2020) and Suyadi et al. (2019) highlight the importance of seasonal variation and nutritional conditions in determining litter size, especially in tropical environments that present challenges such as heat stress and feed availability. The intensive management system implemented in this research likely optimized reproductive performance across age groups through consistent health monitoring and preventive care.

Therefore, we conclude that the differences in polymorphism status and reproductive performance between our study and previous reports are most likely due to a combination of population structure, genetic background, and environmental factors. The high coefficient of variation observed further reinforces that environmental and management factors can mask genetic effects, particularly in studies with limited population sizes. Further studies with broader population coverage and larger sample sizes are needed to comprehensively assess the impact of GDF9 polymorphisms on reproductive performance.

PCR-RFLP analysis

The PCR-RFLP analysis results of the BMP15 and GDF9 genes shown in Figure 3 (BMP15 g.6393 C>G (HaeIII), BMP15 g.6790 C>T (SpeI), GDF9 g.3010 G>A (HpaI), dan GDF9 g.913 T>C (MspI)) indicate uniform DNA patterns (monomorphic) across all samples of Dorper and Local sheep. The restriction enzymes used did not detect any different restriction sites, and the sequencing results in Figure 3 validate these findings. However, at the GDF9|HhaI exon 1 locus, the Local sheep exhibited polymorphism (g.792G>A), while the Dorper sheep remained monomorphic. The HhaI enzyme recognizes the GCG/C cutting site, with a missense mutation involving a G>C base change at this locus.

 

The contrasting polymorphism patterns observed across different sheep populations demonstrate the breed-specific nature of genetic variation at fertility-related loci. While previous studies have documented polymorphic status at GDF9 g.792 in Merino × Garut sheep (Putra et al., 2022), Greek sheep (Liandris et al., 2012), and Kazakh sheep (Amandykova et al., 2023), while Fiky et al. (2017) identified polymorphisms at GDF9 g.913 in Saidi and Ossimi breeds. In contrast, we found that Dorper sheep are monomorphic at GDF9 g.792, suggesting that each breed has evolved its own distinct genetic landscape. This discrepancy underscores the critical influence of breed-specific genetic architecture on allelic diversity patterns.

The monomorphic status observed in Dorper sheep can be attributed to their distinct evolutionary and breeding history. As a composite breed developed through intensive selection for growth performance and environmental adaptability, Dorper sheep have likely experienced genetic bottlenecks and directional selection that reduced allelic diversity at certain loci. The absence of the A allele at the GDF9 g.792 site may reflect either founder effects during breed establishment or selective sweeps that eliminated less favorable variants. Conversely, crossbred populations such as Merino × Garut maintain greater heterozygosity due to ongoing genetic admixture between parental breeds, preserving allelic variants that may have been lost in purebred lines.

This breed-dependent variation in polymorphism status has important implications for marker-assisted selection programs. The effectiveness of genetic markers varies significantly across populations depending on their allelic frequencies and linkage disequilibrium patterns. Our results suggest that genetic improvement strategies must be tailored to the specific genetic architecture of target populations, as universal application of polymorphism-based markers may not be appropriate across all sheep breeds.

It is important to note that the Local sheep included in this study were sourced from a single farm in East Java, and therefore represent a specific sub-population with potentially unique genetic and environmental backgrounds. Local sheep across Indonesia exhibit considerable diversity due to regional adaptations, different management systems, and minimal structured breeding programs. As such, the genetic patterns observed in this study particularly the allele frequencies and polymorphism status of the GDF9 and BMP15 loci may not be representative of all Indonesian local sheep populations. Future research should expand sampling to multiple geographic regions and sub-populations to better understand the broader genetic architecture and its association with prolificacy traits.

These findings highlight the dynamic nature of genetic diversity within livestock species, where population history, selection pressure, and breeding practices interact to shape contemporary allelic distributions. The heterogeneous polymorphism patterns across sheep breeds emphasize the necessity for population-specific genetic characterization before implementing molecular breeding programs, ensuring genetic improvement strategies align with each population’s underlying genetic structure.

Sequencing analysis

The samples sequenced represent the parents of Dorper and Local sheep, with sample selection based on the visualization results of the RFLP analysis. The selected samples are from the polymorphic loci observed in this study and represent each birth type (single, twins, and triplets). The results of sequencing analysis of the BMP15 g.6393 and GDF9 g.792 genes are presented in Figures 4 and 5.

 

 

The sequencing analysis of the BMP15 g.6393 gene shows no base changes at the observed locus, indicating no amino acid changes. This result is consistent with the PCR-RFLP analysis, which showed a monomorphic pattern (Figure 3a). However, the Ensembl gene bank data for this loci BMP15 g.6393 C>G shows a base change from Cytosine (C) to Guanine (G). This mutation is a missense mutation, which may potentially affect the function of the resulting protein.

The sequencing analysis of the GDF9 g.792 G>A exon 1 gene revealed a DNA base substitution from Guanine (G) to Adenine (A), resulting in an amino acid change from Histidine (CAC) to Arginine (CGC). This mutation is classified as a missense mutation. These results are consistent with the PCR-RFLP analysis, which showed polymorphic results (Figure 3d).

Mutations in the GDF9|HhaI exon 1 locus are missense mutations. Irham et al. (2023) stated that a missense mutation occurs when a codon encoding one amino acid changes into a codon encoding a different amino acid, thereby altering the function of the resulting protein. The mutation in the GDF9 g.792 G>A exon 1 gene has been extensively studied in various sheep breeds, such as MEGA sheep (Merino x Garut) by Putra et al. (2022), Kazakh sheep by Amandykova et al. (2023), Ujimqin sheep by Ji et al. (2023), Mehraban sheep by Ahmadi et al. (2016), and Garut sheep by Rahmawati et al. (2019). These studies indicate that livestock with the heterozygous GA genotype at the GDF9 g.792 locus have a higher average litter size compared to those with the homozygous GG genotype.

Allele and genotype frequencies

The analysis results of allele and genotype frequencies are presented in Table 3. The BMP15 g.6393 C>G (HaeIII), BMP15 g.6790 C>T (SpeI), GDF9 g.3010 G>A (HpaI), dan GDF9 g.913 T>C (MspI) loci were monomorphic in both sheep breeds, while only the GDF9|HhaI locus in Local sheep was polymorphic.

The GDF9 g.792 locus showed that the homozygous GG genotype was dominant over heterozygous GA in Local sheep breed (0.83; 0.17), with G allele being predominant (0.91; 0.09). The BMP15 g.6393 C>G (HaeIII), BMP15 g.6790 C>T (SpeI), GDF9 g.3010 G>A (HpaI), dan GDF9 g.913 T>C (MspI) locus showed homozygous genotypes (CC, GG, CC, CC, and TT, respectively) with both genotype and allele frequencies of 1 across all sheep. These values were influenced by the limited sample size and samples originating from homogeneous environments and management systems. Moreover, detailed pedigree records were not available for all individuals in this study, limiting our ability to estimate inbreeding coefficients. However, based on management records, the local sheep originated from a limited number of dam lines, which may contribute to the observed genetic homogeneity and monomorphism at several loci. Damayanti et al. (2023) noted that variations in allele and genotype frequencies can be influenced by various factors such as selection, gene mutations, interpopulation mixing, inbreeding, and outbreeding. The presence of heterozygous individuals in the population is reflected in polymorphic loci.

Genetic polymorphism in a population can be assessed through allele and genotype frequencies. According to Gunawan et al. (2017), a locus is considered polymorphic if its most common allele and genotype frequencies are less than 0.99. The polymorphism value correlates with the number of alleles found at each locus, with more alleles resulting in higher polymorphism values (Riyanto, 2015). Yuniarsih (2011) explains that variations in allele and genotype frequencies may vary due to selection, mutation, gene flow, or inbreeding. The presence of polymorphic loci in a population is indicated by the occurrence of heterozygous individuals. The allele and genotype frequency values of 1 (one) in this study resulted from the small sample size and homogeneous population, leading to no observed diversity.

 

Table 3: Allele and genotype frequencies of BMP15 and GDF9 genes.

Gene	Breed	Sample size	Allele frequency	Genotype frequency
BMP15 g.6393 C>G (HaeIII)	Dorper	17	C (1) G (0)	CC (1) GG (0) CG (0)
	Local	23	C (1) G (0)	CC (1) GG (0) CG (0)
BMP15 g.6637 C>T (HinfI)	Dorper	17	G (1) T (0)	GG (1) TT (0) GT (0)
	Local	23	G (1) T (0)	GG (1) TT (0) GT (0)
BMP15 g.6790 C>T (SpeI)	Dorper	17	C (1) T (0)	CC (1) TT (0) CT (0)
	Local	23	C (1) T (0)	CC (1) TT (0) CT (0)
GDF9 g.792 G>A (HhaI)	Dorper	17	G (1) A (0)	GG (1) AA (0) GA (0)
	Local	23	G (0.91) A (0.09)	GG (0.83) AA (0) GA (0.17)
GDF9 g.3010 G>A (HpaI)	Dorper	17	C (1) T (0)	CC (1) TT (0) TC (0)
	Local	23	C (1) T (0)	CC (1) TT (0) TC (0)
GDF9 g.913 T>C (MspI)	Dorper	17	T (1) C (0)	TT (1) CC (0) TC (0)
	Local	23	T (1) C (0)	TT (1) CC (0) TC (0)

 

Heterozygosity values, hardy-weinberg equilibrium, and degree of polymorphism

Data on heterozygosity values, Hardy-Weinberg equilibrium, and Polymorphic Information Content (PIC) can be seen in Table 4.

 

Table 4: Heterozigozity, hardy-weinberg equilibrium (H-W) and PIC value.

Gene	Breed	Sample size	He	Ho	(X2)	PIC
BMP15 g.6393 C>G (HaeIII)	Dorper	17	0	0	0	0
	Local	23	0	0	0	0
BMP15 g.6637 C>T (HinfI)	Dorper	17	0	0	0	0
	Local	23	0	0	0	0
BMP15 g.6790 C>T (SpeI)	Dorper	17	0	0	0	0
	Local	23	0	0	0	0
GDF9 g.792 G>A (HhaI)	Dorper	17	0	0	0	0
	Local	23	0.172	0.191	0.247	0.157
GDF9 g.3010 G>A (HpaI)	Dorper	17	0	0	0	0
	Local	23	0	0	0	0
GDF9 g.913 T>C (MspI)	Dorper	17	0	0	0	0
	Local	23	0	0	0	0

 

Note: He= Heterozygosity expected, Ho= Heterozygosity observed, X2 tabel 0.05=3.841, PIC= polymorphic information content.

 

The research results showed genetic equilibrium conditions at several analyzed loci. At the BMP15 g.6393, BMP15 g.6637, BMP15 g.6790, GDF9 g.3010, and GDF9 g.913 locus, the Chi-square (X²) values, observed heterozygosity (Ho), and expected heterozygosity (He) were 0, indicating low heterozygosity with equilibrium conditions where Ho=He. The GDF9 g.792 locus was also in equilibrium with Ho > He, where Hardy-Weinberg equilibrium analysis using Chi-square (X²) showed that both Dorper and Local sheep populations were in equilibrium (X²calculated < X²table). Although the Polymorphic Information Content (PIC) value was classified as low (<0.25) according to Carsono et al. (2014) classification, these results still indicate the presence of polymorphism or genetic diversity in the GDF9 g.792 gene in both sheep populations, albeit at a low level.

The population in this study was not in Hardy-Weinberg equilibrium, which was likely caused by small population size and non-random mating that triggered genetic drift and inbreeding. The PIC values at all loci showed that only the GDF9 g.792 locus in Local sheep was polymorphic, although low (<0.25). According to Abramovs et al. (2020) and Noor (2010), Hardy-Weinberg equilibrium is achieved when genotype frequencies remain stable across generations without migration, selection, and mutation. Carsono et al. (2014) classified PIC values into three categories: highly informative (>0.5), moderately informative (0.25–0.5), and low (<0.25). Heterozygosity values also reflect the level of population diversity; low heterozygosity (<0.5) indicates low diversity, while high values (>0.5) indicate high diversity (Tambasco et al., 2003). These results provide preliminary information regarding genetic diversity in Local sheep and Texel crosses, which can be used for further studies related to livestock productivity traits.

Association of BMP15 gene with litter size

The analysis results showed that at all analyzed loci, only one genotype (monomorphic) was detected in each sheep breed: the CC genotype at BMP15|HAEIII and BMP15|SpeI loci, and the GG genotype at the BMP15|HinfI locus. Because no alternative genotypes such as CG, GT, or CT were found in the analyzed samples, there were no genotype variations that could be associated with increased litter size. The results of the association analysis between the BMP15 gene and litter size are presented in Table 5. The difference in average litter size between the two sheep breeds indicates potential influence from other genetic factors or environmental interactions affecting these results. The mutation at the BMP15 g.6637 C>T gene locus is also known as the FecXB variant. The research results showed no diversity in this study, similar to findings by Amandykova et al. (2023) in Kazakh sheep. In contrast, Liandris et al. (2012) found FecXB mutations in Chios and Karagouniki sheep. According to Galloway et al. (2000) and Hanrahan et al. (2004), the FecXB variant in heterozygous condition increases ovulation rate, while homozygotes cause ovarian atrophy and infertility. These heterogeneous observations demonstrate that despite examining identical genetic loci, significant intra-population genetic variation exists, with environmental variables such as nutrition, climatic conditions, and management practice influencing gene expression and epistatic interactions.

Each breed possesses genetic characteristics that affect ovulation rate and fertility capabilities. Scaramuzzi et al. (1993) explained that major genes like Fecβ and FecX control ovulation rate. Fecβ increases plasma FSH concentration, which accelerates follicular development and maturation without altering their initial number. FecX influences ovarian follicular development and increases fertility. The synergy of these two genes modifies sheep reproductive mechanisms, enabling an increase in the number of offspring per reproductive period.

Association of GDF9 gene with litter size

The results of this study showed that GDF9|HpaI and GDF9|MspI loci were monomorphic, thus could not be associated with increased litter size in both sheep breeds. Meanwhile, the GDF9 g.792 G>A loci in Local sheep had genotype variations that could be statistically analyzed for relationships. The results of the association analysis between the GDF9 gene and litter size are presented in Table 6.

 

Table 5: Association of the BMP15 gene with litter size.

Gene	Breed	Sample size	Genotype	Litter size (Mean± SD)
BMP15 g.6393 C>G (HaeIII)	Dorper	17	CC GG CG	1.29 ± 0.47 - -
	Local	23	CC GG CG	1.28 ± 0.46 - -
BMP15 g.6637 C>T (HinfI)	Dorper	17	GG TT GT	1.29 ± 0.47 - -
	Local	23	GG TT GT	1.28 ± 0.46 - -
BMP15 g.6790 C>T (SpeI)	Dorper	17	CC TT CT	1.29 ± 0.47 - -
	Local	23	CC TT CT	1.28 ± 0.46 - -

 

Table 6: Association of the GDF9 gene with litter size.

Gene	Breed	Sample Size	Genotype	Litter size (Mean ± SD)
GDF9 g.792 G>A (HhaI)	Dorper	17	GG AA GA	1.29 ± 0.47 - -
	Local	23	GG AA GA	1.24 ± 0.44 - 1.50 ± 0.58
GDF9 g.3010 G>A (HpaI)	Dorper	17	CC TT TC	1.29 ± 0.47 - -
	Local	23	CC TT TC	1.28 ± 0.46 - -
GDF9 g.913 T>C (MspI)	Dorper	17	TT CC TC	1.29 ± 0.47 - -
	Local	23	TT CC TC	1.28 ± 0.46 - -

 

The GDF9|HhaI exon 1 (g.792G>A) gene polymorphism showed no significant association with litter size (P > 0.05) in Local sheep. However, the GA genotype tended to produce higher litter sizes compared to the GG genotype, indicating potential applications in marker-assisted selection for improving reproductive performance. In contrast, the GDF9|HpaI and GDF9|MspI loci were monomorphic and showed no variation related to litter size.

The lack of significant association between the GDF9 g.792G>A mutation and litter size in this study differs from several previous reports that showed a positive correlation between this mutation and prolificacy. The GDF9|HhaI exon 1 g.792 G>A mutation in this study, in heterozygous condition (GA), has been associated with increased ovulation rate and litter size in various sheep breeds. This aligns with findings by Paz et al. (2015) in Chilota sheep, while Gorlov et al. (2018) reported that Volgograd sheep with GA genotype had higher litter size (1.88) compared to GG (1.22), as did Salks (GA: 1.80; GG: 1.13) and Ujimqin sheep (GA: 1.19; GG: 1.16) as found by Ji et al. (2023). Additionally, Getmantseva et al. (2019) reported that heterozygous sheep had higher birth weights compared to homozygotes.

However, several factors may explain the discrepancy between these results and previous studies. First, the low frequency of the A allele (0.09) in the local sheep population is likely the primary reason for the absence of statistical significance. With such a low frequency, the number of heterozygous individuals (GA) was limited (17%), and no homozygous AA individuals were observed in the sample. Second, the small sample size (n=23 for local sheep) may have reduced the statistical power of the analysis. In genetic association studies, particularly for minor alleles with low frequencies, a large sample size is essential to detect their effects reliably. Third, the absence of homozygous AA individuals may be an artifact of sampling or could genuinely reflect selective pressure against the A allele. Previous studies by Abdoli et al. (2013) and Hanrahan et al. (2004) reported that AA homozygotes cause infertility due to arrested follicular development. This suggests the possibility of negative selection against the A allele in its homozygous form, ultimately reducing its frequency in the population. Although the association was not statistically significant, the presence of the GA genotype may be related to a trend toward increased litter size. However, this result is preliminary and should not yet be used as a basis for Marker-Assisted Selection (MAS) without further validation involving larger sample sizes and more comprehensive genetic studies.

The absence of AA homozygotes for the GDF9 g.792G>A mutation in our dataset, combined with its low allele frequency (0.09), suggests several potential biological and methodological explanations. First, as reported in other studies, the AA genotype may be lethal or lead to infertility, thereby preventing its presence in the breeding population and limiting the allele’s propagation to heterozygous individuals only. Second, the persistence of the A allele at low frequency may reflect a form of balancing selection, wherein heterozygotes (GA) potentially benefit from a reproductive advantage such as a subtle increase in litter size, while homozygotes are selected against. Third, the observed allele frequency may be partially shaped by sampling bias, as the animals were sourced from a single farm representing a limited subpopulation, possibly influenced by founder effects or restricted gene flow. Together, these factors may explain the continued presence of the A allele despite the absence of AA individuals. Broader sampling across diverse populations is needed to clarify the underlying genetic dynamics and validate these hypotheses.

The GDF9 g.913 T>C locus showed no genotype diversity or association with litter size in our study, contrasting with findings by Fiky et al. (2017) who reported polymorphism and significant associations in Saidi and Ossimi sheep breeds. Several factors likely contribute to this lack of diversity in our population. The small sample size may have limited our ability to detect rare variants, while the relatively homogeneous environment and strong natural selection pressure for locally adaptive alleles could have reduced genetic variation over time. Additionally, genetic drift in small, isolated populations often leads to the random loss of allele variants (Bradford et al., 1986; Roberts, 2000). The lack of genetic diversity in the observed population can be attributed to inbreeding management practices, where individuals with close genetic relationships are bred to produce offspring, resulting in a risk of genetic homogeneity and ongoing decline in genetic diversity. Furthermore, genetic drift occurring in small and isolated populations can lead to the loss of allele variants within the population.

CONCLUSSION

Polymorphism was identified at the GDF9|HhaI exon 1 locus, characterized by a point mutation (g.792G>A), resulting in two genotypes (GG and GA) in Local sheep. Although the association was not statistically significant, the presence of the GA genotype may be related to a trend toward increased litter size. However, this result is preliminary and should not yet be used as a basis for Marker-Assisted Selection (MAS) without further validation involving larger sample sizes and more comprehensive genetic studies. The lack of genetic variability at these loci suggests limited diversity within the sampled population. Additionally, the high phenotypic variability and relatively small sample size may have masked potential genetic associations. Future studies should employ larger and stratified populations, and incorporate genomic or pedigree-based relationship data to more accurately disentangle genetic from environmental effects on prolificacy traits in sheep.

ACKNOWLEDGEMENTS

The authors gratefully acknowledge the members of the Animal Biotechnology Research Group Laboratory Faculty of Animal Science Brawijaya University, and Directorate General of Higher Education, The Ministry of Education, Culture, Research and Technology, Indonesia through the PMDSU scholarship. We would like to thank the Brawijaya University for providing funding through the UB Stars scheme 2025, and CV. Kambing Burja for facilitating this research.

NOVELTY STATEMENT

This study provides preliminary insights into the polymorphism of BMP15 and GDF9 genes in full-blood Dorper and Indonesian local sheep raised under tropical conditions, and evaluates their associations with litter size traits. The results revealed that the GDF9 g.792G>A locus in local sheep is polymorphic and may contribute to enhanced reproductive performance, while the BMP15 gene and other GDF9 loci were found to be monomorphic. These findings enrich the currently limited local genetic data and highlight the potential application of Marker-Assisted Selection (MAS) in sheep breeding programs targeting prolificacy traits in Indonesia.

Author’s Contribution

A. As, S. Suyadi, T. E. Susilorini, and K. Kuswati conceptualized the study, developed the methodology, provided resources, and supervised the study. W. A. Septian and A. Ardiantoro conducted the investigation, laboratory analysis, and data visualization. A. As and S. Suyadi wrote the original manuscript. R. F. Putri and C. D. Nugraha revised the manuscript. All authors agreed to the final version of the manuscript.

Conflict of interest

The authors have declared no conflict of interest.

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