Introduction
The water buffaloes play a significant role in agriculture and rural economies, providing milk, meat, and draught power in at least 67 countries, particularly in developing countries. Buffaloes exhibit unique adaptability to variable temperature and moisture conditions, show resistance to diseases and parasites, have long productive lifespans, and thrive on low-quality roughage, making them ideal for sustainable livestock development [1]. Two distinct types of buffalo are recognized globally based on morphology and chromosome number.
The riverine buffalo (Bubalus bubalis bubalis), which includes breeds such as Murrah, Nili-Ravi, and Mediterranean, is distributed from the Indian subcontinent to Italy, Egypt, and South America [2]. This type is renowned for its milk production, contributing approximately 13% of the global milk supply [3]. In contrast, the swamp buffalo (Bubalus bubalis carabanensis), primarily located in Southeast Asia and China, comprises numerous local breeds and is mainly utilized for ploughing, transport, and meat production.
Reproductive performance is pivotal to the overall productivity of buffaloes, directly influencing meat and milk yields, and thereby ultimately determining the economic efficiency of the industry. However, buffaloes exhibit poor reproductive performance [4], characterized by delayed puberty and maturity, silent estrus, anestrus, seasonal breeding patterns, low conception rates, and long calving intervals, all of which strongly constrain the genetic selection. Although reproductive traits have long been considered to exhibit low to moderate heritability, accurately evaluating population genetic parameters and additive genetic variation in these traits is essential for the success of genetic selection programs [5].
Recent developments in high-throughput sequencing technologies and emerging genomic data have created opportunities to decipher the genomic variants affecting reproductive traits in buffaloes. Genome-wide association studies (GWAS), multi-omics techniques, and selection signature analyses have been performed to identify quantitative trait loci (QTLs) and candidate genes associated with buffalo fertility, providing a better understanding of the genetic architecture of fertility traits. In this review, we aim to provide an overview of the genetic parameters of male and female reproductive performance and conduct a comprehensive search for candidate genomic regions and genes associated with reproductive traits in buffaloes.
In addition, the functional enrichment and interaction networks among these candidate genes are discussed.
Description of reproductive traits in buffalo
Fertility is a complex trait influenced by multiple genes and genetic variants, each with minor effects on the phenotype. In females, common phenotypic indicators used to assess reproductive performance include age at first calving (AFC), age at first service (AFS), age at puberty (AAP), age at sexual maturity (ASM), calving ease score (CE), calving interval (CI), conception rate (CR), first artificial insemination (FAI), first calving interval (FCI), first service period (FSP) or service period (SP), gestation length (GL), interval between second and remaining calving (CIV2_12), interval from calving to conception also known as open days (OD), the number of services per conception (NSC), postpartum breeding interval in days (PPBI), second calving age (SCA), third calving age (TCA). In males, semen and testicular traits, including ejaculate volume (EV), mass motility (MM), sperm livability (LS), percentage of abnormal sperm (AS), and sperm concentration (CONC), are used to indicate reproductive ability.
Genetic evaluation of reproductive traits in buffalo
Heritability parameters of reproductive traits
Heritability estimates of reproductive traits are useful for guiding breeding programs aimed at improving reproductive efficiency. Over the last few decades, numerous studies have estimated heritability for reproductive traits in different buffalo populations, with values ranging from low to moderate (Table 1Table 1.Heritability estimate of reproductive traits in female buffalo Traits Heritability Breeds (Numbers /Records) References Age at first calving0.42Brazilian crossbred buffalo (628)[6]0.40 ± 0.15Murrah buffalo (577)[60]0.362 ± 0.086Nili-Ravi buffalo (501)[9]0.36 ± 0.027Thai Swamp buffalo (1034 records)[20]0.35 ± 0.102Egyptian buffalo (907)[19]0.28 ± 0.03Murrah buffalo (827)[61]0.26Italian buffalo (21098 records)[62]0.246 ± 0.10Murrah buffalo (1274)[63]0.226 ± 0.154Surti buffalo (48)[8]0.20 ± 0.06Iranian buffalo (5353 records)[23]0.17 ± 0.02Murrah buffalo (3431 records)[40]0.16 ± 0.04Crossbred Murrah buffalo (2389)[17]0.16Murrah buffalo (2290 records)[28]0.14 ± 0.03Colombian buffalo (4138 records)[64]0.12 ± 0.04Egyptian buffalo (2426 records)[12]0.11 ± 0.06Egyptian buffalo (637)[22]0.11 ± 0.02Mehsana buffalo (7870)[65]0.11Murrah buffalo (662)[66]0.09Mediterranean buffalo (7915)[18]0.08 ± 0.02Egyptian buffalo (1167 records)[67]0.07 ± 0.05Murrah buffalo (1578)[7]Age at first service0.232 ± 0.10Murrah buffalo (1161)[63]Age at puberty0.21Nili-Ravi buffalo (5037 records)[11]Calving ease score0.12 ± 0.02Egyptian buffalo (10240 records)[12]Calving interval0.55 ± 0.131Surti buffalo (158)[8]0.354 ± 0.076Nili-Ravi buffalo (501)[9]0.21 ± 0.14Iranian buffalo (2349 records)[23]0.15Egyptian buffalo (2644 records)[68]0.14 ± 0.10Murrah buffalo (577)[60]0.14 ± 0.07Murrah buffalo (1578)[7]0.12 ± 0.06Murrah buffalo (679)[10]0.11Anatolian buffalo (493)[69]0.1Brazilian crossbred buffalo (628)[6]0.09 ± 0.068Egyptian buffalo (907)[19]0.08Mediterranean buffalo (7915)[18]0.07 ± 0.05Egyptian buffalo (637)[22]0.07Egyptian buffalo (1589 records)[70]0.06 ± 0.01Murrah buffalo (4729 records)[40]0.051 ± 0.004Thai Swamp buffalo (1034 records)[20]0.05 ± 0.01Crossbred Murrah buffalo (2054 /5672 records)[17]0.05Italian buffalo (94028 records)[63]0.03Murrah buffalo (765 records)[28]0.03 ± 0.021Murrah buffalo (702 /1721 records)[71]0.02Murrah buffalo (3392)[24]0.002 ± 0.02Egyptian buffalo (8000 records)[12]Conception rate0.05Murrah buffalo (637)[66]0.02 ± 0.005Murrah buffalo (748 records)[72]Gestation length0.01 ± 0.02Egyptian buffalo (10240 records)[12]Open days0.14 ± 0.03Murrah buffalo (6894 records)[40]0.14Egyptian buffalo (2644 records)[68]0.08Egyptian buffalo (1589 records)[70]0.0001 ± 0.01Egyptian buffalo (8000 records)[12]Service period0.318 ± 0.082Nili-Ravi buffalo (501)[9]0.11Anatolian buffalo (493)[69]0.07 ± 0.065Egyptian buffalo (907)[19]0.02Murrah buffalo (643)[66]). Among all examined traits, AFC and CI have been the most extensively studied.
The highest heritability estimate for AFC was 0.42 in Brazilian crossbred buffaloes [6], while the lowest was 0.07 in Murrah buffaloes [7]. Most studies have indicated that the heritability of CI is low, typically below 0.2. However, a notable exception is the highest recorded heritability for CI-0.55-observed in the Surti buffaloes [8], followed by that of the Nili-Ravi breed [9].
Using the same estimation methodology, a lower heritability for CI was observed in Murrah buffaloes compared to Nili-Ravi buffaloes [9, 10], suggesting that CI in the latter is more responsive to selective breeding. Notably, a previous study on Nili-Ravi buffaloes reported a heritability of 0.21 for AAP [11], a trait that is typically more difficult to record than AFC in buffaloes. Furthermore, the heritabilities of AFS, CE, CR, OD, GL, and SP are relatively low, indicating that these traits may be more effectively improved through appropriate management practices.
Notably, Amin et al. observed low heritability [12] for most reproductive traits in Egyptian buffaloes, suggesting that genetic selection may not be expected to improve reproductive efficiency within this specific population.
Heritability estimates for the same traits exhibit great variation across different buffalo breeds and even among distinct populations within the same breed. These variations can be attributed to environmental factors, breed, management, season, and parity, as well as estimation models and data sources, suggesting the necessity of evaluating each breed or population separately [13]. Compared to buffaloes, heritability in cattle has been estimated for a wider range of reproductive traits, including ovulation rate, superovulatory response, non-return rate, twinning rate, and reproductive hormone levels [14].
Notably, the heritabilities for the number of palpable corpora lutea (CL) and the number of viable embryos (VE), both key superovulatory response traits, are relatively high in cattle, ranging from 0.47 to 0.57 and 0.20 to 0.65, respectively [15]. However, such estimates have seldom been evaluated in buffaloes. Therefore, it is recommended that further research prioritize investigating these traits in buffalo populations.
Compared to female buffaloes, there are fewer heritability estimates available for the reproductive performance of male buffaloes. In the literature on Egyptian buffalo bulls, heritability estimates for semen traits vary widely, ranging from 0.04 for the percentage of abnormal sperm to 0.52 for mass motility [16]. Notably, most estimates for semen characteristics exceed those for female reproductive phenotypes, with the exceptions of abnormal sperm percentage and ejaculate volume [16].
The moderate-to-high heritability estimates for these male reproductive traits, such as mass motility, sperm livability, and sperm concentration [16], may be attributed to their greater additive genetic variances. Consequently, these male reproductive traits represent promising selection indicators for enhancing breeding programs in buffaloes.
Genetic correlations between reproductive and production traits
Numerous estimates of genetic correlations between various fertility traits have been reported in buffaloes. Specifically, the genetic correlation between AFC and CI was reported as 0.28 in crossbred Murrah buffaloes [17], 0.10 and 0.41 in Italian Mediterranean buffaloes [18], 0.38 and 0.61 in Egyptian buffaloes [12, 19], 0.495 and 0.517 in Thai Swamp buffaloes [20]. These findings suggest the potential for simultaneous genetic improvement of both traits through selection in these populations.
In contrast, negative correlations between these two traits have also been reported in Egyptian, Murrah, Mediterranean, and Iranian buffaloes [21,22,23]. These conflicting results within the same breeds, such as Egyptian, Murrah, and Mediterranean buffaloes, may be attributed to environmental variations, management practices, population structure, and sample sizes. Furthermore, Amin et al. [12] and Easa et al. [19] demonstrated strong genetic correlations between CI and OD (0.99), as well as CI and SP (0.96) in Egyptian buffaloes, indicating that the calving interval is highly dependent on OD and SP.
In males, high genetic correlations were observed between AS and multiple traits: AS-MM (0.89), AS-EV (0.85), AS-LS (0.76), AS-CONC (0.82). Similarly, strong correlations were found between sperm concentration and other traits: CONC-EV (0.82), CONC-MM (0.95), CONC-LS (0.92). Conversely, low genetic correlations were found between LS and EV (0.22), as well as between MM and EV (0.17) in Egyptian buffaloes [16].
In addition to the correlation analysis among reproductive traits, numerous studies have also explored the relationships between reproductive and lactation traits. Genetic correlations of –0.22 and –0.25 between milk yield (MY) and CI were reported in Murrah buffaloes [24], Brazilian crossbred buffaloes [25], respectively. Subsequently, Barros et al. [17] and Hossein-Zadeh [23] reported that the genetic correlations between MY and CI were near-zero in Murrah and Iranian buffaloes.
In contrast, Gómez-Carpio et al. [18] showed a positive correlation of 0.48 between MY and CIV2_12 in Mediterranean buffaloes, which may be linked to the breeding goals for increased milk yield. Furthermore, positive genetic correlations between MY and AFC have been reported in Italian and Brazilian buffaloes [17, 18, 26]. Notably, in Egyptian buffaloes, the genetic correlation between MY and AFC was influenced by different lactations; specifically, AFC was positively correlated with MY during the first and second lactations, whereas a negative correlation was observed for the third lactation [22].
The genetic correlation between reproductive and lactation traits indicates that selection for milk production traits can impact both genetic merit and phenotypic traits related to fertility. The findings from the aforementioned studies provide insights into the direction and application of genetic selection across different breeds or populations. Notably, it is essential to adopt balanced breeding strategies that incorporate both production and reproductive traits.
In such cases, it is advisable to use a selection index that assigns appropriate weights to each trait to achieve optimal genetic progress.
Genomic regions and candidate genes associated with reproductive traits in buffalo
Fertility is a complex quantitative trait controlled by multiple minor-effect genes. In this context, reproductive traits have been associated with several quantitative trait locus (QTL) regions and candidate genes. Recently, genome-wide DNA markers based on single-nucleotide polymorphism (SNP) arrays or sequencing have been used to capture the effects of numerous genetic variants.
The identification of QTL regions and key candidate genes associated with reproductive traits could enhance the accuracy of genomic selection and accelerate genetic progress, particularly for traits with low heritability in buffalo breeding programs.
Genomic regions associated with reproductive traits
Unlike cattle, few studies and databases have been conducted to identify QTLs related to reproductive traits in buffaloes. However, due to the high homology between cattle and buffaloes, mapping bovine QTL database onto the buffalo genome has emerged as a useful and common strategy for identifying QTLs associated with buffalo reproduction [27]. Using the single-step genomic best linear unbiased prediction (GBLUP) method in Murrah buffaloes, Araujo Neto et al. [28] revealed that genomic regions on Bubalus bubalis chromosomes (BBU) 3, 12, 21, and 22 were associated with both AFC and FCI; meanwhile genomic regions on BBU 6, 7, 8, 9, and 15 were associated with AFC, and regions on BBU 4,14, and 19 were related to FCI.
Nascimento et al. [29] reported putative QTLs on BBU 1, 2, 3, 16, and 18 in Murrah buffaloes, highlighting the SPATA33 and SPIRE2 genes on BBU18, which are associated with the meiotic process in mammalian germ cells and influence fertility of both sexes [30, 31]. Similarly, dos Santos et al. [32] detected seven genomic regions on BBU 1, 8, 11, 16, and 18 related to AFC in Murrah buffaloes, accounting for 2.92% of the total genetic variance. George et al. [33] identified several QTLs on BBU 1, 2, 3, 9, 10, 11, 16, 18, 21, and 25 associated with AFC, AFS, calving to FAI, and SP in Murrah buffaloes, which overlapped with QTL regions reported in cattle.
Through whole genome copy number variants (CNVs) mapping, Strillacci et al. [34] discovered several QTLs associated with reproductive traits on BBU 1, 6, 9, 10, 16, 17, 18, and 20 in Iranian river buffaloes. Furthermore, George et al. [35] used weighted single-step genome-wide association analysis (wssGWAS) to report genomic regions on BBU 14 and 3 for AFC, BBU17 for CI, and BBU 13 and 9 for FSP in Murrah buffaloes. Kumar et al. [36] noted that TANC1 (BBU2) and SSTR1 (BBU20) are associated with AFC in Murrah buffaloes, aligning with their roles as important fertility QTLs in cattle.
Additionally, Li et al. [37] identified 52 suggestive regulatory loci associated with AFC in river and hybrid buffaloes. Notably, few genomic regions have been identified for males. Nagar et al. [38] reported several genomic regions on BBU 3, 4, 6, 7, 14, 16, 20, 22, and the X-chromosome in Egyptian buffalo bulls that substantially influence ejaculate volume, mass motility, sperm livability, abnormality, and concentration.
Overall, these genomic regions are valuable for marker-assisted selection to improve reproductive efficiency in buffaloes.
Candidate genes associated with reproductive traits
An earlier study identified nine significant SNPs associated with buffalo reproductive traits using the BovineSNP50 BeadChip [39]. Although this represents a meaningful effort to assess the transferability of the bovine 50K SNP array to water buffaloes, the cattle SNP chip does not provide optimal coverage of the buffalo genome. With the availability of the first commercial buffalo genotyping array (90 K) and the development of next generation sequencing (NGS), a substantial number of GWAS and gene network analyses have been conducted, identifying several candidate genes potentially linked to reproductive traits in buffaloes (Table 2Table 2.Candidate genes associated with reproductive traits in buffalo Chr.
Candidate genes Approaches Traits Breeds No. samples/ animals References 1CPNE4GWASCalving to FAIMurrah buffalo120[33]NRIP1GWASSPMurrah buffalo120[33]EIF5A2, RPL22L1GWASASMMurrah buffalo96[42]2PDE11AGWASASMMurrah buffalo96[42]ERICH2GWASPPBIMurrah buffalo96[42]NCKAP5GWASAFCLocal buffalo, hybrid buffalo,Nili-Ravi buffalo, Murrah buffalo120[37]3SLAMF6GWASASMMurrah buffalo96[42]MAP2K6GWASFCIMurrah buffalo950[28]TRNAF-GAAWssGWASAFCThai Swamp buffalo462[20]NDUFS2GWAS,RNA-seqTCAMediterranean buffalo462[41]4IGF2BP3GWASPPBIMurrah buffalo96[42]PDZRN4WssGWASCIThai Swamp buffalo462[20]5TRHDEGWAS, RNA-seqODMediterranean buffalo462[41]6GRID2GWASASM, PPBIMurrah buffalo96[42]AGBL4GWASAFCLocal buffalo, hybrid buffalo,Nili-Ravi buffalo, Murrah buffalo120[37]PMVKGWASAFCMurrah buffalo950[28]IGFBP7GWAS, RNA-seqTCAMediterranean buffalo462[41]ILDR2, TADA1, POGK, MAELWssGWASAFCMurrah buffalo120[35]7EDIL3GWASPPBIMurrah buffalo96[42]8AQP1GWASAFSMurrah buffalo120[33]9FIG 4GWASODMurrah buffalo452[40]ATOX1, SPARC, FAT2WssGWASFSPMurrah buffalo120[35]10GRM1GWASAFCLocal buffalo, hybrid buffalo,Nili-Ravi buffalo, Murrah buffalo120[37]SCG5GWASCIMurrah buffalo452[40]12NRXN1GWASAFCLocal buffalo, hybrid buffalo,Nili-Ravi buffalo, Murrah buffalo120[37]ROCK2GWASAFCMurrah buffalo950[28]BMP10, GFPT1GWASAFC, FCIMurrah buffalo950[28]ABCC4GWASNSCMurrah buffalo452[40]13MYO16GWASAFCMurrah buffalo96[36]14PPP1R42GWASPPBIMurrah buffalo96[42]MACROD2, TASP1, FLRT3WssGWASAFCThai Swamp buffalo462[20]SNX5WssGWASAFCMurrah buffalo120[35]15COLEC10,TNFRSF11B, TRNAC-ACA, MAL2WssGWASAFC, CIThai Swamp buffalo462[20]17TPCN1GWASCIMurrah buffalo452[40]TRPC3, FGF2, SPRY1WssGWASCIMurrah buffalo120[35]18TRNAE-CUCGWASAFSMurrah buffalo120[33]KCNN4, NECTIN2, APOE, APOC2, APOC4ssGWASAFCMurrah buffalo553[32]TBCBGWAS,RNA-seqTCAMediterranean buffalo462[41]19ADCY2GWASFCIMurrah buffalo950[28]MARCH11, FBXL7WssGWASAFCMurrah buffalo120[35]21VOPP1GWASSPMurrah buffalo120[33]23LY86GWASNSCMurrah buffalo452[40]XSELPGWASAFCMurrah buffalo452[40]The table lists GWAS-identified candidate genes, with priority presented to those with annotated biological functions.), primarily focusing on river buffalo.
de Camargo et al. [40] identified several SNPs in the SELP, TPCN1, SCG5, ABCC4, LY86, and FIG 4 genes associated with AFC, CI, NSC, and OD traits in Murrah buffaloes. By integrating GWAS and RNA-seq techniques, Li et al. [41] identified 40 SNPs distributed in 28 genes associated with six reproductive traits (AFC, SCA, TCA, NSC, OD, and CI) in Italian buffaloes, highlighting candidate genes NDUFS2, TBCB, TRHDE, and IGFBP7, with the IGFBP7 gene being validated through functional analysis using RNA interference (RNAi) in buffalo granulosa cells. In addition, candidate genes of ROCK2, PMVK, ADCY2, MAP2K6, BMP10, and GFPT1 have been reported to be associated with AFC and FCI traits in Murrah buffaloes [28].
Another GWAS analysis based on double-digestion restriction-associated DNA tag genotyping -by-sequencing (ddRAD-GBS) technique by Vohra et al. [42] revealed that candidate genes PDE11A, SLAMF6, GRID2, EIF5A2, and RPL22L1 were associated with ASM, while ERICH2, IGF2BP3, PPP1R42, GRID2, and EDIL3 were related to PPBI, with GRID2 being particularly noteworthy as it was associated with both traits. Furthermore, Kumar et al. [36] identified the MYO16 gene, previously reported to be related to milk, protein, and fat yields in Brazilian buffaloes [43], as significantly associated with AFC in Murrah buffaloes. In another GWAS on Murrah buffaloes, George et al. [33] identified five major candidate genes (AQP1, CPNE4, NRIP1, TRNAE-CUC, and VOPP1) associated with FAI, SP, and AFS. dos Santos et al. [32] selected five genes (KCNN4, NECTIN2, APOE, APOC2, and APOC4) involved in T-cell regulation and lipid metabolism pathways as key candidate genes associated with AFC.
George et al. [35] further highlighted ILDR2, SNX5, and MAEL as important genes for reproductive traits. Specifically, ILDR2 is involved in lipid metabolism and glucose homeostasis [44], SNX5 has been linked to the prediction of early pregnancy loss in dairy cows [45], and MAEL is crucial for germ cell differentiation and development [46]. Based on multiple buffalo breeds, Li et al. [37, 47] identified 58 candidate genes associated with AFC, emphasizing AGBL4, GRM1, NCKAP5, and NRXN1, which are primarily enriched in the FOXO and estrogen signaling pathways.
In swamp buffalo, Lomngam et al. [20] reported candidate genes (COLEC10, TNFRSF11B, TRNAC-ACA, MAL2, MACROD2, TASP1, FLRT3, TRNAF-GAA, and PDZRN4) associated with AFC and CI. Notably, few studies are available regarding candidate genes related to male reproductive traits. Nagar et al. [38] identified five novel candidate genes related to LS, AS, MM, and CONC traits in Egyptian buffaloes.
Among the aforementioned genes, NRIP1, TRNAF-GAA, NRXN1, MYO16, and TRNAC-ACA warrant particular attention as they have been previously reported to be associated with milk production traits [40, 43, 48, 49], indicating their potential pleiotropic effects. In summary, these identified candidate genes provide valuable insights into the genetic basis of buffalo reproduction and represent promising targets for future breeding programs.
Selection signatures for fertility in buffalo
Selection processes often leave specific footprints in various regions of the genome, known as selection signatures [50]. The identification of these selection signatures is crucial for uncovering genomic regions, allowing for the targeting of candidate genes related to specific phenotypic traits. The emergence of high-throughput sequencing technologies and SNP genotyping has heightened interest in the genome-wide detection of selection signatures, providing novel insights into the evolution and genetic architecture of important economic traits in buffaloes.
In river buffalo populations, Saravanan et al. [51] identified several genes linked to reproductive traits, including KCND2, ESRR, ASS1, NBAS, NOG, and PPFIBP1, the latter of which has also been reported in cattle [52]. Furthermore, Si et al. [53] observed that the GKAP1 gene, which regulates the apoptosis, number, and motility of germ cells, exhibits the highest level of divergence and is under selection in both river and swamp buffaloes.
Function prediction of main candidate genes
To elucidate specific biological functions, the lists of candidate genes associated with fertility from various studies were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses (Figs. 1Fig. 1.GO analysis of candidate genes associated with reproductive traits in buffalo.
GO terms are categorized into three main domains: Biological Process (BP, blue), Molecular Function (MF, green), and Cellular Component (CC, orange). The X-axis represents the number of candidate genes enriched in each term, while the Y-axis lists the GO functional categories., 2Fig. 2.KEGG enrichment analysis of candidate genes associated with reproductive traits in buffalo.
The bubble plot displays the significantly enriched KEGG pathways. The X-axis indicates the Rich Factor, defined as the ratio of the number of candidate genes to the total number of genes in a specific pathway. The color gradient from blue to red represents the statistical significance, expressed as –log10 (P Value).
The size of each bubble corresponds to the number of genes (gene count) enriched in the respective pathway.), as well as protein-protein interaction (PPI) network analysis (Fig. 3Fig. 3.Protein-Protein Interaction (PPI) network of candidate genes associated with reproductive traits in buffalo.
The network visualizes predicted functional interactions among protein products of the candidate genes. Nodes represent individual proteins, and edges represent documented or predicted functional interactions between them. Both node size and color intensity are scaled according to their degree within the network.).
In terms of biological processes, the most prominent categories included the positive regulation of biological process, multicellular organismal process, and positive regulation of cellular process. Regarding molecular functions, the most represented categories were those associated with protein binding, molecular function activator activity, protein homodimerization activity, and signaling receptor regulator activity. For cellular components, the most frequently represented categories were the extracellular region, extracellular space, and protein-lipid complex.
Importantly, the candidate genes were significantly enriched in pathways such as the estrogen signaling pathway, GnRH signaling pathway, calcium signaling pathway, and cholesterol metabolism, all of which are well-documented to be involved in reproductive processes [54,55,56,57]. In the PPI network analysis, fibroblast growth factor 2 (FGF2) was identified as the central gene, which has been demonstrated to stimulate granulosa cell proliferation and decrease differentiation [58]. Interestingly, apolipoprotein E (APOE), which plays a significant role in mammalian ovarian steroidogenesis [59], was also identified as a key candidate gene.
Conclusions and prospects for the future
This review provides a comprehensive overview of genetic parameter estimates, genomic regions, and candidate genes associated with buffalo reproduction, enhancing our understanding of the genetic architecture underlying fertility. Given the low-to-moderate heritability of traditional reproductive traits in buffaloes, identifying novel indicators with higher heritability is essential to facilitate genomic selection. While numerous QTLs and candidate genes have been identified through GWAS, multi-omics, and selection signature analyses, a significant gap remains between gene discovery and functional validation.
Furthermore, pinpointing causal mutations and genes related to reproductive traits remains a formidable challenge. To ensure balanced genetic progress despite complex correlations between reproductive and production traits, we recommend using a selection index that applies appropriate weights to all relevant traits. Ultimately, improving fertility will require the integration of genomic selection with optimized assisted reproductive technologies, such as estrus synchronization, ovum pick-up and in vitro fertilization (OPU-IVF), and embryo transfer.
Conflict of interests
The authors declare no conflicts of interest.