Two Novel Rare Strongly Linked Missense SNPs (P27R and A85G) Within the GDF9 Gene Were Significantly Associated With Litter Size in Shaanbei White Cashmere (SBWC) Goats

Introduction

The growth differentiation factor 9 (GDF9) gene is a unique member of the transforming growth factor β (TGFβ) superfamily (1), as its protein has six Cys, being different from others in this superfamily that have seven or nine Cys (2, 3). Moreover, its greatest expression is in the ovary while it is widely expressed in 20 different tissues such as the hypothalamus, pituitary, and uterus (4, 5); this indicates that it affects different physiological pathways and metabolism, as well as phenotypic expression to some degree (6). Along with being a powerful intra-ovarian regulator during early folliculogenesis, the GDF9 gene is expressed throughout follicle development, and its mutations may contribute to increased ovulation rates or infertility in female mammals (7, 8).

Based on relevant study and data from the National Center for Biotechnology Information Search database (NCBI), 45 SNP loci have been identified in the goat GDF9 gene (9). Among these, 15 SNPs were identified to have significant associations with litter size in more than 30 goat breeds (10–12). Furthermore, there were some controversial and promising SNPs that had different impacts in different goat breeds (13–16). For instance, three missense mutations, A240V (17), Q320P (18–20), and V397I (21, 22), and three synonymous mutations, L61L (23), N121N (24), and L141L (25, 26), were found to have high mutant frequencies and be significantly associated with litter size in different breeds. However, due to breed-specific effects, several of the above results were not consistent. For instance, in Shaanbei white cashmere (SBWC), Lubei White, Jining Gray, Inner Mongolia cashmere, and Laiwu black goat breeds, the G allele is the major allele in V397I (22, 23, 26), but among the Xinong Saanen dairy goat, Big foot black goat, Guanzhong dairy goat, Jintang black goat, and Yimeng black goat, the A allele is associated with larger litter sizes (27, 28). However, almost all the abovementioned studies focused on a single SNP locus and neglected the fact that quantitative traits are controlled by multiple loci. Therefore, the gap in research regarding the combined effects of multiple loci needed to be addressed.

A combination of whole-genome sequencing and marker-assisted selection (MAS) could satisfy the demand to screen pivotal genes accurately and rapidly (29–31), as well as to assess relationships between their variations and growth and reproductive traits (32–35).

As a powerful high-fertility candidate gene, our group previously found that two strongly linked SNPs, Q320P and V397I, and a 12-bp indel within the GDF9 gene were significant associated with litter size in goats (20, 36). Furthermore, we summarized all reported SNPs within the GDF9 gene (9). Hence, based on our preliminary work, this study aimed to verify a greater number of SNP loci within the GDF9 gene as well as to analyze their relationships with goat litter size, thus providing more information for selecting a population with a high fertility using MAS method in goat rearing.

Materials and Methods

All experiments were approved by the International Animal Care and Use Committee of the Northwest A&F University (IACUC-NWAFU; protocol number NWAFAC1008) and followed local animal welfare guidelines, laws, and policies. The care and use of animals complied with local animal welfare laws and policies.

Sample Collection and DNA Isolation

For this study, 309 ear tissue samples were randomly collected from female SBWC goats (2–3 years) at a goat-breeding farm in Yulin City, Shaanxi Province, China. All selected goats had the same diet and rearing conditions after weaning (37, 38), were healthy, and had records for their first-born litter size and growth traits (e.g., body height, body length, heart girth, body weight, and cannon bone circumference index). Additionally, random selection ensured that individuals were as unrelated as possible (39, 40).

DNA was extracted from ear tissue samples, diluted to 50 ng/μl, and stored at −20°C according to Aljanabi's method (41). The DNA extraction protocol was as follows: 400 μl of buffer (0.4 M NaCl, 10 mM Tris–HCl at pH 8.0, and 2 mM EDTA at pH 8.0), 40 μl of 20% SDS (2% final concentration), and 8 μl of 20 mg/ml proteinase K (400 μg/ml final concentration) were added to the fresh tissue and mixed well. The samples were kept at a constant temperature of 65°C in a water bath shaker for 12–16 h, followed by the addition of 300 μl of 6 M NaCl (NaCl saturated H₂O) and subsequent centrifugation for 30 min at 10,000 r/min. The supernatant was then transferred to fresh tubes. An equal volume of isopropanol was added to each sample, mixed well, and samples were incubated at 20°C for 1 h. Samples were then centrifuged for 20 min at 4°C and at 10,000 r/min. The pellet was washed with 70% ethanol, dried, and finally resuspended in 300–500 μl sterile dH₂O.

Primer Design, PCR Amplification, and Genotyping

Based on the sequence of Capra hircus species (GenBank Accession No. NC_030814.1), a pair of primers (F: 5′-TTTGGTTTTGCTGCTTTGCCT-3′; R: 5′-TCTTTCTTCTTCCCTCCACCCA-3′), which covered P27R, L61L, A85G, L50P, G40G, N112N, and D129D loci, were designed to amplify exon 3 of the goat GDF9 gene using Primer Premier software (Version 6.0). The PCR was carried out in a 25-μl reaction condition containing 1.0 μl of genomic DNA, 0.5 μl of forward and reverse primer separately, 12.5 μl of 2 × MIX (Tsingke, Xi'an, China), and 10.5 μl of ddH₂O. The PCR amplification protocol contained a pre-denaturation at 95°C for 5 min and denaturation at 94°C for 30 s, followed by 18 cycles of denaturation for 30 s at 95°C, annealing for 30 s at 68°C (with a decrease of 1°C per cycle), 30 cycles of elongation at 72°C for 30 s, and a final extension at 72°C for 10 min with subsequent cooling to 4°C (42, 43). Subsequently, PCR products were genotyped by electrophoresis using 2.0% agarose gel, which was stained with ethidium bromide. The PCR product was then directly sequenced by the Tsingke Biotechnology Company (Xi'an, China) using Sanger sequencing technology. Finally, sequence alignment was conducted using BioXM 2.6 (College of Agriculture, Nanjing Agricultural University, Nanjing, China) and Chromas 2.4.1 (Technelysium Pty Ltd, South Brisbane, Queensland, Australia).

Statistical Analyses

Genotype and allele frequencies, Hardy–Weinberg equilibrium (HWE), homozygosity (Ho), heterozygosity (He), effective allele numbers (Ne), and polymorphism information content (PIC) were calculated using PopGene version 1.3.1 (Molecular Biology and Biotechnology Center, University of Alberta, Edmonton, Canada) (44).

Linkage disequilibrium (LD) analysis was conducted on the SHEsis online platform (http://analysis.bio-x.cn) (45). The case of D′ = 1 or r² = 1 indicated a complete LD. Values of D′ < 1; r² > 0.33 indicated strong LD (46, 47).

The general linear models were established to analyze correlations between SNP loci and litter size and growth traits using R_3.2.0 software. For litter size, model I: Y_ijlm = μ + K_i +HYS_j + G_l + ε_ijlm, where Y_ijlm is the litter size phenotypic value, μ is the mean of the overall population, K_i is the effect of kidding years, HYS_j is the mean of population, G_l is the fixed effect of the genotype, and ε_ijlm is the random error (36).

Considering that growth traits had a positive correlation with litter size (39), association between SNP loci and growth traits were analyzed. The association between SNPs and growth traits was considered. Model II: Y_klm = μ₂ + A_k + G_l + ε_klm, where Y_klm is the observation of growth traits on each of the klmth animal, μ₂ is the population mean, A_k is the fixed effect of age of the kth animal, G_l is the fixed effect of genotypes of the lth animal, and ε_klm is the random error.

t-test and the analysis of variance (ANOVA) were conducted to analyze the association between SNP loci and quantitative traits. Moreover, the t-test directed to two group analyses and ANOVA were available for >2 group analyses.

Function Prediction of P27R and A85G Within the Goat GDF9 Gene

As P27R and A85G are missense SNP loci, they may contribute to amino acid type change during encoding of the goat GDF9 gene. Herein, potential effects of SNP loci on protein structures and functions were predicted using three prediction tools, SIFT, PolyPhen-2, and PROVEAN (48–50).

Results

Genotyping of SNP Loci Within the Goat GDF9 Gene

A total of three SNP loci (Table 1; Figure 1) in exon 3 of the GDF9 gene were detected in this study. Based on sequence chromatograms, three SNP loci (P27R, L61L, and A85G) were genotyped in the analyzed SBWC goat population. For both P27R—-where TGC (proline) transformed to TGG (alanine)—-and L61L loci, two genotypes, CC and CG, were identified. For the A85G locus where CCT (alanine) transformed to CCG (glycine), three genotypes (CC, CA, and AA) were verified.

TABLE 1

Table 1. Name information of SNPs within the goat GDF9 gene.

FIGURE 1

Figure 1. Sequence chromatograms of seven SNPs in the goat GDF9 gene run on Chromas. (A) P27R; (B) A85G; (C) L61L; (D) G40G; (E) L50P; (F) N112N; (G) D129D.

Genotypic and Allelic Frequencies of SNP Loci of the GDF9 Gene

Based on PIC values, P27R and A85G displayed low genetic diversity and PIC values were 0.047 and 0.019, respectively (Table 2). The PIC value of L61L was 0.355, demonstrating a medium genetic diversity. Additionally, CC and CG genotypes were identified in the P27R locus, and frequencies of C and G alleles were 0.976 and 0.024, respectively. For the L61L locus, three genotypes (CC, CA, and AA) were identified, and frequencies of C and A alleles were 0.639 and 0.361, respectively. For the A85G locus, in which CC and CG genotypes were detected, the frequency of C and G alleles were 0.990 and 0.010, respectively. Furthermore, both P27R and A85G loci met the HWE principle, whereas L61L did not (Table 2).

TABLE 2

Table 2. Genotypic and allelic frequencies of seven SNPs within the GDF9 gene in Shaanbei white cashmere (SBWC) goats.

Linkage Disequilibrium Analyses

Based on LD analysis results (Table 3; Figure 2), the P27R and A85G loci were discovered to be strongly linked; the D′ and r² values were 0.926 and 0.703, respectively. For L61L with P27R locus, and L61L with A85G locus, D′ values were 0.985 and 0.973, respectively, and r² values were 0.016 and 0.012, respectively.

TABLE 3

Table 3. Linkage disequilibrium parameters (D′ and r²) among P27R, L61L, and A85G loci of the GDF9 gene in Shaanbei white cashmere (SBWC) goats.

FIGURE 2

Figure 2. Linkage disequilibrium plots of the GDF9 gene in Shaanbei white cashmere (SBWC) goats. (A,C): D′ of SBWC goats; (B,D): r² of SBWC goats. The three loci in the red dotted box were detected in this study and the two loci in the black dotted box were detected in our previous study.

Furthermore, in our previous study, two strongly linked SNPs, Q320P and V397I, were identified to be significantly associated with litter size, using the same population as those in this study (20). Combining our previous data regarding two missense SNPs (Q320P and V397I) within the goat GDF9 gene (20), the LD analysis of P27R, L61L, A85G, Q320P, and V397I (Table 4; Figure 2) revealed that neither P27R nor A85G was strongly linked to Q320P or V397I; our previous study verified that these were strongly linked, and significantly affected litter size. Interestingly, L61L was almost found to be strongly linked to both the Q320P and V397I loci. For L61L with Q320P, and L61L with V397I, D′ values were 0.573 and 0.741, respectively, and r² values were 0.306 and 0.324, respectively.

TABLE 4

Table 4. Linkage disequilibrium parameters (D′ and r²) among P27R, L61L, A85G, Q320P, and V397I loci of the GDF9 gene in Shaanbei white cashmere (SBWC) goats.

Association Analyses Between SNP Loci and Litter Size

The association analysis between SNP loci and litter size (Table 5) illustrated that the A85G locus was significantly associated with litter size (P = 0.029), with the heterozygosity genotype displaying a superior phenotype over the homozygosity genotype. However, the P27R locus did not exert a remarkable effect on litter size. In combination genotype analysis of the P27R and A85G loci (Table 6), combination genotypes were significantly correlated to litter size (P = 3.8E−15) and the CG-CG produced the largest litter size. Furthermore, based on our previous data of Q320P and V397I (20), we did a combination genotype analysis, which showed that combination genotypes of P27R, A85G, Q320P, and V397I also exerted significant impacts on litter size (P = 0.001) with the CC-CC-CC-AA being dominant.

TABLE 5

Table 5. Relationship between SNP loci of the GDF9 gene and litter size in Shaanbei white cashmere (SBWC) goats.

TABLE 6

Table 6. Least squares mean and standard error for litter size of different combination genotypes of the GDF9 gene in Shaanbei white cashmere (SBWC) goats.

Association Analyses Between SNP Loci and Growth Traits

Previous studies by our group report a significant positive correlation between litter size and growth traits in our SBWC goat cohorts (20, 36); therefore, the relationship between these two missense SNPs (P27R and A85G) was addressed here. Association analyses between P27R and A85G loci as well as growth traits of SBWC goats revealed that the A85G locus was significantly related to body length (P = 0.001) and heart girth (P = 0.002). Furthermore, the heterozygosity genotype was superior when compared with the wild type (Table 7). No significance was found for P27R and growth traits. However, combination genotypes of P27R and A85G were proved to be significantly associated with hip width (P = 6.9E−8) and the cannon circumference index (P = 1.0E−6), with the phenotype of the homozygosity genotype showing a superior performance over the heterozygosity genotype (P = 6.9E−8; Table 8).

TABLE 7

Table 7. Relationship between the A85G locus of the GDF9 gene and growth parameters in Shaanbei white cashmere (SBWC) goats.

TABLE 8

Table 8. Least squares mean and standard error for growth parameters of different combination genotypes of the SNPs P27R and A85G within the GDF9 gene in Shaanbei white cashmere (SBWC) goats.

Protein Function Prediction of SNP Loci

For protein function prediction of the P27R and A85G loci, the scores of SIFT, PolyPhen-2, and PROVEAN suggested that their influence on the GDF9 protein was benign and probably damaging, respectively. The scores implied that A85G had an impact on protein structure change of the goat GDF9 gene.

Discussion

The GDF9 gene plays a considerable role in the control of somatic cell functions such as follicular proliferation, ovulation, and fertilization, as well as enhancing oocyte development in females (51, 52). Therefore, study of the GDF9 gene and its mutations is worthwhile (53, 54). In the current study, three SNP loci (P27R, L61L, and A85G) were identified within the goat GDF9 gene. The L61L locus is reported to have a negative association with litter size in Jining Gray and Yimeng Black goat breeds (55). However, no significant association has been noted in Wendeng dairy, Liaoning cashmere, Beijing native, Boer, and Lubei goat breeds (23), which was consistent with the findings of this study. This may be due to a breed-specific effect or some degree of linkage between L61L and other SNP loci (22). Furthermore, two novel SNP loci, P27R and A85G, were detected to have low mutation frequencies and be strongly linked. The A85G locus was found to be significantly associated with litter size in SBWC goats while the P27R locus was not. However, combination genotypes of P27R and A85G showed significant association with litter size of SBWC goats with the heterozygosity genotype having a larger litter size. This could imply that these two SNP loci may exert a synergistic effect on litter size in goats. Considering that the SIFT, PolyOhen-2, and PROVEAN scores of P27R and A85G were benign and probably damaging, respectively, A85G might be responsible for changes in protein structure as well as DNA-binding ability. Additionally, this variation may alter GDF9 gene outcomes in terms of post-transcriptional regulation, such as RNA modification (56, 57), alternative splicing (58), and tRNA processing (56). For P27R, which is suggested to have no effect on changes in protein structure, it might be linked with other major polymorphisms exerting indirect effects (20).

In our previous studies, a 12-base pair indel (36) and two strongly linked SNP loci, Q320P and V397I, were significantly associated with litter size in goats (20). Q320P&V397I and P27R&A85G were regarded as block1 and block2, respectively. To investigate the relationship between block1 and block2, LD analyses were performed. Results illustrated that no strong linkage existed between the two blocks, but the combination genotype of the Q320P&V397I&P27R&A85G loci was significantly associated with litter size in goats, and it could be speculated that the two blocks affected litter size synchronously. Given the fact that quantitative traits are controlled by multiple loci, and only two strongly linked blocks have been uncovered to date (20), the identification of novel strongly linked loci, as well as their combined effects, would be promising for MAS breeding.

Already knowing that growth traits are positively correlated with reproductive traits (39), our study set out to determine whether the two strongly linked SNPs had a significant effect on growth traits. Association analyses showed that A85G was significantly associated with growth traits, but this was not true of P27R. However, combination genotype analyses further verified that combination genotypes of P27R and A85G exerted a significant effect on growth traits. These findings hinted that P27R and A85G might have a synergistic effect on growth performance. Therefore, both A85G and P27R could be considered as growth-related loci to some degree.

Conclusion

In conclusion, two novel rare missense SNPs were verified to be strongly linked in this study. Moreover, A85G was significantly associated with litter size, and P27R could have a simultaneous effect. The present study succeeded in establishing a clear and significant correlation between two SNPs (P27R and A85G) within the GDF9 gene with litter size in SBWC goat. Further studies are needed to identify sire effect before the commercial use of these SNPs in MAS.

Data Availability Statement

The datasets analyzed for this study can be found in the Ensembl database under accession numbers listed in Table 1 [http://www.ensembl.org/index.html].

Ethics Statement

The animal study was reviewed and approved by The International Animal Care and Use Committee of the Northwest A&F University (IACUC- NWAFU) (protocol number NWAFAC1008). Written informed consent was obtained from the owners for the participation of their animals in this study.

Author Contributions

YB, JL, and XW came with idea and wrote manuscript. LH, KL, XS, and XL collected the goat samples and isolated of genomic DNA. YB, JL, XW, and LH performed the experiments. YB, LQ, XS, XL, and CP analyzed the data. All authors approved the final version of the manuscript for submission. All authors contributed to the article and approved the submitted version.

Funding

This work was funded by the Shaanxi Provincial Scientific and Technological Innovation Project for Undergraduates of Northwest A&F University (S201910712052).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

Sincere thanks go to all the staff at the Shaanbei White Cashmere goat breeding farm, who made sample collection possible. We would also like to thank the Agricultural Technical Station of Veterinary and Animal Husbandry of Yulin City, Shaanxi Province, P.R. China and the Life Science Research Core Services (LSRCS) of Northwest A&F University (Northern Campus), for providing us with the platform.

Abbreviations

GDF9, growth differentiation factor 9; SNPs, single-nucleotide polymorphisms; PCR, polymerase chain reaction; SBWC, Shaanbei white cashmere goat; P27R, p.proline27alanine; L61L, p.leucine61leucine; A85G, p.alanine85glycine; Q320P, p.glutamine320proline; V397I, p.valine397isoleucine; A240V, p.alanine240valine; N121N, p.asparagine336asparagine; L141L, p.leucine141leucine; LD, linkage disequilibrium; HWE, Hardy–Weinberg equilibrium; Ho, homozygosity; He, heterozygosity; Ne, effective allele number; PIC, polymorphism information content; MAS, marker-assisted selection.

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