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. 2025 Oct 7;36(1):2565161. doi: 10.1080/10495398.2025.2565161

Polymorphism of NCAPG gene and its association with growth traits in Nanjiang Yellow goat

Guolin Chen a,b,‡, Tao Ma a,‡, Jing Luo a,‡, Mancheng Zhang a, Shun Wu a, Yu Chen c, Yong Chen c, Deji Zhuoga d, Yuan Chen a, He Cai a, Hongping Zhang a, Jiazhong Guo a, Jiaxue Cao a, Dinghui Dai a, Li Li a,✉
PMCID: PMC12674292  PMID: 41056441

Abstract

The growth of livestock is influenced by both genetic and environmental factors, with genes like Non-SMC Condensin I complex subunit G (NCAPG) playing key roles in regulating growth. Previous studies have suggested that NCAPG is associated with growth traits in species such as cattle, sheep, and chickens. In this study, we comprehensively assessed NCAPG polymorphisms and their association with body weight (BW), chest circumference (CC), body height (BH), and body length (BL) at six stages (from newborn to 30 months) in 469 Nanjiang Yellow goats, utilizing the Generalized Linear Mixed Models (GLMM). Pearson correlation analysis revealed the strongest correlation between BW and CC (R = 0.91), followed by BW and BL (R = 0.87), and BW and BH (R = 0.89). Additionally, 24 single nucleotide polymorphisms (SNPs) within the NCAPG gene were identified, which were relatively conserved across closely related species. A notable missense mutation (g.5705 A > G) in exon 6 resulted in an amino acid substitution (281 Ile > Met), which may alter the physicochemical properties and conformation of the NCAPG protein. The resulting impairment in condensin function delays chromosome segregation, prolongs metaphase in growth plate stem cells, and enhances proliferative signaling, ultimately leading to variations in body size. Correlation analysis revealed that 13 of these SNPs were concurrently associated with multiple growth traits, including BH, BL, and CC. These polymorphisms in the NCAPG gene represent potential candidates for marker-assisted selection (MAS) to improve body size in Nanjiang Yellow goats.

Keywords: Nanjiang yellow goat, NCAPG, SNP, growth traits, association analysis

Introduction

The growth of livestock results from the combined influence of genetic and environmental factors.1 Superior growth traits typically lead to increased production efficiency and economic benefits, thereby boosting the incomes of farmers.2 The Nanjiang Yellow goat, a meat-type breed, is renowned for its excellent meat production performance. As an artificially selected breed, it possesses key characteristics such as high meat quality, strong adaptability, rapid growth and development, stable genetic traits, and high reproductive capability.3,4 However, significant variation in body size is observed among individuals. Among the primary economic traits of goats, growth traits are crucial for optimizing and expanding meat goat production on a large scale.5 Therefore, understanding the genetics underlying these traits is crucial for their genetic improvement, which relies significantly on the efficient molecular markers associated with superior growth characteristics.6

Non-SMC Condensin I complex subunit G (NCAPG), a mitosis-associated chromosomal condensation protein, is crucial for maintaining the structure of chromosomes.7,8 The structural stability of chromosomes is vital for normal organism development and life activities,9–12 and numerous researchers have delved into the intricate association between NCAPG and various forms of cancer.11,13–15 These studies underscore the ubiquitous upregulation of NCAPG across diverse cancer types, highlighting its pivotal role in tumor cell proliferation, invasion, metastasis, and apoptosis.

Beyond its role in cancer biology, NCAPG has attracted attention in livestock genetics. Genome-wide association studies (GWAS) and selection signature analyses have identified NCAPG as a potential candidate gene for growth traits in cattle, horses, donkeys, sheep, and goats,16–23 with significant associations reported for muscle growth, body size, average daily gain, and carcass weight in cattle.22–26 However, compared with these species, studies on the genetic variations of NCAPG in goats remain limited, and the potential mechanisms linking NCAPG polymorphisms to growth traits in goats are still poorly understood.

To address this gap, the present study focuses on SNPs within the NCAPG gene of Nanjiang Yellow goats, aiming to predict their effects on protein function and to investigate their association with growth traits. This work provides novel insights into the role of NCAPG in caprine growth and offers a potential basis for developing molecular markers to facilitate genetic improvement in goats.

Materials and methods

Collection of blood samples and phenotype data

A total of 469 healthy individuals were sampled, consisting of 205 males and 264 females from the Nanjiang Yellow Goat Breeding Farm in Nanjiang County, Sichuan Province, China. These individuals were subjected to consistent husbandry and management practices. All goats had recorded birth weights (BW0) and were maintained under standardized feeding and environmental conditions. Subsequently, at predetermined 2 months (M2), 4 months (M4), 6 months (M6), 12 months (M12), and 30 months (M30), comprehensive phenotypic assessments were conducted, including body weight (BW), body height (BH), body length (BL), and chest circumference (CC). To ensure data reliability, all measurements were performed by the same trained personnel using calibrated instruments, and each measurement was taken in duplicate. Average values were recorded for analysis to minimize operator bias and technical variability.

Blood samples were aseptically collected via jugular venipuncture, utilizing EDTA-2K as an anticoagulant, and subsequently stored at −20 °C for future analyses. Standard operating procedures were followed throughout sampling and handling to maintain consistency and sample integrity.

DNA quality detection and construction of the mixed Pool

The genomic DNA (gDNA) was extracted from these blood samples using a commercial blood DNA extraction kit that was bought from Tiangen Biochemical Technology Co., Ltd. in Beijing, China. The concentration and purity of DNA extraction were assessed using the NanoDrop 2000 spectrophotometer. DNA samples were deemed eligible if their concentration was greater than 50 ng/μL and their OD260/OD280 ratio was between 1.8 and 2. The qualified gDNA was uniformly diluted to 20 ng/μL. A gDNA pool was conducted by randomly selecting 30 samples (15 males and 15 females) with 5 μL from each.

PCR amplification, sequencing and genotyping

Based on our laboratory previous whole-genome resequencing data, we identified 24 candidate single nucleotide polymorphisms (SNPs) in NCAPG gene of the Nanjiang Yellow goat.27 Specific primers targeting these 24 SNPs were designed using Primer 5.0 software28 based on the NCAPG gene sequence (>NC_030813.1:37858058-37903119 Capra hircus breed San Clemente chromosome 6, ASM170441v1 Gene ID: 102176682; Figure S1) and synthesized by Beijing Qingke Biotechnology Co., Ltd. The following were included in the amplification system: 15 μL 2 × Taq PCR Master mix, 2 μL template, 1.5 μL of each upstream and downstream primer, and 10 μL ultrapure water. Then, PCR amplification was carried out using the mixed gDNA as a template, and the amplified products were sent to Beijing Qingke Biotechnology Co., Ltd. for Sanger sequencing. The PCR amplification primers are listed in Table S1. The amplification system included 15 μL 2 × Taq PCR Master mix, 2 μL template, upstream and downstream primer, 1.5 μL each, and 10 μL ultrapure water. The PCR protocol was set up as follows: 95 °C for 5 minutes, followed by 36 cycles of 95 °C for 30 seconds, annealing for 30 seconds (Table S1 provides the annealing temperatures), 72 °C for 2 minutes, extension at 72 °C for 7 minutes, and finally, 4 °C insulation. BioEdit v7.2.5.0 software29 was used to compare the sequencing findings in order to confirm the SNPs that were previously found using whole genome resequencing.

The gDNA from 469 individuals was provided to Bomiao Biotechnology Co., Ltd (Beijing), where high-throughput genotyping of the aforementioned 24 SNPs was conducted using the Agena Bioscience MassARRAY system with the corresponding 1st-PCRP, 2nd-PCRP, and UEP—SEQ for multiple SNPs designed by Assay Designer 4.0 (Agena Bioscience) (Table S2).

Structure prediction and physicochemical properties analysis of NCAPG protein altered by SNP

We determined the amino acid composition of goat NCAPG protein (Accession: XM_005681456.3, containing 1,017 amino acid residues) using PredictProtein (https://predictprotein.org/). Additionally, the Expasy (https://web.expasy.org/) was used to assess the physicochemical characteristics of the NCAPG protein, and phyre2 (http://www.sbg.bio.ic.ac.uk/phyre2/) and I-TASSER (https://zhanggroup.org/I-TASSER/) were used to predict the protein structure. In the meantime, the glycosylation and phosphorylation sites of the NCAPG protein were predicted using NetPhos-3.1 (NetPhos 3.1 - DTU Health Tech - Bioinformatic Services) and DictyOGlyc-1.1 (DictyOGlyc 1.1 - DTU Health Tech - Bioinformatic Services) in DTU Health Tech Services (https://services.healthtech.dtu.dk/services/), and IMu-tant (https://folding.biofold.org/i-mutant/i-mutant2.0.html) was used to assess stability of the protein.

Data statistical analysis

Using the algorithm in Microsoft Excel, we calculated genotype frequency, allele frequency, effective allele frequency, heterozygosity, homozygosity, Hardy-Weinberg equilibrium (HWE), and polymorphism information content (PIC) of the NCAPG gene. The chi-square test was used to perform initial population genetic assessments (e.g., Hardy–Weinberg equilibrium). Moreover, Haploview software30 was employed to analyse the linkage between SNP loci and construct a linkage map. We employed the Generalized Linear Mixed Models (GLMM) model in SAS v9.4 software31 with the following model formula to measure the relationship between the NCAPG gene and growth attributes in Nanjiang Yellow goats (supplementary section). The model aims to assess the impact of genotype, sex, season, and growth stage (time) on the dependent variable y, with both main effects and interactions considered. The random effects (γi) account for unobserved heterogeneity, and the residual error (ei) captures unexplained variation. For multiple testing correction, we applied false discovery rate control (Benjamini-Hochberg procedure) at q < 0.05.

Results

Correlation of growth traits in Nanjiang Yellow goat

The correlation analysis body weight (BW) and body size (containing BH, BL, and CC) of the Nanjiang Yellow goat was done using growth curves and Pearson correlation coefficients. The study revealed a consistent growth pattern in both body weight and morphometric traits among Nanjiang Yellow goats, demonstrating a significant positive correlation with advancing age from birth until 30 months (Figure 1(A)). The correlation coefficients (R) between BL, BH, and CC, with BW were 0.87, 0.89, and 0.91, respectively (Figure 1(B)). This suggests that CC was the most linked with BW and might be a useful indication for BW estimation.

Figure 1.

Figure 1.

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The growth and development of Nanjiang Yellow goat. A: Growth curve of body size and body weight. B: Correlation between body size and body weight. BL: Body length, CC: Chest circumference, BH: Body height.

Polymorphism of NCAPG gene in Nanjiang Yellow goat

We identified 24 SNPs within the NCAPG gene of Nanjiang Yellow goats (Figure 2) using validated gDNA templates (Figure S1) and PCR-Sanger sequencing. Among these, 22 SNPs were located in intronic regions, while 2 were found in exonic regions (g.5705 A > G and g.12074 A > G) (Table 1, Tables S1–S2, Figure 2). Comparative analysis with genomic variations in sheep and cattle revealed that these 24 SNPs in Nanjiang Yellow goats exist as either wild-type or mutant alleles in closely related species. Notably, sheep exhibited the same mutation at the g.7680 T > A locus (Figure 3). Across the goat population, observed and expected heterozygosity ranged from 0.037 to 0.736 and 0.036 to 0.500, respectively, while polymorphism information content (PIC) values ranged from 0.035 to 0.375. As only one genotype (AG) was identified at locus g.12627 A > G, no further analyses were performed for this site. Of the remaining 23 SNPs, 20 exhibited moderate polymorphism (0.25 < PIC ≤ 0.5), and 3 exhibited low polymorphism (PIC ≤ 0.25). Furthermore, 19 SNPs conformed to Hardy–Weinberg equilibrium (P > 0.05), while 4 SNPs (g.7088 G > A, g.9312 T > A, g.11846 A > G, and g.12361 T > A) deviated from equilibrium, suggesting they are under selection (P < 0.05) (Table 1).

Figure 2.

Figure 2.

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SNPs Of NCAPG gene identified in Nanjiang yellow goats. Red arrows pointed the mutated nucleotides.

Table 1.

Location and amino acid changes of NCAPG SNPs in Nanjiang Yellow goat.

Locus Position Amino acid change No. samples Genotypic frequency
 Allele frequency
 Observed heterozygosity(Ho) Expected heterozygosity(HE) χ2(HWE) P-value Polymorphism information content(PIC)
g.12074 a > G Exon9 No 465 AA(0.073) AG(0.342) GG(0.585) A(0.244) G(0.756) 0.342 0.369 2.309 P > 0.05 0.301
g.3205 T > G Intron3 No 465 GG(0.142) GT(0.419) TT(0.439) G(0.352) T(0.648) 0.419 0.456 2.939 P > 0.05 0.352
g.5705 A > G Exon6 Ile (I)>Met (M) 469 AA(0.486) GA(0.416) GG(0.098) A(0.694) G(0.306) 0.416 0.425 0.204 P > 0.05 0.335
g.6774 A > G Intron7 No 466 AA(0.073) AG(0.358) GG(0.569) A(0.252) G(0.748) 0.358 0.377 1.093 P > 0.05 0.306
g.7088 G > A Intron7 No 466 AA(0.524) GA(0.354) GG(0.122) A(0.701) G(0.299) 0.354 0.419 10.657 p < 0.05 0.332
g.7233 C > T Intron7 No 466 TT(0.910) TC(0.090)   T(0.955) C(0.045) 0.090 0.086 0.087 P > 0.05 0.082
g.7354 T > C Intron7 No 466 CC(0.573) CT(0.354) TT(0.073) C(0.750) T(0.250) 0.354 0.375 1.364 P > 0.05 0.305
g.7437 T > G Intron7 No 466 GG(0.569) GT(0.358) TT(0.073) G(0.748) T(0.252) 0.358 0.377 1.093 P > 0.05 0.306
g.7680 T > A Intron7 No 468 AA(0.613) TA(0.338) TT(0.049) A(0.782) T(0.218) 0.338 0.342 0.043 P > 0.05 0.283
g.9312 T > A Intron7 No 466 AA(0.534) TA(0.343) TT(0.122) A(0.706) T(0.294) 0.343 0.415 13.021 p < 0.05 0.329
g.11638 A > G Intron8 No 429 AA(0.077) AG(0.35) GG(0.573) A(0.252) G(0.748) 0.350 0.377 2.061 P > 0.05 0.306
g.11846 A > G Intron8 No 466 AA(0.122) GA(0.736) GG(0.536) A(0.293) G(0.707) 0.736 0.414 13.523 p < 0.05 0.328
g.12361 T > A Intron9 No 469 AA(0.527) AT(0.351) TT(0.126) A(0.700) T(0.300) 0.351 0.420 13.021 p < 0.05 0.332
g.12627 A > G Intron9 No 469 AG(1.000)     A(0.500) G(0.500)          
g.13815 G > A Intron9 No 466 AA(0.571) GA(0.357) GG(0.073) A(0.749) G(0.251) 0.357 0.376 1.225 P > 0.05 0.305
g.13963 A > T Intron9 No 467 AA(0.075) AT(0.361) TT(0.565) A(0.255) T(0.745) 0.361 0.380 1.228 P > 0.05 0.308
g.21814 A > C Intron12 No 469 AA(0.047) CA(0.325) CC(0.631) A(0.208) C(0.792) 0.325 0.329 0.226 P > 0.05 0.275
g.26816 A > G Intron13 No 466 AA(0.657) AG(0.301) GG(0.043) A(0.807) G(0.193) 0.301 0.312 0.561 P > 0.05 0.263
g.26893 T > C Intron13 No 444 CC(0.903) CT(0.097)   C(0.952) T(0.048) 0.097 0.092 0.104 P > 0.05 0.088
g.27192 A > G Intron13 No 469 GG(0.963) GA(0.037)   G(0.982) A(0.018) 0.037 0.036 0.006 P > 0.05 0.035
g.29006 G > C Intron14 No 466 CC(0.258) CG(0.484) GG(0.26) C(0.499) G(0.501) 0.484 0.500 0.550 P > 0.05 0.375
g.35483 T > C Intron14 No 466 CC(0.135) TC(0.443) TT(0.423) C(0.356) T(0.644) 0.443 0.459 0.604 P > 0.05 0.353
g.38004 A > C Intron14 No 466 AA(0.009) CA(0.161) CC(0.83) A(0.089) C(0.911) 0.161 0.162 0.028 P > 0.05 0.149
g.40816 C > T Intron16 No 466 CC(0.427) TC(0.432) TT(0.142) C(0.643) T(0.357) 0.432 0.459 1.699 P > 0.05 0.354
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Note: χ20.05(2)=5.991, χ20.01(2) =9.2.

Figure 3.

Figure 3.

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Comparison alignment of 24 NCAPG gene SNPs of Nanjiang Yellow goat with sheep and cattle genomic variations. Red indicates mutation sites, while dashes (-) represent positions where alignment with the reference sequence was not achieved.

Additionally, 23 SNPs were organized into three linkage disequilibrium (LD) blocks (blocks 1, 2, and 3), comprising 11, 4, and 4 SNPs, respectively. Four SNPs were not incorporated into any LD block (Table 2, Figure S2). The correlation coefficient between block 1 and block 2 was 1.0, indicating complete linkage, while the correlation coefficient between block 2 and block 3 was 0.93 (Figure S2). Moreover, 11, 7, and 11 combined genotypes were identified in blocks 1, 2, and 3, respectively (Table 4). The most frequent genotypes were GAGGAACCGGAAGGGGG (27.42%) in block 1, AATTCCAA (56.80%) in block 2, and CGTCCCTC (26.50%) in block 3, whereas the least frequent genotypes were AAAGGACTGTTAGGGG (0.94%), GGAAAAAG (0.43%), and CCTTAACC (0.80%) in blocks 1, 2, and 3, respectively (Table 4).

Table 2.

Association analysis of NCAPG SNPs genotype with body size in Nanjiang Yellow goat.

Locus Genotype(N) Average daily gain/g Body height/cm Body length/cm Chest circumference/cm Block
g.3205 T > G GG(66) 51.81 ± 0.961 56.07 ± 1.25b 59.14 ± 1.61 65.37 ± 2.08 –
GT(195) 53.21 ± 0.710 56.68 ± 1.24a 59.41 ± 1.60 65.89 ± 2.07 –
TT(204) 51.53 ± 0.670 56.51 ± 1.24ab 59.11 ± 1.60 65.59 ± 2.07 –
g.5705 A > G AA(228) 51.19 ± 0.624 56.40 ± 1.20 59.06 ± 1.60b 65.51 ± 2.05 Block 1
GA(195) 53.20 ± 0.699 56.70 ± 1.21 59.43 ± 1.60ab 65.90 ± 2.05 Block 1
GG(46) 54.83 ± 1.173 56.61 ± 1.22 59.53 ± 1.62a 65.80 ± 2.07 Block 1
g.6774 A > G AA(34) 49.59 ± 1.437 55.69 ± 1.21b 58.28 ± 1.60b 64.79 ± 2.06b Block 1
AG(167) 53.18 ± 0.729 56.75 ± 1.18a 59.44 ± 1.58a 65.89 ± 2.04a Block 1
GG(265) 51.97 ± 0.628 56.53 ± 1.18a 59.23 ± 1.57a 65.64 ± 2.03a Block 1
g.7088 G > A AA(244) 52.64 ± 0.647 56.74 ± 1.24a 59.46 ± 1.73a 65.87 ± 2.11a Block 1
GA(165) 53.40 ± 0.716 56.87 ± 1.24a 59.60 ± 1.73a 66.05 ± 2.12a Block 1
GG(57) 48.63 ± 1.003 55.45 ± 1.25b 58.11 ± 1.74b 64.57 ± 2.13b Block 1
g.7233 C > T TT(424) 50.91 ± 1.150 56.65 ± 1.22 59.33 ± 1.62 65.79 ± 2.06 Block 1
TC(42) 52.42 ± 0.570 56.50 ± 1.20 59.20 ± 1.60 65.63 ± 2.04 Block 1
g.7354 T > C CC(267) 51.97 ± 0.628 56.52 ± 1.18a 59.22 ± 1.57a 65.62 ± 2.04a Block 1
CT(165) 53.18 ± 0.729 56.77 ± 1.18a 59.46 ± 1.58a 65.91 ± 2.04a Block 1
TT(34) 49.59 ± 1.437 55.69 ± 1.21b 58.28 ± 1.60b 64.79 ± 2.06b Block 1
g.7437 T > G GG(265) 51.97 ± 0.628 56.53 ± 1.18a 59.23 ± 1.57a 65.64 ± 2.03a Block 1
GT(167) 53.18 ± 0.729 56.75 ± 1.18a 59.44 ± 1.58a 65.89 ± 2.04a Block 1
TT(34) 49.59 ± 1.437 55.69 ± 1.21b 58.28 ± 1.60b 64.79 ± 2.06b Block 1
g.7680 T > A AA(287) 52.08 ± 0.627 56.65 ± 1.16a 59.32 ± 1.56a 65.78 ± 2.01a Block 1
TA(158) 52.74 ± 0.732 56.58 ± 1.16a 59.34 ± 1.56a 65.67 ± 2.02a Block 1
TT(23) 49.73 ± 1.858 55.44 ± 1.21b 57.84 ± 1.60b 64.68 ± 2.06b Block 1
g.9312 T > A AA(249) 52.67 ± 0.649 56.74 ± 1.23a 59.46 ± 1.72a 65.87 ± 2.11a Block 1
TA(160) 53.40 ± 0.731 56.85 ± 1.24a 59.58 ± 1.72a 66.05 ± 2.11a Block 1
TT(57) 48.91 ± 1.010 55.48 ± 1.24b 58.14 ± 1.73b 64.57 ± 2.12b Block 1
g.11638 A > G AA(33) 49.50 ± 1.460 55.90 ± 1.30b 58.52 ± 1.83b 64.80 ± 2.06b Block 1
AG(150) 53.86 ± 0.742 56.93 ± 1.28a 59.73 ± 1.82a 66.03 ± 2.05a Block 1
GG(246) 52.56 ± 0.639 56.81 ± 1.27a 59.54 ± 1.82a 65.72 ± 2.05a Block 1
g.11846 A > G AA(57) 48.63 ± 1.002 55.45 ± 1.25b 58.11 ± 1.74b 64.57 ± 2.13b Block 1
GA(159) 53.51 ± 0.726 56.87 ± 1.24a 59.60 ± 1.73a 66.05 ± 2.12a Block 1
GG(250) 52.64 ± 0.642 56.75 ± 1.24a 59.47 ± 1.73a 65.87 ± 2.12a Block 1
g.12074 A > G AA(34) 49.59 ± 1.435 55.71 ± 1.25b 58.27 ± 1.61b 64.78 ± 2.04b Block 1
AG(159) 53.40 ± 0.737 56.72 ± 1.22a 59.45 ± 1.59a 65.92 ± 2.01a Block 1
GG(272) 51.98 ± 0.624 56.47 ± 1.22a 59.21 ± 1.58a 65.63 ± 2.01a Block1
g.12361 T > A AA(247) 52.53 ± 0.644 56.73 ± 1.23a 59.44 ± 1.71a 65.84 ± 2.11a –
AT(163) 53.40 ± 0.724 56.83 ± 1.23a 59.56 ± 1.71a 66.06 ± 2.11a –
TT(59) 48.98 ± 0.989 55.54 ± 1.24b 58.20 ± 1.72b 64.65 ± 2.12b –
g.13815 G > A AA(266) 51.97 ± 0.628 56.53 ± 1.18a 59.23 ± 1.57a 65.64 ± 2.03a Block 2
GA(166) 53.18 ± 0.729 56.75 ± 1.18a 59.44 ± 1.58a 65.89 ± 2.04a Block 2
GG(34) 49.59 ± 1.437 55.69 ± 1.21b 58.28 ± 1.60b 64.79 ± 2.06b Block 2
g.13963 A > T AA(35) 49.81 ± 1.408 55.74 ± 1.21b 58.34 ± 1.60b 64.85 ± 2.06b Block 2
AT(168) 53.18 ± 0.729 56.74 ± 1.18a 59.43 ± 1.57a 65.88 ± 2.03a Block 2
TT(264) 51.97 ± 0.629 56.53 ± 1.18a 59.23 ± 1.57a 65.64 ± 2.03a Block 2
g.21814 A > C AA(22) 49.66 ± 1.928 55.64 ± 1.22b 58.03 ± 1.59b 64.83 ± 2.07 Block 2
CA(151) 53.08 ± 0.754 56.60 ± 1.17a 59.33 ± 1.55a 65.71 ± 2.02 Block 2
CC(296) 51.97 ± 0.613 56.58 ± 1.17a 59.29 ± 1.55a 65.71 ± 2.02 Block 2
g.26816 A > G AA(306) 51.86 ± 0.609 56.58 ± 1.18a 59.26 ± 1.58a 65.66 ± 2.04 Block 2
AG(140) 53.29 ± 0.774 56.56 ± 1.19a 59.32 ± 1.59a 65.75 ± 2.05 Block 2
GG(20) 50.52 ± 2.018 55.63 ± 1.24b 58.01 ± 1.64b 64.77 ± 2.10 Block 2
g.26893 T > C CC(401) 52.88 ± 0.576 56.74 ± 1.28 59.51 ± 1.83 65.73 ± 2.06 –
CT(43) 51.12 ± 1.118 56.61 ± 1.30 59.34 ± 1.84 65.91 ± 2.07 –
g.27192 A > G GG(452) 52.30 ± 0.556 56.50 ± 1.20 59.22 ± 1.59 65.68 ± 2.03 –
GA(17) 49.39 ± 1.935 56.65 ± 1.26 58.99 ± 1.65 65.25 ± 2.10 –
g.29006 G > C CC(120) 52.18 ± 0.796 56.18 ± 1.24b 59.08 ± 1.61ab 65.53 ± 2.05 Block 3
CG(225) 52.29 ± 0.681 56.73 ± 1.23a 59.45 ± 1.62a 65.88 ± 2.05 Block 3
GG(121) 52.12 ± 0.795 56.46 ± 1.24ab 59.02 ± 1.61b 65.44 ± 2.05 Block 3
g.35483 T > C CC(63) 52.22 ± 1.010 56.40 ± 1.25ab 58.92 ± 1.63 65.34 ± 2.06 Block 3
TC(206) 51.73 ± 0.695 56.73 ± 1.24a 59.39 ± 1.62 65.72 ± 2.05 Block 3
TT(197) 52.63 ± 0.677 56.27 ± 1.24b 59.12 ± 1.62 65.66 ± 2.05 Block 3
g.38004 A > C AA(4) 51.34 ± 4.474 56.07 ± 1.51 58.80 ± 1.87 63.65 ± 2.33 Block 3
CA(75) 51.93 ± 0.995 56.48 ± 1.24 59.14 ± 1.61 65.68 ± 2.05 Block 3
CC(387) 52.31 ± 0.577 56.48 ± 1.23 59.23 ± 1.60 65.65 ± 2.04 Block 3
g.40816 C > T CC(199) 51.19 ± 0.657 56.32 ± 1.23b 58.99 ± 1.62b 65.37 ± 2.06b Block 3
TC(201) 53.09 ± 0.692 56.70 ± 1.23a 59.48 ± 1.62a 65.97 ± 2.06a Block 3
TT(66) 53.76 ± 1.011 56.59 ± 1.24ab 59.43 ± 1.63ab 65.94 ± 2.07ab Block 3
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Note: At the same loci, different superscript lowercase letters represent P < 0.05, and the same letter or no letters represent P > 0.05.

Table 4.

Association analysis of combined genotype in linked regions with growth traits in Nanjiang Yellow goat.

Block Combined genotype Proportion/% Body weight/kg Body height/cm Body length/cm Chest circumference/cm
Block1 GAGGAACCGGAAGGGG(116) 27.42 22.02 ± 0.18 56.96 ± 1.26abc 59.71 ± 1.80ab 65.71 ± 2.05bcd
Block1 AAGGAACCGGAAGGGG(77) 18.2 22.01 ± 0.18 56.77 ± 1.26bcd 59.41 ± 1.80bc 65.82 ± 2.05abc
Block1 AAAGGACTGTTAAGAG(63) 14.89 22.04 ± 0.18 56.91 ± 1.27abc 59.81 ± 1.80ab 65.90 ± 2.05abc
Block1 GAAGGACTGTTAAGAG(57) 13.48 22.05 ± 0.18 57.14 ± 1.27ab 59.98 ± 1.81ab 66.31 ± 2.06a
Block1 GGGGAACCGGAAGGGG(43) 10.17 22.06 ± 0.18 56.89 ± 1.27abc 59.92 ± 1.81ab 65.84 ± 2.06abc
Block1 AAAAGGTTTTTTAAAA(22) 5.2 21.97 ± 0.19 55.87 ± 1.30d 58.38 ± 1.83c 64.63 ± 2.08d
Block1 AAAGGACTGTAAAGAG(17) 4.02 22.05 ± 0.19 57.52 ± 1.30a 60.19 ± 1.83a 66.30 ± 2.08ab
Block1 AAAAGGTTTTTAAAAA(11) 2.6 22.08 ± 0.19 56.12 ± 1.30 cd 59.00 ± 1.85bc 65.18 ± 2.10 cd
Block1 GAAGGACTGTAAAGAG(9) 2.13 22.04 ± 0.20 56.30 ± 1.33bcd 59.02 ± 1.86bc 65.75 ± 2.10abcd
Block1 GAAGGACTGTTAGGGG(4) 0.94 21.90 ± 0.22 56.83 ± 1.57abcd 59.57 ± 2.07abc 67.78 ± 2.32a
Block1 AAAGGACTGTTAGGGG(4) 0.94 22.04 ± 0.22 56.62 ± 1.40bcd 59.05 ± 1.92bc 64.80 ± 2.16 cd
Block2 AATTCCAA(263) 56.8 21.82 ± 0.18 55.96 ± 0.55c 58.89 ± 0.68e 64.61 ± 1.00d
Block2 GAATCAAG(125) 27 21.86 ± 0.18 56.98 ± 0.58bc 59.71 ± 0.71de 67.83 ± 1.03c
Block2 GAATCCAA(28) 6.05 21.83 ± 0.18 58.24 ± 0.64ab 61.31 ± 0.77c 69.81 ± 1.12b
Block2 GGAAAAGG(20) 4.32 21.80 ± 0.19 59.62 ± 0.67a 63.17 ± 0.81a 72.75 ± 1.16a
Block2 GAATCAAA(13) 2.8 21.83 ± 0.19 57.12 ± 0.72bc 59.86 ± 0.86d 66.34 ± 1.25c
Block2 GGAACAAG(12) 2.6 21.88 ± 0.19 59.55 ± 0.73a 62.40 ± 0.87b 72.86 ± 1.26a
Block2 GGAAAAAG(2) 0.43 21.51 ± 0.25 58.08 ± 1.30abc 60.63 ± 1.50 cd 70.34 ± 2.15ab
Block3 CGTCCCTC(125) 26.5 21.78 ± 0.17 56.93 ± 1.27ab 59.69 ± 1.81ab 66.00 ± 2.19ab
Block3 CCTTCCTT(66) 13.3 21.75 ± 0.17 56.80 ± 1.28abc 59.69 ± 1.82ab 66.13 ± 2.20ab
Block3 GGCCCCCC(63) 14 21.73 ± 0.17 56.45 ± 1.30abc 59.23 ± 1.81bc 65.61 ± 2.20bc
Block3 CGTTCCTC(50) 10.6 21.77 ± 0.18 57.08 ± 1.28ab 60.01 ± 1.82a 66.65 ± 2.20a
Block3 GGTCCCCC(48) 10.1 21.77 ± 0.17 56.84 ± 1.28abc 59.67 ± 1.82ab 65.97 ± 2.20abc
Block3 CGTCCACC(32) 6.7 21.80 ± 0.17 57.26 ± 1.29a 59.81 ± 1.83ab 66.00 ± 2.21abc
Block3 CCTTCATC(25) 5.3 21.75 ± 0.18 56.60 ± 1.28bc 59.12 ± 1.84bc 65.90 ± 2.22abc
Block3 CCTTCCCC(23) 4.8 21.92 ± 0.18 54.81 ± 1.31d 57.16 ± 1.84d 63.60 ± 2.23d
Block3 CGTTCACC(17) 3.6 21.81 ± 0.18 55.98 ± 1.32c 58.89 ± 1.85bc 65.70 ± 2.24abc
Block3 GGTTCCCC(10) 2.1 21.69 ± 0.19 55.89 ± 1.37 cd 57.99 ± 1.90 cd 64.54 ± 2.29 cd
Block3 CCTTAACC(4) 0.8 21.83 ± 0.21 56.33 ± 1.52bc 59.16 ± 2.04bc 64.01 ± 2.45 cd
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Note: At the same block, different superscript lowercase letters represent P<0.05, and the same letter or no letters represent P>0.05.

The effect of NCAPG mutation g.5705 a > G on the protein

Of the two SNPs identified in exons, g.12074 A > G in exon 9 was a synonymous mutation, whereas g.5705 A > G in exon 6 altered the codon from ATA to ATG, resulting in an amino acid substitution from Isoleucine (Ile, NCAPG-281I) to Methionine (Met, NCAPG-281M) (Table 1). This substitution slightly decreased the proportion of Ile (from 6.50% to 6.40%) and increased the proportion of Met (from 2.10% to 2.20%) in the NCAPG protein (Table S3). Compared with the wild-type g.5705 A (NCAPG-281I), the mutant allele g.5705 G (NCAPG-281M) led to a modest reduction in the total number of atoms, instability index, aliphatic index, and GRAVY score of the NCAPG protein (Table S4, Figure S3). The NCAPG-281I > M substitution was located within the YCG1 (Interval 5–826) and PDS5 (Interval 99–359) domains (Figure S1), suggesting potential structural effects. Protein stability was reduced, as indicated by a negative change in free energy (ΔΔG = −0.826). The g.5705 A > G mutation did not substantially affect the glycosylation or phosphorylation of the protein (Figure S4).

Moreover, the secondary structure of the NCAPG protein was altered by g.5705 A > G mutation (Figure 4(A)). The composition characteristics of NCAPG-281I showed that Alpha helix (Hh), Extended strand (Ee), β-turn (Tt), and Random coil (Cc) account for 69.71%, 1.18%, 0.88%, and 28.22%, respectively; These were altered in NCAPG-281M (Hh 69.52%, Ee 1.47%, Tt 0.98%, and Cc 28.02%) (Table S5). Along with this, the regions with amino acid alterations in the tertiary structure of the NCAPG-281I protein differ from those in the NCAPG-281M protein (Figure 4(B)).

Figure 4.

Figure 4.

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The protein structure prediction of NCAPG-281I and NCAPG-281M. A: Secondary structure prediction of NCAPG-281I and NCAPG-281M. B: Tertiary structure prediction of NCAPG-281I and NCAPG-281M. Note: the red circle represents the area of structural change.

Note: At the same block, different superscript lowercase letters represent P < 0.05, and the same letter or no letters represent P > 0.05.

Association of NCAPG SNPs with growth traits in Nanjiang Yellow goat

Based on the GLMM analysis, SNPs in the NCAPG gene of Nanjiang Yellow goats showed no significant overall association with BW0 and average daily gain (Table S6 and Table 2). However, 19 of the 23 SNPs exhibited significant associations with body size traits at specific developmental stages. In particular, 18 SNPs were associated with BH (p < 0.05), 17 with BL (p < 0.05), and 13 with CC (p < 0.05) (Table S6), with 10 SNPs concurrently influencing all three traits. Age-dependent effects were also observed, as no significant associations were detected for BW0, BW or BL at 4 months, or for BH, BL, and CC at 6 months (Table S6). Moreover, several SNPs exhibited stage-specific effects, such as g.7233 C > T, which was linked to BL only at 30 months, and g.7680 T > A, which was associated with BH at 4 months (Table S6). These results highlight the age- and trait-specific contributions of NCAPG polymorphisms to growth regulation in goats.

Among these body size-related SNPs, 15 were distributed across three linkage blocks. Intriguingly, a total of 10 SNPs, including g.6774 A > G, g.7088 G > A, g.7354 T > C, g.7437 T > G, g.7680 T > A, g.11638 A > G, g.12074 A > G in block 1, g.13815 G > A, g.13963 A > T in block 2, and g.40816 C > T in block 3 were significantly associated with BH, BL, and CC simultaneously. The g.21814 A > C, g.26816 A > G, and g.29006 G > C were significantly correlated with BH and BL (p < 0.05) (Table 3).

Table 3.

Correlation between SNP loci in linked regions and growth traits in Nanjiang Yellow goat.

Block Locus Body weight(kg) Body height(cm) Body length(cm) Chest circumference(cm)
Block1 g.5705 A > G – – * –
Block1 g.6774 A > G – * * *
Block1 g.7088 G > A – * * *
Block1 g.7354 T > C – * * *
Block1 g.7437 T > G – * * *
Block1 g.7680 T > A – * * *
Block1 g.11638 A > G – * * *
Block1 g.12074 A > G – * * *
Block2 g.13815 G > A – * * *
Block2 g.13963 A > T – * * *
Block2 g.21814 A > C – * * –
Block2 g.26816 A > G – * * –
Block3 g.29006 G > C – * * –
Block3 g.35483 T > C – * – –
Block3 g.38004 A > C – – – –
Block3 g.40816 C > T – * – *
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Note: “+” Significantly related, “-” Indicates insignificant correlation.

The analysis of combined genotypes based on linked SNPs revealed that 29 genotype combinations showed no significant association with BW (Table 4). However, goats carrying specific genotypes combinations in Block 2 exhibited superior body size traits. For instance, individuals with the GGAAAAGG genotype displayed the largest BH (59.62 ± 0.67 cm) and BL (63.17 ± 0.81 cm), with a CC of 72.75 ± 1.16 cm, whereas those with the GGAACAAG genotype had the greatest CC (72.86 ± 1.26 cm) alongside relatively high BH (59.55 ± 0.73 cm) and BL (62.40 ± 0.87 cm). In contrast, goats with the CCTTCCCC genotype in Block 3 had the heaviest BW (21.92 ± 0.18 kg) but the smallest body dimensions, including BH (54.81 ± 1.31 cm), BL (57.16 ± 1.84 cm), and CC (63.60 ± 2.23 cm), compared with other Block 2 and 3 genotypes (Table 4). Collectively, these findings suggest that NCAPG polymorphisms influence body weight and size in a genotype- and distribution-dependent manner.

4. Discussion

Growth traits are regarded as a highly economic feature in meat goat production and greatly interest researchers and farmers. It has been demonstrated that NCAPG controls muscle generation in sheep, which influences growth traits.32 This study investigated the genetic polymorphisms of the NCAPG gene and their potential associations with growth-related traits in Nanjiang Yellow goats in order to look into how these polymorphisms affect goat. The correlation research shows that the chest circumference of the Nanjiang Yellow goats, which might be a useful indicator for body weight estimation, has the strongest relationship with their body weight. This aligns with the findings of earlier research.33

The NCAPG gene of the Nanjiang Yellow goat population had 24 SNPs that were confirmed in this investigation. Among these, 19 SNPs agreed with Hardy–Weinberg equilibrium (P > 0.05). The discovery that 4 SNPs were found to be under selection pressure (P < 0.05), which is noteworthy since it suggests that the Nanjiang Yellow goat has experienced some artificial selection. Moreover, 20 SNPs exhibited moderate polymorphism, whereas 3 had low polymorphism indicating an intermediate genetic diversity for the NCAPG gene in the Nanjiang Yellow goat.

In the Nanjiang Yellow goat, this study discovered a g.5705 A > G mutation in exon 6 of the NCAPG gene, which causes an amino acid shift from Ile to Met. This mutation occurs in both YCG1 and PDS5 interaction domains, which are critical for condensin complex assembly. The substitution of Ile with Met results in the loss of hydrophobic interactions within the protein, leading to alterations in secondary structure and modifications in tertiary structure, thereby reducing protein stability. This structural alteration may reduce condensin activity during mitosis, delay chromosome segregation, and enhance proliferative signaling pathways, thereby prolonging the metaphase of growth plate stem cells. Ultimately, these changes contribute to variations in body size in goats.24,25,34,35 According to the protein prediction results, g.5705 A > G modifies local microstructure and domains of the protein but has little impact on its physico-chemical characteristics.

In addition, the 24 SNPs identified in Nanjiang Yellow goats were found to be either wild-type or mutant alleles in closely related species, indicating that these SNPs exhibit a relatively conserved pattern across related species. Interestingly, cattle also have this same amino acid change from Ile to Met, albeit it results from a c.1326 T > G mutation. The sheep also exhibit the same mutation at the g.7680 T > A locus. These findings reveal an intriguing evolutionary pattern of convergent amino acid substitution in NCAPG across ruminant species. While Nanjiang Yellow goats and cattle both exhibit an isoleucine-to-methionine change in the NCAPG protein, this results from distinct genomic mutations (g.5705 A > G in goats vs. c.1326 T > G in cattle), suggesting independent selection for similar molecular functions. Among these 24 SNP loci, 22 SNPs were located in intronic regions that inherently lack coding capacity but may regulate multiple traits through specific mechanisms. For instance, some non-coding SNPs could influence gene expression by altering transcription factors and chromatin structure.36,37

Nowadays, most research on association between NCAPG and growth traits has been done in humans, cattle, horses, poultry, and sheep.38–41 In this study, 24 SNPs within the NCAPG gene were noticed in Nanjiang Yellow goats, 19 of them were shown to have significant correlations with growth traits. Notably, a total of 10 SNPs were associated with BH, BL, and CC at the same time. It is reported that polymorphisms in the leptin (LEP), β-1,4-N-acetylgalactosamine transferase 2 (B4GALNT2), insulin-like growth factor I (IGF-I), and myocyte enhancer factor 2 (MEF2) genes were all intricately linked to the growth performance of Nanjiang Yellow goats.42–45 These studies suggest that the growth traits in Nanjiang Yellow goat are complicated attributes linked to multiple genes, and more research is needed to determine how NCAPG affects growth traits.

Multiple livestock species have provided ample evidence for the critical role of the NCAPG gene in controlling growth-related parameters. The NCAPG gene significantly influences body weight, longissimus muscle development, carcass traits, and body frame size in cattle.11,24,25,35 The c.1326 T > G of NCAPG is the identified QTL potentially affecting divergent bovine fetal growth.35 Similarly, a correlation between NCAPG and rapid growth as well as increased body size has been observed in donkeys through genome-wide selective scanning.21,46 In sheep, a genome-wide scan has also identified an association between the NCAPG gene and traits related to body size, growth, and feed intake.47,48 Furthermore, the LCORL/NCAPG gene exhibits a significant correlation with body height in horses,22,49 while the NCAPG-LCORL locus represents a primary overlap zone for QTL associated with body length in pigs50. These findings strongly support the notion that the NCAPG gene is closely linked to body size traits in livestock and poultry. Future studies on regulatory mechanisms of function verification of NCAPG, such as g.5705 A > G (NCAPG-281 I > M) in exon 6 and g.7088 G > A (in the selected state and distributed in block 1), are therefore strongly recommended.

Conclusions

In summary, this study identified 24 SNPs within the NCAPG gene in Nanjiang Yellow goats, among which several were significantly associated with growth traits such as body height, body length, and chest circumference. Notably, the g.5705 A > G mutation in exon 6, predicted to reduce protein stability, represents a promising candidate variant potentially underlying body size variation. The observed conservation of these polymorphisms across ruminant species further underscores the evolutionary importance of NCAPG in growth regulation. While these findings provide valuable genetic markers that could support marker-assisted selection, it is essential to acknowledge that functional validation—particularly of mutations like g.5705 A > G—is required before their incorporation into breeding strategies. Future work integrating gene expression assays, protein functional analyses, and large-scale association studies will be crucial to establish causality and to fully harness the potential of NCAPG polymorphisms in goat genetic improvement programs.

Supplementary Material

Table S1.xls
LABT_A_2565161_SM1332.xls (35.5KB, xls)
Figure S3. The hydrophobicity of NCAPG-WT and NCAPG-I281M_01.jpg
LABT_A_2565161_SM1331.jpg (587.1KB, jpg)
Table S6.xls
LABT_A_2565161_SM1330.xls (78KB, xls)
Figure S4. Effect of the NCAPG SNP mutations on protein phosphorylation sites_01.jpg
LABT_A_2565161_SM1329.jpg (927.4KB, jpg)
Figure S2. The linkage disequilibrium map of NCAPG SNPs in Nanjiang Yellow goat_01.jpg
LABT_A_2565161_SM1328.jpg (3.1MB, jpg)
Table S5.xls
LABT_A_2565161_SM1327.xls (26.5KB, xls)
Figure S1. Conserved domains on NCAPG_01.jpg
LABT_A_2565161_SM1326.jpg (963.4KB, jpg)
Table S3.xls
LABT_A_2565161_SM1325.xls (30KB, xls)
Table S2.xls
LABT_A_2565161_SM1324.xls (30KB, xls)
Table S4.xls
LABT_A_2565161_SM1323.xls (27.5KB, xls)

Funding Statement

This research was supported by “National Key R&D Plan Project (2021YFD1100200), National Meat Goat Industry Technology System (CARS-38), and Sichuan Provincial Science and Technology Plan Project (2021YFYZ0003)”.

Acknowledgments

We thank the animal experimental teaching demonstration center of Sichuan Agricultural University for the instruments and equipment and Nanjiang Yellow Goat Breeding Farm for providing sample and phenotypic information.

Author contributions

Conceptualization, L.L.; methodology, J.L., T.M and G.C.; software, M.Z. and S.W.; validation, H.Z., J.G. and J.C.; formal analysis, J.L. and G.C.; investigation, Y.C., H.C., and D.D.; resources, D.Z., Y.C., and Y.C.; data curation, G.C., Y.C., and H.C.; writing—original draft preparation, J.L., T.M and G.C.; writing—review and editing, L.L.; visualization, M.Z.; supervision, L.L.; project administration, L.L.; funding acquisition, L.L. All authors have read and agreed to the published version of the manuscript.”

Institutional review board statement

This study was conducted according to the “Code of Practice for Laboratory Animals” (No. 18 of Sichuan Agricultural University [2014]) and was approved by the Animal Care and Use Committee of Sichuan Agricultural University (Approved No. DKY20210018).

Informed consent statement

Not applicable.

Disclosure statement

No potential conflict of interest was reported by the authors.

Data availability statement

The NCAPG gene reference sequence is downloaded from the NCBI (https://www.ncbi.nlm.nih.gov/) with sequence number: NC_030813.1.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1.xls
LABT_A_2565161_SM1332.xls (35.5KB, xls)
Figure S3. The hydrophobicity of NCAPG-WT and NCAPG-I281M_01.jpg
LABT_A_2565161_SM1331.jpg (587.1KB, jpg)
Table S6.xls
LABT_A_2565161_SM1330.xls (78KB, xls)
Figure S4. Effect of the NCAPG SNP mutations on protein phosphorylation sites_01.jpg
LABT_A_2565161_SM1329.jpg (927.4KB, jpg)
Figure S2. The linkage disequilibrium map of NCAPG SNPs in Nanjiang Yellow goat_01.jpg
LABT_A_2565161_SM1328.jpg (3.1MB, jpg)
Table S5.xls
LABT_A_2565161_SM1327.xls (26.5KB, xls)
Figure S1. Conserved domains on NCAPG_01.jpg
LABT_A_2565161_SM1326.jpg (963.4KB, jpg)
Table S3.xls
LABT_A_2565161_SM1325.xls (30KB, xls)
Table S2.xls
LABT_A_2565161_SM1324.xls (30KB, xls)
Table S4.xls
LABT_A_2565161_SM1323.xls (27.5KB, xls)

Data Availability Statement

The NCAPG gene reference sequence is downloaded from the NCBI (https://www.ncbi.nlm.nih.gov/) with sequence number: NC_030813.1.

Articles from Animal Biotechnology are provided here courtesy of Taylor & Francis

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