This study was approved by the Guizhou University Subcommittee of Experimental Animal Ethics (Approval no: EAE-GZU-2023-E045; EAE-GZU-2024-E052) and was conducted in compliance with all relevant China legislations.

The experiments included 153 twelve-month-old healthy Guizhou white goats, obtained from Wuchuan Hongmu Goat Industry Co., Ltd., located in the Guizhou Province, China. The animal cohort comprised 78 male and 75 female goats. All animals were housed under similar conditions and strict environmental control, being fed a balanced diet comprising whole corn plant silage with a 50:50 concentrate-to-fodder ratio as detailed in Table 1. Feeding occurred at 08:00 and 17:00 daily. After a 15-day pre-feeding period, the feeding experiment lasted 90 days. Water was provided ad libitum, and all other management practices were carried out in accordance with the farm’s established protocols. Environmental conditions were maintained at 22°C with 50% humidity, complemented by adequate natural light and ventilation to simulate a natural habitat and minimize stress. Routine handling was conducted by trained personnel using low-stress techniques, and the facility was designed to minimize noise and disturbances. These practices supported the well-being and consistent behavior in goats, as well as ensured minimal environmental impact on growth and meat quality, therefore maximizing reliable and reproducible experimental results.

Animals were desensitized using electric shocks and subsequently slaughtered by exsanguination by severing the jugular vein and carotid artery to ensure complete blood drainage. This method was designed to ensure a quick and humane slaughter while minimizing stress and suffering to the animal. All animals underwent fasting for 24 h and water abstention for 2 h prior to weighing to determine pre-slaughter live weight. Carcass weight was measured post-slaughter and excluded the skin, head, carpal bones of the forelimbs, sections below the tarsal joint of the hind limbs, and internal organs, except for the kidneys and renal fat [21]. Net meat weight was defined as the combined weight of the muscle and fat on the carcass, including the kidneys and renal fat [22]. The loin muscle was extracted by making a transverse cut at the posterior edge of the 12th rib of the left half-carcass. The loin eye muscle area was calculated using the formula:

The grade ruling value (GR value) refers to the tissue thickness at 11 cm from the midline of the back between the 12th and 13th ribs. The thickness of back fat refers to the fat thickness directly above the lumbar muscles between the 12th and 13th ribs. All measurements of the loin muscle height, width, GR value, and the thickness of back fat were conducted using digital calipers (Deli, Hangzhou, China).

Slaughtering percentage, a critical indicator of growth and slaughter performance, was calculated as:

Net meat percentage and carcass net meat percentage, used to assess meat production capacity and slaughter quality, were determined using the formulas below:

Meat quality parameters were evaluated in longissimus dorsi muscle and included the following: meat color, pH value (45 min), dry matter, protein content, fat content, drip loss rate, cooked meat rate, and shear force.

The longissimus thoracis muscle was excised between the 11th and 13th ribs immediately after slaughter to evaluate meat quality traits. Meat color parameters, including lightness (L*), red-green color (a*), and yellow-blue color (b*), were determined in the MiniScan EZ 4000 chroma meter (HunterLab, Reston, VA, USA). Meat pH was measured 45 min post-mortem using the PH100P pH meter (Harveson, Suzhou, China).

Subsequently, 100 g of meat was boiled in water for 30 min, cooled at room temperature for 20 min, and then weighed. The cooked meat rate was calculated as follows:

Then, fragments of the meat sample measuring 5 cm×3 cm×2 cm were placed in a water bath at 80°C until its center temperature [26–30] reached 70°C. The sample was removed and cut perpendicularly to the muscle fibers into fragments measuring 1.5 cm×1 cm×1 cm, and shear force was measured in a C-LM3 muscle tenderness meter (G&R Teck, Thousand Oaks, CA, USA) based on the agricultural industry standard of the People’s Republic of China.

A portion of 5 to 10 g of meat sample was obtained. Multiple samples were taken from the same batch of meat. Each sample was minced separately. For the determination of dry matter content, a subset of these samples was dried using the direct drying method. For measuring crude protein content, a different subset of samples was analyzed using the Kjeldahl method [31]. Similarly, for determining crude fat content, yet another subset of samples was processed using the Soxhlet extraction method [32]. This approach was taken to ensure that the results are representative and to minimize the risk of errors due to potential issues with a single sample. Drip loss rate was determined by suspending a 5 cm×3 cm×2 cm fragment of the longissimus thoracis sample (in a sealed plastic bag at 4°C for 24 h [33]. All methods employed herein were in accordance with the Chinese standard NY/T 1236-2006 (Specifications for Measuring Performance in Sheep and Goat).

Various organs, including the liver, heart, lung, spleen, kidney, small intestine, large intestine, stomach, head and hoof, were weighed, and coefficients were calculated according to the formula: organ coefficient = organ weight/live weight before slaughter.

Blood samples were collected from the jugular vein of each animal included in the study prior to slaughter into EDTA tubes and stored at −20°C. DNA extraction from whole blood was performed using the E.Z.N.A. Blood DNA Kit (OMEGA Bio-Tek, Norcross, GA, USA) according to the manufacturer’s instructions. DNA quantity and quality were assessed using a spectrophotometer (Thermo Scientific, Waltham, MA, USA), and DNA samples were then stored at −20°C until further use.

Primers were designed for all exons of the HMGCS1 gene of Guizhou white goats based on the sequence available in the GenBank database (accession number: NC_030827.1) using Primer Premier 5.0 software (PREMIER Biosoft International, Palo Alto, CA, USA). Designed primers were synthesized by Beijing, Genomics Technology Co., Ltd., China. Primer sequences, product sizes, and annealing temperatures are shown in Table 2.

The polymerase chain reaction (PCR) system (30 μL final volume) consisted of the following: 15 μL of 2×Taq PCR Star Mix (Yeasen Biotechnology, Shanghai, China); 1 μL of forward and reverse primers; 1 μL of DNA template; and 12 μL of ddH₂O. The PCR program was as follows: pre-denaturation at 94°C for 3 min; denaturation at 94°C for 30 s, annealing at 60°C for 30 s, and extension at 72°C for 2 min, for a total of 35 cycles; followed by a final extension at 72°C for 7 min; and storage at 4°C. PCR products were analyzed after horizontal electrophoresis in 1.0% agarose gels and sent to Tsingke Biotechnology Co., Ltd., China, for sequencing, which was performed using Sanger method, the gold standard for SNP detection. DNAStar software (DNAStar Inc, Madison, WI, USA) was employed to compare sequencing results with NCBI original sequences to identify potential SNP loci.

The presence of SNPs in the HMGCS1 nucleotide sequence was identified by peak plotting of PCR sequencing reads using SeqMan software [34]. Wild-type and mutant sequences were aligned and analyzed using MegAlign and ClustalW software in the DNA Star package. Genotype and allele frequencies at each mutation locus were calculated directly, while Hardy-Weinberg equilibrium was assessed using the chi-squared (χ²) test. Polymorphism parameters, including homozygosity (Ho), heterozygosity (He), the number of effective alleles (Ne), and polymorphic information content (PIC), were calculated as previously described elsewhere [35]. Linkage disequilibrium (LD) and haplotype analyses among SNPs were performed using the SHEsis Main11 software [36]. The degree of chain imbalance was evaluated using the r² value, where r² > 0.33 indicated a strong chain imbalance state [37]. Diplotype analyses were conducted based on identified haplotypes.

All experimental data were analyzed in SPSS v.25.0 (IBM, Armonk, NY, USA). Differences in slaughter and meat quality traits between male and female goats were assessed, and the associations between polymorphisms in the HMGCS1 gene and slaughter characteristics, organ coefficients, and meat quality traits were analyzed.

Before statistical analysis, normality and homoscedasticity assumptions were tested. The Shapiro-Wilk test showed that the data approximately met the assumption of normality. Similarly, the Levene test indicates approximately equal variances. The generalized linear model is more flexible and can handle biased data, is applicable to multiple variables with different distributions, and the model handles outliers and heteroscedasticity better even when it is approximately normal. Therefore, we chose the generalized linear model for the analysis.

We first constructed the full model, which included all variables that could be associated with HMGCS1 gene polymorphisms, sex, and other potential influencing factors such as environmental factors, management factors, and individual physiological characteristics. The full model provides a framework for comprehensive assessment of the overall impact of each factor to avoid missing important information. However, there are many dependent variables in the full model, which may have multicollinearity problems, and some variables have no significant influence on the dependent variables, which will make the model too complex and reduce the stability and interpretation. Therefore, we used stepwise regression to construct reduced models. All variables were first included in the model for preliminary fit, and the partial regression sum of squares was calculated for each variable. At each step, variables that contributed the most to the dependent variable and were statistically significant, such as p<0.05, were selected to enter the model, while variables already in the model were reevaluated. If a variable was no longer significant or its addition degraded the overall performance of the model (such as increasing the mean square error, AIC or BIC value, etc.), it was excluded. By continuously adding and removing variables, the model was gradually optimized to obtain a simplified model containing the most significant variables. While retaining key information, the complexity was reduced, and the stability and interpretability were improved, so that the main influencing factors and their mechanisms could be more clearly identified.

The analysis of gender and genotype was stratified. Firstly, a preliminary analysis was conducted in the overall sample to evaluate the overall influence trend of genotype, sex and interaction terms on each studied trait, and to understand the approximate role of gene polymorphism and sex factors at the population level. Next, for the male goat population, data were extracted separately, and only the influence of genotype variables on each trait was considered. Through fitting models and statistical tests (ANOVA), which genotypes in the male population were significantly associated with specific traits were determined. The above procedure was repeated for the female goat population to study the effect of genotype on each trait in females separately. Finally, the differences in the performance of the same genotype between male and female goats were compared, and the statistical test (two independent sample t test) was used to evaluate whether the trait difference was significant and explore whether it was related to gender-related physiological mechanisms. This hierarchical approach allows a more detailed analysis of the effects of sex and genotype and provides a comprehensive perspective on the gene-phenotype relationship.

All results are presented as means ± standard deviation. The generalized linear model used was as follows:

Where g(·) represents the link function, E(Y_ijk) is the expectation of the dependent variable, β₀ is the intercept term, representing the expected value of the dependent variable when all independent variables are zero, β₁, and β₂ represent the effects of genotypes G₁ and G₂ on the dependent variable, respectively, relative to the reference genotype G₃. The determination of these genotype effects was based on comparing the means of the dependent variable across different genotypes. β₃ represents the effect of gender on the dependent variable. This was assessed by comparing the means of the dependent variable between male and female goats. β₄ and β₅ represent the effects of the interaction between genotypes G₁, G₂, and gender on the dependent variable. These interaction effects were evaluated to understand how the relationship between genotype and the dependent variable changes depending on the gender of the goat. G_1i and G_2i are genotype dummy variables (i = 3, the three genotypes were G₁, G₂ and G₃. G_1i and G_2i represent the two genotype dummy variables, with G₃ as the reference group), S_1j is a dummy variable for sex, (j = male, female,when j = male, S_1j = 1,when j = female, S_1j = 0). G_1iS_1j and G_2iS_1j are the interaction terms for genotype and sex, which were included in the model to capture the combined effects of genotype and gender on the dependent variable. These terms allow us to explore how different genotypes of the HMGCS1 gene interact with the sex of the Guizhou white goats to influence the dependent variables. For instance, it could be that a particular combination of genotype and sex leads to a more pronounced effect on slaughter characteristics or meat quality. By accounting for these interactions, we can better understand the underlying mechanisms and avoid oversimplifying the relationship. This not only helps in accurately predicting the outcomes but also provides more in-depth insights into the biological processes at play. Moreover, it enables us to make more refined interpretations of the results and potentially identify gender-specific genetic effects that might have been overlooked otherwise. The e_ijk represents the random error, which accounts for the unexplained variability in the data. [38]. For multiple comparisons of different genotypes, we employed the Bonferroni correction. This correction was used to control the family-wise error rate, ensuring that the probability of making at least one Type I error (rejecting a true null hypothesis) across all comparisons was maintained at an acceptable level [26].

Male and female goats showed differences in overall slaughtering performance. Various parameters such as pre-slaughter live weight, carcass weight, slaughter rate, net meat weight, net meat percentage, carcass net meat rate, GR value, and tail weight differed significantly between male and female goats (p<0.01). Additionally, back fat thickness differed between male and female goats, being more pronounced in male goats compared to female goats (p<0.05).

There were some differences in meat quality characteristics between male and female goats. The dry matter and protein content of female goat meat were significantly different from those of male goats (p<0.05), with female goat meat having higher values. Moreover, the pH value at 45 min in female goat meat was significantly different from that of male goats (p<0.01).

Significant differences were observed in organ coefficients between male and female goats. The heart, liver, and hoof coefficients were significantly higher in male goats compared to females (p<0.01). Additionally, the head coefficient was significantly greater in male goats than in female goats (p<0.05). In contrast, the large intestine, lung, and stomach coefficients were lower in female goats (p<0.01). For the remaining organs, no significant difference was found in organ coefficients (Table 3).

Sequence alignment of the HMGCS1 gene in Guizhou white goats (accession number: NC_030827.1) led to the identification of four SNPs. In this study, the four SNPs identified in the HMGCS1 gene of Guizhou white goats were labelled as g.15523T>C, g.15530G>C, g.18413T>C, and g.19711G>A. All of these SNPs exhibited two alleles and three genotypes (Figure 1). At the g.18413T>C locus, when comparing the wild and mutant-type, the base T was mutated to C. This alteration changed the codon from -ATT- to -ATC-. Interestingly, both codons encode the same amino acid, isoleucine (I), thus resulting in a synonymous SNP (Figure 2). Synonymous SNPs, although they do not change the amino acid sequence, can still have functional implications as they may affect mRNA stability, translation efficiency, or protein folding. The remaining SNPs (g.15523T>C, g.15530G>C, and g.19711G>A) were intronic mutations. Intronic SNPs are located within the non-coding introns and can potentially influence gene expression through mechanisms such as affecting splicing or interacting with regulatory elements. Population genetic analyses were conducted on these SNPs. The results indicated that the homozygosity at each SNP locus exceeded heterozygosity. Additionally, the PIC of these SNPs, which measures the amount of polymorphism in a population, ranged from 0.25<PIC<0.50 for g.15523T>C, g.15530G>C, g.18413T>C, and g.19711G>A, suggesting a moderate level of polymorphism. Chi-squared tests were performed to assess whether the observed genotype frequencies deviated from the expected frequencies under Hardy-Weinberg equilibrium. The results confirmed that all four SNP loci were in Hardy-Weinberg equilibrium (p>0.05) (Table 4).

LD analysis of the HMGCS1 SNPs exhibited D′ values ranging from 0.79 to 1.00 and r² values from 0.54 to 0.85 (Figure 3) [39]. A strong chain imbalance was observed between SNPs g.15523 T>C and g.15530 G>C, with an r² of 0.85 (Table 5). Haplotype analysis identified two dominant haplotypes in the Guizhou white goat population, namely, -CCCA-, -TGTG-, with frequencies of 57.59% and 42.41%, respectively. From haplotype combinations, three diplotypes were identified with frequencies above 5%. The most common diplotype, Hap1/2 (-TC-GC-TC-GA-), occurred in 56.56% of the population. Diplotypes Hap1/1 (-CC-CC-CC-AA-) and Hap2/2 (-TT-GG-TT-GG-) were present in 33.60% and 9.84%, respectively. Haplotypes and diplotypes with frequencies below 5% were not statistically significant and hence excluded from the statistical analysis (Table 6).

In this study, sSNP genotypes were used as a reference for wild-type homozygous genotypes at mutant loci g.15523T> C(TT), g.15530G>C(GG), g.18413T>C(TT) and g.19711G> A(GG). Wild-type homozygous genotypes are the most prevalent and natural state in the studied livestock population. They are the basic benchmark for comparison with other genotypes and help to better understand the effects of SNP mutations on the traits of interest. As for the diplotype reference, we chose the most common diplotype Hap1/2 (-TC-GC-TC-GA-), which is present in 56.56% of the population. This decision was based on its high frequency, suggesting that it represents a typical or baseline state in livestock populations. By using Hap1/2 as a reference diplotype, we can more efficiently analyse the differences and associations of other diplotypes (e.g. Hap1/1 [-CC-CC-CC-AA-] and Hap2/2 [-TT-GG-TT-GG-]) with traits studied in the field of livestock production. This approach allows a more comprehensive understanding of the role of different genotypes and diplotypes in the traits studied in the field of animal science.

The association analysis revealed significant correlations between diplotypes and slaughter characteristics, meat quality traits, and organ coefficients (Table 7). The GR value of Guizhou white goat was significantly lower in diplotypes Hap1/1 (-CC-CC-CC-AA-) and Hap1/2 (-TC-TC-TC-GA-) compared to Hap2/2 (-TT-GG-TT-GG-) (p<0.05). The loin eye muscle area and a* values were significantly higher in Hap1/1 than in Hap1/2 and Hap2/2 (p<0.05). Conversely, shear force and stomach coefficients were significantly larger in diplotype Hap1/2 compared to Hap1/1 and Hap2/2 (p<0.05). The pH value at 45 min, fat content, and drip loss rate were significantly higher in diplotypes Hap2/2 compared to Hap1/1 and Hap1/2 (p<0.05). No significant differences were observed in other slaughter characteristics, meat quality traits, and organ coefficients across the three diploids (p>0.05).

Detailed associations between SNPs in the HMGCS1 gene and slaughter characteristics, meat quality traits, organ coefficients are shown in Tables 8–11. For instance, the tail weight of the TT genotype at g.18413T>C was significantly lower than that of the CC genotype in male goats (p<0.05). Similarly, the loin eye muscle area was significantly larger in the TT genotype compared to TC genotypes in female goats (p<0.05). For most slaughter characteristics, male goats within the same genotype were extremely significantly higher than female goats (p<0.01). Among meat quality traits in male goats, the CC genotype was associated with significantly higher meat color a* values compared to the TC genotype, whereas the TT genotype was associated with higher pH at 45 min compared to the CC genotype (p<0.05). In female goats, protein content was significantly higher in the TT genotype compared to TC genotype, and shear force was highest in the TT genotype compared to TC and CC genotypes (p<0.05). Overall, male goats exhibited highly significantly lower dry matter and protein content but significantly higher cooked meat rate and shear force compared to female goats (p<0.01). Differences in organ coefficients, such as the liver, spleen, kidney, and hoof, were significant across different genotypes in male goats (p<0.05). Significant sex-based differences in organ coefficients were also observed for the same genotype (p<0.01) (Table 8). Further associations of SNPs g.15523T>C, g.15530G>C, and g.19711G>A with slaughter characteristics, meat quality traits, and organ coefficients in Guizhou white goats are shown in Tables 9–11.

This study identified both synonymous and intronic SNPs in the HMGCS1 gene. While synonymous SNPs did not alter the amino acid sequence in the HMGCS1 gene, they can significantly influence meat quality traits. Similarly, intronic SNPs, despite not directly affecting protein coding, may impact growth performance. These findings align with prior research on other livestock species. For instance, synonymous SNPs in SIRT3 and POU1F1 have been significantly associated with goat body weight [40,41], while two synonymous SNPs in SIRT2 were linked to weight traits in Nanyang cattle [42].

Several studies have consistently shown that synonymous SNPs can influence the stability and structure of mRNA, as well as protein folding, thereby affecting gene function and phenotypic traits [43]. Synonymous mutations, despite not altering the amino acid sequence, can introduce codon bias, which may alter the translation mechanism of the cell. This can potentially regulate gene expression, affect protein translation efficiency, and influence protein folding accuracy [44–46]. These changes in translation efficiency may lead to variations in protein expression levels and, consequently, phenotypic differences. For instance, an intronic SNP in the human GRB10 gene has been associated with newborn birth weight [47]. Research suggests that certain SNPs in intronic regions can destabilize RNA and disrupt functional protein expression. They may also affect alternative splicing or interact with genetic elements such as enhancers, thereby regulating gene transcription or translation [48–50]. Therefore, these observations highlight the complex molecular mechanisms underlying the phenotypic impact of SNPs. Further research is needed to unravel these mechanisms, potentially enabling targeted breeding strategies to enhance livestock traits.

This study revealed significant differences in pre-slaughter live weight and carcass weight between male and female goats of similar age (12-months-old) under identical feeding conditions. These findings are consistent with previous studies which have shown that live and carcass weights are influenced by both age and gender [51]. Moreover, significant differences in physiological characteristics and varying hormone levels between male and female goats have been identified. In particular, high estrogen levels are found in female goats, while testosterone dominates in male goats, which may exert an important regulatory role in gene expression [52]. Estrogen may affect gene expression related to fat deposition and muscle development, thus contributing to the differences in meat quality traits compared with male goats [53], which may explain the higher fat content and potentially better tenderness in female goat meat compared to male goat meat. Conversely, testosterone in male goats promotes muscle growth and metabolism, leading to higher lean meat percentages and distinctive muscle fiber characteristics [54].

Based on the data of the present study, it is evident that genotype performance varies between male and female goats, albeit the performance of specific genotypes in male and female goats showed a certain inconsistency. For instance, while no significant differences were found in shear force between male and female goats, the TC genotype showed significantly higher shear force in males compared to female goats (Tables 3, 8). This may be attributed to a higher lean meat percentage in male goats, whereas tenderness is more in female goats. Additionally, differences in certain meat quality traits such as dry matter and protein content between male and female goats may also reflect genotype-specific effects. For instance, certain genotypes (e.g., AA and CC) may play different roles in males and females (Tables 9, 11), influencing these traits differently.

Taken together, understanding sex-based genetic effects provides valuable insights into the relationship between genetics and meat quality traits. Such knowledge is important for advancing precision breeding and genetic improvement efforts in animal husbandry.

Slaughter characteristics and meat quality serve as crucial indicators for assessing breed resources and guiding breed selection and identification. For instance, indicators such as slaughter rate and net meat weight as slaughter characteristics can reflect the development phase of livestock [55]. Slaughter yield is influenced by a variety of factors, such as breed, age, gender, and seasonal factors, among which breed is the most influential [56]. Considering the carcass weight of male Guizhou white goats, it can be stated that it was significantly higher compared to Spanish goats [57], lower compared to Boer×Spanish goats [58], lower compared to Boer×Angora goats, but higher compared to purebred Angora goats [59]. Moreover, back fat thickness of Guizhou white goat was significantly lower compared to Boer×Guizhou white goats [60]. Thus, slaughter performance in Guizhou white goats is moderate and slightly inferior compared to that of crossbreed goats, suggesting a potential for selecting improved slaughter performance characteristics.

Meat quality traits such as dry matter, crude protein, and crude fat significantly influence the nutritional value of goat met [61,62]. This study highlighted key disparities in these traits between male and female Guizhou white goats. Males exhibited lower dry matter and protein content but higher pH value at 45 min postmortem compared to females (p<0.05). Intramuscular fat content and fatty acid composition are crucial factors of the quality of mutton, affecting tenderness, flavor, and nutritional value [63,64]. Compared to other breeds, Guizhou white goats showed protein contents similar to Korean Black goats but lower fat content, lighter meat color (L* and a* values), and higher pH [65]. Similar differences were noted against Boer goats [66]. These findings suggest that crossbreeding Guizhou white goats with breeds of higher meat quality could enhance their overall meat quality traits.

It has been shown that the expression of HMGCS1 was positively associated with cholesterol content in goose liver, being considered a key gene in cholesterol synthesis [67]. Thus, we hypothesized that the HMGCS1 gene could be related to intramuscular fat deposition in Guizhou white goats. However, the analysis of the association between the HMGCS1 gene and meat quality traits in Guizhou white goats revealed that SNPs g.18413T>C, g.15523T>C, g.15530G>C, and g.19711G>A were significantly associated with meat color (a* and b* values), protein content, fat content, dry matter and shear force (p<0.05). No significant differences were found for other meat quality traits. In particular, the intronic SNPs g.15523T>C and g.15530G>C in the HMGCS1 gene were significantly associated with intramuscular fat content (p<0.05). These two loci showed a strong LD state (D′ = 1, r² = 0.84), which suggests a potential synergistic effect on phenotypic traits [39]. It has been suggested that the higher the degree of LD between two SNPs, the greater the likelihood that the two SNPs will be co-inherited in offspring in generational selection [68]. Thus, these novel LD-linked polymorphic loci could serve as critical targets for breeding Guizhou white goats with improved intramuscular fat content.

Organ weight and organ indices provide valuable insights into animal genetics and physiology, serving as reference parameters for research and breeding [69]. However, these metrics are influenced by factors such as body weight, breed, sex, age, and environmental conditions [31]. In 12-month-old Guizhou white goats, organ weights and coefficients were approximately 50% lower than in adult goats [70], likely reflecting rapid early organ development. This fast growth phase may result in lower muscle and fat yields in 12-month-old goats, ultimately impacting meat production indicators [69].

The present study identified significant associations between the HMGCS1 SNP loci and organ coefficients (p<0.05). Differences in organ coefficients among genotypes of Guizhou white goats underscore the complex relationship between genetics and physiology. Notably, most organ coefficients differed significantly between males and females of the same genotype, suggesting sex-based differences in growth rates and developmental patterns. These variations may affect organ growth rate and development, resulting in distinct organ coefficients between the sexes. Understanding the underlying mechanisms and functional consequences of these differences could inform breeding programs and disease management strategies, ultimately enhancing goat health and productivity.