DGAT1 Gene Variants and Udder Biometry: A Genetic Association Study in Nigerian West African Dwarf Goats

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Popoola, Suneel K. Onteru, Dheer Singh, Saidu O. Oseni, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8235032/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The udder is vital for milk production in ruminants, including West African Dwarf (WAD) goats. Udder and teat characteristics influence milk yield and milking efficiency in these animals. The diacylglycerol O-acyltransferase 1 (DGAT1) gene has been identified as a key regulator of lipid metabolism and milk production traits across various species of animals. While DGAT1 polymorphisms have been extensively studied in other species, their association with udder morphology in West African Dwarf (WAD) goats remains unexplored. This study investigates DGAT1 polymorphisms and their relationship with udder traits to enhance genetic improvement strategies. Blood samples from 150 lactating WAD goats (1–8 years old) were collected across intensive, semi-intensive, and extensive management systems in Southwest Nigeria. Genomic DNA was extracted, and allele-specific PCR (AS-PCR) was used for SNP genotyping. Udder and teat traits were also measured. Associations between DGAT1 SNPs and udder traits were analyzed using linear and multiple regression models in R, with age and management system as covariates. Pearson correlations and Manhattan plots were used to visualize the SNP-trait relationships. Results No significant associations were found between the DGAT1 SNP and most udder traits, though suggestive associations were noted for udder depth and circumference (p < 0.10). The SNP effects varied, showing both positive and negative influences, but low R² values indicate that polygenic inheritance and environmental factors dominate. Age and management system significantly affected udder morphology, improving model accuracy, while genotype-trait correlations remained weak. Manhattan plots suggested that multiple loci within DGAT1 or nearby regulatory regions might contribute to trait variation, implying that DGAT1’s impact is likely moderated by genetic and environmental interactions. Conclusion This study found no strong association between the DGAT1 SNP and most udder traits but identified potential effects on udder depth and circumference. The low R² values suggest udder traits are polygenic and environmentally influenced. Including age and management system significantly improved model performance, emphasizing their critical role in udder morphology. While DGAT1 may contribute moderately, its effect likely depends on interactions with other genes and environmental factors. DGAT gene management polymorphism traits udder Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The Nigerian West African Dwarf (WAD) goat is an essential livestock breed in West Africa, valued for its adaptability to harsh environmental conditions, disease resistance, and economic significance in smallholder farming systems (Adebayo et al., 2020). In dairy production, udder traits such as udder depth, udder volume, teat length, and udder circumference play crucial roles in determining milk yield, quality, and overall productivity. These traits are essential for improving dairy goat breeding programs (Yakubu et al., 2018). One of the key candidate genes influencing milk production traits is the diacylglycerol O-acyltransferase 1 (DGAT1) gene. The DGAT1 gene encodes an enzyme involved in triglyceride synthesis, and polymorphisms within this gene have been associated with lipid metabolism, milk yield, fat content, and lactation traits in various livestock species (Bovenhuis et al., 2015; Bovenhuis et al., 2016; Grisart et al., 2002). The DGAT1 was reported as a production-associated gene in several animals including buffalo, sheep, and goat. In goat, DGAT1 gene (Gene ID: 100861225) is located on chromosome 14 with 18 exons ( https://www.ncbi.nlm.nih.gov/gene/100861225 ). The expression of DGAT1 gene has been documented in various tissues including the mammary glands (Farese 2000; Devita 2013). Most genomics scientists believe that single nucleotide polymorphism (SNPs) and genome-wide association studies (GWAS) have a great potential for trait evaluation in dairy animals, particularly dairy goats (Visscher et al., 2012). Despite well-documented role of DGAT1 in milk production, to our knowledge, there is paucity of information on DGAT1 gene SNP association with udder morphology in Nigerian WAD goats. Thus, given the importance of udder traits in dairy goat selection, investigating the relationship between DGAT1 polymorphisms and udder morphological traits in Nigerian WAD goats could provide novel insights into genetic factors affecting its dairy potentials. This research therefore, aims to bridge this gap by assessing DGAT1 polymorphisms and their association with udder biometry traits, thereby, contributing to the genetic improvement of dairy characteristics in Nigerian WAD goats. Materials and Methods Animals and phenotypic measurements A total of 150 WAD goats were sampled from different locations in Southwest Nigeria across different management systems (intensive, semi-intensive and extensive) with mixed age (mean age: 3.69 years, range: 1–8 years) which are of primiparous and multiparous status. The udder and teat traits of these goats were measured according to FAO guidelines for phenotypic characterization of animal genetic resources (FAO 2012) using a flexible measuring tape. Traits measured include: udder perimeter (UP), udder depth (UD), udder length (UL), udder width (UW), udder cleft (UCF), udder circumference (UC), udder volume (UV), teat length(TL), teat width (TW), teat circumference (TC), distance between teats (DBT) and teat height from the ground (THG). Blood samples collection and DNA isolation Blood samples from the phenotyped goats were collected for DNA extraction. Isolation of the DNA was done using Qiagen QIAamp DNA Kits for DNA Extraction (Cat no #51306) as per the manufacturer’s instructions. Integrity of isolated DNA was checked by 1% agarose gel electrophoresis and purity of DNA was assessed by QIAxpert spectrophotometer immediately after extraction. PCR amplification According to DQ380249 accession number which corresponds to a specific DGAT1 gene sequence from goats, was used to design a primer: F 5′ CCCAGACACTTCTACAAGCC3′ and R 3′ TGCCCGATGATGAGTGACAG5'. The primer was designed using Primer 3 software and the specificity of the primer sequence was checked through ‘BLAST’ program. The PCR reaction was carried out for each primer in 12.5 µL of total volume, mixtures containing ~ 150 ng genomic DNA, 6.25 µl of 2 × Dream Taq PCR Master Mix (K1081; Promega), and 5 pmol of each primer. The PCR conditions were as follows: denaturation at 94°C for 5 min, followed by 32 cycles of denaturation at 94°C for 30 seconds, annealing at 58°C for 1 min, extension at 72°C for 30 seconds and final extension at 72°C for 4 min. The PCR products were separated by electrophoresis on 1.5% agarose gels and photographed under UV light. Allelic- Specific Primer designing and Genotyping The DNA sequences of the target gene region were obtained through Sanger sequencing. Sequence alignment and SNP identification were performed using BioEdit sequence alignment editor tool v7.2.5 to detect polymorphic sites. Allele-specific primers (ASP) were designed for a single nucleotide polymorphism (SNP) in the DGAT1 gene using Web-based Allele Specific Primer (WASP) design tool software. Two forward primers were designed: Allele C-specific primer: Perfectly matches the C allele at the 3’ end. Allele T-specific primer: Perfectly matches the T allele at the 3’ end. A common reverse primer was also designed downstream of the SNP. Primers were designed to include the SNP or variant at the 3' end (penultimate mismatch) to ensure specificity for the target allele. The primers designed were: Wildtype Forward Primer 5’: GAGCATCCCCCTGCGCATGTC, Mutant Forward Primer 5': GAGCATCCCCCTGCGCATGTT and Common Reverse Primer 5': GGTCCCGGGCCATCCCGGTAGG. Allele-Specific PCR (AS-PCR) was performed in a 10 µL reaction mixture containing 5µl of 2 × Dream Taq PCR Master Mix (K1081; Promega), 10 ng Template DNA, 0.5 µM Allele C Forward primer (for one reaction), 0.5 µM Allele T Forward primer (for another reaction) and 0.5 µM Common Reverse primer. The PCR conditions were as follows: denaturation at 94°C for 5 min, followed by 32 cycles of denaturation at 94°C for 30 seconds, annealing at 60°C for 1 min, extension at 72°C for 30 seconds and final extension at 72°C for 4 min and was held at 4°C. The PCR products were separated by electrophoresis on 1.5% agarose gels and photographed under UV light. The gel electrophoresis results were analyzed to determine the genotypic pattern. Statistical analyses Genotypes were estimated by counting the electrophoretic pattern of DGAT1 gene. Pearson correlations between udder and teat morphological traits of the goats and genotypes were analysed. The correlations estimated and heatmap plots were constructed with R software (2025) to visualize the correlation matrix and the P-values of each correlation. A linear regression analysis was performed using the linear model function in R to quantify the association between the udder traits and the genotype. The model was applied separately for each trait. The general form of the model is: y i ​= β 0 ​ + β 1 ​X i ​+ ϵ i​ where: y i ​ = value of the udder and teat traits; X i = SNP genotypes (CC, CT, TT); β 0 = the intercept; β 1 = the regression coefficient (Estimate); ϵ i = the residual error term. To control for potential confounding effects and improve model accuracy in explaining variations in udder and teat traits, management system and age of the goats were incorporated in the model. Thus, multiple linear regression model was also employed to assess the association between udder and teat traits and the predictor variables, including SNP effects, management system, and age. The model is represented as: y i = β 0 + β 1 X i + β 2 M i + β 3 A i +ϵ i where: y i = udder or teat traits; β 0 = intercept; β 1 , β 2 , β 3 = regression coefficients; M i = Management systems (Intensive, Semi-intensive, Extensive); A i = Age of the goats; ϵ i = the residual error term. The statistical significance of each predictor was assessed using t-tests, and model fit was evaluated using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The regression estimates (β), t-values, and p-values were extracted using the broom package, and the results were visualized using ggplot2 in R. In the visualization, each predictor is represented with its corresponding regression estimate, confidence intervals, and statistical significance. To identify SNP association with udder and teat traits while controlling for population structure and relatedness, Manhattan plots using the qqman package was also performed in R. Results The association between DGAT 1 gene SNP and udder traits in WAD goats is presented in Table 1. The results revealed varying degrees of genetic influence on these traits, which are critical for milk production. There was no signification association between the SNP and udder traits. However, udder depth and udder circumference revealed suggestive associations with the SNP. Both positive and negative estimated effects of the SNP on udder and teat traits of the goats were obtained. Udder cleft, udder volume and udder depth show relatively larger negative effect sizes. Teat height from ground and teat width exhibits non-significant positive association. Also, coefficient of determination (R²) values was relatively low. Table 1: SNP-Phenotype association for udder and teat traits in Nigerian WAD goats Traits Estimate R 2 p -value Udder perimeter -0.026 0.001 0.900 Udder depth -0.490 0.020 0.084 Udder length -0.149 0.001 0.685 Udder width 0.015 1.57 x 10 -5 0.962 Udder circumference -0.483 0.018 0.099 Udder cleft -0.725 0.015 0.137 Udder volume -0.518 0.001 0.663 Teat length 0.017 0.001 0.762 Teat width 0.131 0.010 0.227 Teat circumference -0.010 0.001 0.884 Distance between teats -0.211 0.008 0.273 Teat height from ground 0.286 0.007 0.318 Given the weak associations observed in the previous result, age and management systems were included as covariates to account for their potential confounding effects on udder traits. The result reveals that there was no significant association between the SNP and udder traits (Table 2). However, relatively higher R² values were obtained compare to previous models without covariates (Table 1), an indication that age and management system contribute to variation in udder traits of the goats. The highest R² values were observed for udder circumference and udder placement, indicating moderate explanatory power. The estimated effects show both positive and negative influences of the SNP on different udder traits. Also, negative effect sizes were observed in udder cleft, udder circumference and udder depth, suggesting a potential unfavorable genetic influence. However, positive and non-signification associations were observed in teat height from ground and teat width. Table 2: SNP-Phenotype association for udder and teat traits in Nigerian WAD goats with age and management system as covariates Traits Estimate R 2 p value Udder perimeter 0.084187 0.158749 0.67087 Udder depth -0.36415 0.129024 0.17738 Udder length 0.007101 0.112591 0.983823 Udder width 0.035501 0.026372 0.910114 Udder circumference -0.31464 0.231984 0.230494 Udder cleft -0.51612 0.125894 0.266426 Udder volume -17.9294 0.046068 0.875888 Teat length 0.025686 0.015921 0.65517 Teat width 0.146286 0.031037 0.17758 Teat circumference 0.002686 0.01911 0.970124 Distance between teats -0.11405 0.162417 0.524716 Teat height from ground 0.255107 0.048074 0.369058 The coefficient plot shows the estimated effects of the management system, age, and SNP genotype on the traits considered in this study (Fig. 1). The result reveal that the intercept (β̂ = 16.52, p = 1.71e-34) is a representative of baseline value of the dependent variable when all the predictors (management system, age and SNP) are zero. A highly significant p-value (p < 0.001) was also observed. Positive coefficients were obtained for management system (β̂ = 0.82, p = 9.28e-03) and SNP (β̂ = 0.35). However, the p-value (p = 0.21) was not statistically significant. A negative coefficient was obtained for age (β̂ = -0.25, p = 0.16) with a non-significant p-value (0.16). The Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values (AIC = 770, BIC = 785) show model complexity and fit. Hierarchical clustering was used with the correlation matrix to explain modular trait groupings and shared phenotypic variation patterns in udder traits of WAD goats (Fig. 2). There exist a cluster of core udder module such as UC, UL,UV,UP and UCF; a width-distance subgroup for UW,DBT and UD; teat morpholgical module for TC,TL and THG as well as a distantly clustered genotype score. Weak and mostly negative correlations were observed between the genotype score representing a composite genetic index with the phenotypic udder traits of the goats. Slight inverse relationships were also observed with UD, UC and UCF Genomic Position-Based Manhattan Plot of DGAT 1 SNP associations with udder traits of WAD goats shows the specific regions within the DGAT1 gene that exhibit associations with udder traits (Fig. 3a). This reveals that SNP associations are distributed across different genomic positions with the significant threshold (p = 0.05) as indicated by the dashed gray line. It is revealed that SNP exhibits varying degrees of association with different traits, with some traits showing stronger associations than others (Fig. 4). Traits such as udder depth, udder circumference, teat width, distance between teat and udder cleft display relatively higher -log10(P-values). However, traits like udder placement, udder width, udder volume, udder length, teat circumference and teat length show weaker associations Discussion The suggestive associations observed for udder depth and udder circumference unlike other non-significant traits indicates potential candidate traits for further analysis. The larger negative effect sizes obtained for udder cleft, udder volume and udder depth are an indication of the presence of an unfavorable allele that may contribute to reduced udder trait expression and potentially impact milk yield negatively. Low determinant of coefficient obtained suggests udder traits are likely influenced by multiple genes (polygenic inheritance) that is, they are likely influenced by multiple genes and environmental factors rather than being controlled by a single genetic variant. Significant association between SNP and udder width, udder depth, teat placement and teat diameter was reported in Murciano-Granadina goats (Luigi-Sierra et al., 2020). Findings from this present study indicate that there is no strong association between the SNP and udder traits measured. Variation of coefficient of determination (R²) values across udder traits obtained with udder circumference showing the highest proportion of variance explained by the SNP suggests that udder circumference may be moderately influenced by DGAT1, while other traits exhibit weaker genetic control. By including age and management system as covariates, it was aimed to reduce environmental noise and isolate the genetic contribution of DGAT1 to udder trait variation. Age is a crucial source of variation influencing mammary gland development and udder morphology (Raphel et al., 2017; Sam et al., 2017; Ahlem et al., 2023). Management system directly have effects on nutrition, health, and overall physiological condition of the animals, which can, in turn, influence udder traits of the goats (Hue-Beauvais et al., 2021). Although, the inclusion of age and management system as covariates account for external influences, but the results revealed that udder traits are influenced by a polygenic gene rather than a single gene like DGAT1. It has been previously confirmed by Mucha et al., (2018) that udder conformation traits in goats are polygenic in nature when 52K goat, SNPs chip developed to evaluate dairy goats' production traits was combined with genotypic and phenotypic data such as milk quantity and udder conformation traits in chromosome 19 and other chromosomes. Also, moderate R² values for UC, UP, and DBT suggest that these traits might be indirectly influenced by DGAT1, potentially through its role in milk secretion and mammary gland development (Farese et al., 2000; Smith et al., 2000; Cases et al., 2004). Chromosome wide associations between SNPS and some udder traits (UW, UD, TP and TD) have been reported in Murciano-Granadina goats (Luigi-Sierra et al., 2020). In a similar study on cattle, Sinha et al., (2022) reported that GG genotype of SNP rs454303072 was found to have wider rear udder, larger udder circumference, longer distance between fore-rear teats and left-right teats in Karan Fries cattle. Whereas, in Sahiwal cattle, AA genotype of this SNP was found to be associated with the higher and wider rear udder, larger udder circumference and wider udder. AA genotype of SNP rs382671389 was found to be associated with longer fore teat in Karan Fries cattle. The TT and CC genotype of SNP rs435289107 was associated with udder type traits in Karan Fries and Sahiwal cattle respectively. The positive coefficient for management system (β̂ = 0.82, p = 9.28e-03) obtained in this study, is an indication that management system has a significant impact on the udder traits. Also, the positive coefficient of SNP (β̂ = 0.35) suggests the association of SNP with an increase in the udder traits. However, the statistically non-significant of p-value shows that there was no strong independent effect of the SNP on the udder traits. A slightly decreasing effect of age on the udder traits of the goats reflects from the negative coefficient obtained for age while non-statistically significant p-values implies that age does not have a strong influence on the udder traits of the goat in the model. Lower values of AIC and BIC generally suggest a better model, while these values alone do not indicate model quality, they can be used for model comparison. Udder traits have been earlier reported to be highly correlated in some breeds of goats; Akkeci goats (Keskin et al., 2005) local goats of Rohilkhand region of India (Upadhyay et al., 2014) WAD (Zahraddeen et al., 2008; Zahraddeen et al., 2007) White Bornu goats (Akporhuarho et al., 2010). The teat traits tend to cluster separately from the udder traits, indicating some independence in their genetic regulation. The moderate and positive correlations between udder depth and distance between teats suggests that deeper udder is associated with wider teat placement while the weak correlation between teat length and udder width implies a minor association between udder and teat dimensions. The correlations between udder and teat traits are consistent with previously reported studies indicating that udder size is a key determinant of milk yield in goats. The specific regions within the DGAT1 gene that exhibit associations with udder traits reveals that SNP associations are distributed across different genomic positions, which implies that multiple loci within DGAT1 may contribute to phenotypic variation in the udder traits. High -log10(P-values) of the SNP suggest a stronger association with one or more udder traits. The borderline significant SNPs (p < 0.10), suggest potential associations that may warrant further investigation. Clustering of certain SNPs within specific genomic regions is an indication of regulatory elements or linkage disequilibrium effects influencing udder morphology, milk production, and teat structure of the goats. The presence of borderline significant SNPs in the genomic position plot suggests that some associations might be trait-specific rather than genome-wide significant, which was validated through the trait-based plot. This allows for a direct comparison of SNP associations across the udder traits. It is revealed that SNP exhibits varying degrees of association with different traits, with some traits showing stronger associations than others. Traits such as udder depth, udder circumference, teat width, distance between teat and udder cleft display relatively higher -log10(P-values), indicating stronger SNP-trait relationships. However, traits like udder placement, udder width, udder volume, udder length, teat circumference and teat length show weaker associations, which implies that genetic variation in these traits may be influenced by factors beyond DGAT1gene. The presence of SNPs with consistent effects across multiple traits suggests pleiotropic relationships, where a single genetic variant (SNP) influences multiple traits. Such relationships suggest that DGAT1 may play a central role in udder morphology, impacting multiple functional and structural aspects simultaneously. Conclusion There was no significant association between the SNP and the udder traits, though suggestive associations were observed for udder depth and circumference. The estimated SNP effects on udder traits varied, showing both positive and negative influences. Low R² values indicate that udder traits in WAD goats are influenced by multiple genes (polygenic inheritance) and environmental factors rather than being controlled by a single genetic variant. The relatively sample size and use of single SNP may have limited detection of additional loci that are influencing udder traits in WAD goats. Further studies using larger populations and multi-traits GWAS are recommended to enhance resolution of genetic variants underlying udder traits of WAD goats. Future GWAS with larger datasets and multi-trait models are recommended to uncover additional variants influencing udder traits in WAD goats. Abbreviations AIC: Akaike Information Criterion; BIC: Bayesian Information Criterion; AS-PCR: Allelic-specific polymerized chain reaction; DGAT1: Diacylglycerol O-acyltransferase 1; DNA: Deoxyribonucleic acid; R2: Coefficient of determination; SNP: Single nucleotide polymorphism; UV: Ultraviolet; WAD: West African Dwarf Declarations Acknowledgements The authors are grateful to the Department of Biotechnology (DBT), Ministry of Science and Technology, India and The World Academic of Sciences for the advancement of science in developing countries, Trieste, Italy and UNESCO programme unit (UNESCO-TWAS) for providing the funds for this project. We are also grateful to Molecular Endocrinology, Functional Genomics and Systems Biology Laboratory, Dairy Animal Biochemistry Division, ICAR-National Dairy Research Institute, Karnal, Haryana, India Authors’ contributions MAP, SKO, DS and SOO considered and designed the study. SKO and DS supervised the study. MAP collected the data on udder morphology blood samples for DNA isolation. MAP, AM, MR, PP and RM undertook the genomic DNA isolation, genotyping and performed statistical analyses. MAP wrote the manuscript. SKO, DS and SOO made substantial contributions to the interpretation of the results and revised the manuscript. All the authors read and approved the final manuscript. Funding This research was co-funded by Department of Biotechnology (DBT), Ministry of Science and Technology, India and The World Academic of Sciences for the advancement of science in developing countries, Trieste, Italy and UNESCO programme unit (UNESCO-TWAS) Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate All procedures were approved by the Federal College of Animal Health and Production Technology – Animal Care and Use Research Ethics Committee (FCAH&PT-ACUREC) approved on 10 October 2023. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author details 1 Department of Animal Health and Production Technology, Federal College of Animal Health and Production Technology, Ibadan, Nigeria 2 Animal Biochemistry Division, ICAR- National Dairy Research Institute, Karnal, Haryana 1320001, India 3 Department of Animal Sciences, Obafemi Awolowo University Ile-Ife, Nigeria 4 Animal Breeding and Genetics Division, ICAR- National Dairy Research Institute, Karnal, Haryana 1320001, India References Adebayo O, Ogah D.M., & Ozoje M.O. (2020). Genetic evaluation of Nigerian West African Dwarf goats for milk production traits. Tropical Animal Health Production, 52(4):1345-1352. 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Zahraddeen D, Butswat I.S.R., Mbap S.T. (2008). Relationship of udder circumference and teat length with milk yield in Red Sokoto and Sahel goats. Nigeria Journal of Animal Production: 35(1):105-109. Zahraddeen D, Butswat I.S.R, Mbap S.T. (2007). Udder and teat traits as possible selection markers for milk yield in local goats of Nigeria. Global Journal of Agricultrual Science. 7(1):23-26. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8235032","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571736492,"identity":"0c8cdd4b-47b6-4529-b490-4b7e99c64562","order_by":0,"name":"Moshood A. 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14:18:44","extension":"html","order_by":27,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":68212,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8235032/v1/a9303a70b065758a5ede7027.html"},{"id":100058360,"identity":"e8785534-5165-42bd-8800-f8ab1afa2276","added_by":"auto","created_at":"2026-01-12 14:18:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":201703,"visible":true,"origin":"","legend":"\u003cp\u003eCoefficient Plot of effects of SNP, management system and age on udder traits of WAD goats. The coefficient plot displays the estimated effects of the management system, age, and SNP genotype on the udder trait of the WAD goats. Intercept (β = 16.52, t (146) = 16.23, p = 1.71e-34). The intercept represents the baseline udder trait value when all predictors (Management System, Age, and SNP) are set to zero. Management System (β = 0.82, t (146) = 2.64, p = 9.28e-03). The positive estimate (β = 0.82) suggests that different management systems significantly affect udder traits, with a higher value associated with certain systems. Age (β = -0.25, t (146) = -1.41, p = 0.16). The negative coefficient (β = -0.25) suggests a potential inverse relationship between age and the udder trait. SNP Effect (β = 0.35, t (146) = 1.25, p = 0.21). The positive β value (0.35) suggests that the presence of a specific DGAT1 SNP allele might be associated with an increase in the udder trait. Model Fit and Predictive Strength; AIC (Akaike Information Criterion) = 770, BIC (Bayesian Information Criterion) = 785)\u003c/p\u003e","description":"","filename":"Binder11.png","url":"https://assets-eu.researchsquare.com/files/rs-8235032/v1/63b94bf6425945973dabbb6c.png"},{"id":100364277,"identity":"f4287f27-44ce-4533-bb73-300bc02907fa","added_by":"auto","created_at":"2026-01-16 07:53:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":661552,"visible":true,"origin":"","legend":"\u003cp\u003eGenotype-Trait Correlation Heatmap. The heatmap presents Pearson correlation coefficients between the genotype data and selected udder and teat traits in West African Dwarf (WAD) goats. The color scale represents correlation strength, with red indicating positive correlations, blue indicating negative correlations, and white indicating weak or no correlation.\u003c/p\u003e","description":"","filename":"Binder12.png","url":"https://assets-eu.researchsquare.com/files/rs-8235032/v1/05c8f818b4f201f27708a2d6.png"},{"id":100364681,"identity":"76d3c0eb-d02b-45b5-80f8-38c934a165e3","added_by":"auto","created_at":"2026-01-16 07:54:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":187087,"visible":true,"origin":"","legend":"\u003cp\u003eGenomic Position-Based Manhattan Plot of DGAT 1 SNP associations with udder traits of WAD goats. The x-axis represents the genomic position (presumably of SNPs within the DGAT1 gene). Each point represents a trait-associated SNP, with colors differentiating chromosomes. Traits with borderline significance (p \u0026lt; 0.10) are highlighted in red. A dashed line at -log10(p) = 1.3 (corresponding to p = 0.05) marks the significance threshold.\u003c/p\u003e","description":"","filename":"Binder13.png","url":"https://assets-eu.researchsquare.com/files/rs-8235032/v1/c94ebe19c2ccf247b5ad1293.png"},{"id":100058358,"identity":"87b93c45-6e1d-4078-81fa-4a4d00d4a84f","added_by":"auto","created_at":"2026-01-12 14:18:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":147494,"visible":true,"origin":"","legend":"\u003cp\u003eTrait-Based Manhattan Plot of DGAT 1 SNP associations with udder traits of WAD goats. The x-axis represents specific udder and teat traits; the dashed red line represents a significance threshold\u003c/p\u003e","description":"","filename":"Binder14.png","url":"https://assets-eu.researchsquare.com/files/rs-8235032/v1/9b889112a70e70ca7bfff106.png"},{"id":107485024,"identity":"99912149-82c6-4709-8083-bea68bf1c310","added_by":"auto","created_at":"2026-04-22 02:33:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1630024,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8235032/v1/ab655df0-210f-4373-b7e4-a2ce64b98f9e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"DGAT1 Gene Variants and Udder Biometry: A Genetic Association Study in Nigerian West African Dwarf Goats","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Nigerian West African Dwarf (WAD) goat is an essential livestock breed in West Africa, valued for its adaptability to harsh environmental conditions, disease resistance, and economic significance in smallholder farming systems (Adebayo et al., 2020). In dairy production, udder traits such as udder depth, udder volume, teat length, and udder circumference play crucial roles in determining milk yield, quality, and overall productivity. These traits are essential for improving dairy goat breeding programs (Yakubu et al., 2018).\u003c/p\u003e \u003cp\u003eOne of the key candidate genes influencing milk production traits is the diacylglycerol O-acyltransferase 1 (DGAT1) gene. The DGAT1 gene encodes an enzyme involved in triglyceride synthesis, and polymorphisms within this gene have been associated with lipid metabolism, milk yield, fat content, and lactation traits in various livestock species (Bovenhuis et al., 2015; Bovenhuis et al., 2016; Grisart et al., 2002). The DGAT1 was reported as a production-associated gene in several animals including buffalo, sheep, and goat. In goat, DGAT1 gene (Gene ID: 100861225) is located on chromosome 14 with 18 exons (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gene/100861225\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gene/100861225\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The expression of DGAT1 gene has been documented in various tissues including the mammary glands (Farese 2000; Devita 2013).\u003c/p\u003e \u003cp\u003eMost genomics scientists believe that single nucleotide polymorphism (SNPs) and genome-wide association studies (GWAS) have a great potential for trait evaluation in dairy animals, particularly dairy goats (Visscher et al., 2012). Despite well-documented role of DGAT1 in milk production, to our knowledge, there is paucity of information on DGAT1 gene SNP association with udder morphology in Nigerian WAD goats. Thus, given the importance of udder traits in dairy goat selection, investigating the relationship between DGAT1 polymorphisms and udder morphological traits in Nigerian WAD goats could provide novel insights into genetic factors affecting its dairy potentials. This research therefore, aims to bridge this gap by assessing DGAT1 polymorphisms and their association with udder biometry traits, thereby, contributing to the genetic improvement of dairy characteristics in Nigerian WAD goats.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAnimals and phenotypic measurements\u003c/h2\u003e \u003cp\u003eA total of 150 WAD goats were sampled from different locations in Southwest Nigeria across different management systems (intensive, semi-intensive and extensive) with mixed age (mean age: 3.69 years, range: 1\u0026ndash;8 years) which are of primiparous and multiparous status. The udder and teat traits of these goats were measured according to FAO guidelines for phenotypic characterization of animal genetic resources (FAO 2012) using a flexible measuring tape. Traits measured include: udder perimeter (UP), udder depth (UD), udder length (UL), udder width (UW), udder cleft (UCF), udder circumference (UC), udder volume (UV), teat length(TL), teat width (TW), teat circumference (TC), distance between teats (DBT) and teat height from the ground (THG).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBlood samples collection and DNA isolation\u003c/h3\u003e\n\u003cp\u003eBlood samples from the phenotyped goats were collected for DNA extraction. Isolation of the DNA was done using Qiagen QIAamp DNA Kits for DNA Extraction (Cat no #51306) as per the manufacturer\u0026rsquo;s instructions. Integrity of isolated DNA was checked by 1% agarose gel electrophoresis and purity of DNA was assessed by QIAxpert spectrophotometer immediately after extraction.\u003c/p\u003e\n\u003ch3\u003ePCR amplification\u003c/h3\u003e\n\u003cp\u003eAccording to DQ380249 accession number which corresponds to a specific DGAT1 gene sequence from goats, was used to design a primer: F 5\u0026prime; CCCAGACACTTCTACAAGCC3\u0026prime; and R 3\u0026prime; TGCCCGATGATGAGTGACAG5'. The primer was designed using Primer 3 software and the specificity of the primer sequence was checked through \u0026lsquo;BLAST\u0026rsquo; program. The PCR reaction was carried out for each primer in 12.5 \u0026micro;L of total volume, mixtures containing\u0026thinsp;~\u0026thinsp;150 ng genomic DNA, 6.25 \u0026micro;l of 2 \u0026times; Dream Taq PCR Master Mix (K1081; Promega), and 5 pmol of each primer. The PCR conditions were as follows: denaturation at 94\u0026deg;C for 5 min, followed by 32 cycles of denaturation at 94\u0026deg;C for 30 seconds, annealing at 58\u0026deg;C for 1 min, extension at 72\u0026deg;C for 30 seconds and final extension at 72\u0026deg;C for 4 min. The PCR products were separated by electrophoresis on 1.5% agarose gels and photographed under UV light.\u003c/p\u003e\n\u003ch3\u003eAllelic- Specific Primer designing and Genotyping\u003c/h3\u003e\n\u003cp\u003eThe DNA sequences of the target gene region were obtained through Sanger sequencing. Sequence alignment and SNP identification were performed using BioEdit sequence alignment editor tool v7.2.5 to detect polymorphic sites. Allele-specific primers (ASP) were designed for a single nucleotide polymorphism (SNP) in the DGAT1 gene using Web-based Allele Specific Primer (WASP) design tool software. Two forward primers were designed: Allele C-specific primer: Perfectly matches the C allele at the 3\u0026rsquo; end. Allele T-specific primer: Perfectly matches the T allele at the 3\u0026rsquo; end. A common reverse primer was also designed downstream of the SNP. Primers were designed to include the SNP or variant at the 3' end (penultimate mismatch) to ensure specificity for the target allele. The primers designed were: Wildtype Forward Primer 5\u0026rsquo;: GAGCATCCCCCTGCGCATGTC, Mutant Forward Primer 5': GAGCATCCCCCTGCGCATGTT and Common Reverse Primer 5': GGTCCCGGGCCATCCCGGTAGG. Allele-Specific PCR (AS-PCR) was performed in a 10 \u0026micro;L reaction mixture containing 5\u0026micro;l of 2 \u0026times; Dream Taq PCR Master Mix (K1081; Promega), 10 ng Template DNA, 0.5 \u0026micro;M Allele C Forward primer (for one reaction), 0.5 \u0026micro;M Allele T Forward primer (for another reaction) and 0.5 \u0026micro;M Common Reverse primer. The PCR conditions were as follows: denaturation at 94\u0026deg;C for 5 min, followed by 32 cycles of denaturation at 94\u0026deg;C for 30 seconds, annealing at 60\u0026deg;C for 1 min, extension at 72\u0026deg;C for 30 seconds and final extension at 72\u0026deg;C for 4 min and was held at 4\u0026deg;C. The PCR products were separated by electrophoresis on 1.5% agarose gels and photographed under UV light. The gel electrophoresis results were analyzed to determine the genotypic pattern.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eGenotypes were estimated by counting the electrophoretic pattern of DGAT1 gene. Pearson correlations between udder and teat morphological traits of the goats and genotypes were analysed. The correlations estimated and heatmap plots were constructed with R software (2025) to visualize the correlation matrix and the P-values of each correlation. A linear regression analysis was performed using the linear model function in R to quantify the association between the udder traits and the genotype. The model was applied separately for each trait. The general form of the model is:\u003c/p\u003e \u003cp\u003ey\u003csub\u003ei\u003c/sub\u003e​= β\u003csub\u003e0\u003c/sub\u003e​ + β\u003csub\u003e1\u003c/sub\u003e​X\u003csub\u003ei\u003c/sub\u003e ​+ ϵ\u003csub\u003ei​\u003c/sub\u003e\u003c/p\u003e \u003cp\u003ewhere: y\u003csub\u003ei\u003c/sub\u003e​ = value of the udder and teat traits; X\u003csub\u003ei\u003c/sub\u003e = SNP genotypes (CC, CT, TT); β\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;the intercept; β\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;the regression coefficient (Estimate); ϵ\u003csub\u003ei\u003c/sub\u003e = the residual error term.\u003c/p\u003e \u003cp\u003eTo control for potential confounding effects and improve model accuracy in explaining variations in udder and teat traits, management system and age of the goats were incorporated in the model. Thus, multiple linear regression model was also employed to assess the association between udder and teat traits and the predictor variables, including SNP effects, management system, and age. The model is represented as:\u003c/p\u003e \u003cp\u003ey\u003csub\u003ei\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;β\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e1\u003c/sub\u003eX\u003csub\u003ei\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e2\u003c/sub\u003eM\u003csub\u003ei\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e3\u003c/sub\u003eA\u003csub\u003ei\u003c/sub\u003e+ϵ\u003csub\u003ei\u003c/sub\u003e\u003c/p\u003e \u003cp\u003ewhere: y\u003csub\u003ei\u003c/sub\u003e= udder or teat traits; β\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;intercept; β\u003csub\u003e1\u003c/sub\u003e, β\u003csub\u003e2\u003c/sub\u003e, β\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;regression coefficients; M\u003csub\u003ei\u003c/sub\u003e = Management systems (Intensive, Semi-intensive, Extensive); A\u003csub\u003ei\u003c/sub\u003e = Age of the goats; ϵ\u003csub\u003ei\u003c/sub\u003e = the residual error term.\u003c/p\u003e \u003cp\u003eThe statistical significance of each predictor was assessed using t-tests, and model fit was evaluated using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The regression estimates (β), t-values, and p-values were extracted using the broom package, and the results were visualized using ggplot2 in R. In the visualization, each predictor is represented with its corresponding regression estimate, confidence intervals, and statistical significance. To identify SNP association with udder and teat traits while controlling for population structure and relatedness, Manhattan plots using the \u003cem\u003eqqman\u003c/em\u003e package was also performed in R.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe association between DGAT 1 gene SNP and udder traits in WAD goats is presented in Table 1. The results revealed varying degrees of genetic influence on these traits, which are critical for milk production. There was no signification association between the SNP and udder traits. However, udder depth and udder circumference revealed suggestive associations with the SNP. Both positive and negative estimated effects of the SNP on udder and teat traits of the goats were obtained. Udder cleft, udder volume and udder depth show relatively larger negative effect sizes. Teat height from ground and teat width exhibits non-significant positive association. Also, coefficient of determination (R\u0026sup2;) values was relatively low.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: SNP-Phenotype association for udder and teat traits in Nigerian WAD goats\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003eTraits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.135%;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9609%;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6791%;\"\u003e\n \u003cp\u003ep -value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003eUdder perimeter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.135%;\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9609%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6791%;\"\u003e\n \u003cp\u003e0.900\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003eUdder depth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.135%;\"\u003e\n \u003cp\u003e-0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9609%;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6791%;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003eUdder length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.135%;\"\u003e\n \u003cp\u003e-0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9609%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6791%;\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003eUdder width\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.135%;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9609%;\"\u003e\n \u003cp\u003e1.57 x 10\u003csup\u003e-5\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6791%;\"\u003e\n \u003cp\u003e0.962\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003eUdder circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.135%;\"\u003e\n \u003cp\u003e-0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9609%;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6791%;\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003eUdder cleft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.135%;\"\u003e\n \u003cp\u003e-0.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9609%;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6791%;\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003eUdder volume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.135%;\"\u003e\n \u003cp\u003e-0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9609%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6791%;\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003eTeat length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.135%;\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9609%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6791%;\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003eTeat width\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.135%;\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9609%;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6791%;\"\u003e\n \u003cp\u003e0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003eTeat circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.135%;\"\u003e\n \u003cp\u003e-0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9609%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6791%;\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003eDistance between teats\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.135%;\"\u003e\n \u003cp\u003e-0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9609%;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6791%;\"\u003e\n \u003cp\u003e0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.225%;\"\u003e\n \u003cp\u003eTeat height from ground\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.135%;\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.9609%;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.6791%;\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eGiven the weak associations observed in the previous result, age and management systems were included as covariates to account for their potential confounding effects on udder traits. The result reveals that there was no significant association between the SNP and udder traits (Table 2). However, relatively higher R\u0026sup2; values were obtained compare to previous models without covariates (Table 1), an indication that age and management system contribute to variation in udder traits of the goats. The highest R\u0026sup2; values were observed for udder circumference and udder placement, indicating moderate explanatory power. The estimated effects show both positive and negative influences of the SNP on different udder traits. Also, negative effect sizes were observed in udder cleft, udder circumference and udder depth, suggesting a potential unfavorable genetic influence. However, positive and non-signification associations were observed in teat height from ground and teat width.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e \u003cstrong\u003eSNP-Phenotype association for udder and teat traits in Nigerian WAD goats\u003c/strong\u003e \u003cstrong\u003ewith age and management system as covariates\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.0705%;\"\u003e\n \u003cp\u003eTraits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6148%;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4448%;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8698%;\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.0705%;\"\u003e\n \u003cp\u003eUdder perimeter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6148%;\"\u003e\n \u003cp\u003e0.084187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4448%;\"\u003e\n \u003cp\u003e0.158749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8698%;\"\u003e\n \u003cp\u003e0.67087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.0705%;\"\u003e\n \u003cp\u003eUdder depth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6148%;\"\u003e\n \u003cp\u003e-0.36415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4448%;\"\u003e\n \u003cp\u003e0.129024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8698%;\"\u003e\n \u003cp\u003e0.17738\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.0705%;\"\u003e\n \u003cp\u003eUdder length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6148%;\"\u003e\n \u003cp\u003e0.007101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4448%;\"\u003e\n \u003cp\u003e0.112591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8698%;\"\u003e\n \u003cp\u003e0.983823\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.0705%;\"\u003e\n \u003cp\u003eUdder width\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6148%;\"\u003e\n \u003cp\u003e0.035501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4448%;\"\u003e\n \u003cp\u003e0.026372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8698%;\"\u003e\n \u003cp\u003e0.910114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.0705%;\"\u003e\n \u003cp\u003eUdder circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6148%;\"\u003e\n \u003cp\u003e-0.31464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4448%;\"\u003e\n \u003cp\u003e0.231984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8698%;\"\u003e\n \u003cp\u003e0.230494\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.0705%;\"\u003e\n \u003cp\u003eUdder cleft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6148%;\"\u003e\n \u003cp\u003e-0.51612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4448%;\"\u003e\n \u003cp\u003e0.125894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8698%;\"\u003e\n \u003cp\u003e0.266426\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.0705%;\"\u003e\n \u003cp\u003eUdder volume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6148%;\"\u003e\n \u003cp\u003e-17.9294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4448%;\"\u003e\n \u003cp\u003e0.046068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8698%;\"\u003e\n \u003cp\u003e0.875888\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.0705%;\"\u003e\n \u003cp\u003eTeat length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6148%;\"\u003e\n \u003cp\u003e0.025686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4448%;\"\u003e\n \u003cp\u003e0.015921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8698%;\"\u003e\n \u003cp\u003e0.65517\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.0705%;\"\u003e\n \u003cp\u003eTeat width\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6148%;\"\u003e\n \u003cp\u003e0.146286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4448%;\"\u003e\n \u003cp\u003e0.031037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8698%;\"\u003e\n \u003cp\u003e0.17758\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.0705%;\"\u003e\n \u003cp\u003eTeat circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6148%;\"\u003e\n \u003cp\u003e0.002686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4448%;\"\u003e\n \u003cp\u003e0.01911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8698%;\"\u003e\n \u003cp\u003e0.970124\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.0705%;\"\u003e\n \u003cp\u003eDistance between teats\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6148%;\"\u003e\n \u003cp\u003e-0.11405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4448%;\"\u003e\n \u003cp\u003e0.162417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8698%;\"\u003e\n \u003cp\u003e0.524716\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 37.0705%;\"\u003e\n \u003cp\u003eTeat height from ground\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.6148%;\"\u003e\n \u003cp\u003e0.255107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.4448%;\"\u003e\n \u003cp\u003e0.048074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.8698%;\"\u003e\n \u003cp\u003e0.369058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe coefficient plot shows the estimated effects of the management system, age, and SNP genotype on the traits considered in this study (Fig. 1). The result reveal that the intercept (\u0026beta;̂ = 16.52, p = 1.71e-34) is a representative of baseline value of the dependent variable when all the predictors (management system, age and SNP) are zero. A highly significant p-value (p \u0026lt; 0.001) was also observed. Positive coefficients were obtained for management system (\u0026beta;̂ = 0.82, p = 9.28e-03) and SNP (\u0026beta;̂ = 0.35). However, the p-value (p = 0.21) was not statistically significant. A negative coefficient was obtained for age (\u0026beta;̂ = -0.25, p = 0.16) with a non-significant p-value (0.16). The Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values (AIC = 770, BIC = 785) show model complexity and fit.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHierarchical clustering was used with the correlation matrix to explain modular trait groupings and shared phenotypic variation patterns in udder traits of WAD goats (Fig. 2). There exist a cluster of core udder module such as UC, UL,UV,UP and UCF; a width-distance subgroup for UW,DBT and UD; teat morpholgical module for TC,TL and THG as well as a distantly clustered genotype score. Weak and mostly negative correlations were observed between the genotype score representing a composite genetic index with the phenotypic udder traits of the goats. Slight inverse relationships were also observed with UD, UC and UCF\u003c/p\u003e\n\u003cp\u003eGenomic Position-Based Manhattan Plot of DGAT 1 SNP associations with udder traits of WAD goats shows the specific regions within the DGAT1 gene that exhibit associations with udder traits (Fig. 3a). This reveals that SNP associations are distributed across different genomic positions with the significant threshold (p = 0.05) as indicated by the dashed gray line. It is revealed that SNP exhibits varying degrees of association with different traits, with some traits showing stronger associations than others (Fig. 4). Traits such as udder depth, udder circumference, teat width, distance between teat and udder cleft display relatively higher -log10(P-values). However, traits like udder placement, udder width, udder volume, udder length, teat circumference and teat length show weaker associations\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe suggestive associations observed for udder depth and udder circumference unlike other non-significant traits indicates potential candidate traits for further analysis. The larger negative effect sizes obtained for udder cleft, udder volume and udder depth are an indication of the presence of an unfavorable allele that may contribute to reduced udder trait expression and potentially impact milk yield negatively. Low determinant of coefficient obtained suggests udder traits are likely influenced by multiple genes (polygenic inheritance) that is, they are likely influenced by multiple genes and environmental factors rather than being controlled by a single genetic variant. Significant association between SNP and udder width, udder depth, teat placement and teat diameter was reported in Murciano-Granadina goats (Luigi-Sierra et al., 2020). Findings from this present study indicate that there is no strong association between the SNP and udder traits measured.\u003c/p\u003e \u003cp\u003eVariation of coefficient of determination (R\u0026sup2;) values across udder traits obtained with udder circumference showing the highest proportion of variance explained by the SNP suggests that udder circumference may be moderately influenced by DGAT1, while other traits exhibit weaker genetic control. By including age and management system as covariates, it was aimed to reduce environmental noise and isolate the genetic contribution of DGAT1 to udder trait variation. Age is a crucial source of variation influencing mammary gland development and udder morphology (Raphel et al., 2017; Sam et al., 2017; Ahlem et al., 2023). Management system directly have effects on nutrition, health, and overall physiological condition of the animals, which can, in turn, influence udder traits of the goats (Hue-Beauvais et al., 2021). Although, the inclusion of age and management system as covariates account for external influences, but the results revealed that udder traits are influenced by a polygenic gene rather than a single gene like DGAT1. It has been previously confirmed by Mucha et al., (2018) that udder conformation traits in goats are polygenic in nature when 52K goat, SNPs chip developed to evaluate dairy goats' production traits was combined with genotypic and phenotypic data such as milk quantity and udder conformation traits in chromosome 19 and other chromosomes. Also, moderate R\u0026sup2; values for UC, UP, and DBT suggest that these traits might be indirectly influenced by DGAT1, potentially through its role in milk secretion and mammary gland development (Farese et al., 2000; Smith et al., 2000; Cases et al., 2004). Chromosome wide associations between SNPS and some udder traits (UW, UD, TP and TD) have been reported in Murciano-Granadina goats (Luigi-Sierra et al., 2020). In a similar study on cattle, Sinha et al., (2022) reported that GG genotype of SNP rs454303072 was found to have wider rear udder, larger udder circumference, longer distance between fore-rear teats and left-right teats in Karan Fries cattle. Whereas, in Sahiwal cattle, AA genotype of this SNP was found to be associated with the higher and wider rear udder, larger udder circumference and wider udder. AA genotype of SNP rs382671389 was found to be associated with longer fore teat in Karan Fries cattle. The TT and CC genotype of SNP rs435289107 was associated with udder type traits in Karan Fries and Sahiwal cattle respectively.\u003c/p\u003e \u003cp\u003eThe positive coefficient for management system (β̂ = 0.82, p\u0026thinsp;=\u0026thinsp;9.28e-03) obtained in this study, is an indication that management system has a significant impact on the udder traits. Also, the positive coefficient of SNP (β̂ = 0.35) suggests the association of SNP with an increase in the udder traits. However, the statistically non-significant of p-value shows that there was no strong independent effect of the SNP on the udder traits. A slightly decreasing effect of age on the udder traits of the goats reflects from the negative coefficient obtained for age while non-statistically significant p-values implies that age does not have a strong influence on the udder traits of the goat in the model. Lower values of AIC and BIC generally suggest a better model, while these values alone do not indicate model quality, they can be used for model comparison.\u003c/p\u003e \u003cp\u003eUdder traits have been earlier reported to be highly correlated in some breeds of goats; Akkeci goats (Keskin et al., 2005) local goats of Rohilkhand region of India (Upadhyay et al., 2014) WAD (Zahraddeen et al., 2008; Zahraddeen et al., 2007) White Bornu goats (Akporhuarho et al., 2010). The teat traits tend to cluster separately from the udder traits, indicating some independence in their genetic regulation. The moderate and positive correlations between udder depth and distance between teats suggests that deeper udder is associated with wider teat placement while the weak correlation between teat length and udder width implies a minor association between udder and teat dimensions. The correlations between udder and teat traits are consistent with previously reported studies indicating that udder size is a key determinant of milk yield in goats.\u003c/p\u003e \u003cp\u003eThe specific regions within the DGAT1 gene that exhibit associations with udder traits reveals that SNP associations are distributed across different genomic positions, which implies that multiple loci within DGAT1 may contribute to phenotypic variation in the udder traits. High -log10(P-values) of the SNP suggest a stronger association with one or more udder traits. The borderline significant SNPs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.10), suggest potential associations that may warrant further investigation. Clustering of certain SNPs within specific genomic regions is an indication of regulatory elements or linkage disequilibrium effects influencing udder morphology, milk production, and teat structure of the goats. The presence of borderline significant SNPs in the genomic position plot suggests that some associations might be trait-specific rather than genome-wide significant, which was validated through the trait-based plot. This allows for a direct comparison of SNP associations across the udder traits. It is revealed that SNP exhibits varying degrees of association with different traits, with some traits showing stronger associations than others. Traits such as udder depth, udder circumference, teat width, distance between teat and udder cleft display relatively higher -log10(P-values), indicating stronger SNP-trait relationships. However, traits like udder placement, udder width, udder volume, udder length, teat circumference and teat length show weaker associations, which implies that genetic variation in these traits may be influenced by factors beyond DGAT1gene. The presence of SNPs with consistent effects across multiple traits suggests pleiotropic relationships, where a single genetic variant (SNP) influences multiple traits. Such relationships suggest that DGAT1 may play a central role in udder morphology, impacting multiple functional and structural aspects simultaneously.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThere was no significant association between the SNP and the udder traits, though suggestive associations were observed for udder depth and circumference. The estimated SNP effects on udder traits varied, showing both positive and negative influences. Low R\u0026sup2; values indicate that udder traits in WAD goats are influenced by multiple genes (polygenic inheritance) and environmental factors rather than being controlled by a single genetic variant. The relatively sample size and use of single SNP may have limited detection of additional loci that are influencing udder traits in WAD goats. Further studies using larger populations and multi-traits GWAS are recommended to enhance resolution of genetic variants underlying udder traits of WAD goats.\u003c/p\u003e \u003cp\u003eFuture GWAS with larger datasets and multi-trait models are recommended to uncover additional variants influencing udder traits in WAD goats.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAIC: Akaike Information Criterion; BIC: Bayesian Information Criterion; AS-PCR: Allelic-specific polymerized chain reaction; DGAT1: Diacylglycerol O-acyltransferase 1; DNA: Deoxyribonucleic acid; R2: Coefficient of determination; SNP: Single nucleotide polymorphism; UV: Ultraviolet; WAD: West African Dwarf\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to the Department of Biotechnology (DBT), Ministry of Science and Technology, India and The World Academic of Sciences for the advancement of science in developing countries, Trieste, Italy and UNESCO programme unit (UNESCO-TWAS) for providing the funds for this project. We are also grateful to Molecular Endocrinology, Functional Genomics and Systems Biology Laboratory, Dairy Animal Biochemistry Division, ICAR-National Dairy Research Institute, Karnal, Haryana, India\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMAP, SKO, DS and SOO considered and designed the study. SKO and DS supervised the study. MAP collected the data on udder morphology blood samples for DNA isolation. MAP, AM, MR, PP and RM undertook the genomic DNA isolation, genotyping and performed statistical analyses. MAP wrote the manuscript. SKO, DS and SOO made substantial contributions to the interpretation of the results and revised the manuscript. All the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was co-funded by Department of Biotechnology (DBT), Ministry of Science and Technology, India and The World Academic of Sciences for the advancement of science in developing countries, Trieste, Italy and UNESCO programme unit (UNESCO-TWAS)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures were approved by the Federal College of Animal Health and Production Technology \u0026ndash; Animal Care and Use Research Ethics Committee (FCAH\u0026amp;PT-ACUREC) approved on 10 October 2023.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Animal Health and Production Technology, Federal College of Animal Health and Production Technology, Ibadan, Nigeria\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eAnimal Biochemistry Division, ICAR- National Dairy Research Institute, Karnal, Haryana 1320001, India\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eDepartment of Animal Sciences, Obafemi Awolowo University Ile-Ife, Nigeria\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003eAnimal Breeding and Genetics Division, ICAR- National Dairy Research Institute, Karnal, Haryana 1320001, India\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdebayo O, Ogah D.M., \u0026amp; Ozoje M.O. (2020). Genetic evaluation of Nigerian West African Dwarf goats for milk production traits. \u003cem\u003eTropical Animal Health Production, 52(4):1345-1352.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eAhlem A, Caraba\u0026ntilde;o M.J, Aicha L, Mouldi A, Farah B.S. \u0026amp; Sghaier N. (2023). Major udder morphology traits and their relationship with milk production in Tunisian local goats. \u003cem\u003eIran Journal of Applied Animal Science, 13(2):297-306.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eAkporhuarho P.O., Orheruata J.A., Otoikhian C.S.O., Igene F.U. (2010). Evaluation of udder size and milk yield of White Bornu (WB) goats reared under on-field research environment. \u003cem\u003eNatural and Applied Science Journal. 11(1):1-6.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eBovenhuis H, Visker M.H.P.W., Poulsen N.A., Sehested J, van Valenberg H.J.F.\u0026amp; van Arendonk J.A.M. (2016). 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Udder and teat traits as possible selection markers for milk yield in local goats of Nigeria. \u003cem\u003eGlobal Journal of Agricultrual Science. 7(1):23-26.\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"DGAT, gene, management, polymorphism, traits, udder","lastPublishedDoi":"10.21203/rs.3.rs-8235032/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8235032/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe udder is vital for milk production in ruminants, including West African Dwarf (WAD) goats. Udder and teat characteristics influence milk yield and milking efficiency in these animals. The diacylglycerol O-acyltransferase 1 (DGAT1) gene has been identified as a key regulator of lipid metabolism and milk production traits across various species of animals. While DGAT1 polymorphisms have been extensively studied in other species, their association with udder morphology in West African Dwarf (WAD) goats remains unexplored. This study investigates DGAT1 polymorphisms and their relationship with udder traits to enhance genetic improvement strategies. Blood samples from 150 lactating WAD goats (1\u0026ndash;8 years old) were collected across intensive, semi-intensive, and extensive management systems in Southwest Nigeria. Genomic DNA was extracted, and allele-specific PCR (AS-PCR) was used for SNP genotyping. Udder and teat traits were also measured. Associations between DGAT1 SNPs and udder traits were analyzed using linear and multiple regression models in R, with age and management system as covariates. Pearson correlations and Manhattan plots were used to visualize the SNP-trait relationships.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eNo significant associations were found between the DGAT1 SNP and most udder traits, though suggestive associations were noted for udder depth and circumference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.10). The SNP effects varied, showing both positive and negative influences, but low R\u0026sup2; values indicate that polygenic inheritance and environmental factors dominate. Age and management system significantly affected udder morphology, improving model accuracy, while genotype-trait correlations remained weak. Manhattan plots suggested that multiple loci within DGAT1 or nearby regulatory regions might contribute to trait variation, implying that DGAT1\u0026rsquo;s impact is likely moderated by genetic and environmental interactions.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study found no strong association between the DGAT1 SNP and most udder traits but identified potential effects on udder depth and circumference. The low R\u0026sup2; values suggest udder traits are polygenic and environmentally influenced. Including age and management system significantly improved model performance, emphasizing their critical role in udder morphology. While DGAT1 may contribute moderately, its effect likely depends on interactions with other genes and environmental factors.\u003c/p\u003e","manuscriptTitle":"DGAT1 Gene Variants and Udder Biometry: A Genetic Association Study in Nigerian West African Dwarf Goats","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 14:18:39","doi":"10.21203/rs.3.rs-8235032/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8506b9c5-f2a7-4801-98d6-4275a805909e","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-18T18:24:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 14:18:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8235032","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8235032","identity":"rs-8235032","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}