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This study was conducted to predict the genetic (co)variance components of growth curve parameters of Moghani sheep breed using the following information: birth weight (N = 7278), 3-mo-old weight (N = 5881), 6-mo-old weight (N = 5013), 9-mo-old weigh (N = 2819], and 12-mo-old weight (N = 2883). The growth parameters (A: maturity weight, B: growth rate, and K: maturity rate) were calculated using Gompertz, Logistic, Brody, and Von Bertalanffy nonlinear models via NLIN procedure of SAS software. The aforementioned models were compared using Akaike information criterion, root mean square error, adjusted co-efficient of determination. Also, both Bayesian (using MTGSAM) and RMEL (using WOMBAT) paradigms were adapted to predict the genetic (co)variance components of growth parameters (A, B, K) due to the best fitted growth models. It was turned out that Von Bertalanffy best fitted to the data in this study. The year of birth and lamb gender had a significant effect on maturity rate (P < 0.01). Also it turned out that within the growth parameter, with increasing (co)variance matrix complexity, the Bayesian paradigm fitted well to the data than the restricted maximum likelihood (REML) one. However, for simple animal model and across all growth parameters, REML outperformed Bayesian. In this way, the h2a predicted (0.15 ± 0.05), (0.11±.05), and (0.04 ± 0.03) for A, B, and K parameters, respectively. Practically, in terms of breeding plan, we could see that genetic improvement of growth parameters in this study is not a tractable strategy to follow up and improvement of the management and environment should be thoroughly considered. In terms of paradigm comparison, REML's bias correction bears up an advantageous approach as far as we are concerned with small sample size. To this end, REML predictions are fairly accurate but the mode of posterior distributions could be overestimated. Finally, the differences between REML and Bayesian estimates were found for all parameter data in this study. We conclude that simulation studies are necessary in order to trade off these parading in the complex random effects scenarios of genetic individual model., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The milk casein genes in goats, are highly polymorphic genes with numerous synonymous and non-synonymous mutations. So far, 20 protein variants have been reported in goats for alpha-S1-casein, eight for beta-casein, 14 for alpha-S2-casein, and 24 for kappa-casein. This review provides a comprehensive overview on identified milk casein protein variants in goat and non-coding DNA sequence variants with some affecting the expression of the casein genes. The high frequency of some casein protein variants in different goat breeds and geographical regions might reflect specific breeding goals with respect to milk processing characteristics, properties for human nutrition and health, or adaptation to the environment. Because protein names, alongside the discovery of protein variants, go through a historical process, we linked old protein names with new ones that reveal more genetic variability. The haplotypes across the cluster of the four genetically linked casein genes are recommended as a valuable genetic tool for discrimination between breeds, managing genetic diversity within and between goat populations, and breeding strategies. The enormous variation in the casein proteins and genes is crucial for producing milk and dairy products with different properties for human health and nutrition, and for genetic improvement depending on local breeding goals., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Rahmatalla, Arends and Brockmann.)

The Bos indicus zebu cattle Butana is the most commonly used indigenous dairy cattle breed in Sudan. In the last years, high-yielding Holstein dairy cattle were introgressed into Butana cattle to improve their milk yield and simultaneously keep their good adaption to extreme environmental conditions. With the focus on the improvement of milk production, other problems arose such as an increased susceptibility to mastitis. Thus, genetic selection for mastitis resistance should be considered to maintain healthy and productive cows. In this study, we tested 10 single nucleotide polymorphisms (SNPs) which had been associated with somatic cell score (SCS) in Holstein cattle for association with SCS in 37 purebred Butana and 203 Butana × Holstein crossbred cattle from Sudan. Animals were genotyped by competitive allele-specific PCR assays and association analysis was performed using a linear mixed model. All 10 SNPs were segregating in the crossbred Butana × Holstein populations, but only 8 SNPs in Sudanese purebred Butana cattle. The SNP on chromosome 13 was suggestively associated with SCS in the Butana × Holstein crossbred population (rs109441194, 13:79,365,467, P BF  = 0.054) and the SNP on chromosome 19 was significantly associated with SCS in both populations (rs41257403, 19:50,027,458, Butana: P BF  = 0.003, Butana × Holstein: P BF  = 6.2 × 10 -16 ). The minor allele of both SNPs showed an increase in SCS. Therefore, selection against the disadvantageous minor allele could be used for genetic improvement of mastitis resistance in the studied populations. However, investigations in a bigger population and across the whole genome are needed to identify additional genomic loci., (© 2022. The Author(s).)

Genetic variations in the four casein genes CSN1S1 , CSN2 , CSN1S2 , and CSN3 have obtained substantial attention since they affect the milk protein yield, milk composition, cheese processing properties, and digestibility as well as tolerance in human nutrition. Furthermore, milk protein variants are used for breed characterization, biodiversity, and phylogenetic studies. The current study aimed at the identification of casein protein variants in five domestic goat breeds from Sudan (Nubian, Desert, Nilotic, Taggar, and Saanen) and three wild goat species [ Capra aegagrus aegagrus (Bezoar ibex), Capra nubiana (Nubian ibex), and Capra ibex (Alpine ibex)]. High-density capture sequencing of 33 goats identified in total 22 non-synonymous and 13 synonymous single nucleotide polymorphisms (SNPs), of which nine non-synonymous and seven synonymous SNPs are new. In the CSN1S1 gene, the new non-synonymous SNP ss7213522403 segregated in Alpine ibex. In the CSN2 gene, the new non-synonymous SNPs ss7213522526, ss7213522558, and ss7213522487 were found exclusively in Nubian and Alpine ibex. In the CSN1S2 gene, the new non-synonymous SNPs ss7213522477, ss7213522549, and ss7213522575 were found in Nubian ibex only. In the CSN3 gene, the non-synonymous SNPs ss7213522604 and ss7213522610 were found in Alpine ibex. The identified DNA sequence variants led to the detection of nine new casein protein variants. New variants were detected for alpha S1 casein in Saanen goats ( CSN1S1 ∗ C1), Bezoar ibex ( CSN1S1 ∗ J), and Alpine ibex ( CSN1S1 ∗ K), for beta and kappa caseins in Alpine ibex ( CSN2 ∗ F and CSN3 ∗ X), and for alpha S2 casein in all domesticated and wild goats ( CSN1S2 ∗ H), in Nubian and Desert goats ( CSN1S2 ∗ I), or in Nubian ibex only ( CSN1S2 ∗ J and CSN1S2 ∗ K). The results show that most novel SNPs and protein variants occur in the critically endangered Nubian ibex. This highlights the importance of the preservation of this endangered breed. Furthermore, we suggest validating and further characterizing the new casein protein variants., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Rahmatalla, Arends, Said Ahmed, Hassan, Krebs, Reissmann and Brockmann.)

The aim of the present study was to assess genetic variation that is characteristic for Sudanese goat breeds in the milk whey protein genes (LALBA and BLG). Four Sudanese goat breeds were screened for variability in LALBA and BLG genes at the DNA level by comparative sequencing of five animals per breed. Sixteen SNPs were identified in LALBA: seven in the upstream region, six synonymous, and three in the 3´-UTR. Three novel synonymous SNPs in exon 2 (ss5197800003, ss5197800012, and ss5197800004) were found in Nubian, Desert, and Nilotic, but not in Taggar goats. One SNP in the promoter of LALBA (rs642745519) modifies a predicted transcription factor binding site for Tcfe2a. The SNPs in the 3'-UTR (rs657915405, rs641559728, and rs664225585) affect predicted miRNA target sites. With respect to haplotypes in the exonic region, haplotype LALBA-A is most frequent in Nubian, Desert, and Nilotic goats, while haplotype LALBA-D is prevalent in Taggar goats. In BLG, 30 SNPs were detected: eight in the upstream gene region, two synonymous, 17 intronic, and three in the 3'-UTR. Among the 30 identified SNPs, 15 were novel. Four of these novel SNPs were located in the upstream gene region, one was synonymous, and ten were intronic. The novel synonymous SNP (ss5197800017), located in exon 2, was only found in Nubian and Nilotic goats. The SNPs ss5197800010 and rs635615192 in the promoter are located in predicted binding sites of transcription factors (M6097, Elk3, Elf5, and GABPA). Among seven haplotypes detected in the coding region, haplotype BLG-A is most frequent in Nubian and Nilotic goats while haplotype BLG-B is most frequent in Desert and Taggar goats. The high variability in regulatory gene regions among Sudanese goats could potentially affect the quality and yield of whey proteins in goat milk and provide a wide resource for genetic improvement of milk production and milk technology characteristics.

Long-term selection of goats for a certain production system and/or different environmental conditions will be reflected in the body morphology of the animals under selection. To investigate the variation contributing to different morphological traits and to identify genomic regions that are associated with body morphological traits in Sudanese goats, we genotyped 96 females belonging to four Sudanese goat breeds with the SNP52 BeadChip. After quality control of the data, the genome-wide association study was performed using 95 goats and 24 027 informative single nucleotide polymorphisms (SNPs). Bicoastal diameter was significantly associated (LOD = 6.32) with snp10185-scaffold1365-620922 on chromosome 2. The minor allele has an additive effect, increasing the bicoastal diameter by 2.6 cm. A second significant association was found between body length and snp56482-scaffold89-467312 on chromosome 3 (LOD = 5.65). The minor allele is associated with increased body length. Additionally, five regions were suggestive for cannon bone, head width, rump length and withers height (LOD > 5). Only one gene (CNTNAP5) is located within the 1-Mb region surrounding the significant SNP for bicoastal diameter on chromosome 2. The body length QTL on chromosome 3 harbors 49 genes. Further research is required to validate the observed associations and to prioritize candidate genes., (© 2018 Stichting International Foundation for Animal Genetics.)

Background: Sudan is endowed with a variety of indigenous goat breeds which are used for meat and milk production and which are well adapted to the local environment. The aim of the present study was to determine the genetic diversity and relationship within and between the four main Sudanese breeds of Nubian, Desert, Taggar and Nilotic goats. Using the 50 K SNP chip, 24 animals of each breed were genotyped., Results: More than 96% of high quality SNPs were polymorphic with an average minor allele frequency of 0.3. In all breeds, no significant difference between observed (0.4) and expected (0.4) heterozygosity was found and the inbreeding coefficients (F IS ) did not differ from zero. F st coefficients for the genetic distance between breeds also did not significantly deviate from zero. In addition, the analysis of molecular variance revealed that 93% of the total variance in the examined population can be explained by differences among individuals, while only 7% result from differences between the breeds. These findings provide evidence for high genetic diversity and little inbreeding within breeds on one hand, and low diversity between breeds on the other hand. Further examinations using Nei's genetic distance and STRUCTURE analysis clustered Taggar goats distinct from the other breeds. In a principal component (PC) analysis, PC1 could separate Taggar, Nilotic and a mix of Nubian and Desert goats into three groups. The SNPs that contributed strongly to PC1 showed high F st values in Taggar goat versus the other goat breeds. PCA allowed us to identify target genomic regions which contain genes known to influence growth, development, bone formation and the immune system., Conclusions: The information on the genetic variability and diversity in this study confirmed that Taggar goat is genetically different from the other goat breeds in Sudan. The SNPs identified by the first principal components show high F st values in Taggar goat and allowed to identify candidate genes which can be used in the development of breed selection programs to improve local breeds and find genetic factors contributing to the adaptation to harsh environments.

The bovine growth hormone receptor (GHR) gene has been identified as a strong positional and functional candidate gene influencing milk production. A non-synonymous single nucleotide polymorphism (SNP) in exon 8 leads to a phenylalanine to tyrosine amino acid substitution (F279Y) in the receptor. The aim of the study was to estimate the effects of the F279Y mutation on milk yield, fat, protein, casein, and lactose yield and content, as well as somatic cell score (SCS), in a German Holstein dairy cattle population. The analysis of 1,370 dairy cows confirmed a strong association of the F279Y polymorphism with milk yield, as well as with fat, protein, and casein contents. Furthermore, increasing effects on lactose yield and content for the 279Y allele were found. Even though the tyrosine variant occurred as the minor allele (16.5%), its substitution effects were 320 kg (305 d), 0.02 kg per day, and 0.07 kg per day for milk, casein, and lactose yields, respectively. The same allele had negative effects on fat, protein, and casein contents. Finally, the high-milk-yield tyrosine allele was also associated with lower SCS (p < 0.05). The data support the high potential of the F279Y polymorphism as a marker for the improvement of milk traits in selection programs.

The effects of genes are commonly estimated using random regression models based on test-day data and only give a general gene effect. Alternatively, lactation curve models can be used to estimate biological and environmental effects, or to predict missing test-day data and perform breeding value estimation. This study combines lactation curve models and estimation of gene effects to represent gene effects in different stages of lactation. The lactation curve models used were based on the Wood, Wilmink, and Ali and Schaeffer models. A random regression test-day model was used to compare estimated gene effects with the results of commonly used models. The well-characterized DGAT1 gene with known effects on milk yield, milk fat, and milk protein production was chosen to test this new approach in a Holstein-Friesian dairy cattle population. The K232A polymorphism and the promoter VNTR (variable number of tandem repeats) of the DGAT1 gene were used. All lactation curve models predicted the production curves sufficiently. Nevertheless, for predicting genotype effects, the Wilmink curve indicated the closest fit to the data. This study shows that the characteristic gene effects for DGAT1 genotypes occur after lactation d 40, which might be explained by a link to other genes affecting metabolic traits. Furthermore, allele substitution effects of allele K of the K232A locus showed that the typical effect of low milk and protein yield is due mainly to a lower overall production level, whereas the higher fat and protein content is reached by increased production toward its peak and fat yield is increased because of a higher production after this peak. Predicting gene effects with production curves gives better insight into the timeline of gene effects. This can be used to form genetic groups, in addition to feeding groups, for managing livestock populations in a more effective way., (Copyright © 2011 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)