Correlation of 20 Single-Nucleotide Polymorphisms with Weight and Wool Traits in Alpine Merino Sheep

SNPs associated with important traits of fine-wool sheep that were previously obtained through genome-wide association analysis screening were verified and analyzed. A total of 20 SNPs related to birth weight, bundle strength, cleaning rate, and fiber diameter were screened using whole-genome resequencing, and the SNPshot assay was used to detect and analyze polymorphisms. This study found that, among the 20 SNPs associated with important traits in Alpine Merino sheep, 8 were monomorphic and 12 were polymorphic, of which 6 showed moderate polymorphisms and 6 showed low polymorphisms. The heterozygosity of the 12 polymorphic loci ranged from 0.10 to 0.49, the effective number of alleles ranged from 1.11 to 1.98, and the polymorphic information content ranged from 0.09 to 0.37. The chi-square test showed that only RHPN2:g.42678119T>G and RALYL:g.90030866A>G were in Hardy-Weinberg disequilibrium (p < 0.05); the other loci were in equilibrium (p > 0.05). These SNPs associated with important traits in Alpine Merino sheep provide a theoretical basis for genomic selection and molecular design breeding.

Association of SLIT3 and ZNF280B Gene Polymorphisms with Wool Fiber Diameter

The SLIT3 gene encodes a secreted protein, and the ZNF280B gene is a member of the transcription factor family. Both genes have multiple biological functions. This study was conducted to investigate the association between SLIT3 and ZNF280B gene polymorphisms and wool fiber diameter and to determine potential molecular marker sites for breeding sheep with fine wool. We used Kompetitive Allele-Specific PCR to type the single nucleotide polymorphism (SNP) loci in the SLIT3 and ZNF280B genes within 1081 Alpine Merino sheep and associated these SNPs with wool fiber diameter. The results revealed one SNP in SLIT3 and ZNF280B, which were each related to sheep fiber diameter. The wool fiber diameters of sheep with the CC genotype in SLIT3 g.478807C>G and AA genotype in ZNF280B g.677G>A were the smallest and differed significantly from the diameters of other genotypes (p < 0.05). These results suggest potential molecular marker sites for fine-wool sheep breeding.

Proteome Analysis of Alpine Merino Sheep Skin Reveals New Insights into the Mechanisms Involved in Regulating Wool Fiber Diameter

Wool fiber is a textile material that is highly valued based on its diameter, which is crucial in determining its economic value. To analyze the molecular mechanisms regulating wool fiber diameter, we used a Data-independent acquisition-based quantitative proteomics approach to analyze the skin proteome of Alpine Merino sheep with four fiber diameter ranges. From three contrasts of defined groups, we identified 275, 229, and 190 differentially expressed proteins (DEPs). Further analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways revealed that pathways associated with cyclic adenosine monophosphate and peroxisome proliferator-activated receptor signaling are relevant to wool fiber diameter. Using the K-means method, we investigated the DEP expression patterns across wool diameter ranges. Using weighted gene co-expression network analysis, we identified seven key proteins (CIDEA, CRYM, MLX, TPST2, GPD1, GOPC, and CAMK2G) that may be involved in regulating wool fiber diameter. Our findings provide a theoretical foundation for identifying DEPs and pathways associated with wool fiber diameter in Alpine Merino sheep to enable a better understanding of the molecular mechanisms underlying the genetic regulation of wool fiber quality.

Estimates of (co)variance components and phenotypic and genetic parameters of growth traits and wool traits in Alpine Merino sheep

The (co)variance components and corresponding phenotypic and genetic parameters for growth traits and wool traits of economic importance were estimated in the Alpine Merino sheep population maintained at Gansu Provincial Sheep Breeding Technology Extension Station in northwestern China. Data from a maximum of 49,474 animals sired by 526 rams and born from 22,531 ewes over 20 years from 2000 to 2019 were used in this study. Birth type, age of dam, birth year, sex and/or management group, and age at measurement were initially fitted as fixed effects in an animal model with various random effects. Genetic groups were defined for all animals by the sire breed and breed genotype interacted with dam-strain flocks and were fitted as one of the random effects. Analyses were conducted using a residual maximum likelihood procedure (ASReml). Seven different animal models were fitted for all traits, and the most appropriate model with relevant random effects was selected through log-likelihood ratio testing. After identifying the appropriate model through single-trait analysis, bivariate analyses were used to obtain the phenotypic and genetic correlations among the traits. The estimates of additive direct heritability for birth weight (BWT), weaning weight (WWT), preweaning growth rate (prwADG), postweaning growth rate (powADG), yearling body weight (YWT), average fibre diameter (AFD), greasy fleece weight (GFW), clean fleece weight (CFW), yield (YLD), yearling wool staple length (YSL), coefficient of variation of average fibre diameter (FDcv) and wool visual fineness counts (VFC) were 0.30, 0.18, 0.18, 0.20, 0.29, 0.20, 0.19, 0.20, 0.35, 0.19, 0.16 and 0.13, respectively, with standard errors ranging from 0.02 to 0.05. The corresponding ratios of genetic group variance to additive genetic variance were significant and, respectively, 0.35, 0.80, 0.62, 0.26, 0.13, 1.06, 0.38, 0.64, 0.09, 0.12, 0.06 and 0.58. These results suggest for these traits that there is potential to exploit both the additive genetic variation and between genetic group variation although for most traits the between group variation was smaller than the variation within groups. Favourable genetic correlations were found among the growth traits, and between growth traits and fleece production traits, and among wool traits GFW, CFW, YSL and YLD. This study provides the required estimates of genetic parameters of both growth and wool traits of the new breed for the design of more effective breeding programmes.

Evaluation of Bayesian alphabet and GBLUP based on different marker density for genomic prediction in Alpine Merino sheep

The marker density, the heritability level of trait and the statistical models adopted are critical to the accuracy of genomic prediction (GP) or selection (GS). If the potential of GP is to be fully utilized to optimize the effect of breeding and selection, in addition to incorporating the above factors into simulated data for analysis, it is essential to incorporate these factors into real data for understanding their impact on GP accuracy, more clearly and intuitively. Herein, we studied the GP of six wool traits of sheep by two different models, including Bayesian Alphabet (BayesA, BayesB, BayesCπ, and Bayesian LASSO) and genomic best linear unbiased prediction (GBLUP). We adopted fivefold cross-validation to perform the accuracy evaluation based on the genotyping data of Alpine Merino sheep (n = 821). The main aim was to study the influence and interaction of different models and marker densities on GP accuracy. The GP accuracy of the six traits was found to be between 0.28 and 0.60, as demonstrated by the cross-validation results. We showed that the accuracy of GP could be improved by increasing the marker density, which is closely related to the model adopted and the heritability level of the trait. Moreover, based on two different marker densities, it was derived that the prediction effect of GBLUP model for traits with low heritability was better; while with the increase of heritability level, the advantage of Bayesian Alphabet would be more obvious, therefore, different models of GP are appropriate in different traits. These findings indicated the significance of applying appropriate models for GP which would assist in further exploring the optimization of GP.

Genomic Prediction of Additive and Dominant Effects on Wool and Blood Traits in Alpine Merino Sheep

Dominant genetic effects may provide a critical contribution to the total genetic variation of quantitative and complex traits. However, investigations of genome-wide markers to study the genomic prediction (GP) and genetic mechanisms of complex traits generally ignore dominant genetic effects. The increasing availability of genomic datasets and the potential benefits of the inclusion of non-additive genetic effects in GP have recently renewed attention to incorporation of these effects in genomic prediction models. In the present study, data from 498 genotyped Alpine Merino sheep were adopted to estimate the additive and dominant genetic effects of 9 wool and blood traits via two linear models: (1) an additive effect model (MAG) and (2) a model that included both additive and dominant genetic effects (MADG). Moreover, a method of 5-fold cross validation was used to evaluate the capability of GP in the two different models. The results of variance component estimates for each trait suggested that for fleece extension rate (73%), red blood cell count (28%), and hematocrit (25%), a large component of phenotypic variation was explained by dominant genetic effects. The results of cross validation demonstrated that the MADG model, comprising additive and dominant genetic effects, did not display an apparent advantage over the MAG model that included only additive genetic effects, i.e., the model that included dominant genetic effects did not improve the capability for prediction of the genomic model. Consequently, inclusion of dominant effects in the GP model may not be beneficial for wool and blood traits in the population of Alpine Merino sheep.