Epigenetic regulation of functional candidate genes for milk production traits in dairy sheep subjected to protein restriction in the prepubertal stage.

BACKGROUND: As the prepubertal stage is a crucial point for the proper development of the mammary gland and milk production, this study aims to evaluate how protein restriction at this stage can affect methylation marks in milk somatic cells. Here, 28 Assaf ewes were subjected to 42.3% nutritional protein restriction (14 animals, NPR) or fed standard diets (14 animals, C) during the prepubertal stage. During the second lactation, the milk somatic cells of these ewes were sampled, and the extracted DNA was subjected to whole-genome bisulfite sequencing. RESULTS: A total of 1154 differentially methylated regions (DMRs) were identified between the NPR and C groups. Indeed, the results of functional enrichment analyses of the genes harboring these DMRs suggested their relevant effects on the development of the mammary gland and lipid metabolism in sheep. The additional analysis of the correlations of the mean methylation levels within these DMRs with fat, protein, and dry extract percentages in the milk and milk somatic cell counts suggested associations between several DMRs and milk production traits. However, there were no phenotypic differences in these traits between the NPR and C groups. CONCLUSION: In light of the above, the results obtained in the current study might suggest potential candidate genes for the regulation of milk production traits in the sheep mammary gland. Further studies focusing on elucidating the genetic mechanisms affected by the identified DMRs may help to better understand the biological mechanisms modified in the mammary gland of dairy sheep as a response to nutritional challenges and their potential effects on milk production.

Low-density SNP panel for efficient imputation and genomic selection of milk production and technological traits in dairy sheep.

The present study aimed to ascertain how different strategies for leveraging genomic information enhance the accuracy of estimated breeding values for milk and cheese-making traits and to evaluate the implementation of a low-density (LowD) SNP chip designed explicitly for that aim. Thus, milk samples from a total of 2,020 dairy ewes from 2 breeds (1,039 Spanish Assaf and 981 Churra) were collected and analyzed to determine 3 milk production and composition traits and 2 traits related to milk coagulation properties and cheese yield. The 2 studied populations were genotyped with a customized 50K Affymetrix SNP chip (Affymetrix Inc.) containing 55,627 SNP markers. The prediction accuracies were obtained using different multitrait methodologies, such as the BLUP model based on pedigree information, the genomic BLUP (GBLUP), and the BLUP at the SNP level (SNP-BLUP), which are based on genotypic data, and the single-step GBLUP (ssGBLUP), which combines both sources of information. All of these methods were analyzed by cross-validation, comparing predictions of the whole population with the test population sets. Additionally, we describe the design of a LowD SNP chip (3K) and its prediction accuracies through the different methods mentioned previously. Furthermore, the results obtained using the LowD SNP chip were compared with those based on the 50K SNP chip data sets. Finally, we conclude that implementing genomic selection through the ssGBLUP model in the current breeding programs would increase the accuracy of the estimated breeding values compared with the BLUP methodology in the Assaf (from 0.19 to 0.39) and Churra (from 0.27 to 0.44) dairy sheep populations. The LowD SNP chip is cost-effective and has proven to be an accurate tool for estimating genomic breeding values for milk and cheese-making traits, microsatellite imputation, and parentage verification. The results presented here suggest that the routine use of this LowD SNP chip could potentially increase the genetic gains of the breeding selection programs of the 2 Spanish dairy sheep breeds considered here.

Study on the concordance between different SNP-genotyping platforms in sheep.

Different SNP genotyping technologies are commonly used in multiple studies to perform QTL detection, genotype imputation, and genomic predictions. Therefore, genotyping errors cannot be ignored, as they can reduce the accuracy of different procedures applied in genomic selection, such as genomic imputation, genomic predictions, and false-positive results in genome-wide association studies. Currently, whole-genome resequencing (WGR) also offers the potential for variant calling analysis and high-throughput genotyping. WGR might overshadow array-based genotyping technologies due to the larger amount and precision of the genomic information provided; however, its comparatively higher price per individual still limits its use in larger populations. Thus, the objective of this work was to evaluate the accuracy of the two most popular SNP-chip technologies, namely, Affymetrix and Illumina, for high-throughput genotyping in sheep considering high-coverage WGR datasets as references. Analyses were performed using two reference sheep genome assemblies, the popular Oar_v3.1 reference genome and the latest available version Oar_rambouillet_v1.0. Our results demonstrate that the genotypes from both platforms are suggested to have high concordance rates with the genotypes determined from reference WGR datasets (96.59% and 99.51% for Affymetrix and Illumina technologies, respectively). The concordance results provided in the current study can pinpoint low reproducible markers across multiple platforms used for sheep genotyping data. Comparing results using two reference genome assemblies also informs how genome assembly quality can influence genotype concordance rates among different genotyping platforms. Moreover, we describe an efficient pipeline to test the reliability of markers included in sheep SNP-chip panels against WGR datasets available on public databases. This pipeline may be helpful for discarding low-reliability markers before exploiting genomic information for gene mapping analyses or genomic prediction.

Genome-wide association studies (GWAS) and post-GWAS analyses for technological traits in Assaf and Churra dairy breeds.

This study aimed to perform a GWAS to identify genomic regions associated with milk and cheese-making traits in Assaf and Churra dairy sheep breeds; second, it aimed to identify possible positional and functional candidate genes and their interactions through post-GWAS studies. For 2,020 dairy ewes from 2 breeds (1,039 Spanish Assaf and 981 Churra), milk samples were collected and analyzed to determine 6 milk production and composition traits and 6 traits related to milk coagulation properties and cheese yield. The genetic profiles of the ewes were obtained using a genotyping chip array that included 50,934 SNP markers. For both milk and cheese-making traits, separate single-breed GWAS were performed using GCTA software. The set of positional candidate genes identified via GWAS was subjected to guilt-by-association-based prioritization analysis with ToppGene software. Totals of 84 and 139 chromosome-wise significant associations for the 6 milk traits and the 6 cheese-making traits were identified in this study. No significant SNPs were found in common between the 2 studied breeds, possibly due to their genetic heterogeneity of the phenotypes under study. Additionally, 63 and 176 positional candidate genes were located in the genomic intervals defined as confidence regions in relation to the significant SNPs identified for the analyzed traits for Assaf and Churra breeds. After the functional prioritization analysis, 71 genes were identified as promising positional and functional candidate genes and proposed as targets of future research to identify putative causative variants in relation to the traits under examination. In addition, this multitrait study allowed us to identify variants that have a pleiotropic effect on both milk production and cheese-related traits. The incorporation of variants among the proposed functional and positional candidate genes into genomic selection strategies represent an interesting approach for achieving rapid genetic gains, specifically for those traits difficult to measure, such as cheese-making traits.

Estimation of genetic parameters for cheese-making traits in Spanish Churra sheep.

The global production of sheep milk is growing, and the main industrial use of sheep milk is cheese making. The Spanish Churra sheep breed is one of the most important native dairy breeds in Spain. The present study aimed to estimate genetic parameters for a wide range of traits influencing the cheese-making ability of Churra sheep milk. Using a total of 1,049 Churra ewes, we studied the following cheese-making traits: 4 traits related to milk coagulation properties (rennet coagulation time, curd-firming time, and curd firmness at 30 and 60 min after addition of rennet), 2 traits related to cheese yield (individual laboratory cheese yield and individual laboratory dried curd yield), and 3 traits measuring curd firmness over time (maximum curd firmness, time to attain maximum curd firmness, and syneresis). In addition, a list of milk traits, including the native pH of the milk and several milk production and composition traits (milk yield; the fat, protein, and dried extract percentages; and the somatic cell count), were also analyzed for the studied animals. After discarding the noncoagulating samples (only 3.7%), data of 1,010 ewes were analyzed with multiple-trait animal models by using the restricted maximum likelihood method to estimate (co)variance components, heritabilities, and genetic correlations. In general, the heritability estimates were low to moderate, ranging from 0.08 (for the individual laboratory dried curd yield trait) to 0.42 (for the fat percentage trait). High genetic correlations were found within pairs of related traits (i.e., 0.93 between fat and dried extract percentages, -0.93 between the log of the curd-firming time and curd firmness at 30 min, 0.70 between individual laboratory cheese yield and individual laboratory dried curd yield, and -0.94 between time to attain maximum curd firmness and syneresis). Considering all the information provided here, we suggest that in addition to the current consideration of the protein percentage trait for improving cheese yield traits, the inclusion of the pH of milk as a measured trait in the Churra dairy breeding program would represent an efficient strategy for improving the cheese-making ability of milk from this breed.

Detection of quantitative trait loci and putative causal variants affecting somatic cell score in dairy sheep by using a 50K SNP chip and whole-genome sequencing.

This study presents a scan of the ovine genome to identify quantitative trait loci (QTL) influencing the somatic cell score (SCS), a classical indicator of subclinical mastitis in sheep, and a subsequent high-resolution analysis of one of the identified QTL regions based on the analysis of whole-genome sequence data sets. A half-sib commercial population of Churra sheep genotyped with a 50K SNP chip was analyzed using linkage analysis (LA) and combined linkage and linkage disequilibrium analysis (LDLA). By LA, 2 5% chromosome-wide significant QTL on OAR5 and OAR25 and one 5% genome-wide significant QTL on ovine chromosome 20 (OAR20) were detected, whereas 22 significant associations were identified by LDLA. Two of the associations detected by LDLA replicated LA-detected effects (OAR20, OAR25). We compared the detected associations with previously reported QTL in sheep and cattle, and functional candidate genes were identified within the estimated confidence intervals. We then performed a high-resolution analysis of the OAR20 QTL region, the most significant QTL region identified by LA that replicated a QTL previously described in Churra sheep for SCS using microsatellite markers. For that, 2 segregating trios of 2 segregating families for the OAR20 QTL (each including the Qq sire and 2 daughters, QQ and qq) were selected for whole-genome sequencing. The bioinformatic analysis of the 6 sequenced samples performed across the genomic interval considered (14.2-41.7 Mb) identified a total of 227,030 variants commonly identified by 2 independent software packages. For the 3 different concordance tests considered, due to discrepancies regarding the QTL peak in the segregating families, the list of mutations concordant with the QTL segregating pattern was processed to identify the variants identified in immune-related genes that show a moderate/high impact on the encoded protein function. Among a list of 85 missense variants concordant with the QTL segregation pattern that were within candidate immune-related genes, 13 variants distributed across 7 genes [PKHD1, NOTCH4, AGER, ENSOARG00000009395 (HLA-C, Homo sapiens), ENSOARG00000015002 (HLA-B, H. sapiens), MOG, and ENSOARG00000018075 (BoLA, Bos taurus, orthologous to human HLA-A] were predicted to cause deleterious effects on protein function. Future studies should assess the possible associations of the candidate variants identified herein in commercial populations with indicator traits of udder inflammation (SCS, clinical mastitis).