Longitudinal data analysis of polymorphisms in the κ-casein and β-lactoglobulin genes shows differential effects along the trajectory of the lactation curve in tropical dairy goats

Validation of single-step genomic BLUP random regression test-day models and SNP effects analysis on milk yield in French Saanen goats

The shape of the lactation curve is linked to an animal's health, feed requirements, and milk production throughout the year. Random regression models (RRM) are widely used for genetic evaluation of total milk production throughout the lactation and for milk yield persistency. Genomic information used with the single-step genomic BLUP method (ssGBLUP) substantially improves the accuracy of genomic prediction of breeding values in the main dairy cattle breeds. The aim of this study was to implement an RRM using ssGBLUP for milk yield in Saanen dairy goats in France. The data set consisted of 7,904,246 test-day records from 1,308,307 lactations of Saanen goats collected in France between 2000 and 2017. The performance of this type of evaluation was assessed by applying a validation step with data targeting candidate bucks. The model was compared with a nongenomic evaluation and a traditional evaluation that use cumulated performance throughout the lactation model (LM). The incorporation of genomic information increased correlations between daughter yield deviations (DYD) and estimated breeding values (EBV) obtained with a partial data set for candidate bucks. The LM and the RRM had similar correlation between DYD and EBV. However, the RRM reduced overestimation of EBV and improved the slope of the regression of DYD on EBV obtained at birth. This study shows that a genomic evaluation from a ssGBLUP RRM is possible in dairy goats in France and that RRM performance is comparable to a LM but with the additional benefit of a genomic evaluation of persistency. Variance of adjacent SNPs was studied with LM and RRM following the ssGBLUP. Both approaches converged on approximately the same regions explaining more than 1% of total variance. Regions associated with persistency were also found.

Bayesian Analysis of the Association between Casein Complex Haplotype Variants and Milk Yield, Composition, and Curve Shape Parameters in Murciano-Granadina Goats

Considering casein haplotype variants rather than SNPs may maximize the understanding of heritable mechanisms and their implication on the expression of functional traits related to milk production. Effects of casein complex haplotypes on milk yield, milk composition, and curve shape parameters were used using a Bayesian inference for ANOVA. We identified 48 single nucleotide polymorphisms (SNPs) present in the casein complex of 159 unrelated individuals of diverse ancestry, which were organized into 86 haplotypes. The Ali and Schaeffer model was chosen as the best fitting model for milk yield (Kg), protein, fat, dry matter, and lactose (%), while parabolic yield-density was chosen as the best fitting model for somatic cells count (SCC × 103 sc/mL). Peak and persistence for all traits were computed respectively. Statistically significant differences (p < 0.05) were found for milk yield and components. However, no significant difference was found for any curve shape parameter except for protein percentage peak. Those haplotypes for which higher milk yields were reported were the ones that had higher percentages for protein, fat, dry matter, and lactose, while the opposite trend was described by somatic cells counts. Conclusively, casein complex haplotypes can be considered in selection strategies for economically important traits in dairy goats.

Transcriptional Profiles of Long Non-coding RNA and mRNA in Sheep Mammary Gland During Lactation Period

Sheep milk and related products have been growing in popularity around the world in recent years. However, the sheep milk industry is limited by low milk yield, and the molecular regulators of ovine lactation remain largely unknown. To investigate the transcriptomic basis of sheep lactation, RNA-Sequencing was used to explore the expression profiles of lncRNA and mRNA of the mammary gland in Hu sheep at three key time points during the lactation stage: 5 days before the expected date of parturition perinatal period (PP), 6 days after parturition early lactation (EL), and 25 days after parturition peak lactation (PL). A total of 1111, 688, and 54 differentially expressed (DE) lncRNAs as well as 1360, 660, and 17 DE mRNAs were detected in the EL vs PP, PL vs PP, and PL vs EL comparisons, respectively. Several prominent mRNAs (e.g., CSN1S1, CSN1S2, PAEP, CSN2, CSN3, and COL3A1) and lncRNAs (e.g., LNC_018483, LNC_005678, LNC_012936, and LNC_004856) were identified. Functional enrichment analysis revealed that several DE mRNAs and target genes of DE lncRNAs were involved in lactation-related pathways, such as MAPK, PPAR, and ECM-receptor interaction. This study enhances our understanding of how transcriptomic profiles change during the lactation period and pave the way for future studies examining sheep lactation.