Population genetic structure analysis of Shall sheep using pedigree information and effect of inbreeding on growth traits

Genomic predictions under different genetic architectures are impacted by mating designs

Mating in animal communities must be managed in a way that assures the performance increase in the progenies without increasing the rate of inbreeding. It has currently become possible to identify millions of single nucleotide polymorphisms (SNPs), and it is feasible to select animals based on genome-wide marker profiles. This study aimed to evaluate the impact of five mating designs among individuals (random, positive and negative assortative, minimized and maximized inbreeding) on genomic prediction accuracy. The choice of these five particular mating designs provides a thorough analysis of the way genetic diversity, relatedness, inbreeding, and biological conditions influence the accuracy of genomic predictions. Utilizing a stochastic simulation technique, various marker and quantitative trait loci (QTL) densities were taken into account. The heritabilities of a simulated trait were 0.05, 0.30, and 0.60. A validation population that only had genotypic records was taken into consideration, and a reference population that had both genotypic and phenotypic records was considered for every simulation scenario. By measuring the correlation between estimated and true breeding values, the prediction accuracy was calculated. Computing the regression of true genomic breeding value on estimated genomic breeding value allowed for the examination of prediction bias. The scenario with a positive assortative mating design had the highest accuracy of genomic prediction (0.733 ± 0.003 to 0.966 ± 0.001). In a case of negative assortative mating, the genomic evaluation's accuracy was lowest (0.680 ± 0.011 to 0.899 ± 0.003). Applying the positive assortative mating design resulted in the unbiased regression coefficients of true genomic breeding value on estimated genomic breeding value. Based on the current results, it is suggested to implement positive assortative mating in genomic evaluation programs to obtain unbiased genomic predictions with greater accuracy. This study implies that animal breeding programs can improve offspring performance without compromising genetic health by carefully managing mating strategies based on genetic diversity, relatedness, and inbreeding levels. To maximize breeding results and ensure long-term genetic improvement in animal populations, this study highlights the importance of considering different mating designs when evaluating genomic information. When incorporating positive assortative mating or other mating schemes into genomic evaluation programs, it is critical to consider the complex relationship between gene interactions, environmental influences, and genetic drift to ensure the stability and effectiveness of breeding efforts. Further research and comprehensive analyzes are needed to fully understand the impact of these factors and their possible complex interactions on the accuracy of genomic prediction and to develop strategies that optimize breeding outcomes in animal populations.

Demography and Genealogical Analysis of Massese Sheep, a Native Breed of Tuscany

This study investigates the genealogical and demographic trends of the Massese sheep breed in Tuscany from 2001 to 2021. The Herd Book kept by the Italian Sheep and Goat Breeders Association (Asso.Na.Pa) provided the data. The descriptive statistics were analyzed using JMP software. The pedigree parameters of a total of 311,056 animals (whole population-WP) were analyzed using CFC, ENDOG, and Pedigree viewer software. A total of 24,586 animals born in the period 2007-2021 represented the Reference Population (RP), and 18,554 animals the Base Population (BP). The demographic results showed an inconsistent trend of offspring registration. This study showed a short period of productivity for both ewes and rams, with means of 1.47 and 19.2 registered newborn ewes and rams, respectively. The genealogical analysis revealed incomplete data, highlighting inaccurate assessments of the relationships among the animals, and inbreeding with large differences among provinces. The average inbreeding coefficient in the WP was 1.16%, and it was 2.26% in the RP. The total number of inbreds was 2790 in the WP, with an average FPED of 13.56%, and 2713 in the RP, with an average FPED of 12.82%. The use of pedigree data is a key and economical approach to calculating inbreeding and relationship coefficients. It is the primary step in genetic management, playing a crucial role in the preservation of a breed. The regular updating of genealogical data is the first step to ensuring the conservation of animal genetic resources, and this study is compromised by the lack of such updates.

Estimation of inbreeding and its effects on growth traits in Beetal goat

The purpose of the present study was to estimate the inbreeding coefficient and its effects on various growth traits in Beetal goat using pedigree records of 963 kids born to 38 sires and 287 dams over the period of 2004 to 2019. The inbreeding coefficients for each individual were obtained under animal model. The effects of inbreeding along with other fixed effects such as year of birth, sex of kid, type of birth, and dam's weight at kidding on growth traits viz., weight at birth (BWT), 3 (WT3), 6 (WT6), 9 (WT9), and 12 (WT12) months of age were studied using least-squares analysis. Additionally, average daily gain and Kleiber ratio up to weaning age (90 days) were studied under the same model. The overall inbreeding coefficient was low in magnitude (1.42%) and ranged from 0 to 25% over the study period. The significant (P < 0.05) increase in average inbreeding coefficient (%) over the years was observed among the studied population. Although, the effects of various factors had significant (P < 0.05) influence on growth traits under least-squares model, the regression of targeted traits on inbreeding were non-significant (P > 0.05) and the same ranged from - 0.06 to 0.04. The present findings indicated that there was no inbreeding depression among the growth traits of Beetal goat. However, as inbreeding (%) raised in recent years only, the scientific efforts must be taken to avoid inbreeding at resourced population by introducing new germplasms at earliest.

Identification of differentially expressed long noncoding RNAs in the ovarian tissue of ewes Shal and Sangsari using RNA-seq

Background

The ovary has an important role in reproductive function. Animal reproduction is dominated by numerous coding genes and noncoding elements. Although long noncoding RNAs (LncRNAs) are important in biological activity, little is known about their role in the ovary and fertility.

Methods

Three adult Shal ewes and three adult Sangsari ewes were used in this investigation. LncRNAs in ovarian tissue from two breeds were identified using bioinformatics analyses, and then target genes of LncRNAs were discovered. Target genes were annotated using the DAVID database, and their interactions were examined using the STRING database and Cytoscape software. The expression levels of seven LncRNAs with their target genes were assessed by real‐time PCR to confirm the RNA‐seq.

Results

Among all the identified LncRNAs, 124 LncRNAs were detected with different expression levels between the two breeds (FDR < 0.05). According to the DAVID database, target genes were discovered to be engaged in one biological process, one cellular component, and 21 KEGG pathways (FDR < 0.05). The PES1, RPS9, EF‐1, Plectin, SURF6, CYC1, PRKACA MAPK1, ITGB2 and BRD2 genes were some of the most crucial target genes (hub genes) in the ovary.

Conclusion

These results could pave the way for future efforts to address sheep prolificacy barriers.