The objective of this study was to uncover genomic regions explaining a substantial proportion of the genetic variance in milk production traits and somatic cell score in a Valle del Belice dairy sheep. Weighted single-step genome-wide association studies (WssGWAS) were conducted for milk yield (MY), fat yield (FY), fat percentage (FAT%), protein yield (PY), protein percentage (PROT%), and somatic cell score (SCS). In addition, our aim was also to identify candidate genes within genomic regions that explained the highest proportions of genetic variance. Overall, the full pedigree consists of 5534 animals, of which 1813 ewes had milk data (15,008 records), and 481 ewes were genotyped with a 50 K single nucleotide polymorphism (SNP) array. The effects of markers and the genomic estimated breeding values (GEBV) of the animals were obtained by five iterations of WssGBLUP. We considered the top 10 genomic regions in terms of their explained genomic variants as candidate window regions for each trait. The results showed that top ranked genomic windows (1 Mb windows) explained 3.49, 4.04, 5.37, 4.09, 3.80, and 5.24% of the genetic variances for MY, FY, FAT%, PY, PROT%, and total SCS, respectively. Among the candidate genes found, some known associations were confirmed, while several novel candidate genes were also revealed, including PPARGC1A, LYPLA1, LEP, and MYH9 for MY; CACNA1C, PTPN1, ROBO2, CHRM3, and ERCC6 for FY and FAT%; PCSK5 and ANGPT1 for PY and PROT%; and IL26, IFNG, PEX26, NEGR1, LAP3, and MED28 for SCS. These findings increase our understanding of the genetic architecture of six examined traits and provide guidance for subsequent genetic improvement through genome selection.

1. Theoretically, haplotype blocks might be a more suitable alternative to SNP genotypes as they were usually better at capturing multi-allelic QTL effects, compared to individual SNP genotypes in a genome-wide association study. The objectives of this study were to identify genomic regions related to egg weight traits by Bayesian methods (BayesA, BayesB, and BayesN) that fit fixed-length haplotypes using GenSel software. 2. Genotypes at 294,705 SNPs, that were common on a 600K Affymetrix chip, were phased for an egg-laying hen population of 1,063 birds. Recorded traits included first egg weight (FEW) and average egg weight at 28, 36, 56, 66, 72 and 80 weeks of age. 2. Fitting 1Mb haplotypes from BayesB resulted in the highest proportion of genetic variance explained for the egg weight traits. Based on the trait, the genetic variance explained by each marker ranged from 27 to 76%. 3. Different haplotype windows associated with egg weight traits only explained a small percentage of the genetic variance. 4. The top one 1-Mb window on GGA1 explained approximately 4.05% of total genetic variance for the FEW. Candidate genes, including PRKAR2B, HMGA2, LEMD3, GRIP1, EHBP1, MAP3K7, and MYH were identified for egg weight traits. 5. Several genomic regions, potentially associated with egg weight traits, were identified, some of which overlapped with known genes and previously reported QTL regions for egg production traits.

The objectives of this study were to identify genomic regions by Bayesian methods (BayesA, BayesB, or BayesN) that fit fixed-length haplotypes or SNPs using GenSel. Covariates for haplo-type alleles of five lengths (125, 250, 500 kb, 1 or 2 Mb) were generated, and rare haplotypes were removed at three thresholds (1, 5, or 10%). Subsequently, we performed gene network analyses to investigate the biological processes shared by genes that were identified for the same across traits. Genotypes at 41,034 SNPs that were common on OvineSNP50 panel were phased for 751 Scottish Blackface (SBF) lambs. This is the first study to quantify the proportion of genetic variance using haplotypes across the whole genome in an SBF population. The genetic variance explained of haplotype-based GWAS was higher than that of SNP-based GWAS in across traits studied. In this population, fitting 500kb haplotypes with a 1% frequency threshold resulted in the highest proportion of genetic variance explained for nematode resistance and fitting 2Mb haplotypes with a 10% frequency threshold improved genetic variance explained for body weight comparable to fitting SNPs by BayesB. Candidate genes, including CXCR4 , STAT4 , CCL1, CCL2, CCL3, CCL5, CCL8, CCL16, CCL18, CARMIL2, and HSPA14 were identified for nematode resistance and ADH5, PPP3CA, and FABP4 for body weight traits. Network analysis provided annotation results linking to all identified candidate genes. This study supported previous results from GWAS of nematode resistance and body weight and revealed additional regions in the ovine genome associated with these economically important traits. These results suggest that network analysis can provide new information regarding biological mechanisms and genes leading to complex phenotypes, like nematode resistance and body weight of lamb.

Domestication and selection significantly changed phenotypic traits in modern domestic animals. To identify the genomic regions associated with prolificacy in this study, 837 ewes from three Iranian indigenous sheep breeds, consisting of Baluchi, Lori-Bakhtiari, and Zandi uniparous breeds, and one Greek highly prolific dairy sheep, namely Chios, were genotyped using OvineSNP50K arrays. Statistical tests were then performed using different and complementary methods based on either site frequency (F_ST) and haplotype (hapFLK) between populations, followed by a pathway analysis of the genes contained in the selected regions. The results revealed that for the top 0.01 percentile of the obtained F_ST values, 16 genomic regions on chromosomes 2, 3, 4, 7, 8, 9, 13, 14, 16, 18, 19, and 20, and for hapFLK values, 3 regions located on chromosomes 3, 7, and 13, were under selection. A bioinformatic analysis of these genomic regions showed that these loci overlapped with potential candidate genes associated with prolificacy in sheep including GNAQ, COL5A2, COL3A1, HECW1, FBN1, COMMD3, RYR1, CCL28, SERPINA14, and HSPA2. These regions also overlapped with some quantitative trait loci (QTLs) linked to prolificacy traits, milk yield, and body weight. These findings suggest that future research could further link these genomic regions to prolificacy traits in sheep.

Salmonella is one of the most common causes of food-borne outbreaks and infection worldwide. The gold standard detection method of Salmonella is cultivation. With time-consuming cultivation, there is a need to investigate rapid and accurate processes. The study evaluated different approaches to detect Salmonella in poultry feces samples. Poultry farm feces samples from 21 cities in Iran were collected from January 2016 to December 2019. Microbiological cultures, serological assays, and multiplex PCR (m-PCR) were used to detect and characterize Salmonella spp. isolates. Serological assays and m-PCR were used to determine the serogroups A, B, C1, C2, D1, E, H, and FliC. The m-PCR was used for the detection of seven Salmonella serovars and a chi-square test was performed to compare the discriminatory power of the methods. Out of 2300 poultry feces samples, 173 (7.5%) and 166 (7.2%) samples were detected as Salmonella spp. by cultivation and m-PCR, respectively. The sensitivity of the molecular method was equal to cultivation at 0.96 (CI = 95%). Assessment of H antigenic subgroups showed the same for both m-PCR and serological tests. Therefore, the matching rate of the two methods for detection of all H antigenic subgroups was 100%. Thus, the relationship between the results obtained from both methods was significant in the contingency table test (P < 0.01). The PCR-based approach confirmed the detection of Salmonella in a shorter period (24–36 hours) compared to the conventional microbiological approach (3–8 days).