Study of Population Structure and Genetic Prediction of Buffalo from Different Provinces of Iran using Machine Learning Method

Considering breeding livestock programs to milk production and type traits based on existence two different ecotypes of Iranian’s buffalo, a study carried out to investigate the population structure of Iranian buffalo and validate its classification accuracy according to different ecotypes from Iran (Azerbaijan and North) using data SNP chip 90K by means Support vector Machine (SVM), Random Forest (RF) and Discriminant Analysis Principal Component (DAPC) methods. A total of 258 buffalo were sampled and genotyped. The results of admixture, multidimensional scaling (MDS), and DAPC showed a close relationship between the animals of different provinces. Two ecotypes indicated higher accuracy of 96% that the Area Under Curve (AUC) confirmed the obtained result of the SVM approach while the DAPC and RF approach demonstrated lower accuracy of 88% and 80 %, respectively. SVM method proved high accuracy compared with DAPC and RF methods and assigned animals to their herds with more accuracy. According to these results, buffaloes distributed in two different ecotypes are one breed, and therefore the same breeding program should be used in the future. The water buffalo ecotype of the northern provinces of Iran and Azerbaijan seem to belong to the same population

Integrated analysis of inflammatory mRNAs, miRNAs, and lncRNAs elucidates the molecular interactome behind bovine mastitis

Abstract Mastitis is known as intramammary inflammation, which has a multifactorial complex phenotype. However, the underlying molecular pathogenesis of mastitis remains poorly understood. In this study, we utilized a combination of RNA-seq and miRNA-seq techniques, along with computational systems biology approaches, to gain a deeper understanding of the molecular interactome involved in mastitis. We retrieved and processed one hundred transcriptomic libraries, consisting of 50 RNA-seq and 50 matched miRNA-seq data, obtained from milk-isolated monocytes of Holstein–Friesian cows, both infected with Streptococcus uberis and non-infected controls. Using the weighted gene co-expression network analysis (WGCNA) approach, we constructed co-expressed RNA-seq-based and miRNA-seq-based modules separately. Module-trait relationship analysis was then performed on the RNA-seq-based modules to identify highly-correlated modules associated with clinical traits of mastitis. Functional enrichment analysis was conducted to understand the functional behavior of these modules. Additionally, we assigned the RNA-seq-based modules to the miRNA-seq-based modules and constructed an integrated regulatory network based on the modules of interest. To enhance the reliability of our findings, we conducted further analyses, including hub RNA detection, protein–protein interaction (PPI) network construction, screening of hub-hub RNAs, and target prediction analysis on the detected modules. We identified a total of 17 RNA-seq-based modules and 3 miRNA-seq-based modules. Among the significant highly-correlated RNA-seq-based modules, six modules showed strong associations with clinical characteristics of mastitis. Functional enrichment analysis revealed that the turquoise module was directly related to inflammation persistence and mastitis development. Furthermore, module assignment analysis demonstrated that the blue miRNA-seq-based module post-transcriptionally regulates the turquoise RNA-seq-based module. We also identified a set of different RNAs, including hub-hub genes, hub-hub TFs (transcription factors), hub-hub lncRNAs (long non-coding RNAs), and hub miRNAs within the modules of interest, indicating their central role in the molecular interactome underlying the pathogenic mechanisms of S. uberis infection. This study provides a comprehensive insight into the molecular crosstalk between immunoregulatory mRNAs, miRNAs, and lncRNAs during S. uberis infection. These findings offer valuable directions for the development of molecular diagnosis and biological therapies for mastitis

Study of whole genome linkage disequilibrium patterns of Iranian water buffalo breeds using the Axiom Buffalo Genotyping 90K Array.

Accuracy of genome-wide association studies, and the successful implementation of genomic selection depends on the level of linkage disequilibrium (LD) across the genome and also the persistence of LD phase between populations. In the present study LD between adjacent SNPs and LD decay between SNPs was calculated in three Iranian water buffalo populations. Persistence of LD phase was evaluated across these populations and effective population size (Ne) was estimated from corrected r2 information. A set of 404 individuals from three Iranian buffalo populations were genotyped with the Axiom Buffalo Genotyping 90K Array. Average r2 and |D'| between adjacent SNP pairs across all chromosomes was 0.27 and 0.66 for AZI, 0.29 and 0.68 for KHU, and 0.32 and 0.72 for MAZ. The LD between the SNPs decreased with increasing physical distance from 100Kb to 1Mb between markers, from 0.234 to 0.018 for AZI, 0.254 to 0.034 for KHU, and 0.297 to 0.119 for MAZ, respectively. These results indicate that a density of 90K SNP is sufficient for genomic analyses relying on long range LD (e.g. GWAS and genomic selection). The persistence of LD phase decreased with increasing marker distances across all the populations, but remained above 0.8 for AZI and KHU for marker distances up to 100Kb. For multi-breed genomic evaluation, the 90K SNP panel is suitable for AZI and KHU buffalo breeds. Estimated effective population sizes for AZI, KHU and MAZ were 477, 212 and 32, respectively, for recent generations. The estimated effective population sizes indicate that the MAZ is at risk and requires careful management

A genome-wide scan for signatures of selection in Azeri and Khuzestani buffalo breeds

Abstract Background Identification of genomic regions that have been targets of selection may shed light on the genetic history of livestock populations and help to identify variation controlling commercially important phenotypes. The Azeri and Kuzestani buffalos are the most common indigenous Iranian breeds which have been subjected to divergent selection and are well adapted to completely different regions. Examining the genetic structure of these populations may identify genomic regions associated with adaptation to the different environments and production goals. Results A set of 385 water buffalo samples from Azeri (N = 262) and Khuzestani (N = 123) breeds were genotyped using the Axiom® Buffalo Genotyping 90 K Array. The unbiased fixation index method (FST) was used to detect signatures of selection. In total, 13 regions with outlier FST values (0.1%) were identified. Annotation of these regions using the UMD3.1 Bos taurus Genome Assembly was performed to find putative candidate genes and QTLs within the selected regions. Putative candidate genes identified include FBXO9, NDFIP1, ACTR3, ARHGAP26, SERPINF2, BOLA-DRB3, BOLA-DQB, CLN8, and MYOM2. Conclusions Candidate genes identified in regions potentially under selection were associated with physiological pathways including milk production, cytoskeleton organization, growth, metabolic function, apoptosis and domestication-related changes include immune and nervous system development. The QTL identified are involved in economically important traits in buffalo related to milk composition, udder structure, somatic cell count, meat quality, and carcass and body weight

Additional file 4: of A genome-wide scan for signatures of selection in Azeri and Khuzestani buffalo breeds

Haploview LD graph for selected regions. (PDF 904 kb

Additional file 12: of A genome-wide scan for signatures of selection in Azeri and Khuzestani buffalo breeds

FST output file. (XLS 9242 kb

Additional file 1: of A genome-wide scan for signatures of selection in Azeri and Khuzestani buffalo breeds

Least-square means and standard deviations of some morphometric and body size traits in Iranian Azeri and Khuzestani water buffalo breeds. (DOCX 19 kb

Additional file 8: of A genome-wide scan for signatures of selection in Azeri and Khuzestani buffalo breeds

Genome wide distribution of iHS from Khuzestani buffalo breed values. (TXT 2864 kb

Additional file 13: of A genome-wide scan for signatures of selection in Azeri and Khuzestani buffalo breeds

Windowed FST output file. (XLS 6816 kb