Analysing the genetic diversity and population structure of five native Turkish cattle breeds using SNP data.

The conservation and sustainable utilization of cattle genetic resources necessitate a comprehensive understanding of their genetic diversity and population structure. This study provides an analysis of five native Turkish cattle breeds: Anatolian Black (ANB), Turkish Grey (TUR), Anatolian Southern Yellow (ASY), East Anatolian Red (EAR), and South Anatolian Red (SAN) using 50 K SNP data. These breeds were compared with three European breeds, Simmental (SIM), Holstein (HOL), and Jersey (JER), and three Asian Zebu breeds: Arabic Zebu (ZAR), Nelore (NEL), and Red Sindhi (RSI). Genetic diversity indices demonstrated moderate heterogeneity among the breeds, with TUR exhibiting the highest observed heterozygosity (Ho = 0.35). Wright's Fst values indicated significant genetic differentiation, particularly between Turkish breeds and both European (Fst = 0.035-0.071) and Asian breeds (Fst = 0.025-0.150). Principal component analysis distinguished the unique genetic profiles of each breed cluster. Admixture analysis revealed degrees of shared genetic ancestry, suggesting historical gene flow between Turkish, European, and Asian breeds. Analysis of molecular variance (AMOVA) attributed approximately 58% of the variation to population differences. Nei's genetic distances highlighted the closer genetic relatedness within Turkish breeds (distance ranges between 0.032 and 0.046) and suggested a more relative affinity of TUR with European breeds. The study's phylogenetic assessments elucidate the nuanced genetic relationships among these breeds, with runs of homozygosity (ROH) analysis indicating patterns of ancestral relatedness and moderate levels of inbreeding, particularly evident in Turkish breeds. Our findings provide valuable insights into the genetic landscape of Turkish cattle, offering a crucial foundation for informed conservation and breeding strategies aimed at preserving these breeds' genetic integrity and heritage.

Two novel outlier detection approaches based on unsupervised possibilistic and fuzzy clustering.

Outliers are data points that significantly deviate from other data points in a data set because of different mechanisms or unusual processes. Outlier detection is one of the intensively studied research topics for identification of novelties, frauds, anomalies, deviations or exceptions in addition to its use for data cleansing in data science. In this study, we propose two novel outlier detection approaches using the typicality degrees which are the partitioning result of unsupervised possibilistic clustering algorithms. The proposed approaches are based on finding the atypical data points below a predefined threshold value, a possibilistic level for evaluating a point as an outlier. The experiments on the synthetic and real data sets showed that the proposed approaches can be successfully used to detect outliers without considering the structure and distribution of the features in multidimensional data sets.