A Machine-Learning-Based Approach to Prediction of Biogeographic Ancestry within Europe.

Data obtained with the use of massive parallel sequencing (MPS) can be valuable in population genetics studies. In particular, such data harbor the potential for distinguishing samples from different populations, especially from those coming from adjacent populations of common origin. Machine learning (ML) techniques seem to be especially well suited for analyzing large datasets obtained using MPS. The Slavic populations constitute about a third of the population of Europe and inhabit a large area of the continent, while being relatively closely related in population genetics terms. In this proof-of-concept study, various ML techniques were used to classify DNA samples from Slavic and non-Slavic individuals. The primary objective of this study was to empirically evaluate the feasibility of discerning the genetic provenance of individuals of Slavic descent who exhibit genetic similarity, with the overarching goal of categorizing DNA specimens derived from diverse Slavic population representatives. Raw sequencing data were pre-processed, to obtain a 1200 character-long binary vector. A total of three classifiers were used-Random Forest, Support Vector Machine (SVM), and XGBoost. The most-promising results were obtained using SVM with a linear kernel, with 99.9% accuracy and F1-scores of 0.9846-1.000 for all classes.

The overview of forensic genetic genealogy.

Forensic genetic genealogy (FGG) benefits largely from popularity of genealogical research within (mostly) American society and the advent of new sequencing techniques that allow typing of challenging forensic samples. It is considered a true breakthrough for both active and especially cold cases where all other resources and methods have failed during investigation. Despite media coverage generally highlighting its powers, the method itself is considered very laborious and the investigation may easily got suspended at every stage due to many factors including no hits in the database or breaks in traceable lineages within the family tree. This review summarizes the scope of FGG use, mentions most concerns and misconceptions associated with the technique and points to the plausible solutions already suggested. It also brings together current guidelines and regulations intended to be followed by law enforcement authorities wishing to utilize genetic genealogy research.

The microbiome and its impact on food allergy and atopic dermatitis in children.

Food allergy (FA) affects 4-10% of children, especially children with atopic dermatitis (AD). During infancy the gut microbiome may determine both the course of FA and tolerance to food allergens. Analogically, the skin microbiome changes in the course of AD. Most studies have associated FA with a lower abundance and diversity of Lactobacillales and Clostridiales, but greater numbers of Enterobacterales, while AD in children has been associated with lower numbers of Staphylococcus epidermidis and S. hominis but an abundance of S. aureus and Streptococcus species. An understanding of the impact of the microbiome on the clinical course of FA and AD may allow for the development of new models of allergy treatment and prevention.

Complete mitogenome data for the Serbian population: the contribution to high-quality forensic databases.

Mitochondrial genome (mtDNA) is a valuable resource in resolving various human forensic casework. The usage of variability of complete mtDNA genomes increases their discriminatory power to the maximum and enables ultimate resolution of distinct maternal lineages. However, their wider employment in forensic casework is nowadays limited by the lack of appropriate reference database. In order to fill in the gap in the reference data, which, considering Slavic-speaking populations, currently comprises only mitogenomes of East and West Slavs, we present mitogenome data for 226 Serbians, representatives of South Slavs from the Balkan Peninsula. We found 143 (sub)haplogroups among which West Eurasian ones were dominant. The percentage of unique haplotypes was 85%, and the random match probability was as low as 0.53%. We support previous findings on both high levels of genetic diversity in the Serbian population and patterns of genetic differentiation among this and ten studied European populations. However, our high-resolution data supported more pronounced genetic differentiation among Serbians and two Slavic populations (Russians and Poles) as well as expansion of the Serbian population after the Last Glacial Maximum and during the Migration period (fourth to ninth century A.D.), as inferred from the Bayesian skyline analysis. Phylogenetic analysis of haplotypes found in Serbians contributed towards the improvement of the worldwide mtDNA phylogeny, which is essential for the interpretation of the mtDNA casework.