[Mathematical model for assessing the level of cross-immunity between strains of influenza virus subtype H3N2].

INTRODUCTION: The WHO regularly updates influenza vaccine recommendations to maximize their match with circulating strains. Nevertheless, the effectiveness of the influenza A vaccine, specifically its H3N2 component, has been low for several seasons. The aim of the study is to develop a mathematical model of cross-immunity based on the array of published WHO hemagglutination inhibition assay (HAI) data. MATERIALS AND METHODS: In this study, a mathematical model was proposed, based on finding, using regression analysis, the dependence of HAI titers on substitutions in antigenic sites of sequences. The computer program we developed can process data (GISAID, NCBI, etc.) and create real-time databases according to the set tasks. RESULTS: Based on our research, an additional antigenic site F was identified. The difference in 1.6 times the adjusted R2, on subsets of viruses grown in cell culture and grown in chicken embryos, demonstrates the validity of our decision to divide the original data array by passage histories. We have introduced the concept of a degree of homology between two arbitrary strains, which takes the value of a function depending on the Hamming distance, and it has been shown that the regression results significantly depend on the choice of function. The provided analysis showed that the most significant antigenic sites are A, B, and E. The obtained results on predicted HAI titers showed a good enough result, comparable to similar work by our colleagues. CONCLUSION: The proposed method could serve as a useful tool for future forecasts, with further study to confirm its sustainability.

The GWAS-MAP|ovis platform for aggregation and analysis of genome-wide association study results in sheep.

In recent years, the number of genome-wide association studies (GWAS) carried out for various economically important animal traits has been increasing. GWAS discoveries provide summary statistics that can be used both for targeted marker-oriented selection and for studying the genetic control of economically important traits of farm animals. In contrast to research in human genetics, GWAS on farm animals often does not meet generally accepted standards (availability of information about effect and reference alleles, the size and direction of the effect, etc.). This greatly complicates the use of GWAS results for breeding needs. Within the framework of human genetics, there are several technological solutions for researching the harmonized results of GWAS, including one of the largest, the GWAS-MAP platform. For other types of living organisms, including economically important agricultural animals, there are no similar solutions. To our knowledge, no similar solution has been proposed to date for any of the species of economically important animals. As part of this work, we focused on creating a platform similar to GWAS-MAP for working with the results of GWAS of sheep, since sheep breeding is one of the most important branches of agriculture. By analogy with the GWAS-MAP platform for storing, unifying and analyzing human GWAS, we have created the GWAS-MAP|ovis platform. The platform currently contains information on more than 34 million associations between genomic sequence variants and traits of meat production in sheep. The platform can also be used to conduct colocalization analysis, a method that allows one to determine whether the association of a particular locus with two different traits is the result of pleiotropy or whether these traits are associated with different variants that are in linkage disequilibrium. This platform will be useful for breeders to select promising markers for breeding, as well as to obtain information for the introduction of genomic breeding and for scientists to replicate the results obtained.

The GWAS-MAP platform for aggregation of results of genome-wide association studies and the GWAS-MAP|homo database of 70 billion genetic associations of human traits.

Hundreds of genome-wide association studies (GWAS) of human traits are performed each year. The results of GWAS are often published in the form of summary statistics. Information from summary statistics can be used for multiple purposes - from fundamental research in biology and genetics to the search for potential biomarkers and therapeutic targets. While the amount of GWAS summary statistics collected by the scientific community is rapidly increasing, the use of this data is limited by the lack of generally accepted standards. In particular, the researchers who would like to use GWAS summary statistics in their studies have to become aware that the data are scattered across multiple websites, are presented in a variety of formats, and, often, were not quality controlled. Moreover, each available summary statistics analysis tools will ask for data to be presented in their own internal format. To address these issues, we developed GWAS-MAP, a high-throughput platform for aggregating, storing, analyzing, visualizing and providing access to a database of big data that result from region- and genome-wide association studies. The database currently contains information on more than 70 billion associations between genetic variants and human diseases, quantitative traits, and "omics" traits. The GWAS-MAP platform and database can be used for studying the etiology of human diseases, building predictive risk models and finding potential biomarkers and therapeutic interventions. In order to demonstrate a typical application of the platform as an approach for extracting new biological knowledge and establishing mechanistic hypotheses, we analyzed varicose veins, a disease affecting on average every third adult in Russia. The results of analysis confirmed known epidemiologic associations for this disease and led us to propose a hypothesis that increased levels of MICB and CD209 proteins in human plasma may increase susceptibility to varicose veins.