Kotsar Yurii

Helicobacter pylori ( H. pylori ) infects the gastric epithelium of approximately half of the global population, and is a well-known risk factor for developing gastric cancer. Despite the clinical significance of H. pylori infection, many genetic factors that contribute to susceptibility remain unidentified. While it is well-established that H. pylori infection can result in gastritis and peptic ulcers, which may progress to gastric cancer, its causal link to other diseases remains unclear. We performed the genome-wide association study (GWAS) for anti- H. pylori IgG antibody titers, which were validated as a surrogate marker for H. pylori infection by the correlation with clinical traits, followed by gene-based and pathway analyses, involving up to 140,863 individuals. This included 56,967 in the discovery phase, and 68,211 in the replication phase from Japanese cohorts, and an additional 15,685 from European populations in a cross-ancestry meta-analysis. We reveal significant associations between H. pylori infection and polymorphisms in the Human Leukocyte Antigen (HLA) class II region within the Major Histocompatibility Complex (MHC), as well as genes related to innate immunity, including CCDC80 , NFKBIZ , TIFA , PSCA , and TRAF3 . Mendelian randomization (MR) analysis revealed that genetic liability to H. pylori infection has both positive and negative causal relationships with a variety of diseases, including autoimmune-related diseases such as Type 1 diabetes, Hashimoto’s disease, atopic dermatitis, as well as traits like body height and weight. These genetic findings strongly support the notion that genetic liability to H. pylori infection influences not only gastrointestinal diseases, but also a broader spectrum of health issues, thereby providing valuable insights for public health strategies and personalized medicine approaches.

We develop a set of programs – the range expansions simulation kit (RESK) – to efficiently simulate range expansions of populations on a square lattice in 1D, 2D (on a strip, and on a disk) and 3D (in a cylinder, and in a sphere). In this study, we present the simulation kit to the public and present some results of using it to simulate a population of diploid individuals with finite genome regions, each containing infinite sites. Specifically, using the programs, we calculate and analyse the temporal evolution of population fitness, as well as fitness on the expansion front, in 3D for the first time. We explore the model over different conditions, compare normalisation methods for fitness, and explore the case of radial (sphere) and axial (cylinder) expansions in 3D, which might apply in the analysis of the different real-life populations, such as viruses/bacteria inside a host, marine species in ocean environments, and potentially in future space colonization planning. In 3D expansions, we find complex spatial fluctuations in deme-average fitness values, different from those in radial 2D expansions. In axial 3D (cylinder) expansions, we determine that the highest-valued deme-average fitness lies along the axis of the expansion. We also find the fluctuation patterns of fitness in 3D cylinder expansions, similar to those previously seen in radial expansions in 2D. In radial 2D (disk) expansions, we find that the fitness of a population undergoing multiple mutations shows a smooth combination of binary segregation pictures against each of those mutations. We confirm the accumulation of deleterious mutations -- a phenomenon known as expansion load -- in all scenarios above. We present the software used in the above to the public as a Julia repository, ready to use with different functions for simulating range expansions with minimal syntax.