COMPUTERIZED ANALYSIS OF THE RELATIONSHIP BETWEEN ALLERGENICITY OF MICROORGANISMS AND THEIR HABITATS

The prevalence of allergic diseases was rapidly increasing in the 20th century. Currently, many people suffer from allergy in industrial countries. Therefore, analysis of allergenic properties of proteins is an urgent task. The following factors were formerly hypothesized to determine the allergenicity of a protein: size, enzymatic properties, and similarity to human proteins. However, no analysis of the relationship between allergenicity of proteins and the habitat of the organisms producing them has been conducted hitherto. We predict allergenicity of proteins from proteomes of more than 500 species of microorganisms. It is shown that the number of allergenic proteins in the proteomes of microorganisms is significantly associated with their pathogenicity, habitat, temperature conditions of the habitat, and oxygen demand

A Novel Framework for Analysis of the Shared Genetic Background of Correlated Traits

We propose a novel effective framework for the analysis of the shared genetic background for a set of genetically correlated traits using SNP-level GWAS summary statistics. This framework called SHAHER is based on the construction of a linear combination of traits by maximizing the proportion of its genetic variance explained by the shared genetic factors. SHAHER requires only full GWAS summary statistics and matrices of genetic and phenotypic correlations between traits as inputs. Our framework allows both shared and unshared genetic factors to be effectively analyzed. We tested our framework using simulation studies, compared it with previous developments, and assessed its performance using three real datasets: anthropometric traits, psychiatric conditions and lipid concentrations. SHAHER is versatile and applicable to summary statistics from GWASs with arbitrary sample sizes and sample overlaps, allows for the incorporation of different GWAS models (Cox, linear and logistic), and is computationally fast

Visualization and Analysis of a Cardio Vascular Disease-and MUPP1-related Biological Network combining Text Mining and Data Warehouse Approaches

Sommer B, Tiys ES, Kormeier B, et al. Visualization and Analysis of a Cardio Vascular Disease-and MUPP1-related Biological Network combining Text Mining and Data Warehouse Approaches. Journal of Integrative Bioinformatics. 2010;7(1):148

Mendelian randomization of genetically independent aging phenotypes identifies LPA and VCAM1 as biological targets for human aging

Length and quality of life are important to us all, yet identification of promising drug targets for human aging using genetics has had limited success. In the present study, we combine six European-ancestry genome-wide association studies of human aging traits—healthspan, father and mother lifespan, exceptional longevity, frailty index and self-rated health—in a principal component framework that maximizes their shared genetic architecture. The first principal component (aging-GIP1) captures both length of life and indices of mental and physical wellbeing. We identify 27 genomic regions associated with aging-GIP1, and provide additional, independent evidence for an effect on human aging for loci near HTT and MAML3 using a study of Finnish and Japanese survival. Using proteome-wide, two-sample, Mendelian randomization and colocalization, we provide robust evidence for a detrimental effect of blood levels of apolipoprotein(a) and vascular cell adhesion molecule 1 on aging-GIP1. Together, our results demonstrate that combining multiple aging traits using genetic principal components enhances the power to detect biological targets for human aging