Метафорична картина світу та її місце у системі світів

Статья посвящается исследованию понятия метафорической картины мира, целесообразность выделения которой автор объясняет тем, что по аналогии с языковой и концептуальной картинами мира, термин "метафорическая картина мира" содержит информацию о сложной структуре многосмысловых значений, которые в силу своей метафорической природе гармонически объединяются.У статті йдеться про поняття метафоричної картини світу, доцільність виділення якої авторка пояснює тим, що за аналогією до мовної й концептуальної картин світу, термін "метафорична картина світу" вміщує інформацію про складну структуру багатосмислових значень, що завдяки своїй метафоричній природі гармонійно поєднуються.The article deals with the notion of metaphorical world picture connected with the general principle of conceptualization. The term "metaphorical world picture" consists of a complex structure of various meanings harmonically combined due to their metaphorical nature

Refined localization of TSC1 by combined analysis of 9q34 and 16pl3 data in 14 tuberous sclerosis families

Tuberous sclerosis (TSC) is a heterogeneous trait. Since 1990, linkage studies have yielded putative TSC loci on chromosomes 9, 11, 12 and 16. Our current analysis, performed on 14 Dutch and British families, reveals only evidence for loci on chromosome 9q34 (TSC1) and chromosome 16p13 (TSC2). We have found no indication for a third locus for TSC, linked or unlinked to either of these chromosomal regions. The majority of our families shows linkage to chromosome 9. We have refined the candidate region for TSC1 to a region of approximately 5 c M between ABL and ABO

Occupational exposure to gases/fumes and mineral dust affect DNA methylation levels of genes regulating expression

Many workers are daily exposed to occupational agents like gases/fumes, mineral dust or biological dust, which could induce adverse health effects. Epigenetic mechanisms, such as DNA methylation, have been suggested to play a role. We therefore aimed to identify differentially methylated regions (DMRs) upon occupational exposures in never-smokers and investigated if these DMRs associated with gene expression levels. To determine the effects of occupational exposures independent of smoking, 903 never-smokers of the LifeLines cohort study were included. We performed three genome-wide methylation analyses (Illumina 450 K), one per occupational exposure being gases/fumes, mineral dust and biological dust, using robust linear regression adjusted for appropriate confounders. DMRs were identified using comb-p in Python. Results were validated in the Rotterdam Study (233 never-smokers) and methylation-expression associations were assessed using Biobank-based Integrative Omics Study data (n = 2802). Of the total 21 significant DMRs, 14 DMRs were associated with gases/fumes and 7 with mineral dust. Three of these DMRs were associated with both exposures (RPLP1 and LINC02169 (2x)) and 11 DMRs were located within transcript start sites of gene expression regulating genes. We replicated two DMRs with gases/fumes (VTRNA2-1 and GNAS) and one with mineral dust (CCDC144NL). In addition, nine gases/fumes DMRs and six mineral dust DMRs significantly associated with gene expression levels. Our data suggest that occupational exposures may induce differential methylation of gene expression regulating genes and thereby may induce adverse health effects. Given the millions of workers that are exposed daily to occupational exposures, further studies on this epigenetic mechanism and health outcomes are warranted

How to deal with the early GWAS data when imputing and combining different arrays is necessary

Genotype imputation has become an essential tool in the analysis of genome-wide association scans. This technique allows investigators to test association at ungenotyped genetic markers, and to combine results across studies that rely on different genotyping platforms. In addition, imputation is used within long-running studies to reuse genotypes produced across generations of platforms. Typically, genotypes of controls are reused and cases are genotyped on more novel platforms yielding a case–control study that is not matched for genotyping platforms. In this study, we scrutinize such a situation and validate GWAS results by actually retyping top-ranking SNPs with the Sequenom MassArray platform. We discuss the needed quality controls (QCs). In doing so, we report a considerable discrepancy between the results from imputed and retyped data when applying recommended QCs from the literature. These discrepancies appear to be caused by extrapolating differences between arrays by the process of imputation. To avoid false positive results, we recommend that more stringent QCs should be applied. We also advocate reporting the imputation quality measure (RT2) for the post-imputation QCs in publications