rs10974346 - GLIS3
Magnitude 2.0 · 2 studies on file
Reported associations
-
Large-scale GWAS of food liking reveals genetic determinants and genetic correlations with distinct neurophysiological traits - Unknown journal (n.d.) · Unknown authors · PubMed 35585065
ABSTRACT: We present the results of a GWAS of food liking conducted on 161,625 participants from the UK-Biobank. Liking was assessed over 139 specific foods using a 9-point scale. Genetic correlations coupled with structural equation modelling identified a multi-level hierarchical map of food-liking with three main dimensions: "Highly-palatable", "Acquired" and "Low-caloric". The Highly-palatable dimension is genetically uncorrelated from the other two, suggesting that independent processes underlie liking high reward foods. This is confirmed by genetic correlations with MRI brain traits which show with distinct associations. Comparison with the corresponding food consumption traits shows a high genetic correlation, while liking exhibits twice the heritability. GWAS analysis id
-
Genetic and observational associations of lung function with gastrointestinal tract diseases: pleiotropic and mendelian randomization analysis - Unknown journal (n.d.) · Unknown authors · PubMed 38102678
ABSTRACT: Background The two-way communications along the gut-lung axis influence the immune function in both gut and lung. However, the shared genetic characteristics of lung function with gastrointestinal tract (GIT) diseases remain to be investigated. Methods We first investigated the genetic correlations between three lung function traits and four GIT diseases. Second, we illustrated the genetic overlap by genome-wide pleiotropic analysis (PLACO) and further pinpointed the relevant tissue and cell types by partitioning heritability. Furthermore, we proposed pleiotropic genes as potential drug targets by drug database mining. Finally, we evaluated the causal relationships by epidemiologic observational study and Mendelian randomization (MR) analysis. Results We found lung function and G
Auto-generated from study metadata. AI-synthesised commentary is added when this entry is regenerated through content-service's LLM mode.