ABSTRACT
OBJECTIVE To investigate the causal association between ticagrelor and risk of infection METHODS Two-sample Mendelian randomization was adopted. Genetic instrumental variables were selected based on the results of the largest genome-wide association analysis to in vivo exposure of ticagrelor and its major active metabolite AR-C124910XX. The causal associations of ticagrelor and its major active metabolite AR-C124910XX with drug indications （coronary artery disease， unstable angina pectoris， myocardial infarction， stroke and ischemic stroke）were analyzed by inverse variance weighted Mendelian randomization model as a positive control for genetic instrumental variables. The causal relationship between ticagrelor and bacterial infection， acute lower respiratory infection， bacterial pneumoniae， pneumoniae，acute upper respiratory infection and sepsis were furtheranalyzed by using this method， and the robustness of the results was assessed by using heterogeneity tests and horizontal 202002030415） pleiotropy tests. RESULTS The increase of area under the curve at steady state （AUCss） of the genetic surrogated ticagrelor significantly reduced the risk of coronary artery disease， myocardial infarction and unstable angina pectoris （P＜0.001）. AUCss genetic instrument variables of its main active metabolite AR-C124910XX failed to pass positive control. Further analysis showed that the increase of the genetic surrogated ticagrelor exposure suggestively reduced the risk of bacterial infection [OR（95%CI）＝0.80（0.65，0.99），P＝0.040] and sepsis [OR （95%CI）＝0.84（0.73， 0.98）， P＝0.023]. The results of the heterogeneity tests showed that there was no heterogeneity in the causal association of the genetic surrogated ticagrelor AUCss with bacterial infection and sepsis （P＞0.05）. The results of horizontal pleiotropy tests showed that the causal association of genetic surrogated ticagrelor AUCss with bacterial infection and sepsis had no effects on horizontal pleiotropy （P＞0.05）. CONCLUSIONS Ticagrelor has a potential role in reducing the risk of sepsis and bacterial infections.
ABSTRACT
With the exacerbation of aging population in China, the number of patients with Alzheimer's disease (AD) is increasing rapidly. AD is a chronic but irreversible neurodegenerative disease, which cannot be cured radically at present. In recent years, in order to intervene in the course of AD in advance, many researchers have explored how to detect AD as early as possible, which may be helpful for effective treatment of AD. Imaging genomics is a kind of diagnosis method developed in recent years, which combines the medical imaging and high-throughput genetic omics together. It studies changes in cognitive function in patients with AD by extracting effective information from high-throughput medical imaging data and genomic data, providing effective guidance for early detection and treatment of AD patients. In this paper, the association analysis of magnetic resonance image (MRI) with genetic variation are summarized, as well as the research progress on AD with this method. According to complexity, the objects in the association analysis are classified as candidate brain phenotype, candidate genetic variation, genome-wide genetic variation and whole brain voxel. Then we briefly describe the specific methods corresponding to phenotypic of the brain and genetic variation respectively. Finally, some unsolved problems such as phenotype selection and limited polymorphism of candidate genes are put forward.