Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

Unilateral contrast neurotoxicity as a stroke mimic after cerebral angiogram

Contrast neurotoxicity (CN) following exposure to iodinated contrast is uncommon, and usually presents as cortical blindness due to bilateral occipital lobe involvement. Unilateral cortical dysfunction due to CN could mimic an acute stroke and has been rarely described. We report the case of an 89-year-old female who developed a transient dense/complete left homonymous-hemianopsia and left-sided tactile extinction after undergoing a right internal carotid (ICA) artery rotational angiogram with a standard high-volume iodinated contrast injection for 3D visualization a 6×4-mm right posterior communicating artery aneurysm with a fetal posterior cerebral artery (PCA) incorporated in the neck. This was associated with transient fullness and loss of gray-white matter differentiation in the right occipital and parietal lobes. The potential mechanism of CN in our case was the injection of a high volume of contrast in the ICA for the rotational angiogram. The presence of a right fetal PCA possibly allowed the contrast to reach the right occipital lobe. CN manifesting as an acute focal neurologic syndrome should be considered in the setting of recent iodinated contrast exposure

Large Scale Cardiovascular Model Personalisation for Mechanistic Analysis of Heart and Brain Interactions

International audienceCerebrovascular diseases have been associated with a variety of heart diseases like heart failure or atrial fibrillation, however the mech-anistic relationship between these pathologies is largely unknown. Until now, the study of the underlying heart-brain link has been challenging due to the lack of databases containing data from both organs. Current large data collection initiatives such as the UK Biobank provide us with joint cardiac and brain imaging information for thousands of individuals , and represent a unique opportunity to gain insights about the heart and brain pathophysiology from a systems medicine point of view. Research has focused on standard statistical studies finding correlations in a phenomenological way. We propose a mechanistic analysis of the heart and brain interactions through the personalisation of the parameters of a lumped cardiovascular model under constraints provided by brain-volumetric parameters extracted from imaging, i.e: ventricles or white matter hyperintensities volumes, and clinical information such as age or body surface area. We applied this framework in a cohort of more than 3000 subjects and in a pathological subgroup of 53 subjects diagnosed with atrial fibrillation. Our results show that the use of brain feature constraints helps in improving the parameter estimation in order to identify significant differences associated to specific clinical conditions