General dimensions of human brain morphometry inferred from genome-wide association data

Understanding the neurodegenerative mechanisms underlying cognitive decline in the general population may facilitate early detection of adverse health outcomes in late life. This study investigates genetic links between brain morphometry, ageing and cognitive ability. We develop Genomic Principal Components Analysis (Genomic PCA) to model general dimensions of brain-wide morphometry at the level of their underlying genetic architecture. Genomic PCA is applied to genome-wide association data for 83 brain-wide volumes (36,778 UK Biobank participants) and we extract genomic principal components (PCs) to capture global dimensions of genetic covariance across brain regions (unlike ancestral PCs that index genetic similarity between participants). Using linkage disequilibrium score regression, we estimate genetic overlap between those general brain dimensions and cognitive ageing. The first genetic PCs underlying the morphometric organisation of 83 brain-wide regions accounted for substantial genetic variance (R2  = 40%) with the pattern of component loadings corresponding closely to those obtained from phenotypic analyses. Genetically more central regions to overall brain structure - specifically frontal and parietal volumes thought to be part of the central executive network - tended to be somewhat more susceptible towards age (r = -0.27). We demonstrate the moderate genetic overlap between the first PC underlying each of several structural brain networks and general cognitive ability (rg  = 0.17-0.21), which was not specific to a particular subset of the canonical networks examined. We provide a multivariate framework integrating covariance across multiple brain regions and the genome, revealing moderate shared genetic etiology between brain-wide morphometry and cognitive ageing

Role of polygenic and environmental factors in the co-occurrence of depression and psychosis symptoms:a network analysis

Depression and psychosis are often comorbid; they also have overlapping genetic and environmental risk factors, including trauma and area-level exposures. The present study aimed to advance understanding of this comorbidity via a network approach, by (1) identifying bridge nodes that connect clusters of lifetime depression and psychosis symptoms and (2) evaluating the influence of polygenic and environmental risk factors in these symptoms. This study included data from European ancestry participants in UK Biobank, a large population-based sample (N = 77,650). In Step 1, a network model identified bridge nodes between lifetime symptoms of depression and psychosis and functional impairment. In Step 2, genetic and environmental risk factors were incorporated to examine the degree to which symptoms associated with polygenic risk scores for depression and schizophrenia, lifetime exposure to trauma and area-level factors (including deprivation, air pollution and greenspace). Feelings of worthlessness, beliefs in unreal conspiracy against oneself, depression impairment and psychosis impairment emerged as bridges between depression and psychosis symptoms. Polygenic risk scores for depression and schizophrenia were predominantly linked with depression and psychosis impairment, respectively, rather than with specific symptoms. Cumulative trauma emerged as a bridge node associating deprivation with feelings of worthlessness and beliefs in unreal conspiracy, indicating that the experience of trauma is prominently linked with the co-occurrence of depression and psychosis symptoms related to negative views of oneself and others. These key symptoms and risk factors provide insights into the lifetime co-occurrence of depression and psychosis

Evaluation of polygenic prediction methodology within a reference-standardized framework.

The predictive utility of polygenic scores is increasing, and many polygenic scoring methods are available, but it is unclear which method performs best. This study evaluates the predictive utility of polygenic scoring methods within a reference-standardized framework, which uses a common set of variants and reference-based estimates of linkage disequilibrium and allele frequencies to construct scores. Eight polygenic score methods were tested: p-value thresholding and clumping (pT+clump), SBLUP, lassosum, LDpred1, LDpred2, PRScs, DBSLMM and SBayesR, evaluating their performance to predict outcomes in UK Biobank and the Twins Early Development Study (TEDS). Strategies to identify optimal p-value thresholds and shrinkage parameters were compared, including 10-fold cross validation, pseudovalidation and infinitesimal models (with no validation sample), and multi-polygenic score elastic net models. LDpred2, lassosum and PRScs performed strongly using 10-fold cross-validation to identify the most predictive p-value threshold or shrinkage parameter, giving a relative improvement of 16-18% over pT+clump in the correlation between observed and predicted outcome values. Using pseudovalidation, the best methods were PRScs, DBSLMM and SBayesR. PRScs pseudovalidation was only 3% worse than the best polygenic score identified by 10-fold cross validation. Elastic net models containing polygenic scores based on a range of parameters consistently improved prediction over any single polygenic score. Within a reference-standardized framework, the best polygenic prediction was achieved using LDpred2, lassosum and PRScs, modeling multiple polygenic scores derived using multiple parameters. This study will help researchers performing polygenic score studies to select the most powerful and predictive analysis methods

High Voltage Operation of heavily irradiated silicon microstrip detectors

We discuss the results obtained from the R&D studies, done within the CMS experiment at LHC related to the behaviour of silicon microstrip prototype detectors when they are operated at high bias voltages before and after heavy irradiation, simulating up to 10 years of LHC running conditions. We have found detectors from several manufacturesrs that are able to work at V_bias > 500 Volts before and after the irradiation procedure, maintaining an acceptable performance with S/N > 14, efficiency close to 100% and few ghost hits

Comparative study of <111> and <100> crystals and capacitance measurements on Si strip detectors in CMS

For the construction of the silicon microstrip detectors for the tracker of the CMS experiment, two different substrate choices were investigated: A high-resistivity (6 k Omega cm) substrate with crystal orientation and a low-resistivity (2 k Omega cm) one with crystal orientation. The interstrip and backplane capacitances were measured before and after the exposure to radiation in a range of strip pitches from 60 mu m to 240 mu m and for values of the width-over-pitch ratio between 0.1 and 0.5. (3 refs)

High-voltage breakdown studies on Si microstrip detectors

The breakdown performance of CMS barrel module prototype detectors and test devices with single and multi-guard structures were studied before and after neutron irradiation up to 2-10/sup 14/ 1 MeV equivalent neutrons. Before irradiation avalanche breakdown occurred at the guard ring implant edges. We measured 100-300 V higher breakdown voltage values for the devices with multi-guard than for devices with single-guard ring, After irradiation and type inversion the breakdown was smoother than before irradiation and the breakdown voltage value increased to 500-600 V for most of the devices. (9 refs)