Dense seismic network provides new insight into the 2007 Upptyppingar dyke intrusion

Factors such as network geometry, network size and phase-picking accuracy have significant\ud effects on the precision of seismic hypocentre locations. In turn, the precision of the hypocentral locations\ud dictates the degree to which morphological details within seismic swarms may be resolved. The Icelandic\ud national seismic network (SIL) is designed to monitor seismic activity across large expanses of Iceland in realtime\ud using automated earthquake detection and location software. Here we examine the performance of the\ud SIL network relative to a much denser, local network of seismometers deployed around the Askja volcano in\ud the Northern Volcanic Zone. A subset of earthquakes from the 2007–2008 dyke intrusion beneath Mt. Upptyppingar\ud is used to compare single- and multi-event hypocentral locations. Specifically, we highlight 288, high\ud signal-to-noise ratio events that occurred during an intensive sequence of earthquakes from 6–24 July 2007,\ud when the temporary Askja network was active. A careful refinement of phase onsets recorded by our wellconfigured,\ud dense network of receivers reveals hypocentres clustered tightly on a planar structure, interpreted\ud as a dyke dipping at 49. The root-mean-square (RMS) misfit to the plane (114 m) is only slightly greater than\ud the uncertainties in relative locations of the earthquakes themselves, and constitutes a three-fold reduction in\ud RMS misfit over SIL relative locations. The improved precision, facilitated predominantly by a more favourable\ud network size and configuration, permits a more detailed analysis of the intrusion

Genomic landscape of adult testicular germ cell tumours in the 100,000 Genomes Project

Testicular germ cell tumours (TGCT), which comprise seminoma and non-seminoma subtypes, are the most common cancers in young men. In this study, we present a comprehensive whole genome sequencing analysis of adult TGCTs. Leveraging samples from participants recruited via the UK National Health Service and data from the Genomics England 100,000 Genomes Project, our results provide an extended description of genomic elements underlying TGCT pathogenesis. This catalogue offers a comprehensive, high-resolution map of copy number alterations, structural variation, and key global genome features, including mutational signatures and analysis of extrachromosomal DNA amplification. This study establishes correlations between genomic alterations and histological diversification, revealing divergent evolutionary trajectories among TGCT subtypes. By reconstructing the chronological order of driver events, we identify a subgroup of adult TGCTs undergoing relatively late whole genome duplication. Additionally, we present evidence that human leukocyte antigen loss is a more prevalent mechanism of immune disruption in seminomas. Collectively, our findings provide valuable insights into the developmental and immune modulatory processes implicated in TGCT pathogenesis and progression

Using genetic variation and environmental risk factor data to identify individuals at high risk for age-related macular degeneration

A major goal of personalized medicine is to pre-symptomatically identify individuals at high risk for disease using knowledge of each individual's particular genetic profile and constellation of environmental risk factors. With the identification of several well-replicated risk factors for age-related macular degeneration (AMD), the leading cause of legal blindness in older adults, this previously unreachable goal is beginning to seem less elusive. However, recently developed algorithms have either been much less accurate than expected, given the strong effects of the identified risk factors, or have not been applied to independent datasets, leaving unknown how well they would perform in the population at large. We sought to increase accuracy by using novel modeling strategies, including multifactor dimensionality reduction (MDR) and grammatical evolution of neural networks (GENN), in addition to the traditional logistic regression approach. Furthermore, we rigorously designed and tested our models in three distinct datasets: a Vanderbilt-Miami (VM) clinic-based case-control dataset, a VM family dataset, and the population-based Age-related Maculopathy Ancillary (ARMA) Study cohort. Using a consensus approach to combine the results from logistic regression and GENN models, our algorithm was successful in differentiating between high- and low-risk groups (sensitivity 77.0%, specificity 74.1%). In the ARMA cohort, the positive and negative predictive values were 63.3% and 70.7%, respectively. We expect that future efforts to refine this algorithm by increasing the sample size available for model building, including novel susceptibility factors as they are discovered, and by calibrating the model for diverse populations will improve accuracy

Chromothripsis orchestrates leukemic transformation in blast phase MPN through targetable amplification of DYRK1A

Chromothripsis, the process of catastrophic shattering and haphazard repair of chromosomes, is a common event in cancer. Whether chromothripsis might constitute an actionable molecular event amenable to therapeutic targeting remains an open question. We describe recurrent chromothripsis of chromosome 21 in a subset of patients in blast phase of a myeloproliferative neoplasm (BP-MPN), which alongside other structural variants leads to amplification of a region of chromosome 21 in ∼25% of patients (‘chr21amp’). We report that chr21amp BP-MPN has a particularly aggressive and treatment-resistant phenotype. The chr21amp event is highly clonal and present throughout the hematopoietic hierarchy. DYRK1A, a serine threonine kinase and transcription factor, is the only gene in the 2.7Mb minimally amplified region which showed both increased expression and chromatin accessibility compared to non-chr21amp BP-MPN controls. We demonstrate that DYRK1A is a central node at the nexus of multiple cellular functions critical for BP-MPN development, including DNA repair, STAT signalling and BCL2 overexpression. DYRK1A is essential for BP-MPN cell proliferation in vitro and in vivo, and DYRK1A inhibition synergises with BCL2 targeting to induce BP-MPN cell apoptosis. Collectively, these findings define the chr21amp event as a prognostic biomarker in BP-MPN and link chromothripsis to a druggable target

Genetic and Functional Dissection of HTRA1 and LOC387715 in Age-Related Macular Degeneration

A common haplotype on 10q26 influences the risk of age-related macular degeneration (AMD) and encompasses two genes, LOC387715 and HTRA1. Recent data have suggested that loss of LOC387715, mediated by an insertion/deletion (in/del) that destabilizes its message, is causally related with the disorder. Here we show that loss of LOC387715 is insufficient to explain AMD susceptibility, since a nonsense mutation (R38X) in this gene that leads to loss of its message resides in a protective haplotype. At the same time, the common disease haplotype tagged by the in/del and rs11200638 has an effect on the transcriptional upregulation of the adjacent gene, HTRA1. These data implicate increased HTRA1 expression in the pathogenesis of AMD and highlight the importance of exploring multiple functional consequences of alleles in haplotypes that confer susceptibility to complex traits

Spatially Uniform ReliefF (SURF) for computationally-efficient filtering of gene-gene interactions

<p>Abstract</p> <p>Background</p> <p>Genome-wide association studies are becoming the de facto standard in the genetic analysis of common human diseases. Given the complexity and robustness of biological networks such diseases are unlikely to be the result of single points of failure but instead likely arise from the joint failure of two or more interacting components. The hope in genome-wide screens is that these points of failure can be linked to single nucleotide polymorphisms (SNPs) which confer disease susceptibility. Detecting interacting variants that lead to disease in the absence of single-gene effects is difficult however, and methods to exhaustively analyze sets of these variants for interactions are combinatorial in nature thus making them computationally infeasible. Efficient algorithms which can detect interacting SNPs are needed. ReliefF is one such promising algorithm, although it has low success rate for noisy datasets when the interaction effect is small. ReliefF has been paired with an iterative approach, Tuned ReliefF (TuRF), which improves the estimation of weights in noisy data but does not fundamentally change the underlying ReliefF algorithm. To improve the sensitivity of studies using these methods to detect small effects we introduce Spatially Uniform ReliefF (SURF).</p> <p>Results</p> <p>SURF's ability to detect interactions in this domain is significantly greater than that of ReliefF. Similarly SURF, in combination with the TuRF strategy significantly outperforms TuRF alone for SNP selection under an epistasis model. It is important to note that this success rate increase does not require an increase in algorithmic complexity and allows for increased success rate, even with the removal of a nuisance parameter from the algorithm.</p> <p>Conclusion</p> <p>Researchers performing genetic association studies and aiming to discover gene-gene interactions associated with increased disease susceptibility should use SURF in place of ReliefF. For instance, SURF should be used instead of ReliefF to filter a dataset before an exhaustive MDR analysis. This change increases the ability of a study to detect gene-gene interactions. The SURF algorithm is implemented in the open source Multifactor Dimensionality Reduction (MDR) software package available from <url>http://www.epistasis.org</url>.</p

SNPs and Other Features as They Predispose to Complex Disease: Genome-Wide Predictive Analysis of a Quantitative Phenotype for Hypertension

Though recently they have fallen into some disrepute, genome-wide association studies (GWAS) have been formulated and applied to understanding essential hypertension. The principal goal here is to use data gathered in a GWAS to gauge the extent to which SNPs and their interactions with other features can be combined to predict mean arterial blood pressure (MAP) in 3138 pre-menopausal and naturally post-menopausal white women. More precisely, we quantify the extent to which data as described permit prediction of MAP beyond what is possible from traditional risk factors such as blood cholesterol levels and glucose levels. Of course, these traditional risk factors are genetic, though typically not explicitly so. In all, there were 44 such risk factors/clinical variables measured and 377,790 single nucleotide polymorphisms (SNPs) genotyped. Data for women we studied are from first visit measurements taken as part of the Atherosclerotic Risk in Communities (ARIC) study. We begin by assessing non-SNP features in their abilities to predict MAP, employing a novel regression technique with two stages, first the discovery of main effects and next discovery of their interactions. The long list of SNPs genotyped is reduced to a manageable list for combining with non-SNP features in prediction. We adapted Efron's local false discovery rate to produce this reduced list. Selected non-SNP and SNP features and their interactions are used to predict MAP using adaptive linear regression. We quantify quality of prediction by an estimated coefficient of determination (R2). We compare the accuracy of prediction with and without information from SNPs

Genotype-informed estimation of risk of coronary heart disease based on genome-wide association data linked to the electronic medical record

<p>Abstract</p> <p>Background</p> <p>Susceptibility variants identified by genome-wide association studies (GWAS) have modest effect sizes. Whether such variants provide incremental information in assessing risk for common 'complex' diseases is unclear. We investigated whether measured and imputed genotypes from a GWAS dataset linked to the electronic medical record alter estimates of coronary heart disease (CHD) risk.</p> <p>Methods</p> <p>Study participants (<it>n </it>= 1243) had no known cardiovascular disease and were considered to be at high, intermediate, or low 10-year risk of CHD based on the Framingham risk score (FRS) which includes age, sex, total and HDL cholesterol, blood pressure, diabetes, and smoking status. Of twelve SNPs identified in prior GWAS to be associated with CHD, four were genotyped in the participants as part of a GWAS. Genotypes for seven SNPs were imputed from HapMap CEU population using the program MACH. We calculated a multiplex genetic risk score for each patient based on the odds ratios of the susceptibility SNPs and incorporated this into the FRS.</p> <p>Results</p> <p>The mean (SD) number of risk alleles was 12.31 (1.95), range 6-18. The mean (SD) of the weighted genetic risk score was 12.64 (2.05), range 5.75-18.20. The CHD genetic risk score was not correlated with the FRS (<it>P </it>= 0.78). After incorporating the genetic risk score into the FRS, a total of 380 individuals (30.6%) were reclassified into higher-(188) or lower-risk groups (192).</p> <p>Conclusion</p> <p>A genetic risk score based on measured/imputed genotypes at 11 susceptibility SNPs, led to significant reclassification in the 10-y CHD risk categories. Additional prospective studies are needed to assess accuracy and clinical utility of such reclassification.</p

A comparison of genomic profiles of complex diseases under different models

Background: Various approaches are being used to predict individual risk to polygenic diseases from data provided by genome-wide association studies. As there are substantial differences between the diseases investigated, the data sets used and the way they are tested, it is difficult to assess which models are more suitable for this task. Results: We compared different approaches for seven complex diseases provided by the Wellcome Trust Case Control Consortium (WTCCC) under a within-study validation approach. Risk models were inferred using a variety of learning machines and assumptions about the underlying genetic model, including a haplotype-based approach with different haplotype lengths and different thresholds in association levels to choose loci as part of the predictive model. In accordance with previous work, our results generally showed low accuracy considering disease heritability and population prevalence. However, the boosting algorithm returned a predictive area under the ROC curve (AUC) of 0.8805 for Type 1 diabetes (T1D) and 0.8087 for rheumatoid arthritis, both clearly over the AUC obtained by other approaches and over 0.75, which is the minimum required for a disease to be successfully tested on a sample at risk, which means that boosting is a promising approach. Its good performance seems to be related to its robustness to redundant data, as in the case of genome-wide data sets due to linkage disequilibrium. Conclusions: In view of our results, the boosting approach may be suitable for modeling individual predisposition to Type 1 diabetes and rheumatoid arthritis based on genome-wide data and should be considered for more in-depth research.This work was supported by the Spanish Secretary of Research, Development and Innovation [TIN2010-20900-C04-1]; the Spanish Health Institute Carlos III [PI13/02714]and [PI13/01527] and the Andalusian Research Program under project P08-TIC-03717 with the help of the European Regional Development Fund (ERDF). The authors are very grateful to the reviewers, as they believe that their comments have helped to substantially improve the quality of the paper

Identifying subtypes of patients with neovascular age-related macular degeneration by genotypic and cardiovascular risk characteristics

<p>Abstract</p> <p>Background</p> <p>One of the challenges in the interpretation of studies showing associations between environmental and genotypic data with disease outcomes such as neovascular age-related macular degeneration (AMD) is understanding the phenotypic heterogeneity within a patient population with regard to any risk factor associated with the condition. This is critical when considering the potential therapeutic response of patients to any drug developed to treat the condition. In the present study, we identify patient subtypes or clusters which could represent several different targets for treatment development, based on genetic pathways in AMD and cardiovascular pathology.</p> <p>Methods</p> <p>We identified a sample of patients with neovascular AMD, that in previous studies had been shown to be at elevated risk for the disease through environmental factors such as cigarette smoking and genetic variants including the complement factor H gene (<it>CFH</it>) on chromosome 1q25 and variants in the <it>ARMS2</it>/HtrA serine peptidase 1 (<it>HTRA1</it>) gene(s) on chromosome 10q26. We conducted a multivariate segmentation analysis of 253 of these patients utilizing available epidemiologic and genetic data.</p> <p>Results</p> <p>In a multivariate model, cigarette smoking failed to differentiate subtypes of patients. However, four meaningfully distinct clusters of patients were identified that were most strongly differentiated by their cardiovascular health status (histories of hypercholesterolemia and hypertension), and the alleles of <it>ARMS2</it>/<it>HTRA1 </it>rs1049331.</p> <p>Conclusions</p> <p>These results have significant personalized medicine implications for drug developers attempting to determine the effective size of the treatable neovascular AMD population. Patient subtypes or clusters may represent different targets for therapeutic development based on genetic pathways in AMD and cardiovascular pathology, and treatments developed that may elevate CV risk, may be ill advised for certain of the clusters identified.</p