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"Holland, Dominic"
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The rest is history : the official book from the makers of the hit podcast
From the chart-topping podcast The Rest is History, a whistle-stop tour through the past - from Alexander the Great to Agatha Christie, the Wars of the Roses to Watergate. The nation's favourite historians Tom Holland and Dominic Sandbrook take on the most curious moments in history, answering the questions we didn't even think to ask: Did the Trojan War actually happen? What was the most disastrous party in history? Was Richard Nixon more like Caligula or Claudius? How did a hair appointment almost blow Churchill's cover? Why did the Nazis believe they were descended from Atlantis? Whether it is sending historical figures to Casa Amor in a series of Love Island, ranking history's most famous eunuchs and pigeons (including Winky, the unsung hero of the Second World War), or debating the meaning of greatness, there is nothing too big or too small for Tom and Dominic to unpick.
BOOK
Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation
Accumulating evidence from genome wide association studies (GWAS) suggests an abundance of shared genetic influences among complex human traits and disorders, such as mental disorders. Here we introduce a statistical tool, MiXeR, which quantifies polygenic overlap irrespective of genetic correlation, using GWAS summary statistics. MiXeR results are presented as a Venn diagram of unique and shared polygenic components across traits. At 90% of SNP-heritability explained for each phenotype, MiXeR estimates that 8.3 K variants causally influence schizophrenia and 6.4 K influence bipolar disorder. Among these variants, 6.2 K are shared between the disorders, which have a high genetic correlation. Further, MiXeR uncovers polygenic overlap between schizophrenia and educational attainment. Despite a genetic correlation close to zero, the phenotypes share 8.3 K causal variants, while 2.5 K additional variants influence only educational attainment. By considering the polygenicity, discoverability and heritability of complex phenotypes, MiXeR analysis may improve our understanding of cross-trait genetic architectures.
To better understand the phenotypic relationships of complex traits it is also important to understand their genetic overlap. Here, Frei et al. develop MiXeR which uses GWAS summary statistics to evaluate the polygenic overlap between two traits irrespective of their genetic correlation.
Journal Article
The rest is history returns : an A-Z of historical curiosities
Tom Holland and Dominic Sandbrook take on some of history's best and most bizarre moments. Charge forth against the traitors of the American Revolution, journey through Baghdad to discover the origins of the 'Arabian Nights' and head to Sicily to witness the first face-off between Carthage and Rome. But that's not all - this book also includes puzzles and a pub quiz. So dust off your tricorne hat, grab your lasso and get ready for a rollicking rollercoaster through the past.
BOOK
Efficient correction of inhomogeneous static magnetic field-induced distortion in Echo Planar Imaging
Single-shot Echo Planar Imaging (EPI) is one of the most efficient magnetic resonance imaging (MRI) acquisition schemes, producing relatively high-definition images in 100 ms or less. These qualities make it desirable for Diffusion Tensor Imaging (DTI), functional MRI (fMRI), and Dynamic Susceptibility Contrast MRI (DSC-MRI). However, EPI suffers from severe spatial and intensity distortion due to B0 field inhomogeneity induced by magnetic susceptibility variations. Anatomically accurate, undistorted images are essential for relating DTI and fMRI images with anatomical MRI scans, and for spatial registration with other modalities. We present here a fast, robust, and accurate procedure for correcting EPI images from such spatial and intensity distortions. The method involves acquisition of scans with opposite phase encoding polarities, resulting in opposite spatial distortion patterns, and alignment of the resulting images using a fast nonlinear registration procedure. We show that this method, requiring minimal additional scan time, provides superior accuracy relative to the more commonly used, and more time consuming, field mapping approach. This method is also highly computationally efficient, allowing for direct “real-time” implementation on the MRI scanner. We further demonstrate that the proposed method can be used to recover dropouts in gradient echo (BOLD and DSC-MRI) EPI images.
Journal Article
Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model
Estimating the polygenicity (proportion of causally associated single nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from a reference panel of 11 million SNPs, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities (as a fraction of the reference panel) ranging from ≃ 2 × 10-5 to ≃ 4 × 10-3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs reaching genome-wide significance at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics.
Journal Article
Understanding the genetic determinants of the brain with MOSTest
Regional brain morphology has a complex genetic architecture, consisting of many common polymorphisms with small individual effects. This has proven challenging for genome-wide association studies (GWAS). Due to the distributed nature of genetic signal across brain regions, multivariate analysis of regional measures may enhance discovery of genetic variants. Current multivariate approaches to GWAS are ill-suited for complex, large-scale data of this kind. Here, we introduce the Multivariate Omnibus Statistical Test (MOSTest), with an efficient computational design enabling rapid and reliable inference, and apply it to 171 regional brain morphology measures from 26,502 UK Biobank participants. At the conventional genome-wide significance threshold of α = 5 × 10
−8
, MOSTest identifies 347 genomic loci associated with regional brain morphology, more than any previous study, improving upon the discovery of established GWAS approaches more than threefold. Our findings implicate more than 5% of all protein-coding genes and provide evidence for gene sets involved in neuron development and differentiation.
Regional brain morphology has a complex genetic architecture. Here the authors present MOSTest, a multivariate statistical framework, apply it to UK Biobank data, and discover hundreds of loci associated with regional brain morphology.
Journal Article
Vertex-wise multivariate genome-wide association study identifies 780 unique genetic loci associated with cortical morphology
•Genetic variants affecting one cortical region often affect other cortical regions.•Standard mass-univariate methods ignore the distributed nature of genetic effects.•Multivariate MOSTest method exploits distributed effects boosting genetic discovery.•Considering fine-grained vertex-wise measures improves genetic discovery further.•Obtained increase in discovery does not come at a cost of poorer generalizability.
Brain morphology has been shown to be highly heritable, yet only a small portion of the heritability is explained by the genetic variants discovered so far. Here we extended the Multivariate Omnibus Statistical Test (MOSTest) and applied it to genome-wide association studies (GWAS) of vertex-wise structural magnetic resonance imaging (MRI) cortical measures from N=35,657 participants in the UK Biobank. We identified 695 loci for cortical surface area and 539 for cortical thickness, in total 780 unique genetic loci associated with cortical morphology robustly replicated in 8,060 children of mixed ethnicity from the Adolescent Brain Cognitive Development (ABCD) Study®. This reflects more than 8-fold increase in genetic discovery at no cost to generalizability compared to the commonly used univariate GWAS methods applied to region of interest (ROI) data. Functional follow up including gene-based analyses implicated 10% of all protein-coding genes and pointed towards pathways involved in neurogenesis and cell differentiation. Power analysis indicated that applying the MOSTest to vertex-wise structural MRI data triples the effective sample size compared to conventional univariate GWAS approaches. The large boost in power obtained with the vertex-wise MOSTest together with pronounced replication rates and highlighted biologically meaningful pathways underscores the advantage of multivariate approaches in the context of highly distributed polygenic architecture of the human brain.
Journal Article
Characterization and Correction of Geometric Distortions in 814 Diffusion Weighted Images
Diffusion Weighted Imaging (DWI), which is based on Echo Planar Imaging (EPI) protocols, is becoming increasingly important for neurosurgical applications. However, its use in this context is limited in part by significant spatial distortion inherent to EPI.
We evaluated an efficient algorithm for EPI distortion correction (EPIC) across 814 DWI scans from 250 brain tumor patients and quantified the magnitude of geometric distortion for whole brain and multiple brain regions.
Evaluation of the algorithm's performance revealed significantly higher mutual information between T1-weighted pre-contrast images and corrected b = 0 images than the uncorrected b = 0 images (p < 0.001). The distortion magnitude across all voxels revealed a median EPI distortion effect of 2.1 mm, ranging from 1.2 mm to 5.9 mm, the 5th and 95th percentile, respectively. Regions adjacent to bone-air interfaces, such as the orbitofrontal cortex, temporal poles, and brain stem, were the regions most severely affected by DWI distortion.
Using EPIC to estimate the degree of distortion in 814 DWI brain tumor images enabled the creation of a topographic atlas of DWI distortion across the brain. The degree of displacement of tumors boundaries in uncorrected images is severe but can be corrected for using EPIC. Our results support the use of distortion correction to ensure accurate and careful application of DWI to neurosurgical practice.
Journal Article
Rates of Decline in Alzheimer Disease Decrease with Age
Age is the strongest risk factor for sporadic Alzheimer disease (AD), yet the effects of age on rates of clinical decline and brain atrophy in AD have been largely unexplored. Here, we examined longitudinal rates of change as a function of baseline age for measures of clinical decline and structural MRI-based regional brain atrophy, in cohorts of AD, mild cognitive impairment (MCI), and cognitively healthy (HC) individuals aged 65 to 90 years (total n = 723). The effect of age was modeled using mixed effects linear regression. There was pronounced reduction in rates of clinical decline and atrophy with age for AD and MCI individuals, whereas HCs showed increased rates of clinical decline and atrophy with age. This resulted in convergence in rates of change for HCs and patients with advancing age for several measures. Baseline cerebrospinal fluid densities of AD-relevant proteins, Aβ(1-42), tau, and phospho-tau(181p) (ptau), showed a similar pattern of convergence with advanced age across cohorts, particularly for ptau. In contrast, baseline clinical measures did not differ by age, indicating uniformity of clinical severity at baseline. These results imply that the phenotypic expression of AD is relatively mild in individuals older than approximately 85 years, and this may affect the ability to distinguish AD from normal aging in the very old. Our findings show that inclusion of older individuals in clinical trials will substantially reduce the power to detect disease-modifying therapeutic effects, leading to dramatic increases in required clinical trial sample sizes with age of study sample.
Journal Article
Combining Polygenic Hazard Score With Volumetric MRI and Cognitive Measures Improves Prediction of Progression From Mild Cognitive Impairment to Alzheimer's Disease
Improved prediction of progression to Alzheimer's Disease (AD) among older individuals with mild cognitive impairment (MCI) is of high clinical and societal importance. We recently developed a polygenic hazard score (PHS) that predicted age of AD onset above and beyond
. Here, we used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to further explore the potential clinical utility of PHS for predicting AD development in older adults with MCI. We examined the predictive value of PHS alone and in combination with baseline structural magnetic resonance imaging (MRI) data on performance on the Mini-Mental State Exam (MMSE). In survival analyses, PHS significantly predicted time to progression from MCI to AD over 120 months (
= 1.07e-5), and PHS was significantly more predictive than
alone (
= 0.015). Combining PHS with baseline brain atrophy score and/or MMSE score significantly improved prediction compared to models without PHS (three-factor model
= 4.28e-17). Prediction model accuracies, sensitivities and area under the curve were also improved by including PHS in the model, compared to only using atrophy score and MMSE. Further, using linear mixed-effect modeling, PHS improved the prediction of change in the Clinical Dementia Rating-Sum of Boxes (CDR-SB) score and MMSE over 36 months in patients with MCI at baseline, beyond both
and baseline levels of brain atrophy. These results illustrate the potential clinical utility of PHS for assessment of risk for AD progression among individuals with MCI both alone, or in conjunction with clinical measures of prodromal disease including measures of cognitive function and regional brain atrophy.
Journal Article