A Known Pathogenic Variant in the Essential Mitochondrial Translation Gene RMND1 Causes a Perrault-Like Syndrome with Renal Defects

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Efficient and exact sampling of simple graphs with given arbitrary degree sequence

Uniform sampling from graphical realizations of a given degree sequence is a fundamental component in simulation-based measurements of network observables, with applications ranging from epidemics, through social networks to Internet modeling. Existing graph sampling methods are either link-swap based (Markov-Chain Monte Carlo algorithms) or stub-matching based (the Configuration Model). Both types are ill-controlled, with typically unknown mixing times for link-swap methods and uncontrolled rejections for the Configuration Model. Here we propose an efficient, polynomial time algorithm that generates statistically independent graph samples with a given, arbitrary, degree sequence. The algorithm provides a weight associated with each sample, allowing the observable to be measured either uniformly over the graph ensemble, or, alternatively, with a desired distribution. Unlike other algorithms, this method always produces a sample, without back-tracking or rejections. Using a central limit theorem-based reasoning, we argue, that for large N, and for degree sequences admitting many realizations, the sample weights are expected to have a lognormal distribution. As examples, we apply our algorithm to generate networks with degree sequences drawn from power-law distributions and from binomial distributions.Comment: 8 pages, 3 figure

Pharmacogenomics in the UK National Health Service: opportunities and challenges

There is increasing interest in pharmacogenomics. However, it is also widely acknowledged that implementation of pharmacogenomics into clinical practice has been slow. Implementation is being undertaken in many centres in the US, but this is not nationwide and often focused on highly specialised academic centres, driven by champions. To date, there has been no implementation on a whole country basis. The UK National Health Service (NHS) is a single integrated healthcare system, which provides free care to all patients at the point of need. Recently, there has been a drive to implement genomic medicine into the NHS, largely spurred on by the success of the 100,000 genomes project. This represents an unprecedented opportunity to implement pharmacogenomics for over 60 million people. In order to discuss the potential for implementing pharmacogenomics into the NHS, the UK Pharmacogenetics and Stratified Medicine Network, NHS England and Genomics England invited experts from academia, the healthcare sector, industry and patient representatives to come together to discuss the opportunities and challenges1. This report highlights the discussions of the workshop with the aim of providing an overview of the issues that need to be considered to enable pharmacogenomic medicine to become mainstream within the NHS

A case-control evaluation of 143 single nucleotide polymorphisms for breast cancer risk stratification with classical factors and mammographic density

Breast Cancer Now. Grant Number: 2015MayPR515National Institute for Health Research. Grant Numbers: IS‐BRC‐1215‐20007, NF‐SI‐0513‐10076Prevent Breast Cancer. Grant Numbers: GA09‐002, GA11‐002Cancer Research UK. Grant Numbers: C1287/A10118, C1287/A16563, C569/A16891National Institutes of Health. Grant Numbers: X01HG007492, U19 CA148065Canadian Institutes of Health Research. Grant Number: GPH‐129344Horizon 2020 Research and Innovation Programme. Grant Numbers: 634935, 633784European Union. Grant Number: HEALTH‐F2‐2009‐22317

On the Refractive Index of Ageing Dispersions of Laponite

Aqueous dispersion of Laponite at low ionic concentration is of interest since it undergoes structural evolution with respect to time, which is usually termed as ageing. In this work we study the refractive index behavior as a function of ageing time, concentration and temperature. We observed that the extended Lorenz-Lorentz equation fitted the refractive index dependence on concentration and temperature very well. The refractive index did not show any dependence on ageing time. However, the dependence of refractive index on concentration showed a marked change as the system underwent transition from an isotropic to a biphasic state. The slope of the refractive index-density data is remarkably close to that of water at all Laponite concentrations. In the context of transport phenomena, optical measurements such as interferometry can exploit the water-like behavior of Laponite dispersions.Comment: 13 pages, 3 figures, to appear in Applied Clay Scienc

Should adjustment for covariates be used in prevalence estimations?

Background Adjustment for covariates (also called auxiliary variables in survey sampling literature) is commonly applied in health surveys to reduce the variances of the prevalence estimators. In theory, adjusted prevalence estimators are more accurate when variance components are known. In practice, variance components needed to achieve the adjustment are unknown and their sample estimators are used instead. The uncertainty introduced by estimating variance components may overshadow the reduction in the variance of the prevalence estimators due to adjustment. We present empirical guidelines indicating when adjusted prevalence estimators should be considered, using gender adjusted and unadjusted smoking prevalence as an illustration. Methods We compare the accuracy of adjusted and unadjusted prevalence estimators via simulation. We simulate simple random samples from hypothetical populations with the proportion of males ranging from 30% to 70%, the smoking prevalence ranging from 15% to 35%, and the ratio of male to female smoking prevalence ranging from 1 to 4. The ranges of gender proportions and smoking prevalences reflect the conditions in 1999–2003 Behavioral Risk Factors Surveillance System (BRFSS) data for Massachusetts. From each population, 10,000 samples are selected and the ratios of the variance of the adjusted prevalence estimators to the variance of the unadjusted (crude) ones are computed and plotted against the proportion of males by population prevalence, as well as by population and sample sizes. The prevalence ratio thresholds, above which adjusted prevalence estimators have smaller variances, are determined graphically. Results In many practical settings, gender adjustment results in less accuracy. Whether or not there is better accuracy with adjustment depends on sample sizes, gender proportions and ratios between male and female prevalences. In populations with equal number of males and females and smoking prevalence of 20%, the adjusted prevalence estimators are more accurate when the ratios of male to female prevalences are above 2.4, 1.8, 1.6, 1.4 and 1.3 for sample sizes of 25, 50, 100, 150 and 200, respectively. Conclusion Adjustment for covariates will not result in more accurate prevalence estimator when ratio of male to female prevalences is close to one, sample size is small and risk factor prevalence is low. For example, when reporting smoking prevalence based on simple random sampling, gender adjustment is recommended only when sample size is greater than 200

Influence of autozygosity on common disease risk across the phenotypic spectrum.

Autozygosity is associated with rare Mendelian disorders and clinically relevant quantitative traits. We investigated associations between the fraction of the genome in runs of homozygosity (FROH) and common diseases in Genes & Health (n = 23,978 British South Asians), UK Biobank (n = 397,184), and 23andMe. We show that restricting analysis to offspring of first cousins is an effective way of reducing confounding due to social/environmental correlates of FROH. Within this group in G&H+UK Biobank, we found experiment-wide significant associations between FROH and twelve common diseases. We replicated associations with type 2 diabetes (T2D) and post-traumatic stress disorder via within-sibling analysis in 23andMe (median n = 480,282). We estimated that autozygosity due to consanguinity accounts for 5%-18% of T2D cases among British Pakistanis. Our work highlights the possibility of widespread non-additive genetic effects on common diseases and has important implications for global populations with high rates of consanguinity

Lack of effect of lowering LDL cholesterol on cancer: meta-analysis of individual data from 175,000 people in 27 randomised trials of statin therapy

<p>Background: Statin therapy reduces the risk of occlusive vascular events, but uncertainty remains about potential effects on cancer. We sought to provide a detailed assessment of any effects on cancer of lowering LDL cholesterol (LDL-C) with a statin using individual patient records from 175,000 patients in 27 large-scale statin trials.</p> <p>Methods and Findings: Individual records of 134,537 participants in 22 randomised trials of statin versus control (median duration 4.8 years) and 39,612 participants in 5 trials of more intensive versus less intensive statin therapy (median duration 5.1 years) were obtained. Reducing LDL-C with a statin for about 5 years had no effect on newly diagnosed cancer or on death from such cancers in either the trials of statin versus control (cancer incidence: 3755 [1.4% per year [py]] versus 3738 [1.4% py], RR 1.00 [95% CI 0.96-1.05]; cancer mortality: 1365 [0.5% py] versus 1358 [0.5% py], RR 1.00 [95% CI 0.93–1.08]) or in the trials of more versus less statin (cancer incidence: 1466 [1.6% py] vs 1472 [1.6% py], RR 1.00 [95% CI 0.93–1.07]; cancer mortality: 447 [0.5% py] versus 481 [0.5% py], RR 0.93 [95% CI 0.82–1.06]). Moreover, there was no evidence of any effect of reducing LDL-C with statin therapy on cancer incidence or mortality at any of 23 individual categories of sites, with increasing years of treatment, for any individual statin, or in any given subgroup. In particular, among individuals with low baseline LDL-C (<2 mmol/L), there was no evidence that further LDL-C reduction (from about 1.7 to 1.3 mmol/L) increased cancer risk (381 [1.6% py] versus 408 [1.7% py]; RR 0.92 [99% CI 0.76–1.10]).</p> <p>Conclusions: In 27 randomised trials, a median of five years of statin therapy had no effect on the incidence of, or mortality from, any type of cancer (or the aggregate of all cancer).</p&gt