A polygenic score (PGS) or polygenic risk score (PRS) is an estimate of an individual's genetic liability to a trait or disease, calculated according to their genotype profile and relevant ...genome-wide association study (GWAS) data. While present PRSs typically explain only a small fraction of trait variance, their correlation with the single largest contributor to phenotypic variation-genetic liability-has led to the routine application of PRSs across biomedical research. Among a range of applications, PRSs are exploited to assess shared etiology between phenotypes, to evaluate the clinical utility of genetic data for complex disease and as part of experimental studies in which, for example, experiments are performed that compare outcomes (e.g., gene expression and cellular response to treatment) between individuals with low and high PRS values. As GWAS sample sizes increase and PRSs become more powerful, PRSs are set to play a key role in research and stratified medicine. However, despite the importance and growing application of PRSs, there are limited guidelines for performing PRS analyses, which can lead to inconsistency between studies and misinterpretation of results. Here, we provide detailed guidelines for performing and interpreting PRS analyses. We outline standard quality control steps, discuss different methods for the calculation of PRSs, provide an introductory online tutorial, highlight common misconceptions relating to PRS results, offer recommendations for best practice and discuss future challenges.
PRSice: Polygenic Risk Score software Euesden, Jack; Lewis, Cathryn M; O'Reilly, Paul F
Bioinformatics,
05/2015, Letnik:
31, Številka:
9
Journal Article
Recenzirano
Odprti dostop
A polygenic risk score (PRS) is a sum of trait-associated alleles across many genetic loci, typically weighted by effect sizes estimated from a genome-wide association study. The application of PRS ...has grown in recent years as their utility for detecting shared genetic aetiology among traits has become appreciated; PRS can also be used to establish the presence of a genetic signal in underpowered studies, to infer the genetic architecture of a trait, for screening in clinical trials, and can act as a biomarker for a phenotype. Here we present the first dedicated PRS software, PRSice ('precise'), for calculating, applying, evaluating and plotting the results of PRS. PRSice can calculate PRS at a large number of thresholds ("high resolution") to provide the best-fit PRS, as well as provide results calculated at broad P-value thresholds, can thin Single Nucleotide Polymorphisms (SNPs) according to linkage disequilibrium and P-value or use all SNPs, handles genotyped and imputed data, can calculate and incorporate ancestry-informative variables, and can apply PRS across multiple traits in a single run. We exemplify the use of PRSice via application to data on schizophrenia, major depressive disorder and smoking, illustrate the importance of identifying the best-fit PRS and estimate a P-value significance threshold for high-resolution PRS studies.
PRSice is written in R, including wrappers for bash data management scripts and PLINK-1.9 to minimize computational time. PRSice runs as a command-line program with a variety of user-options, and is freely available for download from http://PRSice.info
jack.euesden@kcl.ac.uk or paul.oreilly@kcl.ac.uk
Supplementary data are available at Bioinformatics online.
Self-assessment methods are broadly employed in emotion research for the collection of subjective affective ratings. The Self-Assessment Manikin (SAM), a pictorial scale developed in the eighties for ...the measurement of pleasure, arousal, and dominance, is still among the most popular self-reporting tools, despite having been conceived upon design principles which are today obsolete. By leveraging on state-of-the-art user interfaces and metacommunicative pictorial representations, we developed the Affective Slider (AS), a digital self-reporting tool composed of two slider controls for the quick assessment of pleasure and arousal. To empirically validate the AS, we conducted a systematic comparison between AS and SAM in a task involving the emotional assessment of a series of images taken from the International Affective Picture System (IAPS), a database composed of pictures representing a wide range of semantic categories often used as a benchmark in psychological studies. Our results show that the AS is equivalent to SAM in the self-assessment of pleasure and arousal, with two added advantages: the AS does not require written instructions and it can be easily reproduced in latest-generation digital devices, including smartphones and tablets. Moreover, we compared new and normative IAPS ratings and found a general drop in reported arousal of pictorial stimuli. Not only do our results demonstrate that legacy scales for the self-report of affect can be replaced with new measurement tools developed in accordance to modern design principles, but also that standardized sets of stimuli which are widely adopted in research on human emotion are not as effective as they were in the past due to a general desensitization towards highly arousing content.
Causal inference is essential across the biomedical, behavioural and social sciences.By progressing from confounded statistical associations to evidence of causal relationships, causal inference can ...reveal complex pathways underlying traits and diseases and help to prioritize targets for intervention. Recent progress in genetic epidemiology - including statistical innovation, massive genotyped data sets and novel computational tools for deep data mining - has fostered the intense development of methods exploiting genetic data and relatedness to strengthen causal inference in observational research. In this Review, we describe how such genetically informed methods differ in their rationale, applicability and inherent limitations and outline how they should be integrated in the future to offer a rich causal inference toolbox.
Abstract
Background
Polygenic risk score (PRS) analyses have become an integral part of biomedical research, exploited to gain insights into shared aetiology among traits, to control for genomic ...profile in experimental studies, and to strengthen causal inference, among a range of applications. Substantial efforts are now devoted to biobank projects to collect large genetic and phenotypic data, providing unprecedented opportunity for genetic discovery and applications. To process the large-scale data provided by such biobank resources, highly efficient and scalable methods and software are required.
Results
Here we introduce PRSice-2, an efficient and scalable software program for automating and simplifying PRS analyses on large-scale data. PRSice-2 handles both genotyped and imputed data, provides empirical association P-values free from inflation due to overfitting, supports different inheritance models, and can evaluate multiple continuous and binary target traits simultaneously. We demonstrate that PRSice-2 is dramatically faster and more memory-efficient than PRSice-1 and alternative PRS software, LDpred and lassosum, while having comparable predictive power.
Conclusion
PRSice-2's combination of efficiency and power will be increasingly important as data sizes grow and as the applications of PRS become more sophisticated, e.g., when incorporated into high-dimensional or gene set–based analyses. PRSice-2 is written in C++, with an R script for plotting, and is freely available for download from http://PRSice.info.
Recently a dilute nitric acid extraction (0.43 M) was adopted by ISO (ISO-17586:2016) as standard for extraction of geochemically reactive elements in soil and soil like materials. Here we evaluate ...the performance of this extraction for a wide range of elements by mechanistic geochemical modeling. Model predictions indicate that the extraction recovers the reactive concentration quantitatively (>90%). However, at low ratios of element to reactive surfaces the extraction underestimates reactive Cu, Cr, As, and Mo, that is, elements with a particularly high affinity for organic matter or oxides. The 0.43 M HNO3 together with more dilute and concentrated acid extractions were evaluated by comparing model-predicted and measured dissolved concentrations in CaCl2 soil extracts, using the different extractions as alternative model-input. Mean errors of the predictions based on 0.43 M HNO3 are generally within a factor three, while Mo is underestimated and Co, Ni and Zn in soils with pH > 6 are overestimated, for which possible causes are discussed. Model predictions using 0.43 M HNO3 are superior to those using 0.1 M HNO3 or Aqua Regia that under- and overestimate the reactive element contents, respectively. Low concentrations of oxyanions in our data set and structural underestimation of their reactive concentrations warrant further investigation.
•We adapt the CNN architecture to texture analysis.•We introduce an energy layer to discard the overall shape information and focus on texture features.•We evaluate the domain transferability and the ...depth of networks that are from scratch or pretrained.•Our network is simpler than a classic CNN and obtains better classification results on texture datasets.•We combine our texture CNN to a classic CNN (overall shape analysis) and further improve the results.
Deep learning has established many new state of the art solutions in the last decade in areas such as object, scene and speech recognition. In particular Convolutional Neural Network (CNN) is a category of deep learning which obtains excellent results in object detection and recognition tasks. Its architecture is indeed well suited to object analysis by learning and classifying complex (deep) features that represent parts of an object or the object itself. However, some of its features are very similar to texture analysis methods. CNN layers can be thought of as filter banks of complexity increasing with the depth. Filter banks are powerful tools to extract texture features and have been widely used in texture analysis. In this paper we develop a simple network architecture named Texture CNN (T-CNN) which explores this observation. It is built on the idea that the overall shape information extracted by the fully connected layers of a classic CNN is of minor importance in texture analysis. Therefore, we pool an energy measure from the last convolution layer which we connect to a fully connected layer. We show that our approach can improve the performance of a network while greatly reducing the memory usage and computation.
The last year has seen a great deal of new information published relating vestibular dysfunction to cognitive impairment in humans, especially in the elderly. The objective of this review is to ...summarize and critically evaluate this new evidence in the context of the previous literature.
This review will address the recent epidemiological/survey studies that link vestibular dysfunction with cognitive impairment in the elderly; recent clinical investigations into cognitive impairment in the context of vestibular dysfunction, both in the elderly and in the cases of otic capsule dehiscence and partial bilateral vestibulopathy; recent evidence that vestibular impairment is associated with hippocampal atrophy; and finally recent evidence relating to the hypothesis that vestibular dysfunction could be a risk factor for dementia.
The main implication of these recent studies is that vestibular dysfunction, possibly of any type, may result in cognitive impairment, and this could be especially so for the elderly. Such symptoms will need to be considered in the treatment of patients with vestibular disorders.
The development of COVID-19 vaccines is occurring at unprecedented speeds, but require high coverage rates to be successful. This research examines individuals’ psychological beliefs that may act as ...enablers and barriers to vaccination intentions. Using the health beliefs model as a guide to our conceptual framework, we explore factors influencing vaccine hesitancy and health beliefs regarding risks and severity of the disease, along with individual variables such as income, age, religion, altruism, and collectivism. A questionnaire using newly created measures for various antecedents provided 4303 usable responses from Australia, Canada, England, New Zealand, and the United States. A factor analytic and structural equation model indicates that trust in vaccine approval, the perceived effectiveness of the vaccine for protecting others, and conspiracy beliefs are the most significant drivers of intentions to vaccinate. Older people, those seeking employment, and those who have received a recent influenza vaccine are more likely to be vaccinated against COVID-19. The findings have implications for improving communication strategies targeting individuals about the merits of vaccination, particularly focusing on younger individuals and expanded message framing to include altruistic considerations, and to improve government transparency regarding the effectiveness and side effects of vaccines.
Emerging 3D printing technologies are enabling the fabrication of complex scaffold structures for diverse medical applications. 3D printing allows controlled material placement for configuring porous ...tissue scaffolds with tailored properties for desired mechanical stiffness, nutrient transport, and biological growth. However, tuning tissue scaffold functionality requires navigation of a complex design space with numerous trade-offs that require multidisciplinary assessment. Integrated design approaches that encourage iteration and consideration of diverse processes including design configuration, material selection, and simulation models provide a basis for improving design performance. In this review, recent advances in design, fabrication, and assessment of 3D printed tissue scaffolds are investigated with a focus on bone tissue engineering. Bone healing and fusion are examples that demonstrate the needs of integrated design approaches in leveraging new materials and 3D printing processes for specified clinical applications. Current challenges for integrated design are outlined and emphasize directions where new research may lead to significant improvements in personalized medicine and emerging areas in healthcare.
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