Using Mendelian Inheritance To Improve High-Throughput SNP Discovery

Restriction site-associated DNA sequencing or genotyping-by-sequencing (GBS) approaches allow for rapid and cost-effective discovery and genotyping of thousands of single-nucleotide polymorphisms (SNPs) in multiple individuals. However, rigorous quality control practices are needed to avoid high levels of error and bias with these reduced representation methods. We developed a formal statistical framework for filtering spurious loci, using Mendelian inheritance patterns in nuclear families, that accommodates variable-quality genotype calls and missing data—both rampant issues with GBS data—and for identifying sex-linked SNPs. Simulations predict excellent performance of both the Mendelian filter and the sex-linkage assignment under a variety of conditions. We further evaluate our method by applying it to real GBS data and validating a subset of high-quality SNPs. These results demonstrate that our metric of Mendelian inheritance is a powerful quality filter for GBS loci that is complementary to standard coverage and Hardy–Weinberg filters. The described method, implemented in the software MendelChecker, will improve quality control during SNP discovery in nonmodel as well as model organisms.     

Using Antifungal Pharmacodynamics to Improve Patient Outcomes

In vitro and in vivo studies of available and investigational antifungals have broadened our understanding of the pharmacodynamics of these agents as well as the pharmacokinetic/pharmacodynamic characteristics that are associated with efficacy. These data are increasingly being used as surrogate means to answer questions about dosing and administration of antimicrobial agents in order to improve outcomes in patients with invasive fungal infections, as these questions are difficult to answer in clinical trials. The objective of this article is to review the pharmacodynamic activity of widely used classes of antifungal agents, including the azoles, amphotericin B, and the echinocandins, discuss the pharmacokinetic/pharmacodynamic parameters associated with efficacy of these agents in preclinical studies, and describe how this information is being translated into the clinical arena to optimize patient outcomes.     

Multiethnic Genetic Association Studies Improve Power for Locus Discovery

To date, genome-wide association studies have focused almost exclusively on populations of European ancestry. These studies continue with the advent of next-generation sequencing, designed to systematically catalog and test low-frequency variation for a role in disease. A complementary approach would be to focus further efforts on cohorts of multiple ethnicities. This leverages the idea that population genetic drift may have elevated some variants to higher allele frequency in different populations, boosting statistical power to detect an association. Based on empirical allele frequency distributions from eleven populations represented in HapMap Phase 3 and the 1000 Genomes Project, we simulate a range of genetic models to quantify the power of association studies in multiple ethnicities relative to studies that exclusively focus on samples of European ancestry. In each of these simulations, a first phase of GWAS in exclusively European samples is followed by a second GWAS phase in any of the other populations (including a multiethnic design). We find that nontrivial power gains can be achieved by conducting future whole-genome studies in worldwide populations, where, in particular, African populations contribute the largest relative power gains for low-frequency alleles (<5%) of moderate effect that suffer from low power in samples of European descent. Our results emphasize the importance of broadening genetic studies to worldwide populations to ensure efficient discovery of genetic loci contributing to phenotypic trait variability, especially for those traits for which large numbers of samples of European ancestry have already been collected and tested.     

A New Statistic to Evaluate Imputation Reliability

Background

As the amount of data from genome wide association studies grows dramatically, many interesting scientific questions require imputation to combine or expand datasets. However, there are two situations for which imputation has been problematic: (1) polymorphisms with low minor allele frequency (MAF), and (2) datasets where subjects are genotyped on different platforms. Traditional measures of imputation cannot effectively address these problems.

Methodology/Principal Findings

We introduce a new statistic, the imputation quality score (IQS). In order to differentiate between well-imputed and poorly-imputed single nucleotide polymorphisms (SNPs), IQS adjusts the concordance between imputed and genotyped SNPs for chance. We first evaluated IQS in relation to minor allele frequency. Using a sample of subjects genotyped on the Illumina 1 M array, we extracted those SNPs that were also on the Illumina 550 K array and imputed them to the full set of the 1 M SNPs. As expected, the average IQS value drops dramatically with a decrease in minor allele frequency, indicating that IQS appropriately adjusts for minor allele frequency. We then evaluated whether IQS can filter poorly-imputed SNPs in situations where cases and controls are genotyped on different platforms. Randomly dividing the data into “cases” and “controls”, we extracted the Illumina 550 K SNPs from the cases and imputed the remaining Illumina 1 M SNPs. The initial Q-Q plot for the test of association between cases and controls was grossly distorted (λ = 1.15) and had 4016 false positives, reflecting imputation error. After filtering out SNPs with IQS<0.9, the Q-Q plot was acceptable and there were no longer false positives. We then evaluated the robustness of IQS computed independently on the two halves of the data. In both European Americans and African Americans the correlation was >0.99 demonstrating that a database of IQS values from common imputations could be used as an effective filter to combine data genotyped on different platforms.

Conclusions/Significance

IQS effectively differentiates well-imputed and poorly-imputed SNPs. It is particularly useful for SNPs with low minor allele frequency and when datasets are genotyped on different platforms.     

Assessment of Genotype Imputation Performance Using 1000 Genomes in African American Studies

Genotype imputation, used in genome-wide association studies to expand coverage of single nucleotide polymorphisms (SNPs), has performed poorly in African Americans compared to less admixed populations. Overall, imputation has typically relied on HapMap reference haplotype panels from Africans (YRI), European Americans (CEU), and Asians (CHB/JPT). The 1000 Genomes project offers a wider range of reference populations, such as African Americans (ASW), but their imputation performance has had limited evaluation. Using 595 African Americans genotyped on Illumina’s HumanHap550v3 BeadChip, we compared imputation results from four software programs (IMPUTE2, BEAGLE, MaCH, and MaCH-Admix) and three reference panels consisting of different combinations of 1000 Genomes populations (February 2012 release): (1) 3 specifically selected populations (YRI, CEU, and ASW); (2) 8 populations of diverse African (AFR) or European (AFR) descent; and (3) all 14 available populations (ALL). Based on chromosome 22, we calculated three performance metrics: (1) concordance (percentage of masked genotyped SNPs with imputed and true genotype agreement); (2) imputation quality score (IQS; concordance adjusted for chance agreement, which is particularly informative for low minor allele frequency [MAF] SNPs); and (3) average r2hat (estimated correlation between the imputed and true genotypes, for all imputed SNPs). Across the reference panels, IMPUTE2 and MaCH had the highest concordance (91%–93%), but IMPUTE2 had the highest IQS (81%–83%) and average r2hat (0.68 using YRI+ASW+CEU, 0.62 using AFR+EUR, and 0.55 using ALL). Imputation quality for most programs was reduced by the addition of more distantly related reference populations, due entirely to the introduction of low frequency SNPs (MAF≤2%) that are monomorphic in the more closely related panels. While imputation was optimized by using IMPUTE2 with reference to the ALL panel (average r2hat = 0.86 for SNPs with MAF>2%), use of the ALL panel for African American studies requires careful interpretation of the population specificity and imputation quality of low frequency SNPs.     

A Bootstrap Method for Using Imputation Techniques for Data With Missing Values

Bootstrap is a time-honoured distribution-free approach for attaching standard error to any statistic of interest, but has not received much attention for data with missing values especially when using imputation techniques to replace missing values. We propose a proportional bootstrap method that allows effective use of imputation techniques for all bootstrap samples. Five detcnninistic imputation techniques are examined and particular emphasis is placed on the estimation of standard error for correlation coefficient. Some real data examples are presented. Other possible applications of the proposed bootstrap method are discussed.     

提高检验医学本科实习生综合素质探讨

随着生物医学技术、计算机技术、物理化学技术的迅速发展,临床医学对检验医学提出了更高的要求。作为实验室工作人员,在保证结果准确的基础上,还应对临床医生及病人提出的疑问给予合理、正确地解释。此外,还应该具备和临床医生进行交流,以及合理处理病人投诉的能力。五年制检验医学本科生是检验医学的主力军,培养高素质的检验医学本科生,对检验医学的发展将起关键性作用。     

Assisted Knowledge Discovery for the Maintenance of Clinical Guidelines

Background

Improving antibiotic prescribing practices is an important public-health priority given the widespread antimicrobial resistance. Establishing clinical practice guidelines is crucial to this effort, but their development is a complex task and their quality is directly related to the methodology and source of knowledge used.

Objective

We present the design and the evaluation of a tool (KART) that aims to facilitate the creation and maintenance of clinical practice guidelines based on information retrieval techniques.

Methods

KART consists of three main modules 1) a literature-based medical knowledge extraction module, which is built upon a specialized question-answering engine; 2) a module to normalize clinical recommendations based on automatic text categorizers; and 3) a module to manage clinical knowledge, which formalizes and stores clinical recommendations for further use. The evaluation of the usability and utility of KART followed the methodology of the cognitive walkthrough.

Results

KART was designed and implemented as a standalone web application. The quantitative evaluation of the medical knowledge extraction module showed that 53% of the clinical recommendations generated by KART are consistent with existing clinical guidelines. The user-based evaluation confirmed this result by showing that KART was able to find a relevant antibiotic for half of the clinical scenarios tested. The automatic normalization of the recommendation produced mixed results among end-users.

Conclusions

We have developed an innovative approach for the process of clinical guidelines development and maintenance in a context where available knowledge is increasing at a rate that cannot be sustained by humans. In contrast to existing knowledge authoring tools, KART not only provides assistance to normalize, formalize and store clinical recommendations, but also aims to facilitate knowledge building.     

A Projection and Density Estimation Method for Knowledge Discovery

A key ingredient to modern data analysis is probability density estimation. However, it is well known that the curse of dimensionality prevents a proper estimation of densities in high dimensions. The problem is typically circumvented by using a fixed set of assumptions about the data, e.g., by assuming partial independence of features, data on a manifold or a customized kernel. These fixed assumptions limit the applicability of a method. In this paper we propose a framework that uses a flexible set of assumptions instead. It allows to tailor a model to various problems by means of 1d-decompositions. The approach achieves a fast runtime and is not limited by the curse of dimensionality as all estimations are performed in 1d-space. The wide range of applications is demonstrated at two very different real world examples. The first is a data mining software that allows the fully automatic discovery of patterns. The software is publicly available for evaluation. As a second example an image segmentation method is realized. It achieves state of the art performance on a benchmark dataset although it uses only a fraction of the training data and very simple features.     

Using Low Levels of Stochastic Vestibular Stimulation to Improve Balance Function

Low-level stochastic vestibular stimulation (SVS) has been associated with improved postural responses in the medio-lateral (ML) direction, but its effect in improving balance function in both the ML and anterior-posterior (AP) directions has not been studied. In this series of studies, the efficacy of applying low amplitude SVS in 0–30 Hz range between the mastoids in the ML direction on improving cross-planar balance function was investigated. Forty-five (45) subjects stood on a compliant surface with their eyes closed and were instructed to maintain a stable upright stance. Measures of stability of the head, trunk, and whole body were quantified in ML, AP and combined APML directions. Results show that binaural bipolar SVS given in the ML direction significantly improved balance performance with the peak of optimal stimulus amplitude predominantly in the range of 100–500 μA for all the three directions, exhibiting stochastic resonance (SR) phenomenon. Objective perceptual and body motion thresholds as estimates of internal noise while subjects sat on a chair with their eyes closed and were given 1 Hz bipolar binaural sinusoidal electrical stimuli were also measured. In general, there was no significant difference between estimates of perceptual and body motion thresholds. The average optimal SVS amplitude that improved balance performance (peak SVS amplitude normalized to perceptual threshold) was estimated to be 46% in ML, 53% in AP, and 50% in APML directions. A miniature patch-type SVS device may be useful to improve balance function in people with disabilities due to aging, Parkinson’s disease or in astronauts returning from long-duration space flight.     

基于SOM聚类方法在临床检验知识发现中的应用

目的:探讨基于自组织特征映射网络的知识发现方法在建立应用临床检验项目的门诊患者聚类模型中的适用性,为临床检验决策支持系统的建立提供参考.方法:以2009-2011年西安市两所最大的综合医院的内科门诊患者就诊资料为训练样本,运用MatlabR2009b软件包中的SOM Tool Box工具箱建立应用临床检验项目的门诊患者的自组织映射网络聚类模型.用Excel软件绘制柱状图描述各组门诊患者的疾病特征.结果:自组织特征映射网络训练学习后,得出聚类结果,输出结果与临床检验决策真实值具有一定一致性,训练结果表明患者的聚类结果有一定的临床意义.患者性别、年龄、3年累计临床检验项目数、疾病特征对聚类模型的贡献较大.结论:自组织映射方法对应用临床检验项目的门诊患者聚类效果较好,具有较高的应用和推广价值,可为临床检验决策支持系统的研究提供方法学依据.     

Discovery of New Candidate Genes Related to Brain Development Using Protein Interaction Information

Human brain development is a dramatic process composed of a series of complex and fine-tuned spatiotemporal gene expressions. A good comprehension of this process can assist us in developing the potential of our brain. However, we have only limited knowledge about the genes and gene functions that are involved in this biological process. Therefore, a substantial demand remains to discover new brain development-related genes and identify their biological functions. In this study, we aimed to discover new brain-development related genes by building a computational method. We referred to a series of computational methods used to discover new disease-related genes and developed a similar method. In this method, the shortest path algorithm was executed on a weighted graph that was constructed using protein-protein interactions. New candidate genes fell on at least one of the shortest paths connecting two known genes that are related to brain development. A randomization test was then adopted to filter positive discoveries. Of the final identified genes, several have been reported to be associated with brain development, indicating the effectiveness of the method, whereas several of the others may have potential roles in brain development.     

Discovery of Potent ALK Inhibitors Using Pharmacophore-Informatics Strategy

Anaplastic lymphoma kinase is a tyrosine kinase receptor protein belonging to insulin receptor superfamily. Gene fusions in anaplastic lymphoma kinase are associated with non-small cell lung cancer development. Hence, they are of immense importance in targeted therapies. Thus, for the treatment of non-small cell lung cancer, effective anaplastic lymphoma kinase inhibitors are of great significance. Therefore, our objective is to find hit compounds that could have better inhibitory activity than the existing anaplastic lymphoma kinase inhibitors. Keeping this in mind, in the present study pharmacophore based virtual screening was performed to identify possible anaplastic lymphoma kinase inhibitors. Initially, a five-point common pharmacophore hypothesis was generated based on twelve anaplastic lymphoma kinase inhibitors using PHASE module of Schrödinger. Subsequently, common pharmacophore hypothesis-based screening was conducted against in-trials subset of ZINC database and a total of 1000 hits were identified. The molecules obtained were further screened by three stages of docking using GLIDE software. The docking results reveal that six hit molecules showed higher glide score in comparison with the reference molecules. Finally, pharmacokinetic properties of the hit molecules were also analysed using QikProp programme. The results indicate that molecules namely videx, dexecadotril, chloramphenicol, naficillin were found to have good pharmacokinetic properties and human oral absorption. Moreover, videx, naficillin and chloramphenicol were found to have significant inhibitory activity for mutant (F1174L) anaplastic lymphoma kinase. It was also found that videx exhibited crucial interactions with the Met1199 residue of the native and mutant anaplastic lymphoma kinase protein. Furthermore, PASS algorithm predicted anti-neoplastic activity for all the four molecules. Thus these hits are found to be promising leads for anaplastic lymphoma kinase inhibitors. We believe that this study will be useful for the discovery and designing of more potent anaplastic lymphoma kinase inhibitors in the near future.     

Current Practice in Software Development for Computational Neuroscience and How to Improve It

Almost all research work in computational neuroscience involves software. As researchers try to understand ever more complex systems, there is a continual need for software with new capabilities. Because of the wide range of questions being investigated, new software is often developed rapidly by individuals or small groups. In these cases, it can be hard to demonstrate that the software gives the right results. Software developers are often open about the code they produce and willing to share it, but there is little appreciation among potential users of the great diversity of software development practices and end results, and how this affects the suitability of software tools for use in research projects. To help clarify these issues, we have reviewed a range of software tools and asked how the culture and practice of software development affects their validity and trustworthiness.We identified four key questions that can be used to categorize software projects and correlate them with the type of product that results. The first question addresses what is being produced. The other three concern why, how, and by whom the work is done. The answers to these questions show strong correlations with the nature of the software being produced, and its suitability for particular purposes. Based on our findings, we suggest ways in which current software development practice in computational neuroscience can be improved and propose checklists to help developers, reviewers, and scientists to assess the quality of software and whether particular pieces of software are ready for use in research.     

Cancer Gene Discovery Using the Sleeping Beauty Transposon

Epidemiological and molecular data support the hypothesis that cancer results from a series of acquired somatic mutations. Discovering the initial mutations required for oncogenesis has long been a goal of cancer research. To date, the majority of causative mutations have been identified based on their ability to act in a dominant fashion and/or because they are activated by chromosomal translocations. Forward genetic screens are necessary for unbiased discovery of the remaining unknown oncogenic mutations. Two recent projects have demonstrated the feasibility of using the Sleeping Beauty transposon as an insertional mutagen for cancer gene discovery. In this article we discuss the history of cancer gene discovery and propose novel forward genetic screens using Sleeping Beauty transposon aimed at specific tissues and accelerating the discovery of recessive tumor suppressor genes.     

高等医学院校教学秘书队伍建设存在问题及对策

高校教学秘书主要从事教学管理工作,其工作水平的高低直接关系到整个教学秩序的稳定和人才培养质量的优劣。本文对新形势下教学秘书岗位吸引力、工作性质、晋升、薪酬、业务培训及情感激励等因素进行系统分析,认为教学秘书应该认清职责,加强业务学习,勇于探索教学服务与管理新思路并付之实践,为教研室的发展做出更大的贡献;与此同时,院校应逐步实现教学秘书的专业化,以解决教学秘书专业不对口、职称评定错位等诸多问题,以期建立一支稳定的教学秘书队伍,从而切实提高教研室教学管理工作的水平和效率。     

Knowledge and Theme Discovery across Very Large Biological Data Sets Using Distributed Queries: A Prototype Combining Unstructured and Structured Data

As the discipline of biomedical science continues to apply new technologies capable of producing unprecedented volumes of noisy and complex biological data, it has become evident that available methods for deriving meaningful information from such data are simply not keeping pace. In order to achieve useful results, researchers require methods that consolidate, store and query combinations of structured and unstructured data sets efficiently and effectively. As we move towards personalized medicine, the need to combine unstructured data, such as medical literature, with large amounts of highly structured and high-throughput data such as human variation or expression data from very large cohorts, is especially urgent. For our study, we investigated a likely biomedical query using the Hadoop framework. We ran queries using native MapReduce tools we developed as well as other open source and proprietary tools. Our results suggest that the available technologies within the Big Data domain can reduce the time and effort needed to utilize and apply distributed queries over large datasets in practical clinical applications in the life sciences domain. The methodologies and technologies discussed in this paper set the stage for a more detailed evaluation that investigates how various data structures and data models are best mapped to the proper computational framework.     

Using High Spatial Resolution to Improve BOLD fMRI Detection at 3T

For different functional magnetic resonance imaging experiments using blood oxygenation level-dependent (BOLD) contrast, the acquisition of T₂*-weighted scans at a high spatial resolution may be advantageous in terms of time-course signal-to-noise ratio and of BOLD sensitivity when the regions are prone to susceptibility artifacts. In this study, we explore this solution by examining how spatial resolution influences activations elicited when appetizing food pictures are viewed. Twenty subjects were imaged at 3 T with two different voxel volumes, 3.4 μl and 27 μl. Despite the diminution of brain coverage, we found that high-resolution acquisition led to a better detection of activations. Though known to suffer to different degrees from susceptibility artifacts, the activations detected by high spatial resolution were notably consistent with those reported in published activation likelihood estimation meta-analyses, corresponding to taste-responsive regions. Furthermore, these regions were found activated bilaterally, in contrast with previous findings. Both the reduction of partial volume effect, which improves BOLD contrast, and the mitigation of susceptibility artifact, which boosts the signal to noise ratio in certain regions, explained the better detection noted with high resolution. The present study provides further evidences that high spatial resolution is a valuable solution for human BOLD fMRI, especially for studying food-related stimuli.