Knowledge and attitudes regarding genetic testing among Jordanians: An approach towards genomic medicine

BackgroundThe twenty first century can be called the genomic era referring to the rapid development of genetics, and the beginning of genomic medicine. An initial step towards genomic medicine is to evaluate the knowledge and attitude towards genetic testing among different populations. The aims of this study were to assess the genetic knowledge and attitude towards genetic testing among the Jordanian population and patients with immune diseases. In addition, we evaluated the association between knowledge, attitude and several demographic factors of the population.MethodsThis study was performed using an online questionnaire that was distributed to respondents from different regions of Jordan.ResultsA total of 1149 participants were recruited from the Jordanian population. Overall factual genetic knowledge of the participants was good (65.4%), with education level, working or studying in a health-related field and household average monthly income being significant predictors of factual knowledge scores (P = 0.03, P < 0.001 and P < 0.001, respectively). However, factual knowledge results revealed that scores of questions related to diseases were significantly higher than scores of gene-related scientific facts (P < 0.01). Participants of our study reported to have low perceived knowledge on medical uses (39.5%) and social consequences (23.9%) of genetic testing. Regarding the participants’ attitudes, favorable attitudes towards genetic testing were prevailing (91.5%). Favorable attitudes were more prominent among higher educated participants, and participants with higher scores of factual knowledge.ConclusionDespite the fact that our Jordanian-based study revealed a good level of genetic knowledge as well as a favorable attitude towards genetic testing, we realized an imbalance of knowledge between gene-related scientific facts and disease-related concepts as well as between factual and perceived genetic knowledge, which indicates the necessity of increasing the awareness about genetic testing in order to ensure that individuals can take informed decisions that help in the employment of personalized medicine.     

A systems biology approach to genetic studies of complex diseases

Revealing mechanisms underlying complex diseases poses great challenges to biologists. The traditional linkage and linkage disequilibrium analysis that have been successful in the identification of genes responsible for Mendelian traits, however, have not led to similar success in discovering genes influencing the development of complex diseases. Emerging functional genomic and proteomic ('omic') resources and technologies provide great opportunities to develop new methods for systematic identification of genes underlying complex diseases. In this report, we propose a systems biology approach, which integrates omic data, to find genes responsible for complex diseases. This approach consists of five steps: (1) generate a set of candidate genes using gene-gene interaction data sets; (2) reconstruct a genetic network with the set of candidate genes from gene expression data; (3) identify differentially regulated genes between normal and abnormal samples in the network; (4) validate regulatory relationship between the genes in the network by perturbing the network using RNAi and monitoring the response using RT-PCR; and (5) genotype the differentially regulated genes and test their association with the diseases by direct association studies. To prove the concept in principle, the proposed approach is applied to genetic studies of the autoimmune disease scleroderma or systemic sclerosis.     

多基因遗传病基因研究的策略和方法

基因在决定个体表型方面起着决定性的作用。虽然单基因疾病的致病基因的克隆工作取得了显著的进展,但对于多基因疾病来说,仍然存在许多问题,同时也是巨大的挑战。本文综述了多基因疾病的遗传特点和多基因疾病易感基因识别、分离和克隆的一般步骤和存在的问题,介绍了人类基因组计划在此过程中的作用和单核苷酸多态性的应用前景,提出 了最有可能克隆出多基因疾病易感基因的策略和方法。     

Sulfonamide derivatives as multi-target agents for complex diseases

Sulfonamide derivatives are frequently seen structural motifs in medicinal chemistry. Almost a century after Gerhard Domagk’s pioneering work leading to the first sulfonamide antibiotic Prontosil, sulfa-drugs are still widely utilized in various pharmaceutical applications due to their antibacterial, antiviral, antimalarial, antifungal, anticancer, antidepressant, or other properties. In the past few years, the interest in sulfonamides has increased as their broad range of bioactivity and versatile structure make them excellent candidates for repurposing old drugs or developing new multi-target agents in the emerging field of polypharmacology.This digest aims to provide an overview of recent advances in sulfonamide-based bioactive compounds, their importance in drug discovery and development emphasizing multi-target approaches for complex diseases, and their novel contribution to contemporary medicinal chemistry.     

基于组合药物网络的复杂疾病药物组合模式研究

人体作为一个复杂的功能系统。疾病的发生和发展,尤其是复杂疾病,其病理过程往往涉及多环节、多系统。单一药物难以满足复杂疾病的治疗要求,组合药物成为未来药物发展的新趋势。本文在构建组合药物网络的基础上进行MCODE算法聚类,得到33个独立且内部联系紧密的药物模块。其中26组药物模块用于治疗单一复杂疾病。通过详细分析癌症、疼痛、银屑病、细菌感染、类风湿性关节炎、化疗呕吐这六种复杂疾病,归纳总结出这六种疾病的药物组合模式,从而提出复杂疾病多角度的治疗策略。     

Optimal screening for genetic diseases

Screening for genetic diseases is performed in many regions and/or ethnic groups where there is a high prevalence of possibly malign genes. The propagation of such genes can be considered a dynamic externality. Given that many of these diseases are untreatable and give rise to truly tragic outcomes, they are a source of societal concern, and the screening process should perhaps be regulated. This paper incorporates a standard model of genetic propagation into an economic model of dynamic management to derive cost benefit rules for optimal screening. The highly non-linear nature of genetic dynamics gives rise to perhaps surprising results that include discontinuous controls and threshold effects. One insight is that any screening program that is in place for any amount of time should screen all individuals in a target population. The incorporation of genetic models may prove to be useful to several emerging fields in economics such as genoeconomics, neuroeconomics and paleoeconomics.     

A genetic interaction network model of a complex neurological disease

Absence epilepsy (AE) is a complex, heritable disease characterized by a brief disruption of normal behavior and accompanying spike‐wave discharges (SWD) on the electroencephalogram. Only a handful of genes has been definitively associated with AE in humans and rodent models. Most studies suggest that genetic interactions play a large role in the etiology and severity of AE, but mapping and understanding their architecture remains a challenge, requiring new computational approaches. Here we use combined analysis of pleiotropy and epistasis (CAPE) to detect and interpret genetic interactions in a meta‐population derived from three C3H × B6J strain crosses, each of which is fixed for a different SWD‐causing mutation. Although each mutation causes SWD through a different molecular mechanism, the phenotypes caused by each mutation are exacerbated on the C3H genetic background compared with B6J, suggesting common modifiers. By combining information across two phenotypic measures – SWD duration and frequency – CAPE showed a large, directed genetic network consisting of suppressive and enhancing interactions between loci on 10 chromosomes. These results illustrate the power of CAPE in identifying novel modifier loci and interactions in a complex neurological disease, toward a more comprehensive view of its underlying genetic architecture.     

Combination therapeutics in complex diseases

The biological redundancies in molecular networks of complex diseases limit the efficacy of many single drug therapies. Combination therapeutics, as a common therapeutic method, involve pharmacological intervention using several drugs that interact with multiple targets in the molecular networks of diseases and may achieve better efficacy and/or less toxicity than monotherapy in practice. The development of combination therapeutics is complicated by several critical issues, including identifying multiple targets, targeting strategies and the drug combination. This review summarizes the current achievements in combination therapeutics, with a particular emphasis on the efforts to develop combination therapeutics for complex diseases.     

Ancestral processes for non-neutral models of complex diseases

We consider non-neutral models for unlinked loci, where the fitness of a chromosome or individual is not multiplicative across loci. Such models are suitable for many complex diseases, where there are gene-interactions. We derive a genealogical process for such models, called the complex selection graph (CSG). This coalescent-type process is related to the ancestral selection graph, and is derived from the ancestral influence graph by considering the limit as the recombination rate between loci gets large. We analyse the CSG both theoretically and via simulation. The main results are that the gene-interactions do not produce linkage disequilibrium, but do produce dependencies in allele frequencies between loci. For small selection rates, the distributions of the genealogy and the allele frequencies at a single locus are well-approximated by their distributions under a single locus model, where the fitness of each allele is the average of the true fitnesses of that allele with respect to the distribution of alleles at other loci.     

How similar are amino acid mutations in human genetic diseases and evolution

Accumulating evidence indicates that some deleterious mutations responsible for genetic diseases may offer benefits for human to prevent other diseases. Therefore, human genetic diseases and evolution were tentatively regarded as the two sides of the same coin, which stimulated our interest to explore how similar are amino acid mutations in human genetic diseases and evolution. Through a large-scale analysis on amino acid mutation patterns of genetic diseases and evolution of Hominidae (Homo sapiens and Pan troglodytes), it was found that there exist significant correlations between two mutation patterns. Besides, there also exist some evident differences between both mutations, especially those associated with four amino acids C, G, R, and L. These findings are of significance to understanding the subtle connections between human genetic diseases and evolution.     

Quantitative genetic dissection of complex traits in a QTL-mapping pedigree

This paper summarizes and modifies quantitative genetic analyses on a pedigree used to map genetic factors (i.e., QTLs) underlying a complex trait. The total genetic variance can be exactly estimated based on the F₂ family derived from two homozygous parents for alternative alleles at all QTLs of interest. The parents, F₁ hybrids, and two backcrosses are combined to each parent, and the total number of QTLs and the number of dominant QTLs are estimated under the assumptions of gene association with the two parents, equal gene effect, no linkage, and no epistasis among QTLs. Further relaxation for each of the assumptions are made in detail. The biometric estimator for the QTL number and action mode averaged over the entire genome could provide some basic and complementary information to QTL mapping designed to detect the effect and location of specific genetic factors.     

遗传风险评分在复杂疾病遗传学研究中的应用

心血管疾病、2型糖尿病、原发性高血压、哮喘、肥胖、肿瘤等复杂疾病在全球范围内流行,并成为人类死亡的主要原因。越来越多的人开始关注遗传易感性在复杂疾病发病机制中的作用。至今,与复杂疾病相关的易感基因和基因序列变异仍未完全清楚。人们希望通过遗传关联研究来阐明复杂疾病的遗传基础。近年来,全基因组关联研究和候选基因研究发现了大量与复杂疾病有关的基因序列变异。这些与复杂疾病有因果和(或)关联关系的基因序列变异的发现促进了复杂疾病预测和防治方法的产生和发展。遗传风险评分(Genetic risk score,GRS)作为探索单核苷酸多态(Single nucleotide polymorphisms,SNPs)与复杂疾病临床表型之间关系的新兴方法,综合了若干SNPs的微弱效应,使基因多态对疾病的预测性大幅度提升。该方法在许多复杂疾病遗传学研究中得到成功应用。本文重点介绍了GRS的计算方法和评价标准,简要列举了运用GRS取得的系列成果,并对运用过程中所存在的局限性进行了探讨,最后对遗传风险评分的未来发展方向进行了展望。     

Kernel machine approach to testing the significance of multiple genetic markers for risk prediction

There is growing evidence that genomic and proteomic research holds great potential for changing irrevocably the practice of medicine. The ability to identify important genomic and biological markers for risk assessment can have a great impact in public health from disease prevention, to detection, to treatment selection. However, the potentially large number of markers and the complexity in the relationship between the markers and the outcome of interest impose a grand challenge in developing accurate risk prediction models. The standard approach to identifying important markers often assesses the marginal effects of individual markers on a phenotype of interest. When multiple markers relate to the phenotype simultaneously via a complex structure, such a type of marginal analysis may not be effective. To overcome such difficulties, we employ a kernel machine Cox regression framework and propose an efficient score test to assess the overall effect of a set of markers, such as genes within a pathway or a network, on survival outcomes. The proposed test has the advantage of capturing the potentially nonlinear effects without explicitly specifying a particular nonlinear functional form. To approximate the null distribution of the score statistic, we propose a simple resampling procedure that can be easily implemented in practice. Numerical studies suggest that the test performs well with respect to both empirical size and power even when the number of variables in a gene set is not small compared to the sample size.     

Network biology bridges the gaps between quantitative genetics and multi-omics to map complex diseases

With advances in high-throughput sequencing technologies, quantitative genetics approaches have provided insights into genetic basis of many complex diseases. Emerging in-depth multi-omics profiling technologies have created exciting opportunities for systematically investigating intricate interaction networks with different layers of biological molecules underlying disease etiology. Herein, we summarized two main categories of biological networks: evidence-based and statistically inferred. These different types of molecular networks complement each other at both bulk and single-cell levels. We also review three main strategies to incorporate quantitative genetics results with multi-omics data by network analysis: (a) network propagation, (b) functional module-based methods, (c) comparative/dynamic networks. These strategies not only aid in elucidating molecular mechanisms of complex diseases but can guide the search for therapeutic targets.     

Genome-wide multilocus analysis for immune-mediated complex diseases

In postgenomic era, searching and identification of disease genes associated with complex diseases are still one of the great challenge for dissecting human complex diseases. To improve the disease gene localization for complex diseases, a group of closely immune-mediated disease loci were overlapped on each chromosome based on previously reported genome-wide scanning data. Interestingly, five overlapping chromosomal regions (1q21, 2q33, 5q31.1-q33.1, 6p21, and 11q13) were identified by co-localizing disease loci for the following diseases: diabetes, asthma, atopic dermatitis, osteoporosis, and inflammatory bowel disease. The development of specific disease was associated with different combinations of disease loci among five overlapped chromosomal regions. Therefore, the analysis of multiple genetic loci should be considered to determine the effects of multiple genes responsible for complex diseases resulting from the influence of multiple genes.     

Accurate clinical genetic testing for autoinflammatory diseases using the next-generation sequencing platform MiSeq

Autoinflammatory diseases occupy one of a group of primary immunodeficiency diseases that are generally thought to be caused by mutation of genes responsible for innate immunity, rather than by acquired immunity. Mutations related to autoinflammatory diseases occur in 12 genes. For example, low-level somatic mosaic NLRP3 mutations underlie chronic infantile neurologic, cutaneous, articular syndrome (CINCA), also known as neonatal-onset multisystem inflammatory disease (NOMID). In current clinical practice, clinical genetic testing plays an important role in providing patients with quick, definite diagnoses. To increase the availability of such testing, low-cost high-throughput gene-analysis systems are required, ones that not only have the sensitivity to detect even low-level somatic mosaic mutations, but also can operate simply in a clinical setting. To this end, we developed a simple method that employs two-step tailed PCR and an NGS system, MiSeq platform, to detect mutations in all coding exons of the 12 genes responsible for autoinflammatory diseases. Using this amplicon sequencing system, we amplified a total of 234 amplicons derived from the 12 genes with multiplex PCR. This was done simultaneously and in one test tube. Each sample was distinguished by an index sequence of second PCR primers following PCR amplification. With our procedure and tips for reducing PCR amplification bias, we were able to analyze 12 genes from 25 clinical samples in one MiSeq run. Moreover, with the certified primers designed by our short program—which detects and avoids common SNPs in gene-specific PCR primers—we used this system for routine genetic testing. Our optimized procedure uses a simple protocol, which can easily be followed by virtually any office medical staff. Because of the small PCR amplification bias, we can analyze simultaneously several clinical DNA samples with low cost and can obtain sufficient read numbers to detect a low level of somatic mosaic mutations.     

Evolving modular genetic regulatory networks with a recursive,top-down approach

Being able to design genetic regulatory networks (GRNs) to achieve a desired cellular function is one of the main goals of synthetic biology. However, determining minimal GRNs that produce desired time-series behaviors is non-trivial. In this paper, we propose a ‘top-down’ approach to evolving small GRNs and then use these to recursively boot-strap the identification of larger, more complex, modular GRNs. We start with relatively dense GRNs and then use differential evolution (DE) to evolve interaction coefficients. When the target dynamical behavior is found embedded in a dense GRN, we narrow the focus of the search and begin aggressively pruning out excess interactions at the end of each generation. We first show that the method can quickly rediscover known small GRNs for a toggle switch and an oscillatory circuit. Next we include these GRNs as non-evolvable subnetworks in the subsequent evolution of more complex, modular GRNs. Successful solutions found in canonical DE where we truncated small interactions to zero, with or without an interaction penalty term, invariably contained many excess interactions. In contrast, by incorporating aggressive pruning and the penalty term, the DE was able to find minimal or nearly minimal GRNs in all test problems.     

SCtool:识别复杂性状和疾病间遗传关联的工具

识别复杂性状和疾病间遗传关联可以提供有用的病因学见解,并有助于确定可能的因果关系的优先级。尽管已有很多工具可以实现复杂性状和疾病间遗传关联,但是某些工具代码可读性差、并且不同工具基于不同的计算机语言、工具间的串联性较差。因此,本研究基于全基因组关联研究(GWAS)数据,提出了SCtool,一个开源、跨平台和用户友好的软件工具。SCtool整合了ldsc, TwosampleMR和MR-BMA三种软件,其主要功能是基于GWAS汇总水平的数据,识别复杂性状和疾病、复杂性状和复杂性状以及疾病与疾病间的遗传相关性并探究其间潜在的因果关联。最后,使用SCtool揭示了全身性铁状态(铁蛋白,血清铁,转铁蛋白,转铁蛋白饱和度)与表观遗传时钟GrimAge之间的遗传关联。