用于GWAS结果后续研究的PBA方法简介

自提出全基因组关联研究(genome-wide association study,GWAS)设想以来,在人类复杂疾病和水稻农艺性状关联研究方面,GWAS已得到广泛运用。但作为一种典型的单标记研究方法,GWAS不能检测小效应的遗传变异,而稀有变异间的联合效应往往与表型密切相关,因此,需对GWAS结果进行深入的数据挖掘。基于通路的分析方法(pathway-based analysis,PBA)就是利用基因功能、生物代谢通路等相关信息建立的对GWAS结果进行二次挖掘的方法。该方法能从GWAS结果挖掘出与性状、疾病相关联的通路及具有相同功能的基因集等数据,从而获得更多的遗传信息。现对PBA的出现、计算方法和相关软件进行简要综述,以期为人们进行通路分析提供参考。     

利用偏相关系数算法挖掘遗传相互作用数据中的通路基因

在生物系统中,遗传相互作用指的是两个基因同时突变的表型异于它们分别突变表型叠加效果的现象.近年来,随着高通量技术的发展,遗传相互作用的高通量筛选得以实现,产生了大量遗传相互作用数据.通路基因指的是一组在同一条生物通路上相互协作的基因,它们共同完成某一项生命过程.发掘遗传相互作用数据中的通路基因可以研究基因之间如何进行相互作用共同影响某种表型,是理解生物学通路的结构和功能,生物系统进化规律,和研究复杂疾病的重要途径.然而,由于基因多效性以及高维数据处理困难等问题,如何发现遗传相互作用数据中的通路基因面临着很大挑战.本文中,我们通过计算基因间的条件独立性,即删除掉通路中的已知基因的影响后,研究基因间的相关性.而这些通路中的已知基因将在模型中作为种子基因发掘通路中的其他基因.我们将讨论该算法在模拟数据和实际数据中的计算效果,证明该算法在遗传相互作用数据应用中的有效性.     

服务于精准医学的罕见疾病多组学信息注释平台

生物医学技术的发展,以及近几年"精准医学"概念的提出,使得人们对疾病的探究越来越多地深入到分子层面.与此同时,医学诊疗也逐渐趋于个性化.作为发病率低、发病机制复杂、治疗难度极大的一类疾病,罕见疾病越来越受到人们的关注.研究发现,详细的临床表型是破译基因和实现罕见疾病的精准药物的基石.为了服务于精准医学的发展、辅助提高罕见疾病的诊疗水平,我们建立了中文的罕见疾病多组学信息标准化平台eRAM(Encyclopedia of Rare disease Annotation for Precision Medicine, www.unimd.cn/eram).该平台系统地整合了目前可获得的罕见疾病临床表现和分子机制的数据,揭示了许多疾病之间的新关联. eRAM为15942种罕见疾病提供了丰富的注释,包含6147种人类疾病相关表型、31661种哺乳动物表型、10202种来自UMLS的症状、18815种基因和92580种基因型. eRAM同时提供疾病注释体系、疾病网络、疾病预测、病例提交等功能,不仅可以提供有关罕见疾病机制的信息,还可以促进临床医生对罕见疾病做出准确的诊断和治疗决策.     

活血化瘀类中药抗肝纤维化作用的计算药理探讨

如何防治肝纤维化是目前备受关注的问题,活血化瘀类中药治疗肝纤维化有良好的效果。研究发现,虽然活血化瘀类中药有效组分结构相似性不高,但是理化性质比较相似。与实验阶段药物和中药数据库中全部活性成分相比,活血化瘀类中药有效组分的化学空间分布更接近已知药物,约90%的有效组分符合Lipinski五规则。基于靶点蛋白作用通路的研究发现,活血化瘀中药通过直接作用或间接影响纤维化相关蛋白,并且多靶点多通路协同作用于多条生物路径,直接抑制TNF或间接调控NF-kappaB是其中典型的两条途径。这些研究结果可为阐明活血化瘀类中药的作用机制提供更多信息,有助于进一步筛选抗肝纤维化作用药物。     

基于疾病本体的疾病相似性计算方法

疾病相似性研究对于复杂疾病发病机制的理解、诊断、预测和药物研发具有重要意义.最近,研究人员通过集成多种疾病术语库,构建了描述疾病关系的疾病本体(disease ontology,DO),这为从DO角度研究疾病相似性打下了基础.本文综述了基于DO及其注释信息的疾病相似性计算方法,探讨了疾病相似性计算存在的问题和挑战,为疾病相似性进一步的研究提供有益参考.     

NELDA：基于网络嵌入的lncRNA-疾病关联关系预测

目的 长非编码RNA（lncRNAs）参与多种重要的生物学过程并与各种人类疾病密切相关,因此,lncRNA-疾病关联预测研究有助于疾病的诊断、治疗和在分子水平理解人类疾病的发生发展机制。目前,大多数lncRNA-疾病关联预测方法倾向于浅层整合lncRNA和疾病的相关信息,忽略网络拓扑结构中的深层嵌入特征;另外通过随机选取lncRNA-疾病非关联对构建负样本训练集合,影响预测方法的鲁棒性。方法 本文提出一种基于网络嵌入的NELDA方法,预测潜在的lncRNA-疾病关联关系。NELDA首先利用lncRNA 表达谱、疾病本体论和已知的lncRNA-疾病关联关系,构建lncRNA相似性网络、疾病相似性网络和lncRNA-疾病关联网络。然后,通过设计4个深度自编码器分别从lncRNA/疾病的相似性网络、lncRNA-疾病关联网络学习lncRNA和疾病的低维网络嵌入特征。串联lncRNA和疾病的相似性网络嵌入特征及lncRNA和疾病的关联网络嵌入特征,分别输入两个支持向量机分类器预测lncRNA-疾病关联。最后,采用加权融合策略融合两个支持向量机分类器的预测结果,给出lncRNA-疾病关联关系的最终预测结果。另外,根据已知的lncRNA-疾病关联对和疾病语义相似性,设计一种负样本选取策略构建可信度相对较高的lncRNA-疾病非关联对样本集,用以改善分类器的鲁棒性,该策略通过设计一种打分函数为每对lncRNA-疾病进行打分,选取得分较低的lncRNA-疾病对作为lncRNA-疾病非关联对样本（即负样本）。结果 十折交叉验证实验结果表明：NELDA能够有效预测lncRNA-疾病关联关系,其AUC达到0.982 7,比现有LDASR和 LDNFSGB方法分别提高了0.062 7和0.020 7。另外,负样本选取策略与决策级加权融合策略能够有效改善NELDA预测性能。胃癌和乳腺癌案例研究中,29/40（72.5%）预测的与胃癌和乳腺癌关联lncRNAs,在近期文献和公共数据库中能够发现相关的支撑证据。结论 这些实验结果表明,NELDA是一种有效的lncRNA-疾病关联关系预测方法,具有挖掘潜在lncRNA-疾病关联关系的能力。     

结核分枝杆菌耐药相关单碱基突变的计算方法比较

[目的] 耐药结核分枝杆菌（drug-resistant Mycobacterium tuberculosis）的产生给结核病（tuberculosis）的治疗带来巨大困难。[方法] 使用基于全基因组测序的关联分析探究耐药强相关的单核苷酸多态性（single nucleotide polymorphism,SNP）突变,主要有GEMMA、phyc、plink。为了阐明其中最优的耐药相关SNP计算方法,本研究下载NCBI上已有的1504株结核分枝杆菌数据,并获取它们对于3种常见的一线抗结核治疗药物（isoniazid、rifampicin、ethambutol）的耐药性检验结果。并使用这3种耐药相关SNP计算方法计算与结核分枝杆菌耐药相关的SNP;并评估计算得到的耐药相关SNP在预测耐药表型的敏感性和特异性。[结果] 发现通过phyc可以预测到最多的已知耐药相关SNP和最少的耐药无关SNP,而且phyc预测的耐药相关SNP的敏感性和特异性恒定大于52.49%。[结论] phyc在预测结核分枝杆菌耐药相关SNP中结果最准确,但考虑到运行时间和表型数据的更新,GEMMA和plink的结果也应作为参考。     

构建适用于蛋白质家族分类的相似性网络

图聚类用于蛋白质分类问题可以获得较好结果,其前提是将蛋白质之间复杂的相互关系转化为适当的相似性网络作为图聚类分类的输入数据。本文提出一种基于BLAST检索的相似性网络构建方法,从目标蛋白质序列出发,通过若干轮次的BLAST检索逐步从数据库中提取与目标蛋白质直接或间接相关的序列,构成关联集。关联集中序列之间的相似性关系即相似性网络,可作为图聚类算法的分类依据。对Pfam数据库中依直接相似关系难以正确分类的蛋白质的计算表明,按本文方法构建的相似性网络取得了比较满意的结果。     

《自然-医学》:一种通路会诱发阿尔茨海默氏症和朊病毒病

<正>《自然-医学》上的一项研究发现了一种对阿尔茨海默氏症(又称老年痴呆症)和朊病毒疾病发展有促进作用的共同通路。抑制该通路可减缓小鼠体内这两类疾病的发病过程,这意味着开发针对该通路的单一药物可能会对治疗这两类疾病有所帮助。在这两类疾病中,淀粉样前体蛋白和朊蛋白会在细胞表面分别发生分裂。一种名为α-分泌酶分裂的保护性通路会让这些蛋白阻碍在发病     

基于基因表达变异性的通路富集方法研究

当前的通路富集方法主要是基于基因的表达差异,很少有方法从通路变异性(方差)角度对其富集分析．我们注意到用合适的统计量描述通路的变异性时,在疾病表型下一些通路的变异性有明显的上升或者下降．因此本研究假设：通路变异性程度在不同表型中存在差异．本文设计了14种描述通路变异性的统计量与检验方法,检测不同表型下变异性有差异的通路即富集通路,并将富集结果与文献检索结果进行比较,同时,分析不同芯片预处理方法对数据和结果的影响．研究结果表明：5种预处理方法中,多阵列对数健壮算法(RMA)是数据预处理的最优方法;不同表型下通路的变异性程度存在差异;根据文献检索的通路结果,14种基于变异性的通路富集方法中,以通路中各基因欧氏距离的方差做统计量进行permutation检验(方法11)能有效识别显著通路,其富集结果优于基因集富集分析(GSEA)．综上所述,基于通路变异性的通路富集策略具有可行性,不仅对通路富集分析有一定的理论指导意义,而且为人类疾病研究提供新的视角．     

Co-clustering phenome�Cgenome for phenotype classification and disease gene discovery

Understanding the categorization of human diseases is critical for reliably identifying disease causal genes. Recently, genome-wide studies of abnormal chromosomal locations related to diseases have mapped >2000 phenotype–gene relations, which provide valuable information for classifying diseases and identifying candidate genes as drug targets. In this article, a regularized non-negative matrix tri-factorization (R-NMTF) algorithm is introduced to co-cluster phenotypes and genes, and simultaneously detect associations between the detected phenotype clusters and gene clusters. The R-NMTF algorithm factorizes the phenotype–gene association matrix under the prior knowledge from phenotype similarity network and protein–protein interaction network, supervised by the label information from known disease classes and biological pathways. In the experiments on disease phenotype–gene associations in OMIM and KEGG disease pathways, R-NMTF significantly improved the classification of disease phenotypes and disease pathway genes compared with support vector machines and Label Propagation in cross-validation on the annotated phenotypes and genes. The newly predicted phenotypes in each disease class are highly consistent with human phenotype ontology annotations. The roles of the new member genes in the disease pathways are examined and validated in the protein–protein interaction subnetworks. Extensive literature review also confirmed many new members of the disease classes and pathways as well as the predicted associations between disease phenotype classes and pathways.     

基于全局角度网络策略的复杂疾病风险通路识别

复杂疾病的发生发展与机体内生物学通路的功能紊乱有密切联系,从高通量数据出发,利用计算机辅助方法来研究疾病与通路间的关系具有重要意义.本文提出了一个新的基于网络的全局性通路识别方法.该方法利用蛋白质互作信息和通路的基因集组成信息构建复杂的蛋白质-通路网.然后,基于表达谱数据,通过随机游走算法从全局层面优化疾病风险通路.最终,通过扰动方式识别统计学显著的风险通路.将该网络运用于结肠直肠癌风险通路识别,识别出15个与结肠直肠癌发生与发展过程显著相关的通路.通过与其他通路识别方法(超几何检验,SPIA)相比较,该方法能够更有效识别出疾病相关的风险通路.     

Constructing a gene semantic similarity network for the inference of disease genes

MOTIVATION: The inference of genes that are truly associated with inherited human diseases from a set of candidates resulting from genetic linkage studies has been one of the most challenging tasks in human genetics. Although several computational approaches have been proposed to prioritize candidate genes relying on protein-protein interaction (PPI) networks, these methods can usually cover less than half of known human genes. RESULTS: We propose to rely on the biological process domain of the gene ontology to construct a gene semantic similarity network and then use the network to infer disease genes. We show that the constructed network covers about 50% more genes than a typical PPI network. By analyzing the gene semantic similarity network with the PPI network, we show that gene pairs tend to have higher semantic similarity scores if the corresponding proteins are closer to each other in the PPI network. By analyzing the gene semantic similarity network with a phenotype similarity network, we show that semantic similarity scores of genes associated with similar diseases are significantly different from those of genes selected at random, and that genes with higher semantic similarity scores tend to be associated with diseases with higher phenotype similarity scores. We further use the gene semantic similarity network with a random walk with restart model to infer disease genes. Through a series of large-scale leave-one-out cross-validation experiments, we show that the gene semantic similarity network can achieve not only higher coverage but also higher accuracy than the PPI network in the inference of disease genes.     

Disease pathways at the Rat Genome Database Pathway Portal: genes in context-a network approach to understanding the molecular mechanisms of disease

Background

Biological systems are exquisitely poised to respond and adjust to challenges, including damage. However, sustained damage can overcome the ability of the system to adjust and result in a disease phenotype, its underpinnings many times elusive. Unraveling the molecular mechanisms of systems biology, of how and why it falters, is essential for delineating the details of the path(s) leading to the diseased state and for designing strategies to revert its progression. An important aspect of this process is not only to define the function of a gene but to identify the context within which gene functions act. It is within the network, or pathway context, that the function of a gene fulfills its ultimate biological role. Resolving the extent to which defective function(s) affect the proceedings of pathway(s) and how altered pathways merge into overpowering the system's defense machinery are key to understanding the molecular aspects of disease and envisioning ways to counteract it. A network-centric approach to diseases is increasingly being considered in current research. It also underlies the deployment of disease pathways at the Rat Genome Database Pathway Portal. The portal is presented with an emphasis on disease and altered pathways, associated drug pathways, pathway suites, and suite networks.

Results

The Pathway Portal at the Rat Genome Database (RGD) provides an ever-increasing collection of interactive pathway diagrams and associated annotations for metabolic, signaling, regulatory, and drug pathways, including disease and altered pathways. A disease pathway is viewed from the perspective of networks whose alterations are manifested in the affected phenotype. The Pathway Ontology (PW), built and maintained at RGD, facilitates the annotations of genes, the deployment of pathway diagrams, and provides an overall navigational tool. Pathways that revolve around a common concept and are globally connected are presented within pathway suites; a suite network combines two or more pathway suites.

Conclusions

The Pathway Portal is a rich resource that offers a range of pathway data and visualization, including disease pathways and related pathway suites. Viewing a disease pathway from the perspective of underlying altered pathways is an aid for dissecting the molecular mechanisms of disease.

     

Genome-Wide Pathway Association Studies of Multiple Correlated Quantitative Phenotypes Using Principle Component Analyses

Genome-wide pathway association studies provide novel insight into the biological mechanism underlying complex diseases. Current pathway association studies primarily focus on single important disease phenotype, which is sometimes insufficient to characterize the clinical manifestations of complex diseases. We present a multi-phenotypes pathway association study(MPPAS) approach using principle component analysis(PCA). In our approach, PCA is first applied to multiple correlated quantitative phenotypes for extracting a set of orthogonal phenotypic components. The extracted phenotypic components are then used for pathway association analysis instead of original quantitative phenotypes. Four statistics were proposed for PCA-based MPPAS in this study. Simulations using the real data from the HapMap project were conducted to evaluate the power and type I error rates of PCA-based MPPAS under various scenarios considering sample sizes, additive and interactive genetic effects. A real genome-wide association study data set of bone mineral density (BMD) at hip and spine were also analyzed by PCA-based MPPAS. Simulation studies illustrated the performance of PCA-based MPPAS for identifying the causal pathways underlying complex diseases. Genome-wide MPPAS of BMD detected associations between BMD and KENNY_CTNNB1_TARGETS_UP as well as LONGEVITYPATHWAY pathways in this study. We aim to provide a applicable MPPAS approach, which may help to gain deep understanding the potential biological mechanism of association results for complex diseases.     

Alternative pathways of galactose assimilation: could inverse metabolic engineering provide an alternative to galactosemic patients?

The galactose assimilation pathway has been extensively studied as an example of a genetic regulatory switch. Besides the importance of this pathway as a tool in basic biological research, unraveling its structure and regulation is also of major medical importance. Impairment of galactose assimilation is the cause of the genetic metabolic disease known as "galactosemia", while the in vivo activity of the pathway affects the production of glycans. The latter have been connected to tumor metastasis, anti-cancer drug resistance and various cardiovascular diseases. Despite the vast amount of studies, however, galactose assimilation and its interaction with other parts of the metabolic network have not been fully elucidated yet. In yeast and higher eukaryotes, it is still being studied as comprising only the linear Leloir pathway. Recent observations, however, indicate that alternative pathways of galactose assimilation identified in prokaryotes and fungi might also be present in yeast. Such a result is valuable per se, because it could lead to the discovery of these pathways in humans. Even more importantly, these pathways provide alternative phenotypes with known genetic fingerprints that can be used in the context of classical and inverse metabolic engineering to examine and treat the mechanisms of defects of galactose assimilation.     

PathAligner

MOTIVATION: Analysis of metabolic pathways is a central topic in understanding the relationship between genotype and phenotype. The rapid accumulation of biological data provides the possibility of studying metabolic pathways at both the genomic and the metabolic levels. Retrieving metabolic pathways from current biological data sources, reconstructing metabolic pathways from rudimentary pathway components, and aligning metabolic pathways with each other are major tasks. Our motivation was to develop a conceptual framework and computational system that allows the retrieval of metabolic pathway information and the processing of alignments to reveal the similarities between metabolic pathways. RESULTS: PathAligner extracts metabolic information from biological databases via the Internet and builds metabolic pathways with data sources of genes, sequences, enzymes, metabolites etc. It provides an easy-to-use interface to retrieve, display and manipulate metabolic information. PathAligner also provides an alignment method to compare the similarity between metabolic pathways. AVAILABILITY: PathAligner is available at http://bibiserv.techfak.uni-bielefeld.de/pathaligner.     

Relating Diseases by Integrating Gene Associations and Information Flow through Protein Interaction Network

Identifying similar diseases could potentially provide deeper understanding of their underlying causes, and may even hint at possible treatments. For this purpose, it is necessary to have a similarity measure that reflects the underpinning molecular interactions and biological pathways. We have thus devised a network-based measure that can partially fulfill this goal. Our method assigns weights to all proteins (and consequently their encoding genes) by using information flow from a disease to the protein interaction network and back. Similarity between two diseases is then defined as the cosine of the angle between their corresponding weight vectors. The proposed method also provides a way to suggest disease-pathway associations by using the weights assigned to the genes to perform enrichment analysis for each disease. By calculating pairwise similarities between 2534 diseases, we show that our disease similarity measure is strongly correlated with the probability of finding the diseases in the same disease family and, more importantly, sharing biological pathways. We have also compared our results to those of MimMiner, a text-mining method that assigns pairwise similarity scores to diseases. We find the results of the two methods to be complementary. It is also shown that clustering diseases based on their similarities and performing enrichment analysis for the cluster centers significantly increases the term association rate, suggesting that the cluster centers are better representatives for biological pathways than the diseases themselves. This lends support to the view that our similarity measure is a good indicator of relatedness of biological processes involved in causing the diseases. Although not needed for understanding this paper, the raw results are available for download for further study at ftp://ftp.ncbi.nlm.nih.gov/pub/qmbpmn/DiseaseRelations/.     

Genetic diseases of the Kennedy pathways for membrane synthesis

The two branches of the Kennedy pathways (CDP-choline and CDP-ethanolamine) are the predominant pathways responsible for the synthesis of the most abundant phospholipids, phosphatidylcholine and phosphatidylethanolamine, respectively, in mammalian membranes. Recently, hereditary diseases associated with single gene mutations in the Kennedy pathways have been identified. Interestingly, genetic diseases within the same pathway vary greatly, ranging from muscular dystrophy to spastic paraplegia to a childhood blinding disorder to bone deformations. Indeed, different point mutations in the same gene (PCYT1; CCTα) result in at least three distinct diseases. In this review, we will summarize and review the genetic diseases associated with mutations in genes of the Kennedy pathway for phospholipid synthesis. These single-gene disorders provide insight, indeed direct genotype-phenotype relationships, into the biological functions of specific enzymes of the Kennedy pathway. We discuss potential mechanisms of how mutations within the same pathway can cause disparate disease.