Coronary artery disease(CAD) is a complex human disease, involving multiple genes and their nonlinear interactions, which often act in a modular fashion. Genome-wide single nucleotide polymorphism(SNP) profiling provides an effective technique to unravel these underlying genetic interplays or their functional involvements for CAD. This study aimed to identify the susceptible pathways and modules for CAD based on SNP omics. First, the Wellcome Trust Case Control Consortium(WTCCC) SNP datasets of CAD and control samples were used to assess the jointeffect of multiple genetic variants at the pathway level, using logistic kernel machine regression model. Then, an expanded genetic network was constructed by integrating statistical gene–gene interactions involved in these susceptible pathways with their protein–protein interaction(PPI)knowledge. Finally, risk functional modules were identified by decomposition of the network. Of 276 KEGG pathways analyzed, 6 pathways were found to have a significant effect on CAD. Other than glycerolipid metabolism, glycosaminoglycan biosynthesis, and cardiac muscle contraction pathways, three pathways related to other diseases were also revealed, including Alzheimer's disease, non-alcoholic fatty liver disease, and Huntington's disease. A genetic epistatic network of 95 genes was further constructed using the abovementioned integrative approach. Of 10 functional modules derived from the network, 6 have been annotated to phospholipase C activity and cell adhesion molecule binding, which also have known functional involvement in Alzheimer's disease.These findings indicate an overlap of the underlying molecular mechanisms between CAD and Alzheimer's disease, thus providing new insights into the molecular basis for CAD and its molecular relationships with other diseases.
【目的】提出基于知识融合策略构建基因网络方法 ,并应用于双相障碍相关的致病基因网络分析。【方法】将Wellcome Trust Case Control Consortium(WTCCC)提供的双相障碍全基因组单核苷酸多态(SNP)数据与人类蛋白质-蛋白质互作数据库对应的基因做交集。通过单体型全模型logistic回归模型检验获得经多重检验校正统计学显著的基因互作对子,并由此构建致病基因网络以及挖掘连通度显著高于理论分布的核心致病基因。【结果】采用知识融合的方法,将数据维度从482 248个SNP位点降至98 157。经统计模型检验获得3 841个互作基因用于构建双相障碍致病基因网络,并挖掘出115个核心致病基因。其中,在连通度高于30的29个核心基因中,有12个重复了以前的报道(PRKCA,EGFR,ESR1,ATXN1,FYN,CREBBP,TP53,AKT1,CSNK2A1,DLG1,PTN和LYN),另外17个未被报道过的基因从其生物功能以及致病分子机制上看,可能是新的双相障碍易感基因(SMAD3,SRC,GRB2,PIK3R1,ZBTB16,ABL1,APP,EP300,TGFBR1,SYK,YWHAZ,INSR,MAPK1,PRKCB,PRKCD,SMAD2和SVIL)。【结论】本文提出的基于蛋白质-蛋白质互作知识引导的基因网络构建方法是一种可靠的系统性分析方法,有助于全面地了解复杂疾病的分子网络机制和确立核心风险基因。
＜正＞[Objective]To investigate whether single nucleotide polymorphisms（SNPs） in the Mn-superoxide dismutase gene...
LI Xu-dong,LIU Yi-min,GUO Xiao,LIU Bin,LIN Ai-hua,DING Yuan-lin,RAO Shao-qi 1.Guangdong Prevention and Treatment Center for Occupational Diseases,Guangzhou,China
Many cancers apparently showing similar phenotypes are actually distinct at the molecular level,leading to very different responses to the same treatment.It has been recently demonstrated that pathway-based approaches are robust and reliable for genetic analysis of cancers.Nevertheless,it remains unclear whether such function-based approaches are useful in deciphering molecular heterogeneities in cancers.Therefore,we aimed to test this possibility in the present study.First,we used a NCI60 dataset to validate the ability of pathways to correctly partition samples.Next,we applied the proposed method to identify the hidden subtypes in diffuse large B-cell lymphoma(DLBCL).Finally,the clinical significance of the identified subtypes was verified using survival analysis.For the NCI60 dataset,we achieved highly accurate partitions that best fit the clinical cancer phenotypes.Subsequently,for a DLBCL dataset,we identified three hidden subtypes that showed very different 10-year overall survival rates(90%,46% and 20%) and were highly significantly(P = 0.008) correlated with the clinical survival rate.This study demonstrated that the pathwaybased approach is promising for unveiling genetic heterogeneities in complex human diseases.
Genetic studies are traditionally based on single-gene analysis. The use of these analyses can pose tremendous challenges for elucidating complicated genetic interplays involved in complex human diseases. Modern pathway-based analysis provides a technique, which allows a comprehensive understanding of the molecular mechanisms underlying complex diseases. Extensive studies utilizing the methods and applications for pathway-based analysis have significantly advanced our capacity to explore large-scale omics data, which has rapidly accumulated in biomedical fields. This article is a comprehensive review of the pathway-based analysis methods––the powerful methods with the potential to uncover the biological depths of the complex diseases. The general concepts and procedures for the pathway-based analysis methods are introduced and then, a comprehensive review of the major approaches for this analysis is presented. In addition, a list of available pathway-based analysis software and databases is provided. Finally, future directions and challenges for the methodological development and applications of pathway-based analysis techniques are discussed. This review will provide a useful guide to dissect complex diseases.
不宁腿综合征(Restless legs syndrome,RLS)遗传学研究近年来获得了许多重要的进展,极大地丰富了对于这种疾病分子机制的认识。RLS是一种常见的复杂疾病,几个遗传流行病学和双生子研究对RLS遗传组分进行了剖析,说明RLS是一个遗传性很强的性状,其遗传力约为50%。采用基于模型的连锁分析方法或者是不依赖于模型的连锁分析方法目前已定位了5个重要的RLS疾病连锁位点:12q13-23,14q13-21,9p24-22,2q33和20p13,为定位克隆RLS致病基因或者易感基因提供了连锁图谱。最新基于高通量的SNPs分型平台开展的全基因组分析确立3个与RLS显著关联的区域:6p21.2,2p14和15q23。文章结合作者近年来从事不宁腿综合征遗传学的研究工作,对该领域的重要成果进行了汇总和评述。
