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Genomics, Proteomics & Bioinformatics 2014年第5期12卷 210-220页

作者： Lv Jin Xiao-Yu Zuo Wei-Yang Su Xiao-Lei Zhao Man-Qiong Yuan Li-Zhen Han Xiang Zhao Ye-Da Chen Shao-Qi Rao Institute for Medical Systems Biology and Department of Medical Statistics and Epidemiology School of Public HealthGuangdong Medical College Department of Medical Statistics and Epidemiology School of Public Health Sun Yat-Sen University Community Health Service Management Center of Panyu District Department of Statistical Sciences School of Mathematics and Computational Science Sun Yat-Sen University

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 comprehen- sive understanding of the molecular mechanisms underlying complex diseases. Extensive studies uti- lizing 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 path- way-based analysis software and databases is provided. Finally, future directions and challenges for the methodological development and applications of pathway-based analysis techniques are dis- cussed. This review will provide a useful guide to dissect complex diseases.

关键词： complex disease Pathway-based analysisAlgorithms Software and databases

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Identifying disease modules and components of viral infections based on multi-layer networks

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Science China(Information Sciences) 2016年第7期59卷 15-29页

作者： Yuanyuan LI Xiufen ZOU School of Mathematics and Statistics Wuhan University Computational Science Hubei Key Laboratory Wuhan University School of Science Wuhan Institute of Technology

With the emergence of multi-dimensional data, we can more comprehensively analyze the pathogenic mechanisms of complex diseases, and thereby improve the diagnosis, treatment and prevention of these *** study presents a novel multi-layer network-based strategy that integrates multi-dimensional data, and identifies disease-related modules and components of viral infections. We first propose a systematic method that constructs a virus-host interaction network with three layers: a viral protein layer, a host protein layer and a host gene layer. This method combines the data of high-throughput gene expression, viral protein interactions,virus-host interactions, protein-protein interactions and transcriptional regulatory relationships. To extract the underlying mechanisms of viral infections from the multi-layer networks, we identify the intra-layer and crosslayer modules, and investigate the conserved modules across multiple datasets. The essential components in the multi-layer networks are detected by singular-value decomposition. The identified conserved modules and essential components are combined into a functional enrichment analysis that reveals their contributions to influenza virus replication. By this analysis, we elucidate the common and specific mechanisms of the replication cycles of two influenza strains. By combining the different layers of information, we can comprehensively understand pathogenic mechanisms of complex diseases.

关键词： multi-dimensional data multi-layer network complex disease virus infection disease modules

Differential function analysis: identifying structure and activation variations in dysregulated pathways

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Science China(Information Sciences) 2017年第1期60卷 116-131页

作者： Chuanchao ZHANG Juan LIU Qianqian SHI Tao ZENG Luonan CHEN State Key Laboratory of Software Engineering School of Computer Wuhan University Key Laboratory of Systems Biology Innovation Center for Cell Signaling Network Institute of Biochemistry and Cell Biology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences

complex diseases are generally caused by the dysregulation of biological functions rather than individual molecules. Hence, a major challenge of the systematical study on complex diseases is how to capture the differentially regulated biological functions, e.g., pathways. The traditional differential expression analysis(DEA) usually considers the changed expression values of genes rather than functions. Meanwhile, the conventional function-based analysis(e.g., PEA: pathway enrichment analysis) mainly considers the varying activation of functions but disregards the structure change of genetic elements of functions. To achieve precision medicine against complex diseases, it is necessary to distinguish both the changes of functions and their elements from heterogeneous dysregulated pathways during the disease development and progression. In this work, in contrast to the traditional DEA, we developed a new computational framework, namely differential function analysis(DFA), to identify the changes of element-structure and expression-activation of biological functions, based on comparative non-negative matrix factorization(cN MF). To validate the effectiveness of our method, we tested DFA on various datasets, which shows that DFA is able to effectively recover the differential element-structure and differential activation-score of pre-set functional groups. In particular, the analysis of DFA on human gastric cancer dataset, not only capture the changed network-structure of pathways associated with gastric cancer, but also detect the differential activations of these pathways(i.e., significantly discriminating normal samples and disease samples), which is more effective than the state-of-the-art methods, such as GSVA and Pathifier. Totally,DFA is a general framework to capture the systematical changes of genes, networks and functions of complex diseases, which not only provides the new insight on the simultaneous alterations of pathway genes and pathway activations, but a

关键词： complex disease biological function non-negative matrix factorization network structure function activation

Future impact of integrated high-throughput methylome analyses on human health and disease

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Journal of Genetics and Genomics 2008年第7期35卷 391-401页

作者： Lee M Butcher Stephan Beck UCL Cancer Institute Paul O'Gorman Building University College London 72 Huntley Street London WC1E 6BT UK

A spate of high-powered genome-wide association studies （GWAS） have recently identified numerous single-nucleotide polymorphisms （SNPs） robustly linked with complex disease. Despite interrogating the majority of common human variation, these SNPs only account for a small proportion of the phenotypic variance, which suggests genetic factors are acting in concert with non-genetic factors. Although environmental measures are logical covariants for genotype-phenotype investigations, another non-genetic intermediary exists： epigenetics. Epigenetics is the analysis of somatically-acquired and, in some cases, transgenerationally inherited epigenetic modifications that regulate gene expression, and offers to bridge the gap between genetics and environment to understand phenotype. The most widely studied epigenetic mark is DNA methylation. Aberrant methylation at gene promoters is strongly implicated in disease etiology, most notably cancer. This review will highlight the importance of DNA methylation as an epigenetic regulator, outline techniques to characterize the DNA methylome and present the idea of reverse phenotyping, where multiple layers of analysis are integrated at the individual level to create personalized digital phenotypes and, at a phenotype level, to identify novel molecular signatures of disease.

关键词： DNA methylation reverse phenotyping complex disease

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Genetics in Keratoconus: where are we?

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Eye and Vision 2016年第1期3卷 121-130页

作者： Yelena Bykhovskaya Benjamin Margines Yaron S.Rabinowitz Regenerative Medicine Institute and Department of Surgery Cedars-Sinai Medical CenterLos AngelesUSA Cornea Genetic Eye Institute 50 N.La Cienega Blvd.Suite#340Beverly HillsCA 90211USA The Jules Stein Eye Institute University of California Los AngelesLos AngelesUSA

Keratoconus(KC)is a non-inflammatory thinning and protrusion of the cornea in which the cornea assumes a conical *** etiology of this condition at present remains an *** environmental factors have been involved in KC pathogenesis,strong underlining genetic susceptibility has been *** lack of consistent findings among early genetic studies suggested a heterogeneity and complex nature of the genetic contribution to the development of ***,genome-wide linkage studies(GWLS)and genome-wide association studies(GWAS)were ***-generation sequencing(NGS)-based genomic screens are also currently being carried *** of these recently developed comprehensive genetic tools led to a much greater success and increased reproducibility of genetic findings in *** of the LOX gene identified through GWLS has been confirmed in multiple cohorts of KC patients around the *** susceptibility region located at the 2q21.3 chromosomal region near the RAB3GAP1 gene identified through GWAS was independently *** variants in the ZNF469 gene(mutated in corneal dystrophy Brittle Cornea Syndrome)and in the TGFBI gene(mutated in multiple corneal epithelial–stromal TGFBI dystrophies)have been repeatedly identified in familial and sporadic KC patients of different *** comprehensive strategies using quantitative endophenotypes have been successfully employed to bring further understanding to the genetics of *** genetic determinants including the COL5A1 gene have been identified in the GWAS of KC-related trait central corneal *** recent discoveries confirmed the importance of the endophenotype approach for studying complex genetic diseases such as KC and showed that different connective tissue disorders may have the same genetic determinants.

关键词： Keratoconus Genetics complex disease Genetic variation Linkage Genetic association Sequencing Corneal dystrophy Genotyping

Copy number variation related disease genes

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Frontiers of Electrical and Electronic Engineering in China 2018年第2期6卷 99-112页

作者： Chaima Aouiche Xuequn Shang Bolin Chen School of Computer Science Northwestern Polytechnical University Xi'an 710072 China

Background： One of the most important and challenging issues in biomedicine and genomics is how to identify disease related genes. Datasets from high-throughput biotechnologies have been widely used to overcome this issue from various perspectives, e.g., epigenomics, genomics, transcriptomics, proteomics, metabolomics. At the genomic level, copy number variations （CNVs） have been recognized as critical genetic variations, which contribute significantly to genomic diversity. They have been associated with both common and complex diseases, and thus have a large influence on a variety of Mendelian and somatic genetic disorders. Results： In this review, based on a variety of complex diseases, we give an overview about the critical role of using CNVs for identifying disease related genes, and discuss on details the different high-throughput and sequencing methods applied for CNV detection. Some limitations and challenges concerning CNV are also highlighted. Conclusions： Reliable detection of CNVs will not only allow discriminating driver mutations for various diseases, but also helps to develop personalized medicine when integrating it with other genomic features.

关键词： CNV disease gene complex disease targeted approach genome-wide approach whole exome sequencing