Gene modules associated with human diseases revealed by network analysis

ABSTRACTDespite many genes associated with human diseases have been identified, disease mechanisms often remain elusive due to the lack of understanding how disease genes are connected functionally at pathways level. Within biological networks, disease genes likely map to modules whose identification facilitates etiology studies but remains challenging. We describe a systematic approach to identify disease-associated gene modules. A gene co-expression network based on the graphical Gaussian model (GGM) was constructed using the GTEx dataset and assembled into 652 gene modules. Screening these modules identified those with disease genes enrichment for obesity, cardiomyopathy, hypertension, and autism, which illuminated the molecular pathways underlying their pathogenesis. Using mammalian phenotypes derived from mouse models, potential disease candidate genes were identified from these modules. Also analyzed were epilepsy, schizophrenia, bipolar disorder, and depressive disorder, revealing shared and distinct disease modules among brain disorders. Thus, disease genes converge on modules within our GGM gene co-expression network, which provides a general framework to dissect genetic architecture of human diseases.

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Tumour-specific Causal Inference Discovers Distinct Disease Mechanisms Underlying Cancer Subtypes

Scientific Reports ◽

10.1038/s41598-019-48318-7 ◽

2019 ◽

Vol 9 (1) ◽

Author(s):

Yifan Xue ◽

Gregory Cooper ◽

Chunhui Cai ◽

Songjian Lu ◽

Baoli Hu ◽

...

Keyword(s):

Causal Inference ◽

The Cancer Genome Atlas ◽

Common Disease ◽

Precision Oncology ◽

Breast Cancers ◽

Cancer Subtypes ◽

Disease Mechanisms ◽

Distinct Disease ◽

Somatic Genome ◽

Survival Patterns

Abstract Cancer is a disease mainly caused by somatic genome alterations (SGAs) that perturb cellular signalling systems. Furthermore, the combination of pathway aberrations in a tumour defines its disease mechanism, and distinct disease mechanisms underlie the inter-tumour heterogeneity in terms of disease progression and responses to therapies. Discovering common disease mechanisms shared by tumours would provide guidance for precision oncology but remains a challenge. Here, we present a novel computational framework for revealing distinct combinations of aberrant signalling pathways in tumours. Specifically, we applied the tumour-specific causal inference algorithm (TCI) to identify causal relationships between SGAs and differentially expressed genes (DEGs) within tumours from the Cancer Genome Atlas (TCGA) study. Based on these causal inferences, we adopted a network-based method to identify modules of DEGs, such that the member DEGs within a module tend to be co-regulated by a common pathway. Using the expression status of genes in a module as a surrogate measure of the activation status of the corresponding pathways, we divided breast cancers (BRCAs) into five subgroups and glioblastoma multiformes (GBMs) into six subgroups with distinct combinations of pathway aberrations. The patient groups exhibited significantly different survival patterns, indicating that our approach can identify clinically relevant disease subtypes.

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Analysis of Topological Parameters of Complex Disease Genes Reveals the Importance of Location in a Biomolecular Network

Genes ◽

10.3390/genes10020143 ◽

2019 ◽

Vol 10 (2) ◽

pp. 143 ◽

Cited By ~ 8

Author(s):

Xiaohui Zhao ◽

Zhi-Ping Liu

Keyword(s):

Complex Disease ◽

Significant Degree ◽

Clustering Coefficient ◽

Gene Location ◽

Disease Genes ◽

Complex Disorders ◽

Topological Parameters ◽

Topological Features ◽

Disease Mechanisms ◽

Biomolecular Network

Network biology and medicine provide unprecedented opportunities and challenges for deciphering disease mechanisms from integrative viewpoints. The disease genes and their products perform their dysfunctions via physical and biochemical interactions in the form of a molecular network. The topological parameters of these disease genes in the interactome are of prominent interest to the understanding of their functionality from a systematic perspective. In this work, we provide a systems biology analysis of the topological features of complex disease genes in an integrated biomolecular network. Firstly, we identify the characteristics of four network parameters in the ten most frequently studied disease genes and identify several specific patterns of their topologies. Then, we confirm our findings in the other disease genes of three complex disorders (i.e., Alzheimer’s disease, diabetes mellitus, and hepatocellular carcinoma). The results reveal that the disease genes tend to have a higher betweenness centrality, a smaller average shortest path length, and a smaller clustering coefficient when compared to normal genes, whereas they have no significant degree prominence. The features highlight the importance of gene location in the integrated functional linkages.

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COMPARATIVE ANALYSIS OF DISEASE-ASSOCIATED MUTATIONS IN THE CODING REGIONS OF ALTERNATIVELY AND CONSTITUTIVELY SPLICED HUMAN GENES

Journal of Biological System ◽

10.1142/s0218339008002563 ◽

2008 ◽

Vol 16 (02) ◽

pp. 241-253

Author(s):

QIANLI HUANG ◽

YONG LI ◽

JESSE LI-LING ◽

HUIFANG HUANG ◽

XUEPING CHEN ◽

...

Keyword(s):

Alternative Splicing ◽

Codon Usage ◽

Codon Usage Bias ◽

Human Disease ◽

Molecular Mechanisms ◽

Human Diseases ◽

Disease Genes ◽

Single Mutation ◽

Coding Regions ◽

Human Disease Genes

To better understand the evolutionary and molecular mechanisms of alternative splicing causing human diseases, we have systematically compared the pattern, the distribution and the density of disease-associated mutations as well as the influence of codon usage bias on the single mutation between alternatively and constitutively spliced genes through analysis of the large datasets from human disease genes. The results indicated that: 1. The most common pattern of single mutation in alternatively and constitutively spliced genes are, respectively, C/T (25.17%), (22.81%) and G/A (21.54%), (22.73%), suggesting that the two types of disease genes are prone to C → T and G → A mutations. 2. There is an overall preponderance for transitions over transversions in alternatively (62.88% versus 37.12%) and constitutively (64.41% versus 35.59%) spliced disease genes. 3. For the second base of codons, there exist significant differences in transitions and transversions between the two types of genes. 4. Our data indicated that the single mutation tends to occur preferentially when the upstream neighboring-nucleotide is C or G in human disease genes. 5. Codon usage bias and synonymous codon usage have great influence on the single mutation in both alternatively and constitutively spliced genes. The GC content and gene length also have very evident influence on such mutations. Our results seem to imply that disease-associated mutations within the coding regions of alternatively spliced human disease genes have different mechanisms from constitutively spliced genes. Such findings may facilitate understanding the molecular mechanism of alternative splicing causing human diseases, and the development of gene therapies for such diseases.

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Distinct disease mechanisms in peripheral neuropathies due to altered peripheral myelin protein 22 gene dosage or a Pmp22 point mutation

Neurobiology of Disease ◽

10.1016/j.nbd.2004.10.023 ◽

2005 ◽

Vol 18 (3) ◽

pp. 656-668 ◽

Cited By ~ 42

Author(s):

Guya Giambonini-Brugnoli ◽

Johanna Buchstaller ◽

Lukas Sommer ◽

Ueli Suter ◽

Ned Mantei

Keyword(s):

Point Mutation ◽

Gene Dosage ◽

Myelin Protein ◽

Peripheral Neuropathies ◽

Peripheral Myelin Protein 22 ◽

Peripheral Myelin Protein ◽

Disease Mechanisms ◽

Distinct Disease

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CoCoCoNet: Conserved and Comparative Co-expression Across a Diverse Set of Species

10.1101/2020.04.21.053900 ◽

2020 ◽

Author(s):

John Lee ◽

Manthan Shah ◽

Sara Ballouz ◽

Megan Crow ◽

Jesse Gillis

Keyword(s):

Alternative Model ◽

Model Systems ◽

Model Organisms ◽

Disease Genes ◽

Gene Module ◽

Link Type ◽

Network Properties ◽

Gene Modules ◽

Human Disease Genes ◽

Insight Into

ABSTRACTCo-expression analysis has provided insight into gene function in organisms from Arabidopsis to Zebrafish. Comparison across species has the potential to enrich these results, for example by prioritizing among candidate human disease genes based on their network properties, or by finding alternative model systems where their co-expression is conserved. Here, we present CoCoCoNet as a tool for identifying conserved gene modules and comparing co-expression networks. CoCoCoNet is a resource for both data and methods, providing gold-standard networks and sophisticated tools for on-the-fly comparative analyses across 14 species. We show how CoCoCoNet can be used in two use cases. In the first, we demonstrate deep conservation of a nucleolus gene module across very divergent organisms, and in the second, we show how the heterogeneity of autism mechanisms in humans can be broken down by functional groups, and translated to model organisms. CoCoCoNet is free to use and available to all at https://milton.cshl.edu/CoCoCoNet, with data and R scripts available at ftp://milton.cshl.edu/data.

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Rare Genetic Variation Underlying Human Diseases and Traits: Results from 200,000 Individuals in the UK Biobank

10.1101/2020.11.29.402495 ◽

2020 ◽

Author(s):

Sean J. Jurgens ◽

Seung Hoan Choi ◽

Valerie N. Morrill ◽

Mark Chaffin ◽

James P. Pirruccello ◽

...

Keyword(s):

Cardiovascular Disease ◽

Genetic Variation ◽

Exome Sequencing ◽

Human Diseases ◽

Disease Genes ◽

Uk Biobank ◽

Novel Genes ◽

Pathogenic Variants ◽

Rare Genetic Variation ◽

The Uk

AbstractBackgroundMany human diseases are known to have a genetic contribution. While genome-wide studies have identified many disease-associated loci, it remains challenging to elucidate causal genes. In contrast, exome sequencing provides an opportunity to identify new disease genes and large-effect variants of clinical relevance. We therefore sought to determine the contribution of rare genetic variation in a curated set of human diseases and traits using a unique resource of 200,000 individuals with exome sequencing data from the UK Biobank.Methods and ResultsWe included 199,832 participants with a mean age of 68 at follow-up. Exome-wide gene-based tests were performed for 64 diseases and 23 quantitative traits using a mixed-effects model, testing rare loss-of-function and damaging missense variants. We identified 51 known and 23 novel associations with 26 diseases and traits at a false-discovery-rate of 1%. There was a striking risk associated with many Mendelian disease genes including: MYPBC3 with over a 100-fold increased odds of hypertrophic cardiomyopathy, PKD1 with a greater than 25-fold increased odds of chronic kidney disease, and BRCA2, BRCA1, ATM and PALB2 with 3 to 10-fold increased odds of breast cancer. Notable novel findings included an association between GIGYF1 and type 2 diabetes (OR 5.6, P=5.35×10−8), elevated blood glucose, and lower insulin-like-growth-factor-1 levels. Rare variants in CCAR2 were also associated with diabetes risk (OR 13, P=8.5×10−8), while COL9A3 was associated with cataract (OR 3.4, P=6.7×10−8). Notable associations for blood lipids and hypercholesterolemia included NR1H3, RRBP1, GIGYF1, SCGN, APH1A, PDE3B and ANGPTL8. A number of novel genes were associated with height, including DTL, PIEZO1, SCUBE3, PAPPA and ADAMTS6, while BSN was associated with body-mass-index. We further assessed putatively pathogenic variants in known Mendelian cardiovascular disease genes and found that between 1.3 and 2.3% of the population carried likely pathogenic variants in known cardiomyopathy, arrhythmia or hypercholesterolemia genes.ConclusionsLarge-scale population sequencing identifies known and novel genes harboring high-impact variation for human traits and diseases. A number of novel findings, including GIGYF1,represent interesting potential therapeutic targets. Exome sequencing at scale can identify a meaningful proportion of the population that carries a pathogenic variant underlying cardiovascular disease.

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Identifying digenic disease genes using machine learning in the undiagnosed diseases network

10.1101/2020.05.31.125716 ◽

2020 ◽

Author(s):

Souhrid Mukherjee ◽

Joy D Cogan ◽

John H Newman ◽

John A Phillips ◽

Rizwan Hamid ◽

...

Keyword(s):

Biological Networks ◽

Rapid Screening ◽

Disease Genes ◽

Individual Genome ◽

Genetic Causes ◽

Gene Pairs ◽

Monogenic Diseases ◽

Variant Gene ◽

Undiagnosed Diseases ◽

Undiagnosed Diseases Network

ABSTRACTRare diseases affect hundreds of millions of people worldwide, and diagnosing their genetic causes is challenging. The Undiagnosed Diseases Network (UDN) was formed in 2014 to identify and treat novel rare genetic diseases, and despite many successes, more than half of UDN patients remain undiagnosed. The central hypothesis of this work is that many unsolved rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating even just pairs of variants for potential to cause disease is currently infeasible. To address this challenge, we developed DiGePred, a random forest classifier for identifying candidate digenic disease gene pairs using features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the DiGePred classifier using DIDA, the largest available database of known digenic disease causing gene pairs, and several sets of non-digenic gene pairs, including variant pairs derived from unaffected relatives of UDN patients. DiGePred achieved high precision and recall in cross-validation and on a held out test set (PR area under the curve >77%), and we further demonstrate its utility using novel digenic pairs from the recent literature. In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings like the UDN. Finally, to facilitate the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work facilitates the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.

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