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

Genes ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 143 ◽  
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.

scholarly journals Multiple Sclerosis Biomarker Discoveries by Proteomics and Metabolomics Approaches

2021 ◽  
Vol 16 ◽  
pp. 117727192110133
Author(s):  
Ameneh Jafari ◽  
Amirhesam Babajani ◽  
Mostafa Rezaei-Tavirani
Keyword(s):  
Multiple Sclerosis ◽  
Complex Disease ◽  
Biomarker Discovery ◽  
Response To Treatment ◽  
Inflammatory Disorder ◽  
Protein Marker ◽  
Complex Disorders ◽  
New Information ◽  
The Central Nervous System ◽  
Monitoring Treatment

Multiple sclerosis (MS) is an autoimmune inflammatory disorder of the central nervous system (CNS) resulting in demyelination and axonal loss in the brain and spinal cord. The precise pathogenesis and etiology of this complex disease are still a mystery. Despite many studies that have been aimed to identify biomarkers, no protein marker has yet been approved for MS. There is urgently needed for biomarkers, which could clarify pathology, monitor disease progression, response to treatment, and prognosis in MS. Proteomics and metabolomics analysis are powerful tools to identify putative and novel candidate biomarkers. Different human compartments analysis using proteomics, metabolomics, and bioinformatics approaches has generated new information for further clarification of MS pathology, elucidating the mechanisms of the disease, finding new targets, and monitoring treatment response. Overall, omics approaches can develop different therapeutic and diagnostic aspects of complex disorders such as multiple sclerosis, from biomarker discovery to personalized medicine.

scholarly journals Biomolecular Network-Based Synergistic Drug Combination Discovery

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Xiangyi Li ◽  
Guangrong Qin ◽  
Qingmin Yang ◽  
Lanming Chen ◽  
Lu Xie
Keyword(s):  
Drug Combination ◽  
Complex Disease ◽  
Recent Decade ◽  
Drug Combinations ◽  
Network Medicine ◽  
Disease Therapy ◽  
Biomolecular Network ◽  
Disease Related Genes ◽  
Disease Specific ◽  
Synergistic Drug Combinations

Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing.

Shaking the tree: mapping complex disease genes with linkage disequilibrium

The Lancet ◽  
2005 ◽  
Vol 366 (9492) ◽  
pp. 1223-1234 ◽  
Author(s):  
Lyle J Palmer ◽  
Lon R Cardon
Keyword(s):  
Linkage Disequilibrium ◽  
Complex Disease ◽  
Disease Genes

scholarly journals Genotyping Error Detection Through Tightly Linked Markers

Genetics ◽  
2003 ◽  
Vol 164 (3) ◽  
pp. 1161-1173
Author(s):  
Guohua Zou ◽  
Deyun Pan ◽  
Hongyu Zhao
Keyword(s):  
Error Detection ◽  
Complex Disease ◽  
Disease Genes ◽  
Genotyping Error ◽  
Genotyping Errors ◽  
Detection Rates ◽  
Nuclear Families ◽  
Additional Information ◽  
Number Of Children ◽  
Multiple Markers

Abstract The identification of genotyping errors is an important issue in mapping complex disease genes. Although it is common practice to genotype multiple markers in a candidate region in genetic studies, the potential benefit of jointly analyzing multiple markers to detect genotyping errors has not been investigated. In this article, we discuss genotyping error detections for a set of tightly linked markers in nuclear families, and the objective is to identify families likely to have genotyping errors at one or more markers. We make use of the fact that recombination is a very unlikely event among these markers. We first show that, with family trios, no extra information can be gained by jointly analyzing markers if no phase information is available, and error detection rates are usually low if Mendelian consistency is used as the only standard for checking errors. However, for nuclear families with more than one child, error detection rates can be greatly increased with the consideration of more markers. Error detection rates also increase with the number of children in each family. Because families displaying Mendelian consistency may still have genotyping errors, we calculate the probability that a family displaying Mendelian consistency has correct genotypes. These probabilities can help identify families that, although showing Mendelian consistency, may have genotyping errors. In addition, we examine the benefit of available haplotype frequencies in the general population on genotyping error detections. We show that both error detection rates and the probability that an observed family displaying Mendelian consistency has correct genotypes can be greatly increased when such additional information is available.

scholarly journals Rare variants in the genetic background modulate the expressivity of neurodevelopmental disorders

2018 ◽  
Author(s):  
Lucilla Pizzo ◽  
Matthew Jensen ◽  
Andrew Polyak ◽  
Jill A. Rosenfeld ◽  
Katrin Mannik ◽  
...  
Keyword(s):  
Family History ◽  
Genetic Background ◽  
Rare Variants ◽  
De Novo ◽  
Genetic Diagnosis ◽  
Clinical Information ◽  
Disease Genes ◽  
Complete Evaluation ◽  
Rare Cnvs ◽  
Complex Disorders

AbstractPurposeTo assess the contribution of rare variants in the genetic background towards variability of neurodevelopmental phenotypes in individuals with rare copy-number variants (CNVs) and gene-disruptive mutations.MethodsWe analyzed quantitative clinical information, exome-sequencing, and microarray data from 757 probands and 233 parents and siblings who carry disease-associated mutations.ResultsThe number of rare secondary mutations in functionally intolerant genes (second-hits) correlated with the expressivity of neurodevelopmental phenotypes in probands with 16p12.1 deletion (n=23, p=0.004) and in probands with autism carrying gene-disruptive mutations (n=184, p=0.03) compared to their carrier family members. Probands with 16p12.1 deletion and a strong family history presented more severe clinical features (p=0.04) and higher burden of second-hits compared to those with mild/no family history (p=0.001). The number of secondary variants also correlated with the severity of cognitive impairment in probands carrying pathogenic rare CNVs (n=53) or de novo mutations in disease genes (n=290), and negatively correlated with head size among 80 probands with 16p11.2 deletion. These second-hits involved known disease-associated genes such as SETD5, AUTS2, and NRXN1, and were enriched for genes affecting cellular and developmental processes.ConclusionAccurate genetic diagnosis of complex disorders will require complete evaluation of the genetic background even after a candidate gene mutation is identified.

scholarly journals Deconstruction of Vulnerability to Complex Diseases: Enhanced Effect Sizes and Power of Intermediate Phenotypes

2007 ◽  
Vol 7 ◽  
pp. 124-130 ◽  
Author(s):  
David Goldman ◽  
Francesca Ducci
Keyword(s):  
Effect Size ◽  
Complex Disease ◽  
Complex Diseases ◽  
Effect Sizes ◽  
Complex Disorders ◽  
Large Samples ◽  
Intermediate Phenotypes ◽  
Genome Association ◽  
Whole Genome Association ◽  
Disease Loci

The deconstruction of vulnerability to complex disease with the help of intermediate phenotypes, including the heritable and disease-associated endophenotypes, is a legacy of Henri Begleiter. Systematic searches for genes influencing complex disorders, including bipolar disorder, have recently been completed using whole genome association (WGA), identifying a series of validated loci. Using this information, it is possible to compare effect sizes of disease loci discovered in very large samples to the effect sizes of replicated functional loci determining intermediate phenotypes that are of essential interest in psychiatric disorders. It is shown that the genes influencing intermediate phenotypes tend to have a larger effect size. Furthermore, the WGA results reveal that the number of loci of large effect size for complex diseases is limited, and yet multiple functional loci have already been identified for intermediate phenotypes relevant to psychiatric diseases, and without the benefit of WGA.

scholarly journals Microfluidic organ-on-a-chip model of the outer blood–retinal barrier with clinically relevant read-outs for tissue permeability and vascular structure

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Yusuf B. Arık ◽  
Wesley Buijsman ◽  
Joshua Loessberg-Zahl ◽  
Carlos Cuartas-Vélez ◽  
Colin Veenstra ◽  
...  
Keyword(s):  
Complex Disease ◽  
Vascular Structure ◽  
Blood Retinal Barrier ◽  
Future Studies ◽  
Disease Mechanisms ◽  
Visual Disorders ◽  
Organ On A Chip ◽  
Tissue Permeability

This organ-on-a-chip device of the outer blood retinal barrier will allow future studies of complex disease mechanisms and treatments of visual disorders using clinically relevant endpoints in vitro.

Using endophenotypes for pathway clusters to map complex disease genes

2006 ◽  
Vol 30 (2) ◽  
pp. 143-154 ◽  
Author(s):  
Wen-Harn Pan ◽  
Ke-Shiuan Lynn ◽  
Chun-Houh Chen ◽  
Yi-Lin Wu ◽  
Chung-Yen Lin ◽  
...  
Keyword(s):  
Complex Disease ◽  
Disease Genes

scholarly journals Synchronized age-related gene expression changes across multiple tissues in human and the link to complex diseases

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Jialiang Yang ◽  
◽  
Tao Huang ◽  
Francesca Petralia ◽  
Quan Long ◽  
...  
Keyword(s):  
Gene Expression ◽  
Complex Disease ◽  
Principal Component ◽  
Tissue Expression ◽  
Whole Body ◽  
Disease Genes ◽  
Related Gene ◽  
Tissue Specific ◽  
Age Related ◽  
Holistic Understanding

Abstract Aging is one of the most important biological processes and is a known risk factor for many age-related diseases in human. Studying age-related transcriptomic changes in tissues across the whole body can provide valuable information for a holistic understanding of this fundamental process. In this work, we catalogue age-related gene expression changes in nine tissues from nearly two hundred individuals collected by the Genotype-Tissue Expression (GTEx) project. In general, we find the aging gene expression signatures are very tissue specific. However, enrichment for some well-known aging components such as mitochondria biology is observed in many tissues. Different levels of cross-tissue synchronization of age-related gene expression changes are observed and some essential tissues (e.g., heart and lung) show much stronger “co-aging” than other tissues based on a principal component analysis. The aging gene signatures and complex disease genes show a complex overlapping pattern and only in some cases, we see that they are significantly overlapped in the tissues affected by the corresponding diseases. In summary, our analyses provide novel insights to the co-regulation of age-related gene expression in multiple tissues; it also presents a tissue-specific view of the link between aging and age-related diseases.

Sign in / Sign up

Export Citation Format

Share Document