Current Problems of Complex Disease Genes Mapping

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.

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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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Using endophenotypes for pathway clusters to map complex disease genes

Genetic Epidemiology ◽

10.1002/gepi.20136 ◽

2006 ◽

Vol 30 (2) ◽

pp. 143-154 ◽

Cited By ~ 25

Author(s):

Wen-Harn Pan ◽

Ke-Shiuan Lynn ◽

Chun-Houh Chen ◽

Yi-Lin Wu ◽

Chung-Yen Lin ◽

...

Keyword(s):

Complex Disease ◽

Disease Genes

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

Scientific Reports ◽

10.1038/srep15145 ◽

2015 ◽

Vol 5 (1) ◽

Cited By ~ 98

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.

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Enhancer redundancy predicts gene pathogenicity and informs complex disease gene discovery

10.1101/459123 ◽

2018 ◽

Cited By ~ 7

Author(s):

Xinchen Wang ◽

David B. Goldstein

Keyword(s):

Complex Disease ◽

Genome Wide Association Study ◽

Regulatory Elements ◽

Disease Genes ◽

Transcriptional Regulatory Elements ◽

Regulatory Domains ◽

Disease Gene Discovery ◽

Expression Of Genes ◽

Genome Wide ◽

Transcriptional Regulatory

AbstractNon-coding transcriptional regulatory elements are critical for controlling the spatiotemporal expression of genes. Here, we demonstrate that the number of bases in enhancers linked to a gene reflects its disease pathogenicity. Moreover, genes with redundant enhancer domains are depleted of cis-acting genetic variants that disrupt gene expression, and are buffered against the effects of disruptive non-coding mutations. Our results demonstrate that dosage-sensitive genes have evolved robustness to the disruptive effects of genetic variation by expanding their regulatory domains. This resolves a puzzle in the genetic literature about why disease genes are depleted of cis-eQTLs, suggesting that eQTL information may implicate the wrong genes at genome-wide association study loci, and establishes a framework for identifying non-coding regulatory variation with phenotypic consequences.

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The New Paradigm of Network Medicine to Analyze Breast Cancer Phenotypes

International Journal of Molecular Sciences ◽

10.3390/ijms21186690 ◽

2020 ◽

Vol 21 (18) ◽

pp. 6690 ◽

Cited By ~ 1

Author(s):

Anna Maria Grimaldi ◽

Federica Conte ◽

Katia Pane ◽

Giulia Fiscon ◽

Peppino Mirabelli ◽

...

Keyword(s):

Breast Cancer ◽

Complex Disease ◽

Ex Vivo ◽

The Cancer Genome Atlas ◽

Disease Genes ◽

Network Medicine ◽

New Paradigm ◽

Data Set ◽

Disease Heterogeneity ◽

Tumor Subtypes

Breast cancer (BC) is a heterogeneous and complex disease as witnessed by the existence of different subtypes and clinical characteristics that poses significant challenges in disease management. The complexity of this tumor may rely on the highly interconnected nature of the various biological processes as stated by the new paradigm of Network Medicine. We explored The Cancer Genome Atlas (TCGA)-BRCA data set, by applying the network-based algorithm named SWItch Miner, and mapping the findings on the human interactome to capture the molecular interconnections associated with the disease modules. To characterize BC phenotypes, we constructed protein–protein interaction modules based on “hub genes”, called switch genes, both common and specific to the four tumor subtypes. Transcriptomic profiles of patients were stratified according to both clinical (immunohistochemistry) and genetic (PAM50) classifications. 266 and 372 switch genes were identified from immunohistochemistry and PAM50 classifications, respectively. Moreover, the identified switch genes were functionally characterized to select an interconnected pathway of disease genes. By intersecting the common switch genes of the two classifications, we selected a unique signature of 28 disease genes that were BC subtype-independent and classification subtype-independent. Data were validated both in vitro (10 BC cell lines) and ex vivo (66 BC tissues) experiments. Results showed that four of these hub proteins (AURKA, CDC45, ESPL1, and RAD54L) were over-expressed in all tumor subtypes. Moreover, the inhibition of one of the identified switch genes (AURKA) similarly affected all BC subtypes. In conclusion, using a network-based approach, we identified a common BC disease module which might reflect its pathological signature, suggesting a new vision to face with the disease heterogeneity.

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