Prediction and experimental validation of osteoporosis susceptibility genes by computational disease gene identification strategy

Bone ◽  
2008 ◽  
Vol 43 ◽  
pp. S44-S45
Author(s):  
Qingyang Huang ◽  
Li Gloria H.Y. Li ◽  
Annie W.C. Kung
Keyword(s):  
Experimental Validation ◽  
Disease Gene ◽  
Susceptibility Genes ◽  
Gene Identification ◽  
Identification Strategy

scholarly journals Exploring Candidate Genes for Epilepsy by Computational Disease-Gene Identification Strategy

2010 ◽  
Vol 13 (2) ◽  
pp. 35-40 ◽  
Author(s):  
Y Sha ◽  
Q Liu ◽  
Y Wang ◽  
C Dong ◽  
L Song
Keyword(s):  
Candidate Genes ◽  
Complex Disease ◽  
Disease Gene ◽  
Experimental Tests ◽  
Susceptibility Genes ◽  
Gene Identification ◽  
Receptor Signaling ◽  
Bioinformatic Tools ◽  
Network Analyses ◽  
Identification Strategy

Exploring Candidate Genes for Epilepsy by Computational Disease-Gene Identification StrategyEpilepsy is a complex disease with a strong genetic component. So far, studies have focused on experimental validation or genome-wide linkage scans for epilepsy susceptibility genes in multiple populations. We have used four bioinformatic tools (SNPs3D, PROSPECTR and SUSPECTS, GenWanderer, PosMed) to analyze 16 susceptibility loci selected from a literature search. Pathways and regulatory network analyses were performed using the Ingenuity Pathways Analysis (IPA) software. We identified a subset of 48 candidate epilepsy susceptibility genes. Five significant canonical pathways, in four typical networks, were identified: GABA receptor signaling, interleukin-6 (IL-6) signaling, G-protein coupled receptor signaling, type 2 diabetes mellitus signaling and airway inflammation in asthma. We concluded that online analytical tools provide a powerful way to reveal candidate genes which can greatly reduce experimental time. Our study contributes to further experimental tests for epilepsy susceptibility genes.

scholarly journals Prediction of osteoporosis candidate genes by computational disease-gene identification strategy

2008 ◽  
Vol 53 (7) ◽  
pp. 644-655 ◽  
Author(s):  
Qing-Yang Huang ◽  
Gloria H. Y. Li ◽  
William M. W. Cheung ◽  
You-Qiang Song ◽  
Annie W. C. Kung
Keyword(s):  
Candidate Genes ◽  
Disease Gene ◽  
Gene Identification ◽  
Identification Strategy

scholarly journals False Discovery Rate in Linkage and Association Genome Screens for Complex Disorders

Genetics ◽  
2003 ◽  
Vol 164 (2) ◽  
pp. 829-833
Author(s):  
Chiara Sabatti ◽  
Susan Service ◽  
Nelson Freimer
Keyword(s):  
Gene Mapping ◽  
False Discovery Rate ◽  
Disease Gene ◽  
Susceptibility Genes ◽  
Complex Disorders ◽  
Disease Gene Mapping ◽  
False Discovery ◽  
Simple Step ◽  
Multiple Comparison Procedure ◽  
Step Down

Abstract We explore the implications of the false discovery rate (FDR) controlling procedure in disease gene mapping. With the aid of simulations, we show how, under models commonly used, the simple step-down procedure introduced by Benjamini and Hochberg controls the FDR for the dependent tests on which linkage and association genome screens are based. This adaptive multiple comparison procedure may offer an important tool for mapping susceptibility genes for complex diseases.

Human Gene Mapping and Disease Gene Identification

2007 ◽  
pp. 207-230
Author(s):  
Robert L. Nussbaum ◽  
Roderick R. McInnes ◽  
Huntington F. Willard ◽  
Ada Hamosh
Keyword(s):  
Gene Mapping ◽  
Disease Gene ◽  
Human Gene ◽  
Gene Identification ◽  
Human Gene Mapping

scholarly journals Disease gene identification by using graph kernels and Markov random fields

2014 ◽  
Vol 57 (11) ◽  
pp. 1054-1063 ◽  
Author(s):  
BoLin Chen ◽  
Min Li ◽  
JianXin Wang ◽  
Fang-Xiang Wu
Keyword(s):  
Random Fields ◽  
Markov Random Fields ◽  
Disease Gene ◽  
Gene Identification ◽  
Graph Kernels ◽  
Markov Random

Techniques for the Identification of Genes Involved in Psychiatric Disorders

2005 ◽  
Vol 39 (7) ◽  
pp. 542-549 ◽  
Author(s):  
Ian P. Blair ◽  
Philip B. Mitchell ◽  
Peter R. Schofield
Keyword(s):  
Psychiatric Disorders ◽  
Susceptibility Genes ◽  
Genome Project ◽  
Mental Illnesses ◽  
Gene Identification ◽  
Great Promise ◽  
Psychiatric Genetics ◽  
Biochemical Basis ◽  
Genetic Traits ◽  
The Future

Objective: Most psychiatric disorders are complex genetic traits involving both genetic and environmental risk factors. This paper aims to review the gene identification strategies being applied bymolecular geneticists in their efforts to elucidate the genetic and molecular basis of psychiatric disorders. Future strategies will also be canvassed. Method: The psychiatric genetic literature was reviewed to identify current strategies applied to gene identification, with examples provided where available. The future strategies and applications that will arise from genome projects, including the International Haplotype Mapping Project, are also discussed. Results: Many advances in the techniques of gene discovery, and the increasing resources available, are rapidly being adopted by researchers and applied to the complex problem of identifying susceptibility genes for mental illnesses. Perhaps the single most important advance to date is the Human Genome Project and all that has stemmed from the vast quantity of information that this endeavour has provided. With these technological advances and the massive increase of publicly available genetic resources, several genes have recently been implicated in the susceptibility to psychiatric illnesses including schizophrenia and depression. After many years of fruitless endeavours, these recent reports indicate that the labours of researchers in psychiatric genetics are beginning to show exciting results. Conclusions: Identification of these susceptibility genes holds great promise, with the unravelling of the molecular and biochemical basis of some conditions now being a more realistic and tangible goal. The increasing number of genes being identified augers well for the future treatment of psychiatric disorders. The genes identified, and the pathways of genes and proteins that they implicate, will provide potential novel targets for new therapeutic drugs. Psychiatric genetics appears to be poised for significant advances in our knowledge and understanding of the molecular genetic basis of mental illness.

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