Abstract
A systematic study has been conducted of all available reports in PubMed and OMIM (Online Mendelian Inheritance in Man) to examine the genetic and molecular basis of quantitative genetic loci (QTL) of diabetes with the main focus on genes and polymorphisms. The major question is, What can the QTL tell us? Specifically, we want to know whether those genome regions differ from other regions in terms of genes relevant to diabetes. Which genes are within those QTL regions, and, among them, which genes have already been linked to diabetes? whether more polymorphisms have been associated with diabetes in the QTL regions than in the non-QTL regions. Our search revealed a total of 9038 genes from 26 type 1 diabetes QTL, which cover 667,096,006 bp of the mouse genomic sequence. On one hand, a large number of candidate genes are in each of these QTL; on the other hand, we found that some obvious candidate genes of QTL have not yet been investigated. Thus, the comprehensive search of candidate genes for known QTL may provide unexpected benefit for identifying QTL genes for diabetes.
Keywords: Quantitative trait loci, type 1 diabetes, insulin-dependent diabetes mellitus (IDDM), candidate gene, polymorphism, mouse
Current Genomics
Title: Genetic and Molecular Basis of QTL of Diabetes in Mouse: Genes and Polymorphisms
Volume: 9 Issue: 5
Author(s): Peng Gao, Yan Jiao, Qing Xiong, Cong-Yi Wang, Ivan Gerling and Weikuan Gu
Affiliation:
Keywords: Quantitative trait loci, type 1 diabetes, insulin-dependent diabetes mellitus (IDDM), candidate gene, polymorphism, mouse
Abstract: A systematic study has been conducted of all available reports in PubMed and OMIM (Online Mendelian Inheritance in Man) to examine the genetic and molecular basis of quantitative genetic loci (QTL) of diabetes with the main focus on genes and polymorphisms. The major question is, What can the QTL tell us? Specifically, we want to know whether those genome regions differ from other regions in terms of genes relevant to diabetes. Which genes are within those QTL regions, and, among them, which genes have already been linked to diabetes? whether more polymorphisms have been associated with diabetes in the QTL regions than in the non-QTL regions. Our search revealed a total of 9038 genes from 26 type 1 diabetes QTL, which cover 667,096,006 bp of the mouse genomic sequence. On one hand, a large number of candidate genes are in each of these QTL; on the other hand, we found that some obvious candidate genes of QTL have not yet been investigated. Thus, the comprehensive search of candidate genes for known QTL may provide unexpected benefit for identifying QTL genes for diabetes.
Export Options
About this article
Cite this article as:
Gao Peng, Jiao Yan, Xiong Qing, Wang Cong-Yi, Gerling Ivan and Gu Weikuan, Genetic and Molecular Basis of QTL of Diabetes in Mouse: Genes and Polymorphisms, Current Genomics 2008; 9 (5) . https://dx.doi.org/10.2174/138920208785133253
DOI https://dx.doi.org/10.2174/138920208785133253 |
Print ISSN 1389-2029 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5488 |
Call for Papers in Thematic Issues
Advanced AI Techniques in Big Genomic Data Analysis
The thematic issue on "Advanced AI Techniques in Big Genomic Data Analysis" aims to explore the cutting-edge methodologies and applications of artificial intelligence (AI) in the realm of genomic research, where vast amounts of data pose both challenges and opportunities. This issue will cover a broad spectrum of AI-driven strategies, ...read more
Genomic Insights into Oncology: Harnessing Machine Learning for Breakthroughs in Cancer Genomics.
This special issue aims to explore the cutting-edge intersection of genomics and oncology, with a strong emphasis on original data and experimental validation. While maintaining the focus on how machine learning and advanced data analysis techniques are revolutionizing our understanding and treatment of cancer, this issue will prioritize contributions that ...read more
Integrating Artificial Intelligence and Omics Approaches in Complex Diseases
Recent advancements in AI and omics methodologies have revolutionized the landscape of biomedical research, enabling us to extract valuable information from vast amounts of complex data. By combining AI algorithms with omics technologies such as genomics, proteomics, metabolomics, and transcriptomics, researchers can obtain a more comprehensive and multi-dimensional analysis of ...read more
Integrating Machine Learning with Genome Science: Pioneering Developments and Future Directions
Integrating machine learning (ML) with genome science is driving transformative advancements in fields such as genomics, personalized medicine, and drug discovery. Genomic data is vast and complex, making traditional analysis methods inadequate for uncovering deep insights. Machine learning, particularly deep learning models like convolutional neural networks (CNNs) and recurrent neural ...read more
Related Journals
- Author Guidelines
- Graphical Abstracts
- Fabricating and Stating False Information
- Research Misconduct
- Post Publication Discussions and Corrections
- Publishing Ethics and Rectitude
- Increase Visibility of Your Article
- Archiving Policies
- Peer Review Workflow
- Order Your Article Before Print
- Promote Your Article
- Manuscript Transfer Facility
- Editorial Policies
- Allegations from Whistleblowers
- Announcements
Related Articles
-
Role of Infrared Spectroscopy in Medicinal Plants Research in Pakistan
Current Bioactive Compounds Adrenomedullin and Nitric Oxide: Implications for the Etiology and Treatment of Primary Brain Tumors
CNS & Neurological Disorders - Drug Targets Recent Findings Confirm LIM Domain Kinases as Emerging Target Candidates for Cancer Therapy
Current Cancer Drug Targets The Quest for Surrogate Markers of Angiogenesis: A Paradigm for Translational Research in Tumor Angiogenesis and Anti- Angiogenesis Trials
Current Molecular Medicine Molecular Biomarkers for Lung Adenocarcinoma: A Short Review
Current Cancer Therapy Reviews Copper(II) Complexes with Saccharinate and Glutamine as Antitumor Agents: Cytoand Genotoxicity in Human Osteosarcoma Cells
Anti-Cancer Agents in Medicinal Chemistry Role of Moving Average Analysis for Development of Multi-Target (Q)SAR Models
Mini-Reviews in Medicinal Chemistry Apoptosis-related BCL2-family Members: Key Players in Chemotherapy
Anti-Cancer Agents in Medicinal Chemistry Patent Selections
Recent Patents on Drug Delivery & Formulation Protein Interaction Domains: Structural Features and Drug Discovery Applications (Part 2)
Current Medicinal Chemistry Immune Modulation of Asian Folk Herbal Medicines and Related Chemical Components for Cancer Management
Current Medicinal Chemistry Role of Sequence Variations in <i>AhR</i> Gene Towards Modulating Smoking Induced Lung Cancer Susceptibility in North Indian Population: A Multiple Interaction Analysis
Current Genomics Subject Index To Volume 5
Anti-Infective Agents in Medicinal Chemistry Synthesis and Evaluation of Cytotoxic Activity of Some Pyrroles and Fused Pyrroles
Anti-Cancer Agents in Medicinal Chemistry Genetic Determinants of Amyotrophic Lateral Sclerosis as Therapeutic Targets
CNS & Neurological Disorders - Drug Targets Survey of Recent Literature Related to the Biologically Active 4(3H)-Quinazolinones Containing Fused Heterocycles
Current Medicinal Chemistry Analysis of the Concordance in the EGFR Pathway Status Between Primary Tumors and Related Metastases of Colorectal Cancer Patients:Implications for Cancer Therapy
Current Cancer Drug Targets HIV-1 Vpr: Regulator of Viral Survival
Current HIV Research Targeting Hypoxia-Inducible Factor-1 (HIF-1) Signaling in Therapeutics: Implications for the Treatment of Inflammatory Bowel Disease
Recent Patents on Inflammation & Allergy Drug Discovery Patent Annotations
Recent Patents on Anti-Cancer Drug Discovery