A Haplotype Analysis System for Genes Discovery of Common Diseases

2011 ◽  
pp. 214-230
Author(s):  
Takashi Kido
Keyword(s):  
Haplotype Analysis ◽  
Complex Disease ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Current Theory ◽  
Theory And Practice ◽  
Discovery Research ◽  
Common Diseases ◽  
The Common ◽  
Analysis System

This chapter introduces computational methods for detecting complex disease loci with haplotype analysis. It argues that the haplotype analysis, which plays a major role in the study of population genetics, can be computationally modeled and systematically implemented as a means for detecting causative genes of complex diseases. In this chapter, the author provides a review of issues on haplotype analysis and proposes the analysis system which integrates a comprehensive spectrum of functions on haplotype analysis for supporting disease association studies. The explanation of the system and some real examples of the haplotype analysis will not only provide researchers with better understanding of current theory and practice of genetic association studies, but also present a computational perspective on the gene discovery research for the common diseases.

A Haplotype Analysis System for Genes Discovery of Common Diseases

2011 ◽  
pp. 590-604
Author(s):  
Takashi Kido
Keyword(s):  
Haplotype Analysis ◽  
Complex Disease ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Current Theory ◽  
Theory And Practice ◽  
Discovery Research ◽  
Common Diseases ◽  
The Common ◽  
Analysis System

This chapter introduces computational methods for detecting complex disease loci with haplotype analysis. It argues that the haplotype analysis, which plays a major role in the study of population genetics, can be computationally modeled and systematically implemented as a means for detecting causative genes of complex diseases. In this chapter, the author provides a review of issues on haplotype analysis and proposes the analysis system which integrates a comprehensive spectrum of functions on haplotype analysis for supporting disease association studies. The explanation of the system and some real examples of the haplotype analysis will not only provide researchers with better understanding of current theory and practice of genetic association studies, but also present a computational perspective on the gene discovery research for the common diseases.

A Haplotype Analysis System for Genes Discovery of Common Diseases

2011 ◽  
pp. 2109-2122
Author(s):  
Takashi Kido
Keyword(s):  
Haplotype Analysis ◽  
Complex Disease ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Current Theory ◽  
Theory And Practice ◽  
Discovery Research ◽  
Common Diseases ◽  
The Common ◽  
Analysis System

This chapter introduces computational methods for detecting complex disease loci with haplotype analysis. It argues that the haplotype analysis, which plays a major role in the study of population genetics, can be computationally modeled and systematically implemented as a means for detecting causative genes of complex diseases. In this chapter, the author provides a review of issues on haplotype analysis and proposes the analysis system which integrates a comprehensive spectrum of functions on haplotype analysis for supporting disease association studies. The explanation of the system and some real examples of the haplotype analysis will not only provide researchers with better understanding of current theory and practice of genetic association studies, but also present a computational perspective on the gene discovery research for the common diseases.

A Haplotype Analysis System for Genes Discovery of Common Diseases

2008 ◽  
pp. 1674-1687
Author(s):  
Takashi Kido
Keyword(s):  
Haplotype Analysis ◽  
Complex Disease ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Current Theory ◽  
Theory And Practice ◽  
Discovery Research ◽  
Common Diseases ◽  
The Common ◽  
Analysis System

This chapter introduces computational methods for detecting complex disease loci with haplotype analysis. It argues that the haplotype analysis, which plays a major role in the study of population genetics, can be computationally modeled and systematically implemented as a means for detecting causative genes of complex diseases. In this chapter, the author provides a review of issues on haplotype analysis and proposes the analysis system which integrates a comprehensive spectrum of functions on haplotype analysis for supporting disease association studies. The explanation of the system and some real examples of the haplotype analysis will not only provide researchers with better understanding of current theory and practice of genetic association studies, but also present a computational perspective on the gene discovery research for the common diseases.

scholarly journals Field synopsis and systematic meta-analyses of genetic association studies in isolated dystonia

2018 ◽  
Author(s):  
Olena Ohlei ◽  
Valerija Dobricic ◽  
Katja Lohmann ◽  
Christine Klein ◽  
Christina Lill ◽  
...  
Keyword(s):  
Genetic Association ◽  
Large Scale ◽  
Complex Disease ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Sufficient Information ◽  
Genetic Associations ◽  
Pubmed Database ◽  
The Status ◽  
Meta Analyses

AbstractBackground and objectivesDystonia is a genetically complex disease with both monogenic and polygenic causes. For the latter, numerous genetic associations studies have been performed with largely inconsistent results. The aim of this study was to perform a field synopsis including systematic meta-analyses of genetic association studies in isolated dystoniaMethodsFor the field synopsis we systematically screened and scrutinized the published literature using NCBI’s PubMed database. For genetic variants with sufficient information in at least two independent datasets, random-effects meta-analyses were performed, including meta-analyses stratified by ethnic descent and dystonia subtypes.ResultsA total of 3,575 articles were identified and scrutinized resulting in the inclusion of 42 independent publications allowing 134 meta-analyses on 45 variants across 17 genes. While our meta-analyses pinpointed several significant association signals with variants in TOR1A, DRD1, and ARSG, no single variant displayed compelling association with dystonia in the available data.ConclusionsOur study provides an up-to-date summary of the status of dystonia genetic association studies. Additional large-scale studies are needed to better understand the genetic causes of isolated dystonia.

scholarly journals Microarray genotyping resource to determine population stratification in genetic association studies of complex disease

BioTechniques ◽  
2004 ◽  
Vol 37 (6) ◽  
pp. 977-985 ◽  
Author(s):  
Scott J. Tebbutt ◽  
Jian-Qing He ◽  
Kelly M. Burkett ◽  
Jian Ruan ◽  
Igor V. Opushnyev ◽  
...  
Keyword(s):  
Genetic Association ◽  
Population Stratification ◽  
Complex Disease ◽  
Association Studies ◽  
Genetic Association Studies

scholarly journals Ghrelin in Diabetes and Metabolic Syndrome

2010 ◽  
Vol 2010 ◽  
pp. 1-11 ◽  
Author(s):  
Leena Pulkkinen ◽  
Olavi Ukkola ◽  
Marjukka Kolehmainen ◽  
Matti Uusitupa
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Metabolic Syndrome ◽  
Complex Disease ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Public Health Problem ◽  
Basic Research ◽  
Global Public Health

Metabolic syndrome is a cluster of related risk factors for cardiovascular disease, type 2 diabetes and liver disease. Obesity, which has become a global public health problem, is one of the major risk factors for development of metabolic syndrome and type 2 diabetes. Obesity is a complex disease, caused by the interplay between environmental and genetic factors. Ghrelin is one of the circulating peptides, which stimulates appetite and regulates energy balance, and thus is one of the candidate genes for obesity and T2DM. During the last years both basic research and genetic association studies have revealed association between the ghrelin gene and obesity, metabolic syndrome or type 2 diabetes

scholarly journals Systemic Sclerosis is a Complex Disease Associated Mainly with Immune Regulatory and Inflammatory Genes

2014 ◽  
Vol 8 (1) ◽  
pp. 29-42 ◽  
Author(s):  
Jingxiao Jin ◽  
Chou Chou ◽  
Maria Lima ◽  
Danielle Zhou ◽  
Xiaodong Zhou
Keyword(s):  
Systemic Sclerosis ◽  
Genetic Association ◽  
Dna Cleavage ◽  
Complex Disease ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Genome Wide Association Studies ◽  
Functional Changes ◽  
Circulating Autoantibodies ◽  
Genetic Contributions

Systemic sclerosis (SSc) is a fibrotic and autoimmune disease characterized clinically by skin and internal organ fibrosis and vascular damage, and serologically by the presence of circulating autoantibodies. Although etiopathogenesis is not yet well understood, the results of numerous genetic association studies support genetic contributions as an important factor to SSc. In this paper, the major genes of SSc are reviewed. The most recent genome-wide association studies (GWAS) are taken into account along with robust candidate gene studies. The literature search was performed on genetic association studies of SSc in PubMed between January 2000 and March 2014 while eligible studies generally had over 600 total participants with replication. A few genetic association studies with related functional changes in SSc patients were also included. A total of forty seven genes or specific genetic regions were reported to be associated with SSc, although some are controversial. These genes include HLA genes, STAT4, CD247, TBX21, PTPN22, TNFSF4, IL23R, IL2RA, IL-21, SCHIP1/IL12A, CD226, BANK1, C8orf13-BLK, PLD4, TLR-2, NLRP1, ATG5, IRF5, IRF8, TNFAIP3, IRAK1, NFKB1, TNIP1, FAS, MIF, HGF, OPN, IL-6, CXCL8, CCR6, CTGF, ITGAM, CAV1, MECP2, SOX5, JAZF1, DNASEIL3, XRCC1, XRCC4, PXK, CSK, GRB10, NOTCH4, RHOB, KIAA0319, PSD3 and PSOR1C1. These genes encode proteins mainly involved in immune regulation and inflammation, and some of them function in transcription, kinase activity, DNA cleavage and repair. The discovery of various SSc-associated genes is important in understanding the genetics of SSc and potential pathogenesis that contribute to the development of this disease.

scholarly journals Athlome Project Consortium: a concerted effort to discover genomic and other “omic” markers of athletic performance

2016 ◽  
Vol 48 (3) ◽  
pp. 183-190 ◽  
Author(s):  
Yannis P. Pitsiladis ◽  
Masashi Tanaka ◽  
Nir Eynon ◽  
Claude Bouchard ◽  
Kathryn N. North ◽  
...  
Keyword(s):  
Genetic Association ◽  
Athletic Performance ◽  
Complex Disease ◽  
Association Studies ◽  
Genetic Association Studies ◽  
Basic Research ◽  
Nucleotide Polymorphisms ◽  
Gene Effects ◽  
Athletic Status ◽  
Retrospective View

Despite numerous attempts to discover genetic variants associated with elite athletic performance, injury predisposition, and elite/world-class athletic status, there has been limited progress to date. Past reliance on candidate gene studies predominantly focusing on genotyping a limited number of single nucleotide polymorphisms or the insertion/deletion variants in small, often heterogeneous cohorts (i.e., made up of athletes of quite different sport specialties) have not generated the kind of results that could offer solid opportunities to bridge the gap between basic research in exercise sciences and deliverables in biomedicine. A retrospective view of genetic association studies with complex disease traits indicates that transition to hypothesis-free genome-wide approaches will be more fruitful. In studies of complex disease, it is well recognized that the magnitude of genetic association is often smaller than initially anticipated, and, as such, large sample sizes are required to identify the gene effects robustly. A symposium was held in Athens and on the Greek island of Santorini from 14–17 May 2015 to review the main findings in exercise genetics and genomics and to explore promising trends and possibilities. The symposium also offered a forum for the development of a position stand (the Santorini Declaration). Among the participants, many were involved in ongoing collaborative studies (e.g., ELITE, GAMES, Gene SMART, GENESIS, and POWERGENE). A consensus emerged among participants that it would be advantageous to bring together all current studies and those recently launched into one new large collaborative initiative, which was subsequently named the Athlome Project Consortium.

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