权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Cluster Based Selection of Candidate Genes in Genome Wide Association Studies.

全基因组关联研究中候选基因的基于聚类的选择。

基本信息

批准号：
8122169
负责人：
Laura Kelly Vaughan
金额：
$ 15.06万
依托单位：
UNIVERSITY OF ALABAMA AT BIRMINGHAM
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-09-01 至 2013-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8122169
关键词：
Address Biochemical Pathway Biochemistry Bioinformatics Biological Biology Candidate Disease Gene Characteristics Cluster Analysis Code Collaborations Complex Computational Biology Computer software Data Databases Diabetes Mellitus Discipline Disease Electronics Gene Cluster Gene Expression Gene Family Genes Genetic Genetic Polymorphism Genome Goals Health Heart Diseases Hereditary Disease Human Genetics Imagery Individual Influentials Internet Investigation K-Series Research Career Programs Link Mentors Methods Molecular Biology Nucleotides Obesity Online Systems Ontology Pattern Phenotype Polymorphic Microsatellite Marker Procedures Process Research Research Personnel Signal Transduction Source Specialist Testing Time Training Work base genetic association genome wide association study interest medical specialties sound trait

项目摘要

DESCRIPTION (provided by applicant): The candidate's overall goal for this mentored career development award is to gain the expertise and training to enable her to become an independent researcher in statistical genetics and bioinformatics, and to act as a facilitator for cross-discipline collaborative research. To achieve this aim, the candidate has compiled a mentoring team consisting of experts in a variety of fields, all of which have a proven track record of successful collaboration. Genome wide association (GWA) studies may result in hundreds, if not thousands, of SNPs showing significant association with the trait(s) under investigation. For many complex traits, such as obesity, there are arguably thousands of genuinely influential polymorphisms with small effects. Hence, one of the challenges that will be faced by investigators will be the selection and prioritization of SNPs from within the lengthy lists of apparently associated SNPs. Building upon research from related high-dimensional biology fields such as microarray based gene expression studies, the candidate proposes to address the issue of candidate gene selection and interpretation of GWA studies. The proposed research introduces the use of cluster analysis in GWA studies, which will group genes together based on their similarity derived from functional annotations such as Gene Ontology and biochemical pathway information. Specifically, the aims of this project are: 1) implement an automated procedure to identify genes in the study and retrieve annotation information, 2) cluster genes based on shared annotations, and 3) evaluate a battery of methods for determining which clusters of genes are associated with the phenotype. By testing the clusters, instead of all the markers in the study, the multiple test correction burden is decreased and power is increased. The identification of genetic contribution to complex diseases such as obesity, heart disease and diabetes will be key in developing an understanding and and [sic] possible treatments for the many health problems associated with these and other diseases. The methods utilized will be packaged as a web-based software which will be made freely available. The components of the mentored career development award will build upon the candidate's background in molecular biology, biochemistry and statistical genetics in order to yield an independent investigator capable of leading future research into the inheritance of human genetic diseases.

描述（由申请人提供）：候选人的总体目标是获得专业知识和培训，使她能够成为统计遗传学和生物信息学的独立研究人员，并作为跨学科合作研究的促进者。为了实现这一目标，候选人组建了一个由各个领域的专家组成的指导团队，所有这些专家都有成功合作的良好记录。全基因组关联（GWA）研究可能会导致数百个（如果不是数千个）SNP显示出与研究中的性状显著相关。对于许多复杂的性状，如肥胖，可以说有数千个真正有影响力的多态性，但影响很小。因此，研究人员将面临的挑战之一是从冗长的明显相关的SNP列表中选择SNP并对其进行优先排序。基于相关高维生物学领域的研究，如基于微阵列的基因表达研究，候选人提出解决候选基因选择和GWA研究解释的问题。拟议的研究介绍了聚类分析在GWA研究中的使用，该研究将根据基因本体论和生化途径信息等功能注释中的相似性将基因分组在一起。具体来说，该项目的目标是：1）实现一个自动化的程序来识别研究中的基因并检索注释信息，2）基于共享注释对基因进行聚类，以及3）评估一组用于确定哪些基因簇与表型相关的方法。通过测试聚类，而不是研究中的所有标记物，减少了多重检验的校正负担，提高了功效。确定遗传因素对肥胖、心脏病和糖尿病等复杂疾病的影响，将是理解这些疾病和其他疾病相关的许多健康问题和可能的治疗方法的关键。所采用的方法将被打包成一个基于网络的软件，免费提供。指导职业发展奖的组成部分将建立在候选人在分子生物学，生物化学和统计遗传学的背景，以产生一个独立的调查员能够领导未来的研究人类遗传疾病的遗传。