Statistical Models for Genetic Studies, Using Network and Integrative Analysis
Statistical Models for Genetic Studies, Using Network and Integrative Analysis
批准号:
10134596
负责人:
Dongjun Chung
金额:
$25.79万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-07-21 至 2021-04-30
中文摘要
全基因组关联研究(GWAS)已经确定了数以万计的相关遗传变异
英文摘要
Genome-wide association studies (GWAS) have identified tens of thousands of genetic variants associated
with hundreds of phenotypes and diseases, which in some cases have provided clinical and medical benefits
to patients with novel biomarkers and therapeutic targets. However, investigation of complex traits often
suffers from limited statistical power due to polygenicity, high dimensionality, and moderate sample size. While
it is practically challenging and costly to recruit patients to attain sufficient sample size to identify all associated
genetic variants, we recently showed that statistical power to identify risk associated genetic variants can be
significantly increased by 1) considering genetic basis shared among multiple phenotypes, namely pleiotropy,
and 2) incorporating genomic and genetic annotation data. However, effective integration of these datasets
becomes statistically more challenging as the number of genetic studies and annotation data increases.
The objective of this proposal is to develop statistical methods and software to improve identification and
interpretation of risk variants and to promote understanding of genetic relationship among phenotypes. This
objective will be attained by pursuing four specific aims. In Aim 1, we will develop a Bayesian graphical model
to identify risk variants and construct a phenotype network, by integrating multiple GWAS datasets with various
annotation data. In Aim 2, we will develop a Bayesian graphical model to build a phenotype network from
biomedical literature. In Aim 3, we will develop a statistical method to construct meta-annotations that can
effectively summarize high dimensional annotation data without losing interpretability. In Aim 4, we will apply
these methods to genetic studies of vascular complications and autoimmune diseases in African American
populations, with PubMed literature and various annotation datasets. The proposed research is innovative
because it proposes a novel statistical framework that integrates multiple GWAS, biomedical literature, and
annotation datasets to improve identification and interpretation of risk variants. The proposed research is
significant because it is expected to help improve diagnosis and treatment of diseases with more effective
identification of risk variants and enhanced understanding of common etiology among diseases.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Statistical Power Calculation Framework for Spatially Resolved Transcriptomics Experiments
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批准号:10629262
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项目类别:
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资助金额:$23.0万
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财政年份:2022
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负责人:Dongjun Chung
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依托单位:
Statistical Power Calculation Framework for Spatially Resolved Transcriptomics Experiments
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批准号:10453133
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项目类别:
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资助金额:$19.15万
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财政年份:2022
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负责人:Dongjun Chung
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依托单位:
The Genetic Basis of Opioid Dependence Vulnerablility in a Rodent Model
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批准号:10454143
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项目类别:
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资助金额:$78.26万
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财政年份:2018
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负责人:Dongjun Chung
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依托单位:
The Genetic Basis of Opioid Dependence Vulnerablility in a Rodent Model
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批准号:9982281
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项目类别:
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资助金额:$85.24万
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财政年份:2018
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负责人:Dongjun Chung
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依托单位:
The Genetic Basis of Opioid Dependence Vulnerablility in a Rodent Model
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批准号:10223254
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项目类别:
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资助金额:$92.18万
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财政年份:2018
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负责人:Dongjun Chung
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依托单位:
The Genetic Basis of Opioid Dependence Vulnerablility in a Rodent Model
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批准号:9788389
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项目类别:
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资助金额:$77.16万
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财政年份:2018
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负责人:Dongjun Chung
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依托单位:
Statistical Models for Genetic Studies, Using Network and Integrative Analysis
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批准号:9920162
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项目类别:
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资助金额:$33.28万
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财政年份:2016
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负责人:Dongjun Chung
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依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
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批准号:--
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项目类别:合作创新研究团队
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资助金额:--
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批准年份:2024
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负责人:姚韬
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依托单位:
新型手性NAD(P)H Models合成及生化模拟
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批准号:20472090
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项目类别:面上项目
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资助金额:23.0万元
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批准年份:2004
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负责人:王乃兴
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依托单位: