Methods for Integrative Genomic Data Analysis
综合基因组数据分析方法
基本信息
- 批准号:9752369
- 负责人:
- 金额:$ 43.08万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2018
- 资助国家:美国
- 起止时间:2018-09-01 至 2022-06-30
- 项目状态:已结题
- 来源:
- 关键词:AddressAlgorithmic SoftwareAlzheimer&aposs DiseaseAreaBiologicalBiological ProcessChargeChronic Kidney FailureCollaborationsComplexComputer softwareComputing MethodologiesDataData AnalysesData SetDependenceDevelopmentDiseaseDisease PathwayDocumentationFunctional disorderGene ExpressionGenesGeneticGenomicsGenotype-Tissue Expression ProjectGrantGraphHigh-Throughput Nucleotide SequencingIndividualKidneyKidney DiseasesLeadMeasurementMeasuresMediatingMediationMethodologyMethodsModelingNetwork-basedPathway interactionsPennsylvaniaPhenotypePlayPublic HealthRandomizedRegulator GenesResearch PersonnelSample SizeSoftware ToolsStatistical MethodsStatistical ModelsStructureSyndromeSystemTechnologyUniversitiesWorkbasebiological systemscardiometabolismcausal variantcomputerized toolsdisease phenotypedisorder riskepigenomicsexperimental studygene functiongene interactiongenetic analysisgenetic associationgenetic variantgenome wide association studygenome-widegenomic datahigh dimensionalityhigh throughput technologyhuman diseaseinsightinterestmetabolomicsmultidimensional datanext generation sequencingnovelprogramssimulationstatisticstheoriestreatment response
项目摘要
Abstract
The broad, long-term objective of this project concerns the development of novel statistical methods, theory
and computational tools for statistical modeling of large-scale multiple high-dimensional genomic data motivated
by important biological questions and experiments. New high-throughput technologies and next generation sequencing are generating various types of very high-dimensional genetics, genomic, epigenomics, metabolomics data
in order to obtain an integrative understanding of various complex phenotypes. As the types and complexity of
the data increase and as the questions being addressed become more sophisticated, statistical methods that can
both integrate these genomic data and incorporate information about gene function and pathways are required in
order to draw valid statistical and biological inferences. The specific aims of the current project are to develop new
statistical models and methods for causal integrative analysis of eQTL data with genome wide genetic association
data (GWAS) in order to identify the possible causal genes and pathways for disease phenotypes. Motivated by
analysis of diverse genomic data, the first aim is to develop novel causal mediation analysis methods to identify the
genes that mediate the effects of genetic variants on disease phenotypes by constructing gene regulatory networks
based on eQTL data. Aim 2 is to develop high-dimensional instrumental variables (HDIV) regression models in
order to identify the phenotype-causing genes using eQTLs as possible instrumental variables. Aims 3 develops
methods for estimating the genetic relatedness between disease phenotype and gene expressions in order to identify the possible disease causing genes and biological pathways. Finally, Aim 4 is to develop statistical methods
that can effectively integrate GTEx data with GWAS association summary statistics in order to identify possible
causal disease genes and pathways. These methods hinge on novel integration of methods for multiple related
high-dimensional regressions and high-dimensional causal inference. The new methods can be applied to different
types of genomic data and will ideally help facilitate the identification of genes and their complex interactions as
well as the biological pathways underlying various complex human diseases. The work proposed here will contribute statistical methodology and theory for modeling high-dimensional genomic data and to studying complex
phenotypes and biological systems and o er insights into each of the biological areas represented by the various
data sets, including Alzheimer's disease, cardiometabolic syndrome, and chronic kidney disease. All algorithms
and software tools developed under this grant and detailed documentation will be made available free-of-charge to
interested researchers.
摘要
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Hongzhe Lee其他文献
Hongzhe Lee的其他文献
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{{ truncateString('Hongzhe Lee', 18)}}的其他基金
Statistical Methods for Microbiome and Metagenomics
微生物组和宏基因组学的统计方法
- 批准号:
9447252 - 财政年份:2017
- 资助金额:
$ 43.08万 - 项目类别:
Statistical Methods for Microbiome and Metagenomics
微生物组和宏基因组学的统计方法
- 批准号:
9983111 - 财政年份:2017
- 资助金额:
$ 43.08万 - 项目类别:
Statistical Methods for Microbiome and Metagenomics
微生物组和宏基因组学的统计方法
- 批准号:
10707092 - 财政年份:2017
- 资助金额:
$ 43.08万 - 项目类别:
Statistical Methods for Next-Generation Sequence Data
下一代序列数据的统计方法
- 批准号:
8500393 - 财政年份:2012
- 资助金额:
$ 43.08万 - 项目类别:
Statistical Methods for Next-Generation Sequence Data
下一代序列数据的统计方法
- 批准号:
8643260 - 财政年份:2012
- 资助金额:
$ 43.08万 - 项目类别:
Statistical Methods for Next-Generation Sequence Data
下一代序列数据的统计方法
- 批准号:
8237259 - 财政年份:2012
- 资助金额:
$ 43.08万 - 项目类别:
Training in Ophthalmic Statistical Genetics and Bioinformatics
眼科统计遗传学和生物信息学培训
- 批准号:
8075190 - 财政年份:2011
- 资助金额:
$ 43.08万 - 项目类别:
Training in Ophthalmic Statistical Genetics and Bioinformatics
眼科统计遗传学和生物信息学培训
- 批准号:
8494622 - 财政年份:2011
- 资助金额:
$ 43.08万 - 项目类别:
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