Penalized likelihood methods for estimation and testing with genomic data
Penalized likelihood methods for estimation and testing with genomic data
批准号:
9043646
负责人:
Jean V. Morrison
金额:
$2.05万
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:
2016-03-15 至 2016-09-15
关键词:
AlgorithmsBasic ScienceBiologicalCellsChromosomesClinicalCollaborationsCommunitiesComplexDNA MethylationDataData SourcesDeoxyribonuclease IDevelopmentDigestionDiseaseEncyclopedia of DNA ElementsFoundationsFutureGene ExpressionGene Expression RegulationGeneticGenomeGenomicsGoalsHistocompatibility TestingImageryJointsKnowledgeLinkMeasurementMethodsMethylationModelingOutcomePlayPositioning AttributeProcessResearchResearch ProposalsRoleSpecific qualifier valueStatistical MethodsStructureTechniquesTestingTimeTissuesTranslatingVariantWorkbasecell typecomputerized toolsdisorder preventionenvironmental changeflexibilitygenomic datagenomic profileshuman tissuein vivointerestmethylation patternnovelpublic health relevanceresearch studytheoriestooltrait
中文摘要
英文摘要
DESCRIPTION (provided by applicant): In recent years the scientific community has acquired vast amounts of genomic data fueled by the promise of discovering the genetic and regulatory foundations of disease and phenotypic variation. This promise has not yet been fully realized, in part due to the limitations of our current statistical and computational tools. The problem of studying phenotypic associations with sequence variants is now well studied, but tools utilizing non-sequence data types and integrating multiple data sources are less well established. Many non-sequence data types possess spatial correlation with respect to genetic position - we might expect these data to follow a smooth function of position along the chromosome. In this research proposal we will develop adaptive methods for simultaneously estimating these functions or genomic profiles and discovering regions in which they are associated with clinically or biologically relevant outcomes. These methods are born out of a single penalized regression framework called Joint Adaptive Differential Estimation (JADE) and will be implemented in efficient, scalable algorithms. The suite of JADE methods will include binary and quantitative trait analysis, adaptive spatially varying clustering, and significance testing. These broad, flexible capabilities offer many possibilities for relating spatially structured genomic data typesto biological or clinical outcomes, or to other binary or quantitative genomic information such as gene expression levels. In our applications we will focus on DNA methylation data in a variety of healthy tissue types available from the Encyclopedia of DNA Elements (ENCODE) consortium and DNase I data in collaboration with a lab exploring in vivo changes in gene regulation associated with environmental changes.
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专著(0)
科研奖励(0)
会议论文
Mendelian randomization for modern data: Integrating data resources to improve accuracy of causal estimates.
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批准号:10716241
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项目类别:
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资助金额:$35.88万
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财政年份:2023
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负责人:Jean V. Morrison
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依托单位:
海外基金