Computational Characterization of Genetic Heterogeneity
遗传异质性的计算表征
基本信息
- 批准号:8417550
- 负责人:
- 金额:$ 37.3万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2013
- 资助国家:美国
- 起止时间:2013-09-01 至 2016-08-31
- 项目状态:已结题
- 来源:
- 关键词:AddressAlgorithmsAttentionBiologicalBiomedical ResearchClinicalClinical DataCollaborationsCollectionComputational TechniqueComputer softwareDNA SequenceDataData AnalysesDetectionDevelopmentDiagnosticDiseaseEpigenetic ProcessFrequenciesGene ClusterGene MutationGenesGeneticGenetic HeterogeneityGenomeHeterogeneityIndividualInheritedInstitutesKnowledgeLeadMalignant NeoplasmsMeasurementMedicineMethodsMutateMutationOvarianPathway interactionsReaction TimeRegulatory PathwayResearchResearch PersonnelSamplingSignal PathwaySomatic MutationSourceStatistical ModelsTechniquesTechnologyTestingThe Cancer Genome AtlasTimeUniversitiesVariantWashingtonWorkbasecancer genomecombinatorialdata sharingdesignepigenomegene interactiongenetic variantgenome sequencingnext generationnovelresponsesound
项目摘要
Genetic heterogeneity is a common feature of many diseases, with different causal variants, or
mutations, present in different individuals with the disease. Genetic heterogeneity complicates
the identification of the genetic basis of disease, as any modest sized study will contain
individuals with different causal genetic variants. One reason for this heterogeneity is that causal
variants are present in groups of genes that interact in various cellular signaling and regulatory
pathways. Genetic heterogeneity demands the testing of combinations of variants, rather than
individual variants, for association with a disease. However, while individual variants can be
tested exhaustively for association, combinations of variants cannot, as there are too many
combinations to test, and the number of samples required for statistical significance would be
astronomical. We propose to develop new computational and statistical approaches to identify
combinations of variants that are associated with a disease. In contrast to existing approaches,
we do not restrict attention to known pathways or groups of genes a priori. Rather, our algorithms
utilize genome-scale interaction networks and combinational/statistical constraints to identify
combinations of variants and rigorously assess their statistical significance. Further, we extend
these approaches to find associations between combinations of variants and various clinical
parameters such as survival time or response to treatment. We will apply these techniques to
cancer genome sequencing projects including The Cancer Genome Atlas (TCGA), in
collaboration with several biomedical research groups. Successful completion of the proposed
research will facilitate the study of genetically heterogeneous diseases - and in particular cancer
- using only a modest number of samples that is attainable with present DNA sequencing
technologies.
遗传异质性是许多疾病的共同特征,具有不同的因果变异,或
突变,存在于不同的疾病个体中。遗传异质性使
确定疾病的遗传基础,因为任何中等规模的研究都将包含
具有不同致病基因变异的个体。这种异质性的一个原因是,
变异存在于基因组中,这些基因在各种细胞信号传导和调节中相互作用。
路径。遗传异质性要求测试变异的组合,而不是
个体变异与疾病的关联。然而,虽然个体变体可以
尽管对关联性进行了详尽的测试,但变异体的组合不能,因为有太多的
组合进行检验,统计学显著性所需的样本数量为
天文数字我们建议开发新的计算和统计方法来识别
与疾病相关的变体的组合。与现有方法相比,
我们并不把注意力限制在先验的已知途径或基因组上。相反,我们的算法
利用基因组规模的相互作用网络和组合/统计约束,
变异的组合,并严格评估其统计学意义。此外,我们扩展
这些方法发现变异体组合与各种临床
参数,如生存时间或对治疗的反应。我们将把这些技术应用于
癌症基因组测序项目,包括癌症基因组图谱(TCGA),
与几个生物医学研究小组合作。圆满完成拟议的
研究将促进对遗传异质性疾病的研究,特别是癌症。
- 仅使用现有DNA测序可获得的适度数量的样品
技术.
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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Benjamin Raphael其他文献
Benjamin Raphael的其他文献
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{{ truncateString('Benjamin Raphael', 18)}}的其他基金
Pathway, Network and Spatiotemporal Integration of Cancer Genomics Data
癌症基因组数据的路径、网络和时空整合
- 批准号:
10704174 - 财政年份:2021
- 资助金额:
$ 37.3万 - 项目类别:
Pathway, Network and Spatiotemporal Integration of Cancer Genomics Data
癌症基因组数据的路径、网络和时空整合
- 批准号:
10301898 - 财政年份:2021
- 资助金额:
$ 37.3万 - 项目类别:
Comprehensive and Robust Tools for Analysis of Tumor Heterogeneity and Evolution
用于分析肿瘤异质性和进化的全面而强大的工具
- 批准号:
10269002 - 财政年份:2020
- 资助金额:
$ 37.3万 - 项目类别:
Comprehensive and Robust Tools for Analysis of Tumor Heterogeneity and Evolution
用于分析肿瘤异质性和进化的全面而强大的工具
- 批准号:
10700040 - 财政年份:2020
- 资助金额:
$ 37.3万 - 项目类别:
Comprehensive and Robust Tools for Analysis of Tumor Heterogeneity and Evolution
用于分析肿瘤异质性和进化的全面而强大的工具
- 批准号:
10059032 - 财政年份:2020
- 资助金额:
$ 37.3万 - 项目类别:
Comprehensive and Robust Tools for Analysis of Tumor Heterogeneity and Evolution
用于分析肿瘤异质性和进化的全面而强大的工具
- 批准号:
10677268 - 财政年份:2020
- 资助金额:
$ 37.3万 - 项目类别:
Pathway and Network Integration of Cancer Genomics and Clinical Data
癌症基因组学和临床数据的通路和网络整合
- 批准号:
9765287 - 财政年份:2016
- 资助金额:
$ 37.3万 - 项目类别:
Pathway and Network Integration of Cancer Genomics and Clinical Data
癌症基因组学和临床数据的通路和网络整合
- 批准号:
9211127 - 财政年份:2016
- 资助金额:
$ 37.3万 - 项目类别:
Analytical Approaches to Massive Data Computation with Applications to Genomics
海量数据计算的分析方法及其在基因组学中的应用
- 批准号:
8825472 - 财政年份:2013
- 资助金额:
$ 37.3万 - 项目类别:
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