Powering whole genome sequence-based genetic discovery for common human diseases
为常见人类疾病提供基于全基因组序列的基因发现
基本信息
- 批准号:10168752
- 负责人:
- 金额:$ 25万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-07-31 至 2022-09-30
- 项目状态:已结题
- 来源:
- 关键词:AllelesAutomobile DrivingBase SequenceBiologicalCodeCommunitiesComplexComputational BiologyComputer softwareComputing MethodologiesDataData SetDatabasesDiseaseElementsEnsureEtiologyFoundationsGenesGeneticGenetic ModelsGenetic StructuresGenetic VariationGenomicsGoalsGroupingHeritabilityHumanHuman GeneticsIndividualKnowledgeLearningLearning ModuleMeasuresMediationMendelian disorderMethodsModelingNational Human Genome Research InstituteNon-Insulin-Dependent Diabetes MellitusPathway interactionsPatient CarePhenotypePlayPolygenic TraitsPopulation ControlRandomizedRegulatory ElementResearch PersonnelResourcesRiskRoleSamplingSchemeSchizophreniaSignal TransductionStatistical MethodsTestingTimeTo specifyTranslatingUntranslated RNAVariantWeightanalytical methodanalytical toolbaseburden of illnessdata resourcedata sharingdisease phenotypedisorder preventionepigenomicsgenetic analysisgenetic architecturegenetic associationgenome sequencinghuman diseaseimprovedindividualized preventioninsightnovelnovel strategiesopen sourceoutcome forecastphenotypic datapleiotropismpopulation stratificationpower analysisprecision medicineprogramsrare variantscale uptooltraituser-friendlywhole genome
项目摘要
PROJECT SUMMARY/ABSTRACT
The coming NHGRI Centers for Common Disease Genomics (CCDG) and Centers for Mendelian Genomics
(CMG) plan to generate whole genome sequencing (WGS) data on over 200,000 individuals. WGS will provide
comprehensive and complete genetic data across coding and non-coding variation, presenting an
unprecedented opportunity for discovery in the genetic analysis of human diseases. However, a lack of
powerful analytic tools that fully realize the potential of these data has emerged as a bottleneck for effectively
translating rich information contained in these massive WGS data into meaningful insights about human
diseases. There is a pressing need to develop powerful and robust analytic methods for WGS that can
accelerate genetic discoveries. To meet this need, we have assembled an interdisciplinary team of
computational biologists, geneticists, and statisticians. Building on our extensive track record in sequencing
studies, statistical genetics, functional analysis and computational biology, we will power the next round of
genetic discoveries by (1) building a massive WGS control sample and developing the methods for
incorporating these controls in studies of complex and Mendelian diseases; (2) creating more powerful
statistical methods for rare variant analysis through the incorporation of functional and regulatory information
and advanced statistical tools; (3) establishing methods to analyze multiple phenotypes to boost the power for
association and understand how different phenotypes relate genetically. These methods will enhance our
ability to identify novel associations across a wide range of genetic architectures, from Mendelian diseases
driven by a strong acting allele to complex polygenic traits. Novel associations promise to lay the foundation for
gaining new insight into the biological mechanisms driving disease and be the bedrock for precision prevention
and medicine strategies. We will collaborate with the investigators of the Genome Sequencing Program, and
will share the developed data resources, tools and methods with the community through user-friendly open
source software and educational modules.
项目摘要/摘要
即将到来的NHGRI公共疾病基因组学中心(CCDG)和Mendelian基因组学中心
(CMG)计划生成超过200,000个人的全基因组测序(WGS)数据。 WGS将提供
跨编码和非编码变化的全面而完整的遗传数据,呈现
在人类疾病的遗传分析中发现的前所未有的机会。但是,缺乏
充分意识到这些数据潜力的强大分析工具已成为有效的瓶颈
将这些大量WGS数据中包含的丰富信息转化为有关人类的有意义的见解
疾病。迫切需要为WGS开发强大而强大的分析方法
加速遗传发现。为了满足这种需求,我们组建了一个跨学科团队
计算生物学家,遗传学家和统计学家。基于我们在测序中广泛的往绩
研究,统计遗传学,功能分析和计算生物学,我们将为下一轮提供动力
通过(1)构建大量WGS控制样本并开发方法的遗传发现
将这些控制纳入复杂和孟德尔疾病的研究中; (2)创建更强大的
通过合并功能和调节信息的稀有变体分析的统计方法
和高级统计工具; (3)建立分析多种表型的方法,以增强
关联并了解不同的表型如何在遗传上关联。这些方法将增强我们的
能够从孟德尔疾病中识别各种遗传结构的新型关联
由强大的作用等位基因驱动到复杂的多基因性状。新颖的协会有望为
获得对驱动疾病的生物学机制的新见解,并成为预防精确的基岩
和医学策略。我们将与基因组测序计划的调查人员合作,并
将通过用户友好开放与社区共享开发的数据资源,工具和方法
来源软件和教育模块。
项目成果
期刊论文数量(0)
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科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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{{ truncateString('XIHONG LIN', 18)}}的其他基金
Statistical Methods for Integrative Analysis of Large-Scale Multi-Ethnic Whole Genome Sequencing Studies and Biobanks of Common Diseases
大规模多民族全基因组测序研究和常见疾病生物样本库综合分析的统计方法
- 批准号:
10622567 - 财政年份:2022
- 资助金额:
$ 25万 - 项目类别:
Powering whole genome sequence-based genetic discovery for common human diseases- Extended 2021-2022.
为常见人类疾病提供基于全基因组序列的基因发现 - 延期 2021-2022 年。
- 批准号:
10355760 - 财政年份:2021
- 资助金额:
$ 25万 - 项目类别:
Powering whole genome sequence-based genetic discovery for common human diseases
为常见人类疾病提供基于全基因组序列的基因发现
- 批准号:
10085285 - 财政年份:2020
- 资助金额:
$ 25万 - 项目类别:
Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer Research
癌症研究中大量遗传和基因组数据分析的统计方法
- 批准号:
9120850 - 财政年份:2015
- 资助金额:
$ 25万 - 项目类别:
Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer Research
癌症研究中大量遗传和基因组数据分析的统计方法
- 批准号:
10676866 - 财政年份:2015
- 资助金额:
$ 25万 - 项目类别:
Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer Research
癌症研究中大量遗传和基因组数据分析的统计方法
- 批准号:
9321418 - 财政年份:2015
- 资助金额:
$ 25万 - 项目类别:
Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer Research
癌症研究中大量遗传和基因组数据分析的统计方法
- 批准号:
9980301 - 财政年份:2015
- 资助金额:
$ 25万 - 项目类别:
Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer Research
癌症研究中大量遗传和基因组数据分析的统计方法
- 批准号:
9752258 - 财政年份:2015
- 资助金额:
$ 25万 - 项目类别:
Statistical Methods for Analysis of Massive Genetic and Genomic Data in Cancer Research
癌症研究中大量遗传和基因组数据分析的统计方法
- 批准号:
8955524 - 财政年份:2015
- 资助金额:
$ 25万 - 项目类别:
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