Integrative approaches for mapping the genetic risk of complex traits
绘制复杂性状遗传风险的综合方法
基本信息
- 批准号:10112280
- 负责人:
- 金额:$ 45.53万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2017
- 资助国家:美国
- 起止时间:2017-03-01 至 2022-02-28
- 项目状态:已结题
- 来源:
- 关键词:AccountingAddressAreaAwarenessBig DataBiochemicalBiologicalBiological AssayBody mass indexCatalogsChromosome MappingCollaborationsComplexComputer softwareComputing MethodologiesCouplingDataData SetDevelopmentDiseaseEpigenetic ProcessGene ExpressionGenesGeneticGenetic DiseasesGenetic RiskGenetic VariationGenetic studyGenomicsGoalsHeightHeterogeneityIndividualJointsLinkLinkage DisequilibriumLipidsMeasurementMeasuresMediationMethodologyMethodsModelingModificationMutationNoiseObesityPhenotypePrivacyPublicationsResearchResourcesSignal TransductionSiteStatistical MethodsTechniquesTissuesVariantbasecausal variantclinically relevantcohortcostdisorder riskexperiencefunctional genomicsgenome wide association studygenome-widegenomic datagenomic locushigh riskinsightmultiple omicsnew therapeutic targetpersonalized medicinepleiotropismpreventrisk predictionrisk variantsuccesstooltraittranscriptometranscriptomics
项目摘要
PROJECT SUMMARY/ABSTRACT
Although genome-wide association studies (GWAS) have been extremely successful in identifying numerous
risk loci for complex traits and diseases, at the vast majority of these loci, the causal mechanism between
genetic variation and disease risk remains largely unknown. This prohibits the development of novel drug
targets, personalized treatments or accurate prediction of high-risk individuals. In the quest to address this gap,
post-GWAS studies are experiencing a “big data” revolution driven by the exponentially decreasing costs of
high-throughput genomic assays. Multiple layers of data (genetic variation, transcriptome levels, epigenetic
modifications, localization of tissue-specific regulatory sites, etc.) are routinely collected in increasingly large
cohorts of individuals. This raises the need for new computational and statistical methods that are able to
integrate various types of data (genetic, epigenetic, transcriptomic) to understand the causal mechanism of
disease at GWAS risk loci. Here we propose to develop new methods and techniques and to apply them to
gain insights to the genetic basis of common disease and traits. Importantly, we aim to circumvent genomic
privacy issues (that often prohibit access to large-scale GWAS data) by proposing techniques that operate
directly at the summary statistic level (e.g. variant effect sizes). We will apply existing and newly developed
methods on GWAS summary data sets over 30 traits and diseases spanning more than 1,000,000 phenotype
measurements, joint with a catalogue of over 7,000 biochemical and evolutionary genetic metrics of
functionality as well as over 10,000 individuals for which genetic variation, gene expression and disease status
has been measured.
项目摘要/摘要
尽管全基因组关联研究(GWAS)已经非常成功地识别了
对于复杂性状和疾病的危险基因座,在绝大多数这些基因座上,因果机制之间
基因变异和疾病风险在很大程度上仍是未知的。这禁止了新药的开发
目标、个性化治疗或对高危个体的准确预测。在寻求解决这个差距的过程中,
后Gwas时代的研究正在经历一场由以下因素推动的大数据革命:
高通量基因组分析。多层数据(遗传变异、转录组水平、表观遗传学
修饰、组织特异性调控位点的定位等)按惯例收集的数量越来越大
一群群的个体。这增加了对新的计算和统计方法的需求,这些方法能够
整合各种类型的数据(遗传、表观遗传、转录本),以了解
疾病发生在GWA型高危基因座。在这里,我们建议开发新的方法和技术,并将它们应用于
深入了解常见疾病和特征的遗传基础。重要的是,我们的目标是绕过基因组
隐私问题(通常禁止访问大规模GWAS数据),因为提出了操作技术
直接在汇总统计级别(例如,不同的效果大小)。我们将应用现有的和新开发的
方法:对超过100万个表型的30多个性状和疾病数据集进行汇总
测量,连同超过7,000个生化和进化遗传指标的目录
以及超过10,000个个体的基因变异、基因表达和疾病状态
已经被测量过。
项目成果
期刊论文数量(30)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Leveraging expression from multiple tissues using sparse canonical correlation analysis and aggregate tests improves the power of transcriptome-wide association studies.
- DOI:10.1371/journal.pgen.1008973
- 发表时间:2021-04
- 期刊:
- 影响因子:4.5
- 作者:Feng H;Mancuso N;Gusev A;Majumdar A;Major M;Pasaniuc B;Kraft P
- 通讯作者:Kraft P
Partitioning gene-level contributions to complex-trait heritability by allele frequency identifies disease-relevant genes.
通过等位基因频率划分基因水平对复杂性状遗传力的贡献,可以识别疾病相关基因。
- DOI:10.1016/j.ajhg.2022.02.012
- 发表时间:2022
- 期刊:
- 影响因子:9.8
- 作者:Burch,KathrynS;Hou,Kangcheng;Ding,Yi;Wang,Yifei;Gazal,Steven;Shi,Huwenbo;Pasaniuc,Bogdan
- 通讯作者:Pasaniuc,Bogdan
Large-scale transcriptome-wide association study identifies new prostate cancer risk regions.
- DOI:10.1038/s41467-018-06302-1
- 发表时间:2018-10-04
- 期刊:
- 影响因子:16.6
- 作者:Mancuso N;Gayther S;Gusev A;Zheng W;Penney KL;Kote-Jarai Z;Eeles R;Freedman M;Haiman C;Pasaniuc B;PRACTICAL consortium
- 通讯作者:PRACTICAL consortium
Optimized design of single-cell RNA sequencing experiments for cell-type-specific eQTL analysis.
- DOI:10.1038/s41467-020-19365-w
- 发表时间:2020-10-30
- 期刊:
- 影响因子:16.6
- 作者:Mandric I;Schwarz T;Majumdar A;Hou K;Briscoe L;Perez R;Subramaniam M;Hafemeister C;Satija R;Ye CJ;Pasaniuc B;Halperin E
- 通讯作者:Halperin E
Multivariate genome-wide association analysis by iterative hard thresholding.
- DOI:10.1093/bioinformatics/btad193
- 发表时间:2023-04-03
- 期刊:
- 影响因子:0
- 作者:
- 通讯作者:
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Bogdan Pasaniuc其他文献
Bogdan Pasaniuc的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Bogdan Pasaniuc', 18)}}的其他基金
Metrics and methods for cross-population fine mapping
跨人群精细制图的指标和方法
- 批准号:
8544432 - 财政年份:2012
- 资助金额:
$ 45.53万 - 项目类别:
Metrics and methods for cross-population fine mapping
跨人群精细制图的指标和方法
- 批准号:
8305856 - 财政年份:2012
- 资助金额:
$ 45.53万 - 项目类别:
相似海外基金
Rational design of rapidly translatable, highly antigenic and novel recombinant immunogens to address deficiencies of current snakebite treatments
合理设计可快速翻译、高抗原性和新型重组免疫原,以解决当前蛇咬伤治疗的缺陷
- 批准号:
MR/S03398X/2 - 财政年份:2024
- 资助金额:
$ 45.53万 - 项目类别:
Fellowship
CAREER: FEAST (Food Ecosystems And circularity for Sustainable Transformation) framework to address Hidden Hunger
职业:FEAST(食品生态系统和可持续转型循环)框架解决隐性饥饿
- 批准号:
2338423 - 财政年份:2024
- 资助金额:
$ 45.53万 - 项目类别:
Continuing Grant
Re-thinking drug nanocrystals as highly loaded vectors to address key unmet therapeutic challenges
重新思考药物纳米晶体作为高负载载体以解决关键的未满足的治疗挑战
- 批准号:
EP/Y001486/1 - 财政年份:2024
- 资助金额:
$ 45.53万 - 项目类别:
Research Grant
Metrology to address ion suppression in multimodal mass spectrometry imaging with application in oncology
计量学解决多模态质谱成像中的离子抑制问题及其在肿瘤学中的应用
- 批准号:
MR/X03657X/1 - 财政年份:2024
- 资助金额:
$ 45.53万 - 项目类别:
Fellowship
CRII: SHF: A Novel Address Translation Architecture for Virtualized Clouds
CRII:SHF:一种用于虚拟化云的新型地址转换架构
- 批准号:
2348066 - 财政年份:2024
- 资助金额:
$ 45.53万 - 项目类别:
Standard Grant
The Abundance Project: Enhancing Cultural & Green Inclusion in Social Prescribing in Southwest London to Address Ethnic Inequalities in Mental Health
丰富项目:增强文化
- 批准号:
AH/Z505481/1 - 财政年份:2024
- 资助金额:
$ 45.53万 - 项目类别:
Research Grant
ERAMET - Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
ERAMET - 快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
- 批准号:
10107647 - 财政年份:2024
- 资助金额:
$ 45.53万 - 项目类别:
EU-Funded
BIORETS: Convergence Research Experiences for Teachers in Synthetic and Systems Biology to Address Challenges in Food, Health, Energy, and Environment
BIORETS:合成和系统生物学教师的融合研究经验,以应对食品、健康、能源和环境方面的挑战
- 批准号:
2341402 - 财政年份:2024
- 资助金额:
$ 45.53万 - 项目类别:
Standard Grant
Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
- 批准号:
10106221 - 财政年份:2024
- 资助金额:
$ 45.53万 - 项目类别:
EU-Funded
Recite: Building Research by Communities to Address Inequities through Expression
背诵:社区开展研究,通过表达解决不平等问题
- 批准号:
AH/Z505341/1 - 财政年份:2024
- 资助金额:
$ 45.53万 - 项目类别:
Research Grant