Dissecting the molecular aetiology of complex traits using high dimensional omic data
Dissecting the molecular aetiology of complex traits using high dimensional omic data
批准号:
MR/S003886/1
负责人:
Thomas Richardson
金额:
$39.03万
依托单位:
依托单位国家:
英国
项目类别:
Fellowship
财政年份:
2018
资助国家:
英国
项目状态:
已结题
起止时间:
2018 至 --
中文摘要
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英文摘要
The discovery of genetic variants associated with complex traits is increasing at an exponential rate. It is now of vital importance to develop our understanding of the molecular mechanisms which can help explain these findings, in order to improve our capability to prevent and treat disease. Advancements in high-throughput sequencing technologies present an unprecedented opportunity to address this challenge and ascertain the biological and clinical relevance of results from genome-wide association studies (GWAS). However, there is an increasing abundance of data being generated on diverse types of molecular "omic" traits, accompanied by the rapid development of partially overlapping and often untested methodologies. To overcome this challenge, there needs to be focused research into the most appropriate and efficient manner to harness large-scale 'omic data to elucidate the molecular determinants of complex disease.The research outlined in this fellowship proposal can be delineated into five categories, with the overall aim of harnessing large-scale data to improve patient healthcare in-line with the UK's industrial strategy. The opportunity presented by HDR-UK will allow me to address some of the most crucial limitations in molecular aetiology. Specifically, there needs to be extensive research into tissue-specificity for 'omic traits, systematic frameworks to appropriately appraise molecular mediation and methods to improve causal inference in this paradigm. I also intend on applying novel, state-of-art-methods to available 'omic data to elucidate findings which have translational value for therapeutic evaluation. Finally, I will build publicly accessible computational tools to automate fundamental analyses in this paradigm and resources to disseminate the findings of this fellowship. Using health informatics to harness large-scale, high throughput data to develop our understanding of the causal pathway from genetic variation to complex disease is the overarching theme of this research project. This research will lead to several high impact publications to improve our understanding of the molecular determinants of disease, as well as web tools that should help disseminate the product of this project and also assist colleagues with their endeavors in this field. As such, this work most closely aligns with the HDR-UK priorities concerning health informatics and accelerating medicines discovery.
期刊论文(9)
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科研奖励(0)
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Leveraging brain cortex-derived molecular data to elucidate epigenetic and transcriptomic drivers of neurological function and disease
利用大脑皮层衍生的分子数据来阐明神经功能和疾病的表观遗传和转录组驱动因素
DOI:
10.1101/429134
发表时间:
2018
期刊:
影响因子:
--
作者:
[Hatcher C]
通讯作者:
Hatcher C
Combined effect of PNPLA3, TM6SF2, and HSD17B13 variants on risk of cirrhosis and hepatocellular carcinoma in the general population
PNPLA3、TM6SF2 和 HSD17B13 变异对普通人群肝硬化和肝细胞癌风险的综合影响
DOI:
10.1016/j.atherosclerosis.2021.06.120
发表时间:
2021
期刊:
Atherosclerosis
影响因子:
5.3
作者:
[Gellert-Kristensen H]
通讯作者:
Gellert-Kristensen H
DOI:
10.1371/journal.pgen.1009224
发表时间:
2021-01
期刊:
PLoS genetics
影响因子:
4.5
作者:
[Baird DA, Liu JZ, Zheng J, Sieberts SK, Perumal T, Elsworth B, Richardson TG, Chen CY, Carrasquillo MM, Allen M, Reddy JS, De Jager PL, Ertekin-Taner N, Mangravite LM, Logsdon B, Estrada K, Haycock PC, Hemani G, Runz H, Smith GD, Gaunt TR, AMP-AD eQTL working group]
通讯作者:
AMP-AD eQTL working group
DOI:
10.1093/hmg/ddaa256
发表时间:
2021-02-25
期刊:
Human molecular genetics
影响因子:
3.5
作者:
[Brandkvist M, Bjørngaard JH, Ødegård RA, Åsvold BO, Smith GD, Brumpton B, Hveem K, Richardson TG, Vie GÅ]
通讯作者:
Vie GÅ
Autonomous Drones for Nature Conservation Missions
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批准号:EP/X029077/1
-
项目类别:Research Grant
-
资助金额:$67.6万
-
财政年份:2023
-
负责人:Thomas Richardson
-
依托单位:
Collaborative Research: Graphical and Algebraic Models for Multivariate Categorical Data
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批准号:0505865
-
项目类别:Standard Grant
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资助金额:$0.0万
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财政年份:2005
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负责人:Thomas Richardson
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依托单位:
Graphical Markov Models with Interpretable Structure
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批准号:9972008
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项目类别:Continuing Grant
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资助金额:$15.5万
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财政年份:1999
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负责人:Thomas Richardson
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依托单位:
国内基金
海外基金
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批准号:82371616
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项目类别:面上项目
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负责人:姚晨成
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