Integrating genome-scale data to reveal causal mechanisms in type 2 diabetes

整合基因组规模数据揭示 2 型糖尿病的因果机制

• 批准号: 10120279
• 负责人: Anna Louise Gloyn
• 金额: --
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-05 至 2022-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10120279
• 关键词: Integrating; genome; scale; data; reveal

 DESCRIPTION (provided by applicant): Type 2 diabetes has emerged as one of the leading threats to global health. The rapid rise in diabetes prevalence in both industrialized and emerging economies bears testament to the failures of prevention, and high rates of complication in those with diabetes highlight the inadequacies of current therapeutic approaches. Major gaps in our understanding of the mechanisms responsible for the development of diabetes represent obstacles to innovation with respect to novel preventative and therapeutic strategies. Human genetics provides an increasingly-powerful approach for addressing these deficiencies and providing mechanistic insights into disease that can result in health-related benefits. This proposal seeks to use information from human genetic discovery efforts that have, in recent years, identified over 100 regions of the genome which harbor DNA sequence variants influencing T2D-risk. There has been limited progress in turning these discoveries into mechanistic insights but several recent technological and analytical advances have transformed the situation, and it is these that we plan to exploit. Our first aim is to home i on the specific DNA sequence changes driving the risk-associations in these regions. The aggregation of very large genetic datasets, particularly when derived from a range of ethnic groups, makes it possible to define the subset of these variants likely to be driving the T2D-risk effect. We will take extensive genetic data sets collected as part of large international consortia and apply existing and novel approaches to derive the most precise localization of these T2D-risk variants yet obtained. Having identified these variants, the second aim is to understand the cellular processes they perturb. Recently, it has become possible to generate detailed functional maps of the genome from key diabetes- relevant human tissues, including the pancreatic islet. These maps define elements crucial for regulating cellular activity. We will use these maps to highlight the specific elements that contain T2D-causal variants, and initiate experimental studies to test the functional hypotheses that emerge. The third aim seeks to connect these T2D-associated functional elements to the specific genes, proteins, networks and pathways that mediate their effects. We will aggregate data from a variety of existing and novel public and proprietary sources, each of which provides complementary clues to the relevance of the regional genes to T2D development. Most medicines act on specific protein targets, and these efforts will result in novel protein targets that are directly implicated in human disease. An essential feature of this proposal is that it relies on extensive data sets that have already been collected, or, in some cases, are being generated with existing funding. The funding we request here will support the further integration of these data, and also enable its dissemination to the wider research community, most particularly via the AMP-T2DGENES consortium portal.



项目成果

Whole blood co-expression modules associate with metabolic traits and type 2 diabetes: an IMI-DIRECT study.

• DOI: 10.1186/s13073-020-00806-6
• 发表时间: 2020-12-01
• 期刊: Genome medicine
• 影响因子: 12.3
• 作者: Gudmundsdottir V;Pedersen HK;Mazzoni G;Allin KH;Artati A;Beulens JW;Banasik K;Brorsson C;Cederberg H;Chabanova E;De Masi F;Elders PJ;Forgie I;Giordano GN;Grallert H;Gupta R;Haid M;Hansen T;Hansen TH;Hattersley AT;Heggie A;Hong MG;Jones AG;Koivula R;Kokkola T;Laakso M;Løngreen P;Mahajan A;Mari A;McDonald TJ;McEvoy D;Musholt PB;Pavo I;Prehn C;Ruetten H;Ridderstråle M;Rutters F;Sharma S;Slieker RC;Syed A;Tajes JF;Thomas CE;Thomsen HS;Vangipurapu J;Vestergaard H;Viñuela A;Wesolowska-Andersen A;Walker M;Adamski J;Schwenk JM;McCarthy MI;Pearson E;Dermitzakis E;Franks PW;Pedersen O;Brunak S
• 通讯作者: Brunak S

Analysis of overlapping genetic association in type 1 and type 2 diabetes.

• DOI: 10.1007/s00125-021-05428-0
• 发表时间: 2021-06
• 期刊: Diabetologia
• 影响因子: 8.2
• 作者: Inshaw JRJ;Sidore C;Cucca F;Stefana MI;Crouch DJM;McCarthy MI;Mahajan A;Todd JA
• 通讯作者: Todd JA

Large-Scale Analyses Provide No Evidence for Gene-Gene Interactions Influencing Type 2 Diabetes Risk.

• DOI: 10.2337/db20-0224
• 发表时间: 2020-11
• 期刊: Diabetes
• 影响因子: 7.7
• 作者: Nag A;McCarthy MI;Mahajan A
• 通讯作者: Mahajan A

Genome-Wide Association Study of Peripheral Artery Disease.

• DOI: 10.1161/circgen.119.002862
• 发表时间: 2021-10
• 期刊: Circulation. Genomic and precision medicine
• 作者: van Zuydam NR;Stiby A;Abdalla M;Austin E;Dahlström EH;McLachlan S;Vlachopoulou E;Ahlqvist E;Di Liao C;Sandholm N;Forsblom C;Mahajan A;Robertson NR;Rayner NW;Lindholm E;Sinisalo J;Perola M;Kallio M;Weiss E;Price J;Paterson A;Klein B;Salomaa V;Palmer CNA;Groop PH;Groop L;McCarthy MI;de Andrade M;Morris AP;Hopewell JC;Colhoun HM;Kullo IJ;GoLEAD Consortium, SUMMIT Consortium†
• 通讯作者: GoLEAD Consortium, SUMMIT Consortium†

Deep learning models predict regulatory variants in pancreatic islets and refine type 2 diabetes association signals.

深度学习模型可预测胰岛的调节变异并细化 2 型糖尿病关联信号。

• DOI: 10.7554/elife.51503
• 发表时间: 2020
• 期刊: eLife
• 影响因子: 7.7
• 作者: Wesolowska-Andersen,Agata;ZhuoYu,Grace;Nylander,Vibe;Abaitua,Fernando;Thurner,Matthias;Torres,JasonM;Mahajan,Anubha;Gloyn,AnnaL;McCarthy,MarkI
• 通讯作者: McCarthy,MarkI