Exploiting the protein-protein interaction network to identify common genetic variants associated with complex diseases

利用蛋白质-蛋白质相互作用网络来识别与复杂疾病相关的常见遗传变异

• 批准号: MR/K021613/1
• 负责人: Alessia David
• 金额: $ 47.7万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FK021613%2F1
• 关键词: Exploiting; protein; interaction; network; identify

Thanks to recent advances in technology, it is nowadays possible to analyse our genetic material, the DNA. The DNA of two persons differs by approximately 4.5 million small genetic variants, which are the likely cause of the different predisposition to important diseases such as cancer, diabetes and heart disorders. It is, thus, important to understand which of these numerous variants are responsible for disease and the different response to therapies. This cannot be achieved with traditional biology experiments, but requires the use of complex computer algorithms. This project aims at developing such a computer program, which combines biological, mathematical and statistical tools with the vast amount of genetic and biological information available on the internet and the power of modern computers. In particular, the proposed study aims to evaluate the effect of genetic variants on health, by studying how these affect the structure and interaction between proteins, thus, causing disease. We will focus on cardiovascular disorders, diabetes, obesity and cancer which affect millions of people in the UK alone, but the method created will be applicable to any human disease. This computer program will be able to identify potentially damaging DNA variations and will represent an invaluable tool for researchers around the world who are engaged in the effort of understanding and treating diseases.

由于最近的技术进步，现在可以分析我们的遗传物质，DNA。两个人的DNA有大约450万个小的遗传变异，这可能是导致癌症、糖尿病和心脏病等重要疾病的不同倾向的原因。因此，重要的是要了解这些众多的变异是负责疾病和对治疗的不同反应。这是传统生物学实验无法实现的，需要使用复杂的计算机算法。该项目旨在开发这样一种计算机程序，它将生物、数学和统计工具与互联网上的大量遗传和生物信息以及现代计算机的能力相结合。特别是，拟议的研究旨在评估遗传变异对健康的影响，通过研究这些变异如何影响蛋白质之间的结构和相互作用，从而导致疾病。我们将专注于心血管疾病，糖尿病，肥胖和癌症，仅在英国就影响数百万人，但所创建的方法将适用于任何人类疾病。该计算机程序将能够识别潜在的破坏性DNA变异，并将为世界各地从事理解和治疗疾病的研究人员提供宝贵的工具。

项目成果

Mutations in IGSF10 cause self-limited delayed puberty

IGSF10突变导致自限性青春期延迟

• DOI: 10.1530/endoabs.39.oc5.1
• 发表时间: 2015
• 期刊: Endocrine Abstracts
• 作者: Howard S

Exploring the cellular basis of human disease through a large-scale mapping of deleterious genes to cell types.

• DOI: 10.1186/s13073-015-0212-9
• 发表时间: 2015-09-01
• 期刊: Genome medicine
• 影响因子: 12.3
• 作者: Cornish AJ;Filippis I;David A;Sternberg MJ
• 通讯作者: Sternberg MJ

IGSF10 mutations dysregulate gonadotropin-releasing hormone neuronal migration resulting in delayed puberty.

• DOI: 10.15252/emmm.201606250
• 发表时间: 2016-06
• 期刊: EMBO molecular medicine
• 影响因子: 11.1
• 作者: Howard SR;Guasti L;Ruiz-Babot G;Mancini A;David A;Storr HL;Metherell LA;Sternberg MJ;Cabrera CP;Warren HR;Barnes MR;Quinton R;de Roux N;Young J;Guiochon-Mantel A;Wehkalampi K;André V;Gothilf Y;Cariboni A;Dunkel L
• 通讯作者: Dunkel L

Role of IGSF10 mutations in self-limited delayed puberty

IGSF10突变在自限性青春期延迟中的作用

• DOI: 10.1016/s0140-6736(16)00401-3
• 发表时间: 2016
• 期刊: The Lancet
• 作者: Howard S

Contributions of Function-Altering Variants in Genes Implicated in Pubertal Timing and Body Mass for Self-Limited Delayed Puberty.

• DOI: 10.1210/jc.2017-02147
• 发表时间: 2018-02-01
• 期刊: The Journal of clinical endocrinology and metabolism
• 作者: Howard SR;Guasti L;Poliandri A;David A;Cabrera CP;Barnes MR;Wehkalampi K;O'Rahilly S;Aiken CE;Coll AP;Ma M;Rimmington D;Yeo GSH;Dunkel L
• 通讯作者: Dunkel L