Systems-biology based approaches for the identification of genes controlling complex phenotypic traits

基于系统生物学的方法来识别控制复杂表型性状的基因

• 批准号: BB/I016287/1
• 金额: $ 12.73万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Training Grant
• 财政年份: 2011
• 资助国家: 英国
• 起止时间: 2011 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FI016287%2F1
• 关键词: Systems; biology; based; approaches; identification

Next-generation sequencing technologies are revolutionizing the way we do research in molecular biology and genetics. One of the leading companies driving the development of next-generation technologies is Illumina. The cost of DNA sequencing has dropped so much that within the next five years sequencing whole genomes for many individuals will become a standard technique, as being undertaken in the Wellcome Trust Sanger Institute's UK10K project. However, we are just at the very beginning of analysing and understanding these massive amounts of data. This project will develop new bioinformatics methodologies for the analysis of next-generation sequence data. Individuals of one species show many differences in DNA sequence, many variants appear to be without phenotypic effect. But recent publications demonstrated elegantly that analysing the protein-coding sequences in few individuals is sufficient to identify the gene responsible for monogenic traits, for example responsible for particular genetically inherited diseases (Choi 2009; Ng 2009; Ng 2010). In these cases the strong phenotypic effect of the individual sequence variants allowed to exclude all previously known sequence variants from the candidate lists. However, most traits are not determined by single genes, but rather depend on many different genes. Sequence variants contributing to such complex traits will be much harder to identify, because individual variants might not have any phenotypic effects unless they occur in combination with other sequence variants, i.e. we cannot exclude previously known sequence variants per se any longer. Prediction of the deleterious effects of individual sequence variants on the amino-acid sequence of the protein products can provide further evidence for the identification of causal variants, e.g. (Ng 2003), though this approach on its own is not powerful enough to identify the causal gene(s). The aim of this project is to establish a systems approach utilizing biological networks in combination with sequence analysis methods to identify sequence variants in silico that are likely to be important for complex phenotypic traits. The underlying assumption is that multiple sequence variants that hit different proteins involved in functionally related processes will in combination lead to phenotypic effects. This project will use gene networks, protein networks and metabolic networks that we have collected from public data repositories and publications to examine the function and potential impact of sequence variants on the biological system. The approaches developed here will be relevant for the study of biological organisms in general; they will also be very instrumental for the identification of genetic effects contributing to complex phenotypes, which could be relevant for breeding of plants and animals, as well as to improve our understanding of complex diseases such as Crohn's disease, Psoriasis and Cancer. Large-scale sequence data for individuals suffering from these disorders are currently being obtained within the Department and by Illumina and will be available for analysis. The outcomes of this project will benefit researchers in the areas of genetics, bioinformatics, gene and protein networks, systems biology and ultimately disease processes. Bioinformatics software developed as part of this project will be made available free of charge as open source software. Molecular biologists will benefit as users of our software for the analysis of their sequence data and the exploration biological networks; the project will thus support the design of novel experimental approaches. Choi, M. (2009). Proc Natl Acad Sci U S A 106(45): 19096-19101. Ng, P. C. (2003). Nucleic Acids Res 31(13): 3812-3814. Ng, S. B. (2010). Nat Genet 42(1): 30-35. Ng, S. B. (2009). Nature 461(7261): 272-276.

下一代测序技术正在彻底改变我们在分子生物学和遗传学方面的研究方式。Illumina是推动下一代技术发展的领先公司之一。DNA测序的成本已经大幅下降，在未来五年内，对许多人的整个基因组进行测序将成为一项标准技术，正如威康信托桑格研究所的UK10K项目所做的那样。然而，我们才刚刚开始分析和理解这些海量数据。该项目将为分析下一代序列数据开发新的生物信息学方法。一个物种的个体在DNA序列上表现出许多差异，许多变体似乎没有表型效应。但最近的出版物优雅地表明，分析少数个体中的蛋白质编码序列足以鉴定负责单基因性状的基因，例如负责特定遗传遗传性疾病的基因（Choi 2009; Ng 2009; Ng 2010）。在这些情况下，单个序列变体的强表型效应允许从候选列表中排除所有先前已知的序列变体。然而，大多数性状不是由单个基因决定的，而是取决于许多不同的基因。导致这种复杂性状的序列变异将更难识别，因为个体变异可能不具有任何表型效应，除非它们与其他序列变异组合出现，即我们不能再排除先前已知的序列变异本身。预测单个序列变体对蛋白质产物的氨基酸序列的有害影响可以为鉴定致病变体提供进一步的证据，例如（Ng 2003），尽管这种方法本身不足以鉴定致病基因。该项目的目的是建立一个系统的方法，利用生物网络与序列分析方法相结合，以确定在硅片上的序列变异可能是重要的复杂的表型性状。潜在的假设是，多个序列变体击中参与功能相关过程的不同蛋白质，将组合导致表型效应。该项目将使用我们从公共数据库和出版物中收集的基因网络，蛋白质网络和代谢网络来研究序列变异对生物系统的功能和潜在影响。这里开发的方法将是相关的生物有机体的研究一般，他们也将是非常有用的遗传效应的识别有助于复杂的表型，这可能是相关的植物和动物的育种，以及提高我们的理解复杂的疾病，如克罗恩病，牛皮癣和癌症。目前，该部门和Illumina正在获得患有这些疾病的个人的大规模序列数据，并将用于分析。该项目的成果将使遗传学、生物信息学、基因和蛋白质网络、系统生物学以及最终疾病过程等领域的研究人员受益。作为该项目一部分开发的生物信息学软件将作为开放源码软件免费提供。分子生物学家将受益于我们的软件的用户分析他们的序列数据和探索生物网络;该项目将因此支持新的实验方法的设计。崔，M。（2009年）。Proc Natl Acad Sci U S A 106（45）：19096 - 19101. Ng，P. C.（2003年）的报告。Nucleic Acids Res 31（13）：3812 - 3814. Ng，S. B。（2010年）。Nat Genet 42（1）：30 - 35. Ng，S. B。（2009年）。Nature 461（7261）：272 - 276.