QTLNetMiner - Mining Gene Networks from QTL Intervals

发现哪些基因决定了作物、动物或人类的特定生物特征，这是一个非常重要的发现。这类知识有许多应用，包括：为动物或人类疾病确定新的生物标记物，这可能导致新的诊断；设计新药物的筛选；帮助选择生产力更高或对疾病等应激反应具有抵抗力的作物或牲畜的新品种。然而，在农作物或动物基因组中寻找这些候选基因就像大海捞针，收集支持选择一个基因而不是另一个基因的证据更令人望而生畏。这是因为证据分散在不同的互联网数据库中，并且以不兼容的形式存在，不容易链接或整合在一起。生物学家用来开始寻找候选基因的一个非常重要的信息类别是遗传学。经典的遗传学方法使用群体和家系的研究，并使用统计方法来识别最可能的基因组片段，这些基因组片段被称为数量性状基因座(QTL)。然而，复杂性状的性质意味着可以为某一特定性状确定许多QTL。例如，最近在甘蓝型油菜上的一项研究确定了47个与种子产量相关的QTL，在猪身上的研究总共发现了400多个与肥度有关的QTL。多年来，对农作物和家畜复杂性状的研究一直是通过选择育种改良它们的重要辅助手段。直到最近，QTL定位的重点一直是基于使用相对较少的(数百个)遗传标记构建的遗传图谱，这些遗传标记被相当大的遗传距离分开。通过将遗传图谱与新获得的基因组序列信息联系起来，现在就有可能列出构成每个QTL的基因。这些研究表明，植物和动物中的典型QTL通常包含相当大的基因组部分--通常是数百个基因。虽然遗传学提高了找到合适基因的机会，将典型基因组中的选择从大约22,000个减少到特定QTL的数百个基因，但在实验室评估候选基因仍然是一项艰巨而昂贵的任务。此外，在癌症等疾病中变得越来越明显，复杂的表型可能是一组看似独立的基因通过不同生物关系网络相互作用的结果。我们计划在这个项目中开发的软件建立在以前资助的BBSRC研究的基础上，在这些研究中，我们开发了整合不同生物信息源的通用方法，并使用基于网络的方法探索基因和蛋白质之间的关系。我们的方法帮助生物学家挖掘基因之间的信息网络和相互作用，以便更明智地判断哪些基因或基因网络与特定性状有关。在这个项目中，我们将进一步开发软件，并采用我们的方法为代表重要作物和农场动物物种的四个物种创建适合生物学家的网站原型，其中遗传学和QTL数据可以与包括科学文献在内的其他数据资源相结合。之所以选择这些物种，是因为它们对BBSRC的重要性，以及围绕改善我们的粮食和能源(生物能源)供应的安全的国家优先事项。特别是，我们将开发集成的数据和网络生物学资源作为网站，供农场动物研究社区使用；从而将我们的数据集成研究的应用转化为BBSRC资助的生物学的一个新领域。除了为生物学家开发几个新的资源外，我们还希望证明，Ondex数据集成平台可以以成本效益的方式适应生物研究的新领域。

Discovering which genes determine a particular biological trait in crops, animals or humans is a very important finding. There are many applications of such knowledge including: identifying new biomarkers for animal or human diseases which can lead to new diagnostics; designing screens for new drugs, and helping to select new varieties of crop or livestock animals with improved productivity or resistance to stresses such as disease. Searching for these candidate genes in a crop or animal genome is, however, like searching for a needle in a haystack and gathering the evidence that supports the choice of one gene over another is even more daunting. This is because the evidence is scatted among different internet databases and in incompatible forms that are not easily linked together or integrated. One very important class of information used by biologists to begin their search for candidate genes is genetics. Classical genetics methods use studies of populations and families and employ statistical methods to identify the most likely genome segments that are known as Quantitative Trait Loci (QTL). The nature of complex traits, however, means that many QTL may be identified for a particular trait. For example, a recent study in Brassica napus identified 47 QTLs which were relevant for seed yield and studies in pig have discovered in total more than 400 QTLs related to fatness. For many years, the study of complex traits in crops and livestock animals has been an important adjunct to their improvement through selective breeding. Until recently, the focus on mapping of QTLs has been based on genetic maps constructed using relatively small numbers (hundreds) of genetic markers separated by quite large genetic distances. By linking the genetic maps with newly obtained genome sequence information it is now possible to list the genes that underlie each QTL. These studies show that typical QTLs in both plants and animals generally encompass quite sizeable parts of the genome - typically several hundred genes. While genetics improves the chances of finding the right gene (or genes), reducing the options down from 22,000 or so found in a typical genome, to hundreds genes for a particular QTL, it is still a daunting and expensive task to evaluate candidate gene in the laboratory. Furthermore, as is becoming apparent in diseases such as cancer, a complex phenotype may be the consequence of groups of seemingly independent genes interacting through a network of different biological relationships. The software we plan to develop in this project builds on previously-funded BBSRC research in which we have developed general methods for integrating different sources of biological information and exploring the relationships among genes and proteins using network-based approaches. Our methods help biologists mine the networks of information and interactions among genes in order to make better-informed judgments about which gene or gene networks are involved in a particular trait. In this project we will further develop the software and adapt our methods to create prototypes of biologist-friendly web sites for four species representing important crop and farm animal species where genetics and QTL data can be combined with other data resources, including the scientific literature. These species have been chosen because of their importance to the BBSRC and national priorities around improving the security of our food and energy (bioenergy) supplies. In particular, we will develop integrated data and network biology resources as web sites for use by the farm animal research community; thus translating the applications of our data integration research into a new area of BBSRC-funded biology. In addition to developing several novel resources for biologists, we wish to demonstrate that the Ondex data integration platform can be adapted to new areas of biological research in a cost-effective manner.