Network-based algorithms for target identification and drug repositioning from genetic associations

在遗传学领域，常见变异的全基因组关联研究（GWAS）和外显子组测序- 基于分析是阐明遗传变异与特定基因之间关系的常见策略 表型。虽然这些方法有其优点，但它们也有很大的局限性，例如它们无法 识别导致遗传易感性的复杂生物相互作用，它们无法整合不同的 但相关的表型，以及它们无法通过组织分离遗传变异的影响。如果表型是 仅表现为多种因素复杂相互作用的结果，不可能成功隔离 通过研究仅针对一种结果特征或疾病的基因型-表型关联来分析各个部分。 为了影响疾病，药物需要作用于正确的目标和正确的组织。生物信息学方法 整合多个关键层的信息来揭示有效的药物将解决一个关键的未满足的需求，因为 预计多种因素的复杂相互作用构成了大多数人类表型和疾病的基础。 该提案的总体目标是开发整合基因和表组范围的算法 将结果与染色体结构数据和功能关系网络关联起来，以识别基因 产生复杂的表型和改变它们的药物。这些算法将提供一种新的、独特的 意味着研究复杂性状和结果的遗传病因，增加其可解释性和 最终是从高吞吐量关联测试中产生的见解。该提案的理由是 强大的组织特异性方法将为遗传学家、拥有生物样本库的研究人员以及那些 能够获取其他广泛的表型数据，以有效地重新定位药物并确定新的靶点。 提出了解决这一挑战的不同方面的补充算法作为具体目标：（AIM 1） 开发将外显子组测序结果与生物网络相结合以识别基因的算法 以及与特定组织表型相关的途径； (目标 2) 开发集成的算法 3D 基因组结构通过生物网络具有强大的关联性，以识别表型背后的基因 在特定组织中； (目标 3) 开发算法来识别特异性改变基因区域的药物 与复杂表型相关的基因网络。方法将应用于全表组分析 Geisinger Health System MyCode® 生物储存库和一部分候选者将通过分子验证 化验。 这项资助的成果，即基因和药物的组织特异性网络分析算法，是 预计会产生积极的翻译影响，因为此类算法使研究人员能够翻译 将现有数据资源转化为致病基因和有效药物。

In the field of genetics, genome-wide association studies of common variants (GWAS) and exome sequencing- based analyses are a common strategy to elucidate the relationship between genetic variants and a specific phenotype. While these approaches have strengths, they also have significant limitations such as their inability to identify complex biological interactions that lead to genetic predispositions, their inability to integrate distinct but related phenotypes, and their inability to separate genetic variants effects by tissue. If a phenotype is manifest only as a result of the complex interplay of multiple factors, it can be impossible to successfully isolate individual parts by investigating genotype-phenotype associations for only one outcome trait or disease alone. To affect a disease, drugs need to act on the right target and in the right tissue. Bioinformatics approaches that integrate multiple key layers of information to reveal effective drugs will address a critical unmet need because it is expected that a complex interplay of factors forms the basis for most human phenotypes and diseases. The overall objective of this proposal is the development of algorithms that integrate gene and phenome-wide association results with chromosome structure data and functional relationship networks to identify genes that give rise to complex phenotypes and drugs that modify them. These algorithms will provide a new and unique means to study the genetic etiology of complex traits and outcomes, increasing the interpretability of and ultimately the insights generated from high throughput association testing. The proposal's rationale is that robust tissue-specific methods will open the door for geneticists, researchers with biorepositories, and those with access to other extensive phenotyping data to effectively reposition drugs and identify new targets. Complementary algorithms to address distinct aspects of this challenge are proposed as specific aims: (AIM 1) Development of algorithms that integrate exome sequencing results with biological networks to identify genes and pathways associated with phenotypes in specific tissues; (AIM 2) Development of algorithms that integrate 3D genome structure with robust associations via biological networks to identify genes underlying phenotypes in specific tissues; (AIM 3) Development of algorithms that identify drugs that specifically alter regions of gene- gene networks associated with a complex phenotype. Methods will be applied to phenome-wide analysis of the Geisinger Health System MyCode® biorepository and a subset of candidates will be validated via molecular assays. The outcomes of this grant, namely algorithms for tissue-specific network analysis of genes and drugs, are expected to generate positive translational impact because such algorithms enable researchers to translate existing data resources into causal genes and effective drugs.