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和外显子组测序- 基于分析是阐明基因变异和特定基因之间关系的一种常用策略 表型。虽然这些方法有优点，但它们也有显著的局限性，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，可能不可能成功分离 通过只研究一种结果性状或疾病的基因型-表型关联来研究个体部分。 要影响一种疾病，药物需要作用于正确的靶点和正确的组织。生物信息学方法 集成多个关键信息层以揭示有效药物将解决一个关键的未得到满足的需求，因为 预计，复杂的因素相互作用形成了大多数人类表型和疾病的基础。 这项提议的总体目标是开发整合基因和表型的算法。 将结果与染色体结构数据和功能关系网络相关联，以识别 会产生复杂的表型和修饰它们的药物。这些算法将提供一种新的和独特的 研究复杂性状和结果的遗传病因学的方法，增加对和 最终，从高吞吐量关联测试中获得的见解。这项提议的理由是 强大的组织特异性方法将为遗传学家、拥有生物信息库的研究人员以及那些 通过获取其他广泛的表型数据，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：(目标1) 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的开发 和特定组织中与表型相关的途径；(目标2)开发整合了 3D基因组结构与通过生物网络识别潜在表型基因的强健关联 在特定组织中；(目标3)开发识别特定改变基因区域的药物的算法-- 与复杂表型相关的基因网络。方法将被应用于表观范围的分析 盖辛格健康系统MyCode®生物库和候选生物仓库的子集将通过分子验证 化验。 这项资助的结果，即基因和药物的组织特定网络分析的算法，是 预计将产生积极的翻译影响，因为这样的算法使研究人员能够 将现有的数据资源转化为因果基因和有效药物。

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.