Cross-species network approach to predict epistatic cancer susceptibility genes

项目总结/摘要 人类基因组计划的完成带来了技术的迅速进步， 基因组数据这一巨大成就激发了国际社会作出令人难以置信的努力， 编目人类遗传变异，并将这种变异与人类表型联系起来， 更个性化的医疗虽然最新的技术提供了前所未有的能力进行基因组- 广泛关联研究（GWAS）旨在识别特定人类疾病的个体易感性基因座，GWAS是 （由于多假设检验问题）筛选多基因-基因 相互作用赋予人类易感性。为了克服这种限制，在本申请中，我们提出了一种 新的跨物种（酵母到人类）比较系统遗传学策略，以确定基因-基因和 人类乳腺癌易感性的途径-途径相互作用。具体来说，我们假设 细胞对DNA损伤的敏感性可以作为乳腺癌的中间表型， 易感性，以及与DNA损伤反应途径中缺陷协同作用的基因和途径 也会协同作用使人类更容易患乳腺癌。在目标1中，我们将利用现有的 我们在酵母中进行全基因组筛选的新数据（R 01 CA 129604- 01 A1基于表型的方法， 发现乳腺癌风险的潜在基因相互作用; PI：Paulovich），以确定基因-基因相互作用可能 是人类乳腺癌易感性的基础。相互作用的酵母基因的假定人类直系同源物将被 使用复杂的数据分析工具进行识别。然后将使用综合基因组学分析来进一步 基于基因组学，优先考虑与乳腺癌易感性相关的高概率基因对 来自人类乳腺癌的数据集和网络。尽管模式生物可以在基因上 改变和他们的环境操纵，以测试预测的贡献，特定的基因变异， 风险，有限制的单一或多个基因敲除或突变的方法作为唯一的手段来测试 因此，在目标2中，我们将测试人类乳腺上皮细胞（HMEC）的功能意义 在酵母中发现的合成或协同基因-基因相互作用，并在目标1中优先考虑。在目标3中，基因- 将检测在HMEC中功能验证的基因相互作用与乳腺癌易感性的相关性 使用现有和新兴的GWAS数据集对人类乳腺癌进行研究。利用DNA敏感性的前提是 损伤作为中间表型是合理的，因为有大量证据表明， 乳腺癌的遗传易感性这项工作将补充和扩大目前的 GWAS研究，因为风险的显著增加可能仅在考虑变异等位基因时才明显， 因此，这些风险等位基因在GWA研究中将经常被遗漏。

Project Summary/Abstract Completion of the human genome project has resulted in rapid advances in technology as well as a deluge of genomic data. This tremendous accomplishment has sparked an incredible international community effort to catalog human genetic variation and to relate this variation to human phenotypes with the ultimate payoff of more personalized medicine. While the latest technologies provide unprecedented ability to conduct genome- wide association studies (GWAS) to identify individual susceptibility loci for a given human disease, GWAS are underpowered (due to the multiple hypotheses testing problem) to screen for the multiple gene-gene interactions conferring susceptibility in humans. To overcome this limitation, in this application, we propose a novel, cross-species (yeast-to-human) comparative systems genetics strategy to identify gene-gene and pathway-pathway interactions underlying human breast cancer susceptibility. Specifically, we hypothesize that cellular sensitivity to DNA damage can be used as an intermediate phenotype for breast cancer susceptibility, and that genes and pathways that synergize with defects in the DNA damage response pathway will also synergize to produce breast cancer susceptibility in humans. In Aim 1, we will leverage existing and emerging data from our genome-wide screens in yeast (R01 CA 129604-01A1 Phenotype-based approach to find gene interactions underlying breast cancer risk; PI: Paulovich) to identify gene-gene interactions likely to underlie susceptibility for breast cancer in humans. Putative human orthologs of interacting yeast genes will be identified using sophisticated data analysis tools. An integrative genomics analysis will then be used to further prioritize gene pairs with high probability of contributing to breast cancer susceptibility based on genomics datasets and networks derived from human breast cancers. Although model organisms can be genetically altered and their environments manipulated to test predictions about contributions of specific gene variants to risk, there are limitations of single or multiple gene knockout or mutant approaches as the sole means to test predictions; hence in Aim 2 we will test the functional significance in human mammary epithelial cells (HMEC) of synthetic or synergistic gene-gene interactions discovered in yeast and prioritized in Aim 1. In Aim 3, gene- gene interactions functionally verified in HMEC will be tested for association with breast cancer susceptibility using existing and emerging GWAS datasets on human breast cancer. The premise of using sensitivity to DNA damage as an intermediate phenotype is reasonable given the abundance of evidence that defects in this pathway cause germline predisposition to breast cancer. This work will complement and extend the current GWAS studies since significant increases in risk may only be apparent when variant alleles are considered in combination (epistasis), and hence these risk alleles will be frequently missed in GWA studies.