Toward improved understanding of sex differences in drug response: developing gene and pathway-based informatics methods to examine sex-differential genetic effects

研究综述 女性发生不良事件的风险高出1.5倍以上，包括严重不良事件，如 药物性肝损伤1997年至2000年，10种药物中有8种退出市场， 对女性的健康风险更大。虽然这种增加的风险部分是由于妇女服用过量， 这些差异的大部分也与生物学性别差异有关;然而， 这些差异背后的原因我们知之甚少。此外，不研究女性的一个经常被引用的原因是， 由于月经周期而存在性别内变异。在整个药物开发过程中， 性别很少被考虑，标签通常被排除在分析之外，即使是在计算层面。遗传 以全基因组关联研究（GWAS）和基因表达水平为形式的数据，提供了独特的 分析性的影响的机会;他们允许洞察生物功能和检查 在这种情况下，无标记数据是可能的，因为性别很容易估算。此外，基于网络 对这些数据的分析具有通过依赖于先验信息来增加信噪比的益处 关于基因-基因相互作用和途径，也有助于结果的生物学解释。提高 了解性别差异的影响，我建议利用遗传数据，以实现以下具体 目的：1）利用基因表达数据，研究不同年龄组性别差异的分子效应。 器官水平和性别内的差异，由于月经周期激素的变异，2）发展网络为基础的 检测GWAS中性别差异效应的方法，以及3）性别间和性别内变异性之间的联系 药物反应。我的工作将提高对性和药物反应之间相互作用的理解， 深入了解这些相互作用背后的机制。随着成功和进一步评估，这 分析将通过考虑性别相关的变异性来改善药物开发过程， 不良事件。 我的长期职业目标是成为一名独立的学术研究人员， 研究生物学、疾病和药物反应的性别间和性别内差异。在我的研究期间 培训，我将通过深化我的研究技能，建立合作，发表论文， 参加研讨会和会议，参加额外的相关课程，以及教学和指导 学生我已经做好了实现这些目标的准备;我的培训将与Russ博士一起进行 奥特曼在指导学生方面有着非常成功的记录，而在斯坦福大学， 拥有令人难以置信的教育资源和协作的尖端研究环境。

RESEARCH SUMMARY Women are at more than 1.5-fold higher risk for adverse events, including serious adverse events such as drug induced liver injury. Between 1997 and 2000, eight out of the ten drugs withdrawn from the market proposed greater health risks to women. While some of this increased risk is due to women being overdosed, a large portion of these differences are also related to biological sex differences; however, the mechanisms behind these differences are poorly understand. In addition, an often-cited reason for not studying women is the presence of within-sex variability due to the menstrual cycle. Throughout the drug development pipeline, sex is rarely considered, and labels are routinely left out of analysis, even at the computational level. Genetic data, in the forms of Genome-Wide Association Studies (GWAS) and gene expression levels, provide unique opportunities for analyzing the effects of sex; they allow for insights into biological function and examination of unlabeled data is possible in this case because sex can be easily imputed. Additionally, network-based analysis of these data has the benefit of increasing the signal-to-noise ratio by relying on prior information about gene-gene interactions and pathways, and also aids in the biological interpretation of results. To improve understanding of sex-differential effects, I propose to leverage genetic data to accomplish following specific aims: 1) use gene expression data to investigate the molecular effects of between-sex differences at the organ-level and within-sex differences due to menstrual cycle hormone variability, 2) develop network-based methods for detecting sex-differential effects in GWAS, and 3) link identified between- and within-sex variability to drug response. My work will improve understanding of interactions between sex and drug response, and provide insight into the mechanisms behind these interactions. With success and further evaluation, this analysis will improve the drug development process by taking sex-related variability into account, decreasing adverse events. My long-term career goal is to become an independent academic researcher developing informatics methods to study between and within sex variability in biology, disease, and drug response. During my fellowship training, I will work toward this goal by deepening my research skills, building collaborations, publishing papers, attending seminars and conferences, taking additional relevant coursework, and teaching and mentoring students. I am exceptionally well-poised to achieve these goals; my training will take place with Dr. Russ Altman, who has an extremely successful track record of mentoring students, and at Stanford University, which has incredible educational resources and a collaborative cutting-edge research environment.