Integrated Clinical and Transcriptomic Profiling to Characterize Disease Phenotype

项目总结 外显子组和全基因组测序正日益成为癌症的常规方法[1]，常见 疾病[2]和罕见疾病的诊断。[3]尽管他们取得了成功，但我们有能力充分解释其临床相关性 个人基因组变异仍然是一个显著的差距[4-6]。考虑到这一点，最关键的需要是更多 将遗传变异与疾病病因联系起来的基因-表型数据。这项建议的目的是 改进对遗传变异的临床解释；特别是通过开发综合方法 预测遗传变异对临床表型的影响。这项提议解决了这个假设，并得到了支持 根据初步数据，将患者转录数据与基因和临床数据相结合(与 每个单独)提供了对疾病自然历史的更好的机械性理解，从最初的呈现到 进步。 特定的目的被设计成每个独立地添加大量的功能基因组信息， 在先前可获得的患者基因数据的基础上，进一步解析临床表型。目标1 建立一个全面的、广泛共享的患者转录(和遗传)变异数据集 躯体获得性心脏病患者的多种癌症、心血管和血栓/出血表型 骨髓增生性肿瘤(MPN)和其他罕见的遗传性血液疾病(HBD)。目标2：有条不紊 确定MPN和HBD临床相关亚组之间的差异RNA表达和处理 病人。目标3将这些元素结合在一起-并应用了两种集成的贝叶斯和机器学习 方法，河[24](RNA信息的调节变异效应)和套索[25](最小绝对收缩和 选择运算符)，以解析稀有变体的功能和临床相关性；并识别最 疾病风险或进展的预测。 这些目标的完成将为整合转录数据提供新的科学知识 改善其他遗传性(和罕见)疾病的临床基因组分析。此外，该项目将使 首席研究员，发展基因组医学信息学和数据科学方面的专业知识 这补充了她目前在生物物理学、生物化学和转化血液学方面的背景。组合在一起 在斯坦福大学通过课程作业、研讨会、一对一咨询等方式进行额外的信息学培训 来自项目导师，以及与更广泛的统计、生物信息学和基因组学社区的互动，这 该项目将使首席研究员做好准备，开始在基因组医学方面的独立学术生涯。

PROJECT SUMMARY Exome and whole-genome sequencing are becoming increasingly routine approaches in cancer[1], common disease[2]and rare disease diagnosis.[3] Despite their success, our ability to fully interpret the clinical relevance of personal genome variation remains a significant gap[4-6]. Considering this, the most crucial need is more genotype-phenotype data that link genetic variation with disease causation. The objective of this proposal is to improve the clinical interpretation of genetic variation; in particular, by developing integrative approaches that predict the effect of genetic variation on clinical phenotype. This proposal addresses the hypothesis, supported by preliminary data, that combining patient transcriptomic data with genotypic and clinical data (as opposed to each alone) offers a better mechanistic understanding of disease natural history, from initial presentation to progression. The specific aims are designed such that each independently add substantial functional genomic information, over and above previously available patient genetic data, to further resolve the clinical phenotype. Aim 1 establishes a comprehensive and widely-shared dataset of patient transcriptomic (and genetic) variation across multiple cancer, cardiovascular and thrombosis/bleeding phenotypes, in patients with somatically-acquired myeloproliferative neoplasms (MPN) and select other rare heritable blood diseases (HBD). Aim 2 methodically determines differential RNA expression and processing between clinically-relevant subgroups of MPN and HBD patients. Aim 3 brings these elements together – and applies two integrative Bayesian and machine learning approaches, RIVER[24] (RNA-informed variant effect on regulation) and LASSO[25] (Least Absolute Shrinkage and Selection Operator), to resolve the functional and clinical relevance of rare variants; and identify signatures most predictive of disease risk or progression. Completion of these aims will contribute new scientific knowledge on how integrating transcriptomic data improves clinical genomic analyses in other genetic (and rare) diseases. In addition, this project will enable the Principal Investigator to develop expertise in the informatics and data science aspects of genomic medicine that complement her current background in biophysics, biochemistry and translational hematology. Combined with additional informatics training at Stanford University through coursework, seminars, one-on-one advising from project mentors, and interactions with the wider statistics, bioinformatics and genomics communities, this project will prepare the Principal Investigator to launch an independent academic career in genomic medicine.