Statistical Methods for Personal Genome Interpretation

项目摘要 该提案的目的是通过以下方式改善个人基因组遗传变异的临床解释： 开发统计方法来预测个人遗传变异对下游的影响， 全转录组基因表达水平和复杂疾病和其他临床相关疾病的风险 性状这个建议是基于这样一个假设，即个人转录组变异在 确定复杂的性状和疾病易感性，转录组变异具有遗传学意义。 这是可以从个人遗传变异中预测出来的。具体目标涉及以下三个方面： 遗传变异、转录组变异和复杂性状之间的关系。具体而言，目标1是 开发方法来预测个体遗传变异对个体表达水平的影响， 目的2是开发从全基因组预测转录组范围基因表达水平的方法 测序数据，包括罕见和常见变异效应;目标3是开发方法， 将基因表达变异信息纳入基于基因型的疾病风险预测模型， 而不需要在应用模型期间测量基因表达水平以预测 未来的个人。这些目标的完成将为临床医生和研究人员提供新的工具来解释 个人基因组，通过预测未知意义的个体变异和全球 全基因组变异对转录组变异的影响以及复杂疾病和其他疾病的风险 临床相关特征此外，该项目将使主要研究者能够发展以下方面的专门知识： 统计学，以补充她目前在遗传学，生物物理学，生物化学和计算 生物学再加上在斯坦福大学通过课程、研讨会、一个- 来自项目共同导师的一对一建议，以及与更广泛的统计学和生物统计学的互动 社区，该项目将准备首席研究员启动一个独立的学术生涯， 统计基因组学

PROJECT SUMMARY The objective of this proposal is to improve clinical interpretation of genetic variation in personal genomes by developing statistical methods to predict the downstream effects of personal genetic variation on transcriptome-wide gene expression levels and on risks for complex diseases and other clinically relevant traits. This proposal is based on the hypothesis that personal transcriptome variation plays an important role in determining complex traits and disease susceptibilities, and that transcriptome variation has a genetic component that is predictable from personal genetic variation. The specific aims address three aspects of the relationship between genetic variation, transcriptome variation, and complex traits. In particular, Aim 1 is to develop methods to predict the effects of individual genetic variants on the expression levels of individual genes; Aim 2 is to develop methods to predict transcriptome-wide gene expression levels from whole genome sequencing data, including both rare and common variant effects; and Aim 3 is to develop methods to incorporate information on gene expression variation into genotype-based disease risk prediction models, without requiring gene expression levels to be measured during application of the models to predict risk in future individuals. Completion of these aims will provide novel tools for clinicians and researchers to interpret personal genomes, by predicting regulatory effects of individual variants of unknown significance and global effects of whole-genome variation on transcriptome variation and risks for complex diseases and other clinically relevant traits. In addition, this project will enable the Principal Investigator to develop expertise in statistics to complement her current background in genetics, biophysics, biochemistry, and computational biology. Combined with additional statistical training at Stanford University through coursework, seminars, one- on-one advising from the project co-mentors, and interactions with the wider statistics and biostatistics communities, this project will prepare the Principal Investigator to launch an independent academic career in statistical genomics.