Statistical Methods for Personal Genome Interpretation

个人基因组解读的统计方法

• 批准号: 10229521
• 负责人: Nilah Monnier Ioannidis
• 金额: $ 19.03万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10229521
• 关键词: Address; Affect; Biochemistry; Biometry; Biophysics; Bipolar Disorder; Classification; Clinical; Communities; Complement; Complex; Computational Biology; Data; Data Set; Databases; Dependence; Disease; Disease model; Disease susceptibility; Future; Gene Expression; Genes; Genetic; Genetic Markers; Genetic Variation; Genome; Genomics; Genotype; Goals; Heart failure; Individual; Insulin Resistance; Measurement; Measures; Mentors; Methods; Modeling; Outcome; Pattern; Personal Genetic Information; Play; Population; Positioning Attribute; Principal Investigator; Public Health; Research Personnel; Risk; Role; Statistical Methods; Structure; Training; Universities; Variant; base; career; clinical application; clinically relevant; disease heterogeneity; disorder risk; gene expression variation; genetic information; genetic predictors; genetic testing; genetic variant; genome sequencing; genome wide association study; genome-wide; improved; novel; personalized medicine; rare variant; response; risk prediction model; statistics; tool; trait; transcriptome; variant of unknown significance; whole genome

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.

项目总结 这项建议的目标是通过以下方式改善对个人基因组遗传变异的临床解释 发展统计方法来预测个人遗传变异的下游影响 转录组范围的基因表达水平以及复杂疾病和其他临床相关疾病的风险 特征。这一建议是基于这样一种假设，即个人转录组变异在 决定复杂的性状和疾病易感性，以及转录组变异具有遗传 可以从个人遗传变异中预测到的成分。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 发展预测个体遗传变异对个体表达水平影响的方法 基因；目标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，包括罕见和常见的变异效应；目标3是开发方法来 将基因表达变异信息纳入基于基因的疾病风险预测模型， 不需要在模型应用期间测量基因表达水平来预测风险 未来的个体。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，通过预测未知意义和全球范围的个体变异的调控效应 全基因组变异对转录组变异和复杂疾病等风险的影响 临床上相关的特征。此外，该项目将使首席调查员能够在以下方面发展专门知识 统计学，以补充她目前在遗传学、生物物理学、生物化学和计算方面的背景 生物学。结合斯坦福大学通过课程作业、研讨会进行的额外统计培训，一 来自项目共同导师的一对一建议，以及与更广泛的统计和生物统计的互动 社区，该项目将使首席调查员在#年开始独立的学术生涯。 统计基因组学。

项目成果

Characterizing uncertainty in predictions of genomic sequence-to-activity models.

描述基因组序列到活性模型预测的不确定性。

• DOI: 10.1101/2023.12.21.572730
• 发表时间: 2023
• 期刊: bioRxiv : the preprint server for biology
• 作者: Bajwa,Ayesha;Rastogi,Ruchir;Kathail,Pooja;Shuai,RichardW;Ioannidis,NilahM
• 通讯作者: Ioannidis,NilahM

Personal transcriptome variation is poorly explained by current genomic deep learning models.

• DOI: 10.1038/s41588-023-01574-w
• 发表时间: 2023-12
• 期刊: NATURE GENETICS
• 影响因子: 30.8
• 作者: Huang, Connie;Shuai, Richard W.;Baokar, Parth;Chung, Ryan;Rastogi, Ruchir;Kathail, Pooja;Ioannidis, Nilah M.
• 通讯作者: Ioannidis, Nilah M.