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.
Interpretation of personal genome data is an essential component of personalized medicine approaches to improving public health, which aim to predict differences in clinical outcomes and disease risks between individuals using genetic information. This project will develop statistical tools to predict the consequences of personal genetic variation on downstream variation in gene expression levels and clinically relevant traits.
| Ioannidis, Nilah M; Wang, Wei; Furlotte, Nicholas A et al. (2018) Gene expression imputation identifies candidate genes and susceptibility loci associated with cutaneous squamous cell carcinoma. Nat Commun 9:4264 |
