# Statistical Methods for Personal Genome Interpretation

> **NIH NIH R00** · UNIVERSITY OF CALIFORNIA BERKELEY · 2020 · $194,914

## Abstract

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.

## Key facts

- **NIH application ID:** 10018525
- **Project number:** 5R00HG009677-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Nilah Monnier Ioannidis
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $194,914
- **Award type:** 5
- **Project period:** 2019-09-15 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10018525

## Citation

> US National Institutes of Health, RePORTER application 10018525, Statistical Methods for Personal Genome Interpretation (5R00HG009677-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10018525. Licensed CC0.

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