# Integrated Clinical and Transcriptomic Profiling to Characterize Disease Phenotype

> **NIH NIH K08** · STANFORD UNIVERSITY · 2020 · $190,941

## Abstract

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.

## Key facts

- **NIH application ID:** 9948713
- **Project number:** 5K08HG010061-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Anandi Krishnan
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $190,941
- **Award type:** 5
- **Project period:** 2018-09-17 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9948713

## Citation

> US National Institutes of Health, RePORTER application 9948713, Integrated Clinical and Transcriptomic Profiling to Characterize Disease Phenotype (5K08HG010061-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9948713. Licensed CC0.

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