# Predicting Phenotype by Using Transcriptomic Alteration as Endophenotype

> **NIH NIH R01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2020 · $336,880

## Abstract

Project Summary
Modern studies of the genetic architecture underlying human complex traits or diseases generally fall into three
designs of association relationship: the association between genetic variants and disease, the association
between genetic variants and expression (e.g. expression quantitative trait loci, eQTL), and the association
between gene expression and disease. Many promising findings are discovered, including thousands of single
nucleotide polymorphisms found to be associated with common diseases. While these findings provide us with
valuable insights into the genetic architecture of common diseases and the shared heritability among diseases,
what missing are the mechanisms, including the exact causal variants, the direction of their effects, and the
orders of events, which forms the foundational hypothesis that we would like to solve through the studies in this
proposal. With the inspiration of many recent discoveries that a substantial fraction of the disease-associated
genetic variants is located in regulatory regions, in this proposal, we combine bioinformatics, statistical
genetics, precision medicine, and phenotype and electronic medical record (EMR) data mining to develop
novel analytical strategies that maximally leverage regulatory information from both genotype and expression,
aiming to predict phenotype using transcriptomic alteration with DNA variation. We propose the following three
major aims. (1) To build a unified genetic model for the prediction of phenotype by combining genetic and
transcriptomic associations. Functional and regulatory annotation data generated from the ENCODE,
FANTOM5, GENCODE, the Epigenomic Roadmap, and GTEx will be effectively incorporated to infer an
important endophenotype, the genetically determined expression component, for better prediction of
phenotype or disease outcome. (2) To develop a maximum likelihood based link test and a phenotype-specific
regulatory network approach to resolve genotype-phenotype causality relationships mediated by gene
expression. (3) To extensively evaluate the approaches in schizophrenia and apply them to broad phenotypes
using the Vanderbilt biobank (BioVU) genotype and linked electronic medical data. Building on our previous
studies and strong preliminary data, this proposal is timely for studying the genetic architecture in human
complex diseases and traits by dissecting the genetic components contributed from regulatory roles of variants
at the gene expression level. It is highly significant because it tackles the strong limitations in numerous
genome-wide association studies (GWAS) and next-generation sequencing (NGS) for inferring causality and
translational potentials in the emerging fields of precision medicine. The successful completion of this project
will not only advance our understanding of genetic components in schizophrenia and a broad spectrum of
phenotypes or clinical outcomes, but also provide useful methods and tools to the public commun...

## Key facts

- **NIH application ID:** 9980998
- **Project number:** 5R01LM012806-04
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** Zhongming Zhao
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $336,880
- **Award type:** 5
- **Project period:** 2017-09-14 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9980998

## Citation

> US National Institutes of Health, RePORTER application 9980998, Predicting Phenotype by Using Transcriptomic Alteration as Endophenotype (5R01LM012806-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9980998. Licensed CC0.

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