# Delineation of genetic architecture underlying complex traits at molecular, individual and population levels

> **NIH NIH R35** · STANFORD UNIVERSITY · 2020 · $353,250

## Abstract

Abstract
Deciphering the genetic basis of complex traits is a central goal of human genetics and precision medicine.
Current research in my group aims to develop computational approaches that fill knowledge gaps in two
areas. First, the success of genome-wide associations (GWAS) has largely confined to populations of
European descent; minority individuals are under-represented, and as a result, our understanding of
disease etiology in minority populations lags behind. We have a long-standing commitment to develop and
apply novel analytic frameworks, which aim to accelerate biomedical research in minority populations.
Building upon our existing work, we will develop genetic risk assessment approaches for minority individuals
that judiciously leverage both trans-ethnic and ethnic-specific information. We will also develop models for
characterizing the genetic basis underlying population-level phenotypic differences. A second area, which
we will investigate in parallel, is to use molecular phenotypes, in particular proteomics data, to elucidate the
biological relationship between GWAS loci and disease outcomes. A fundamental limitation of GWAS is that
it does not reveal the mechanisms through which DNA-level variation manifests into phenotypes; this is
particularly problematic because a large fraction of GWAS SNPs falls into non-coding regions. The mapping
of gene expression quantitative traits (eQTL) using RNA expression has provided a rich source of
information regarding gene regulation. On the other hand, the genetic basis of post-transcriptional regulation
and its impact on complex traits and diseases is poorly understood. We hypothesize that proteomics data
allows us to gain understanding about post-transcriptional regulation, and protein abundance provides a
heritable marker linking between RNA and phenotypes. Making use of proteomic data, such as those
generated through GTEx, we propose novel analytic approaches for uncovering pQTLs, for using protein as
intermediate phenotype in disease risk assessment, and for identifying candidate proteins that link GWAS
loci and phenotype. Ultimately, we envision that the ensemble of methods we develop, by capitalizing on
multi-ethnic cohorts and multi-omics data, will contribute to the implementation of individualized prevention
and intervention strategies for people of all races and ethnicities.

## Key facts

- **NIH application ID:** 9901591
- **Project number:** 5R35GM127063-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Hua Tang
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $353,250
- **Award type:** 5
- **Project period:** 2018-04-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9901591

## Citation

> US National Institutes of Health, RePORTER application 9901591, Delineation of genetic architecture underlying complex traits at molecular, individual and population levels (5R35GM127063-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9901591. Licensed CC0.

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