# Reverse Engineering Quantitative Genetic Variation

> **NIH NIH R01** · CLEMSON UNIVERSITY · 2020 · $453,893

## Abstract

PROJECT SUMMARY
Risk for most human diseases is attributable to segregating alleles at many interacting genes with
environmentally sensitive effects. Future developments towards personalized precision medicine require a
predictive understanding of how DNA sequence variants give rise to phenotypic variation through modulation
of regulatory gene networks. This is challenging in human populations because variants associated with
complex traits are embedded in relatively large local linkage disequilibrium (LD) blocks, within which
segregating molecular polymorphisms are not independent. Thus, these variants are not necessarily causal,
but could be in LD with the true common or rare causal variant(s) within the same LD block. Furthermore, the
majority of variants associated with complex traits are in intergenic regions, up- or down-stream of coding
regions, or in introns. These variants are presumably regulatory and affect variation in gene expression.
Formally proving the causal relationships between molecular genetic variation, genetic variation in gene
expression and other intermediate molecular phenotypes, and genetic variation in quantitative trait phenotypes
is not possible in human populations. The Drosophila melanogaster Genetic Reference Panel (DGRP) was
generated in our laboratories and consists of 205 inbred, sequenced lines derived from single inseminated
females collected from the Raleigh, NC Farmer’s Market. We have used the DGRP to perform genome wide
association (GWA) mapping for many organismal quantitative traits as well as genome wide gene expression,
which has generated testable hypotheses about the genotype-phenotype map, including sex-, genetic
background- and environment-specific effects. The precision of GWA mapping in the DGRP is excellent
because of rapid local decline of LD with physical distance. Here, we propose to test these hypotheses using
CRISPR/Cas9 mediated precise allelic replacement to functionally validate (1) additive, epistatic and
environment-specific effects of common variants that affect chill coma recovery time; (2) pleiotropic, epistatic
and environment-specific effects of rare variants; and (3) novel transcribed regions (NTRs) and cis-trans
transcriptional networks, and evaluate their effects on genome-wide expression and quantitative traits. These
proposed studies will enable us to evaluate the direct and pleiotropic effects of common and rare variants, in
both genic and intergenic regions, that are shared and distinct between males and females, both with respect
to organismal quantitative trait phenotypes as well as genome wide gene expression. We will be able to
explicitly evaluate the existence and magnitude of epistatic interactions for organismal phenotypes and gene
expression traits and create “designer” genotypes between epistatically interacting alleles in defined genetic
backgrounds. These studies will greatly advance our understanding of how subtle naturally occurring molecular
variation impacts...

## Key facts

- **NIH application ID:** 9915941
- **Project number:** 5R01GM128974-03
- **Recipient organization:** CLEMSON UNIVERSITY
- **Principal Investigator:** Robert R. H Anholt
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $453,893
- **Award type:** 5
- **Project period:** 2018-08-23 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9915941

## Citation

> US National Institutes of Health, RePORTER application 9915941, Reverse Engineering Quantitative Genetic Variation (5R01GM128974-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9915941. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*