# Determining the source of missing heritability

> **NIH NIH R01** · HARVARD MEDICAL SCHOOL · 2020 · $330,816

## Abstract

Project Summary
Most human traits are complex/quantitative. Similarly, many common human diseases are
complex; they typically are not caused by a small number of genes, but instead are influenced
by hundreds if not thousands of genes. Little is known about quantitative traits due to
conceptual, experimental, and analytical limitations. This proposal aims to address several key
questions: 1) what are the genes that can drive a quantitative trait and how are they interrelated,
2) what are the genes that drive variation in a quantitative trait in natural populations, and 3)
how do the phenotypes of each individual quantitative gene combine to determine the overall
phenotype of the trait, i.e. are gene-gene interactions important. The induction of galactose and
phosphate metabolic genes in the budding yeast Saccharomyces cerevisiae are classical
Eukaryotic model systems for probing signaling. Preliminary results described in this proposal
show that these responses are also complex traits. Our laboratory has developed high-
throughput flow cytometry methods that are essential for accurately determining the effects of
genes on quantitative traits both among natural variants and mutant strains. Building on our
experimental strengths, we will combine fluorescence reporter strains with a series of deletion or
dosage perturbation libraries. We will generate the most comprehensive list of quantitative
genes yet in each of these traits, and assess the interplay of these quantitative genes within and
between traits. Using allele swaps combined with bulk segregant analysis and classical linkage
we will determine the extent to which alleles of quantitative genes vary in nature. By combining
between zero to four alleles or deletion of quantitative genes, we will be able to directly test the
importance of gene-gene interactions. This combination of approaches should greatly enhance
our understanding of complex traits and have direct relevance for human disease.

## Key facts

- **NIH application ID:** 9980925
- **Project number:** 5R01GM120122-05
- **Recipient organization:** HARVARD MEDICAL SCHOOL
- **Principal Investigator:** Michael Springer
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $330,816
- **Award type:** 5
- **Project period:** 2016-09-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9980925

## Citation

> US National Institutes of Health, RePORTER application 9980925, Determining the source of missing heritability (5R01GM120122-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9980925. Licensed CC0.

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