# A path to personalized phenotypic prediction: unlocking the context-dependency of allelic effects

> **NIH NIH R35** · PRINCETON UNIVERSITY · 2020 · $405,000

## Abstract

The long-term goal of my research program is to understand the biological basis for individual
variation. The genetic architecture of complex traits is not a static blueprint of the phenotype as it
was previously thought; rather, it is highly dynamic and context-dependent. I seek to understand
how genes interact with each other and their environment to shape variation between individuals
and what factors control the degree of individual variability. Technological advances have
recently fueled the ascent of personal genomics and the promise of precision medicine. The
success of medical genetics will depend on its capacity to personalize, however, individualized
prediction is a grand challenge. When the average effect of an allele does not capture a specific
allelic contribution under certain conditions (whether due to genetic background or the
environment), the link between genotype and phenotype will be missed. Given such context
dependency, understanding how genotypic variation influences variation in an individual's
phenotype demands a shift in focus from population averages to individual effects. Globally, we
are witnessing the rise of complex diseases related to dramatic changes in our daily
environments. These disorders have a clear environmental basis, but they also show strong
familial correlations: susceptibility to these diseases is highly heritable. Despite considerable
effort and resources, we have made little progress in understanding the genetic basis of these
common conditions. This highlight the need for a different approach to identify the causal genetic
factors underlying disorders characterized by non-additive interactions. To date, a key limitation
to address this problem has been that small sample sizes and skewed allele frequency spectrum
limit the power of detecting genetic associations. We have solved this problem by creating a new
community resource made of large, synthetic outbred populations. This enables us to break
away from traditional, artificial and underpowered approaches that have relied on inbred strains.
In parallel, we have developed a molecular and analytical pipeline allowing us to sequence
thousands of single flies at high throughput with very low cost and reliable accuracy. With this
new and versatile resource, we can rear thousands of genetically unique flies drawn from a
common genetic pool, expose them to a range of different environments, and contrast the
ensuing genetic architectures. Our inability to make progress in human genetics for diseases
with strong environmental components suggests a fundamental knowledge gap that my research
addresses in a powerful model system. Given that in humans there is extreme variation and
stochasticity in environmental exposure, we need a predictive framework that can accommodate
these individual-specific impacts. My research program paves a path to personalized phenotypic
prediction by unlocking the context dependence of allelic effects.

## Key facts

- **NIH application ID:** 9999591
- **Project number:** 5R35GM124881-04
- **Recipient organization:** PRINCETON UNIVERSITY
- **Principal Investigator:** Julien Ayroles
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $405,000
- **Award type:** 5
- **Project period:** 2017-09-18 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9999591

## Citation

> US National Institutes of Health, RePORTER application 9999591, A path to personalized phenotypic prediction: unlocking the context-dependency of allelic effects (5R35GM124881-04). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/9999591. Licensed CC0.

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