# Understanding complex trait architecture through population genomics

> **NIH NIH R01** · UNIVERSITY OF MINNESOTA · 2021 · $409,677

## Abstract

Project Summary
Evolutionary medicine leverages the power of naturally occurring phenotypic differences to investigate
genetic mechanisms underlying diseases, including neurological and metabolic disorders. The Mexican
cavefish exhibits a dramatic evolution of sleep loss, hyperphagia, and obesity compared to a surface fish of
the same species, thus, providing a unique and powerful model for identifying genetic factors regulating
these traits. The proposed experiments will implement genomic and transgenic technology in Mexican
cavefish to identify genetic loci that contribute to sleep, feeding, and metabolism. In addition, these
experiments have potential to identify whether shared genetic architecture underlies changes in these traits,
providing insight into the relationship between sleep loss and metabolic disorders. Specifically, the
contribution of cave-associated alleles in Hypocretin receptor 2 to sleep, feeding, and adiposity within a
surface fish genetic background will be investigated. Beyond the immediate scientific goals, transgenic and
genomic tools and techniques will be established and will provide a valuable resource and can be applied to
address questions beyond the scope in this proposal, including in investigations of eye-degeneration and
autism.

## Key facts

- **NIH application ID:** 10144482
- **Project number:** 5R01GM127872-04
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** Alex C Keene
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $409,677
- **Award type:** 5
- **Project period:** 2018-05-15 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10144482

## Citation

> US National Institutes of Health, RePORTER application 10144482, Understanding complex trait architecture through population genomics (5R01GM127872-04). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10144482. Licensed CC0.

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