# Evaluating a powerful genetic mapping framework to discover lifespan extension genes in Drosophila

> **NIH NIH R21** · UNIVERSITY OF KANSAS LAWRENCE · 2024 · $232,564

## Abstract

PROJECT SUMMARY
 A plethora of environmental factors contribute to inter-individual differences in lifespan, yet a significant
genetic component to lifespan variation has been demonstrated in a number of species, including humans.
Characterizing the complex genetic basis of lifespan can provide insights into the mechanisms of aging, the
molecular processes underlying age-related disease risk, and will inform treatments directed at maintaining
health during aging. Direct genetic analysis of lifespan variation in humans is difficult due to the array of
uncontrollable environmental, sociological, and economic factors at play. As a consequence, the genetic
dissection of lifespan in humans has met with mixed success, and only a handful of robust lifespan-associated
variants are known. Given these challenges, non-human model systems – that can be subject to exquisite
environmental control, and allow great flexibility in the design of genetic experiments – remain critical tools for
understanding the biology of aging, and evaluating interventions that can extend life- and healthspan.
 Numerous studies have sought to employ the fruit fly model system, Drosophila melanogaster, to
identify QTL (Quantitative Trait Loci) contributing to lifespan and age-related phenotypes. But these studies
have variously suffered from very low power to identify causative variants, poor resolution to localize QTL, are
open to the confounding effects of inbreeding depression, or are experimentally inefficient. We have pioneered
a novel approach that mitigates all these concerns, extending the “extreme QTL” or X-QTL methodology
originally developed by plant breeders and yeast geneticists. We establish a highly-recombinant population
from a set of sequenced strains, rear thousands of outbred diploid individuals in a “common garden”
environment, select and sequence pools of both phenotypically-extreme and control individuals, and identify
QTL via changes in allele frequency between groups. This strategy affords high power to map even very
modest effect QTL, and localizes them to sufficiently small intervals that follow-up, gene-level functional testing
is possible. We have successfully employed this approach to efficiently and powerfully dissect several traits.
 In the present proof-of-concept project we plan to extend our X-QTL method to dissect genetic variation
for lifespan, as well as variation in the response to dietary restriction, a well-described life-extending treatment.
Extensive simulations, along with our empirical work on other traits, suggest this is by far the highest-powered
study to date aimed at identifying longevity genes in Drosophila. Results will impact the aging/lifespan field in
two major ways. First, we can discover novel loci impacting lifespan traits, future characterization of which will
implicate new pathways in the control of variation. Second, we can rigorously characterize differences in the
genetic architecture of lifespan between sexes, and under...

## Key facts

- **NIH application ID:** 10866801
- **Project number:** 1R21AG086734-01
- **Recipient organization:** UNIVERSITY OF KANSAS LAWRENCE
- **Principal Investigator:** ANTHONY Douglas LONG
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $232,564
- **Award type:** 1
- **Project period:** 2024-06-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10866801

## Citation

> US National Institutes of Health, RePORTER application 10866801, Evaluating a powerful genetic mapping framework to discover lifespan extension genes in Drosophila (1R21AG086734-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10866801. Licensed CC0.

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