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

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 normal and dietary restricted regimes. If successful, future elaborations of our fundamental approach could compare the genetic basis lifespan across multiple, diverse populations, and contrast genetic architecture across a range of lifespan-enhancing treatments.