The world population is rapidly growing older, and population aging will be one of the most important social and health problems in the coming half-century. Life span is a typical quantitative trait, with natural variation attributable to segregating variants at multiple interacting loci, the effects of which are sensitive to the environment to which the individuals are exposed. However, only a handful of candidate genes associated with variation in life span in human populations have been identified. In this application we propose to identify evolutionarily conserved genetic networks causally associated with life span by using the powerful Drosophila model system, which enables us to accurately measure life span while precisely controlling both genetic background and environmental conditions. We propose an integrated systems genetics analysis of life span using the Drosophila melanogaster Genetic Reference Population (DGRP), which consists of wild-derived inbred lines with sequenced genomes. The lines are genetically variable for all phenotypes measured to date, including life span.
The Specific Aims of this application are: (1) To map causal alleles associated with variation in life span with high resolution using a combination of genome wide association and linkage mapping;(2) To derive causal transcriptional co-expression networks affecting life span, placing novel loci identified by genetic mapping in appropriate biological context, and to use allele specific expression to test the systems level predictions;and (3) To use mutations and RNAi to functionally test effects on life span of genes implicated by the statistical analyses of natural variation and architecture of transcriptional networks, and use the recently developed system for integrating transgenes in the same genomic location to perform tests for causal effects of natural alleles. Results from these studies are likely to uncover novel genes and evolutionarily conserved cellular pathways associated with variation in life span. Because many genes in Drosophila have human orthologues, general insights derived from our proposed studies will have translational implications for human genetic studies on life span;moreover, we argue that insights derived from systems genetic studies of life span will have a broad impact on our general understanding of quantitative traits.
The world population is rapidly growing older, and population aging will be one of the most important social and health problems in the coming half-century. Life span is a complex trait with significant genetic variation in human populations. However, only a few loci affecting variation in lifespan have been identified, and the effects of variants associated with longevity and age-related diseases are typically small and account for little of the population variance. Human orthologues of genes identified in model organisms are excellent candidate genes affecting longevity because of the broad conservation of genes and genetic pathways across eukaryotic taxa. This study utilizes a new genetic resource, fully sequenced Drosophila lines, to map molecular variants affecting life span and derive gene expression networks causally associated with longevity. The novel genes and pathways we identify in Drosophila can be incorporated as candidate genes in human linkage and association studies.