Genetic analysis of variation in lifespan using a multiparental advanced intercross Drosophila mapping population

Abstract

BackgroundConsiderable natural variation for lifespan exists within human and animal populations. Genetically dissecting this variation can elucidate the pathways and genes involved in aging, and help uncover the genetic mechanisms underlying risk for age-related diseases. Studying aging in model systems is attractive due to their relatively short lifespan, and the ability to carry out programmed crosses under environmentally-controlled conditions. Here we investigate the genetic architecture of lifespan using the Drosophila Synthetic Population Resource (DSPR), a multiparental advanced intercross mapping population.ResultsWe measured lifespan in females from 805 DSPR lines, mapping five QTL (Quantitative Trait Loci) that each contribute 4–5 % to among-line lifespan variation in the DSPR. Each of these QTL co-localizes with the position of at least one QTL mapped in 13 previous studies of lifespan variation in flies. However, given that these studies implicate >90 % of the genome in the control of lifespan, this level of overlap is unsurprising. DSPR QTL intervals harbor 11–155 protein-coding genes, and we used RNAseq on samples of young and old flies to help resolve pathways affecting lifespan, and identify potentially causative loci present within mapped QTL intervals. Broad age-related patterns of expression revealed by these data recapitulate results from previous work. For example, we see an increase in antimicrobial defense gene expression with age, and a decrease in expression of genes involved in the electron transport chain. Several genes within QTL intervals are highlighted by our RNAseq data, such as Relish, a critical immune response gene, that shows increased expression with age, and UQCR-14, a gene involved in mitochondrial electron transport, that has reduced expression in older flies.ConclusionsThe five QTL we isolate collectively explain a considerable fraction of the genetic variation for female lifespan in the DSPR, and implicate modest numbers of genes. In several cases the candidate loci we highlight reside in biological pathways already implicated in the control of lifespan variation. Thus, our results provide further evidence that functional genetics tests targeting these genes will be fruitful, lead to the identification of natural sequence variants contributing to lifespan variation, and help uncover the mechanisms of aging.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-016-0419-9) contains supplementary material, which is available to authorized users.

Naturally-segregating Variation atUgt86DdContributes to Nicotine Resistance inDrosophila melanogaster

“…In addition to Marriage et al (2014), various studies have identified very large effect QTL with the DSPR (KING et al 2012b;COGNI et al 2016), and for such QTL finding and validating the causative locus using the battery of tools available for D. melanogaster is within reach. However, QTL effects are typically more modest (KISLUKHIN et al 2013;HIGHFILL et al 2016), often do not implicate unambiguous candidates, and may regularly be generated by noncoding variants (ALBERT AND KRUGLYAK 2015). Such features ensure that some fraction of causative variants will always remain uncharacterized.…”

Thermal‐specific patterns of longevity and fecundity in a set of heat‐sensitive and heat‐resistant genotypes of Drosophila melanogaster

“…Given the well‐known association between longevity and fecundity, it is interesting to test these two classes of traits for their possible associations to heat‐resistance genotypes at elevated temperature (e.g., Loeschcke et al., ). QTL mapping in recombinant inbred lines (RIL) from crosses between heat‐selected populations provided information about genomic regions affecting heat resistance (Norry et al., , 2007a, ; Morgan & Mackay, ; Arias et al., ; Sambucetti et al., ), as well as longevity and fecundity (Defays et al., ; Sambucetti et al., ; Highfill et al., ). This information can be used to establish fly stocks with alternative genotypes for heat resistance, which can be used to test fitness‐related traits of individuals carrying heat‐sensitive genotypes in comparison to individuals carrying heat‐resistant genotypes (e.g., Loeschcke et al., ).…”

Dissection of complex, fitness-related traits in multiple Drosophila mapping populations offers insight into the genetic control of stress resistance

“…A. Distribution of mean female lifespan in DSPR pB RILs (Highfill et al (2016). RILs with low female starvation resistance (in the bottom 25% of the distribution) are shown in solid black symbols, while other RILs are shown in gray.…”