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

Abstract

44We leverage two complementary Drosophila melanogaster mapping panels to genetically dissect 45 starvation resistance, an important fitness trait. Using >1600 genotypes from the multiparental 46Drosophila Synthetic Population Resource (DSPR) we map numerous starvation stress QTL that 47 the DGRP. We found strong phenotypic correlations among studies, but extremely low overlap 59 in the sets of genomewide significant sites. Despite this, our analyses revealed that the most 60 highly-associated variants from each study typically showed the same additive effect sign in 61 independent studies, in contrast to otherwise equivalent sets of random variants. This 62 consistency provides evidence for reproducible trait-associated sites in a widely-used mapping 63 panel, and highlights the polygenic nature of starvation resistance. 64 65 Periods of food scarcity and suboptimal nutrient resources present an important 66 challenge for nearly all species (McCue 2010), and this form of environmental stress can limit the 67 survival of individuals with poor nutritional status and reduced stress resistance (Harshman et al. 68 1999;Lee and Jang 2014). As a result, starvation stress resistance has direct implications for the 69 fitness of individuals as they experience resource variability in natural populations. Starvation 70 resistance is a classic quantitative, fitness-related trait that is associated with several other 71 phenotypes that influence survival, lifespan, and reproduction (Service and Rose 1985; Da Lage 72 et al

A natural genetic variation screen identifies insulin signaling, neuronal communication, and innate immunity as modifiers of hyperglycemia in the absence of Sirt1

“…We used the DGRP as an unbiased source of natural genetic variation for this screen. This is the first time a genetic model of metabolic dysfunction has been put to this use, as previous screens have either focused on dietary stress as a source of metabolic disease or on the impact of genotype on metabolic parameters under non-stressed conditions (Mackay et al 2012;Ivanov et al 2015;Nelson et al 2016;Jehrke et al 2018;Everman et al 2019). We observed very little overlap in modifier candidates between our observations and these studies.…”

“…Model organism tools, such as the Drosophila Genetic Reference Panel (DGRP), provide a way to study of the impact of natural genetic variation on diseases such as diabetes (Mackay et al 2012;He et al 2014;Ivanov et al 2015;Nelson et al 2016;Jehrke et al 2018;Everman et al 2019). The DGRP is a collection of ~200 isogenic strains derived from a wild population, such that each strain represents one wild-derived genome (Mackay et al 2012).…”

“…The utility of the DGRP in identifying candidate modifiers of metabolic disease has already been demonstrated numerous times, with screens associated with misfolded insulin, high sugar and high fat feeding, and starvation resistance already documented (Mackay et al 2012;He et al 2014;Ivanov et al 2015;Nelson et al 2016;Jehrke et al 2018;Everman et al 2019). While some of these used biochemical assays to precisely measure metabolite levels in flies as a quantitative phenotype for the screen (Nelson et al 2016;Jehrke et al 2018;Everman et al 2019), several used more general physiological measurements such as starvation resistance and lifespan (Mackay et al 2012;Ivanov et al 2015). Although effective, the use of this kind of general readout reduces the specificity of the modifiers identified.…”

Powerful, efficient QTL mapping in Drosophila melanogaster using bulked phenotyping and pooled sequencing

“…Second, in a study using inbred lines one might collect multiple phenotypes on the same set of lines, allowing examination of genetic correlations among traits (e.g., (Dickson et al . 2016; Everman et al . 2019)).…”

“…These assumptions are reasonable since QTL exhibiting >2% heritability are commonly observed in RIL-based QTL mapping studies employing the DSPR (see for instance (Najarro et al . 2015)and(Everman et al . 2019)), Beavis effects are subtle for large experiments (Beavis et al .…”