Fine-Mapping Nicotine Resistance Loci inDrosophilaUsing a Multiparent Advanced Generation Inter-Cross Population

Abstract

Animals in nature are frequently challenged by toxic compounds, from those that occur naturally in plants as a defense against herbivory, to pesticides used to protect crops. On exposure to such xenobiotic substances, animals mount a transcriptional response, generating detoxification enzymes and transporters that metabolize and remove the toxin. Genetic variation in this response can lead to variation in the susceptibility of different genotypes to the toxic effects of a given xenobiotic. Here we use Drosophila melanogaster to dissect the genetic basis of larval resistance to nicotine, a common plant defense chemical and widely used addictive drug in humans. We identified quantitative trait loci (QTL) for the trait using the DSPR (Drosophila Synthetic Population Resource), a panel of multiparental advanced intercross lines. Mapped QTL collectively explain 68.4% of the broad-sense heritability for nicotine resistance. The two largest-effect loci-contributing 50.3 and 8.5% to the genetic variation-map to short regions encompassing members of classic detoxification gene families. The largest QTL resides over a cluster of ten UDP-glucuronosyltransferase (UGT) genes, while the next largest QTL harbors a pair of cytochrome P450 genes. Using RNAseq we measured gene expression in a pair of DSPR founders predicted to harbor different alleles at both QTL and showed that Ugt86Dd, Cyp28d1, and Cyp28d2 had significantly higher expression in the founder carrying the allele conferring greater resistance. These genes are very strong candidates to harbor causative, regulatory polymorphisms that explain a large fraction of the genetic variation in larval nicotine resistance in the DSPR.A routine part of life for all organisms is avoiding, and if necessary metabolizing, toxic substances encountered in the environment. A common challenge for animals are those toxins produced by potential prey and plant hosts as chemical defenses against predation and herbivory (Glendinning 2002(Glendinning , 2007. Understanding how organisms overcome these defenses can give us insight into the evolution of host specialization, which can often involve an organism overcoming the defenses of a particular host, avoiding competition by making use of a resource toxic to other species (for example, Hungate et al. 2013). Many animals, especially insects, are also commonly exposed to chemical pesticides used to protect crop plants. As a consequence of this strong evolutionary pressure, there are a number of examples of insecticide resistance arising in natural populations (Crow 1957). Understanding the biology and molecular genetics underlying resistance to insecticides (Perry et al. 2011;Ffrench-Constant 2013) is valuable in the design of pest management strategies. In addition, humans are frequently exposed to an array of potentially harmful compounds, notably pharmaceuticals. Given the desire to achieve maximal drug efficacy while minimizing dosage and avoiding adverse drug responses, elaborating the mechanisms of drug metabolism, and the genet...

Dissecting complex traits using the Drosophila Synthetic Population Resource

“…Collectively, our single experiment was able to map eight QTL influencing ADH activity, explaining approximately 60% of the genetic variation for the trait. We have subsequently used the DSPR to map QTL explaining large fractions of the heritable variation for traits such as nicotine resistance [30], and the response to widely-used chemotheraphy drugs [31,32]. Several other groups are also using the DSPR to study additional metabolic, morphological, and behavioral traits (www.FlyRILs.org/Projects).…”

“…The simplest format is to directly phenotype the RILs (e.g., [14,30]), but this may be problematic for fitness-related traits, such as many behavioral and reproductive characters, that are expected to suffer from inbreeding depression. A second strategy is to phenotype the F 1 of crosses between pairs of A and B RILs (e.g., [31,33]).…”

“…This is considerably greater resolution than traditional QTL mapping, and in flies such intervals will typically implicate <50 genes, and often suggest clear candidates [14,30-32,39]. An appealing theoretical aspect of the DSPR is the idea of “phasing” mapped QTL, thereby reducing the candidate causative variant list to just a few dozen candidates.…”

Genetic dissection of intraspecific variation in a male-specific sexual trait in Drosophila melanogaster

“…Following the method described in Marriage et al (2014), the broad-sense heritability of the trait was estimated separately for each subpopulation by calculating the genetic and phenotypic variance components from a linear model of the form: Y ijk = μ + b i + g ij + ɛ ijk , where Y ijk is the k th observation of the j th genotype in the i th batch, μ is the grand mean, b i is the random effect of batch, g ij is the random effect of RIL cross genotype nested within batch and ɛ ijk is the error term. The components were calculated in R (http://www.R-project.org) using the lme and VarCorr functions in the nlme package (Pinheiro et al , 2011).…”

Bayesian Modeling of Haplotype Effects in Multiparent Populations

“…, Talbot et al 1999; Valdar et al 2006a,b; Huang et al 2009; Aylor et al 2011; Huang et al 2011; Collaborative Cross Consortium 2012; Baud et al 2013; Marriage et al 2014; Tsaih et al 2014). In a multiparent cross, a select set of known founders is combined and bred to an advanced generation to create a population of individuals whose genomes are mosaics of the original founder haplotypes; this multiparent population is then well suited for detection of quantitative trait loci (QTL) mapping through linkage disequilibrium (LD) mapping—that is, QTL mapping based on inferred descent.…”