Identifying Loci Contributing to Natural Variation in Xenobiotic Resistance in XXXX .

@entity1 @entity137 @entity5834 @entity72281 @entity238
Natural populations exhibit a great deal of interindividual genetic variation in the response to toxins, exemplified by the variable clinical efficacy of pharmaceutical drugs in @entity1 , and the evolution of pesticide resistant insects. Such variation can result from several phenomena, including variable metabolic detoxification of the xenobiotic, and differential sensitivity of the molecular target of the toxin. Our goal is to genetically dissect variation in the response to xenobiotics, and characterize naturally-segregating polymorphisms that modulate @entity137 . Here, we use the @entity238 Synthetic Population Resource (DSPR), a multiparent advanced intercross panel of recombinant inbred lines, to identify QTL (Quantitative Trait Loci) underlying xenobiotic resistance, and employ @entity5834 as a model toxic compound. Phenotyping over 1,700 genotypes led to the identification of ten QTL, each explaining 4.5-14.4% of the broad-sense heritability for @entity5834 resistance. Four QTL harbor members of the cytochrome P450 family of detoxification enzymes, which represent strong a priori candidate genes. The case is especially strong for @entity72281 , with multiple lines of evidence indicating the gene causally impacts @entity5834 resistance. @entity72281 is implicated by QTL mapped in both panels of DSPR RILs, is significantly upregulated in the presence of @entity5834 , and RNAi knockdown robustly decreases @entity5834 tolerance. Furthermore, copy number variation at @entity72281 is strongly associated with phenotype in the DSPR, with a trend in the same direction observed in the DGRP ( @entity238 Genetic Reference Panel). No additional plausible causative polymorphisms were observed in a full genomewide association study in the DGRP, or in analyses restricted to QTL regions mapped in the DSPR. Just as in @entity1 populations, replicating modest-effect, naturally-segregating causative variants in an association study framework in flies will likely require very large sample sizes.
