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" Shared Genomic Regions Underlie Natural Variation in Diverse Toxin Responses. "
Evans, Kathryn S; Brady, Shannon C; Bloom, Joshua S; Tanny, Robyn E; Cook, Daniel E; Giuliani, Sarah E; Hippleheuser, Stephen W; Zamanian, Mostafa; Andersen, Erik C
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AL
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| Record Number
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909445
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LA5k44j3hk
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| Title & Author
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Shared Genomic Regions Underlie Natural Variation in Diverse Toxin Responses. [Article]\ Evans, Kathryn S; Brady, Shannon C; Bloom, Joshua S; Tanny, Robyn E; Cook, Daniel E; Giuliani, Sarah E; Hippleheuser, Stephen W; Zamanian, Mostafa; Andersen, Erik C
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2018
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Lawrence Berkeley National Laboratory
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| Abstract
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Phenotypic complexity is caused by the contributions of environmental factors and multiple genetic loci, interacting or acting independently. Studies of yeast and Arabidopsis often find that the majority of natural variation across phenotypes is attributable to independent additive quantitative trait loci (QTL). Detected loci in these organisms explain most of the estimated heritable variation. By contrast, many heritable components underlying phenotypic variation in metazoan models remain undetected. Before the relative impacts of additive and interactive variance components on metazoan phenotypic variation can be dissected, high replication and precise phenotypic measurements are required to obtain sufficient statistical power to detect loci contributing to this missing heritability. Here, we used a panel of 296 recombinant inbred advanced intercross lines of Caenorhabditis elegans and a high-throughput fitness assay to detect loci underlying responses to 16 different toxins, including heavy metals, chemotherapeutic drugs, pesticides, and neuropharmaceuticals. Using linkage mapping, we identified 82 QTL that underlie variation in responses to these toxins, and predicted the relative contributions of additive loci and genetic interactions across various growth parameters. Additionally, we identified three genomic regions that impact responses to multiple classes of toxins. These QTL hotspots could represent common factors impacting toxin responses. We went further to generate near-isogenic lines and chromosome substitution strains, and then experimentally validated these QTL hotspots, implicating additive and interactive loci that underlie toxin-response variation.
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