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Shared Genomic Regions Underlie Natural Variation in Diverse Toxin Responses

机译:共有的基因组区域构成了多种毒素反应的自然变异

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摘要

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.
机译:表型复杂性是由环境因素和多个遗传位点,相互作用或独立发挥作用引起的。酵母和拟南芥的研究通常发现,大多数表型的自然变异都可归因于独立的加性定量性状基因座(QTL)。在这些生物中检测到的基因座可以解释大多数估计的遗传变异。相比之下,后生动物模型中表型变异的许多可遗传成分仍未被发现。在可以剖析累加和交互方差成分对后生动物表型变异的相对影响之前,需要进行高重复性和精确的表型测量,以获得足够的统计能力,以检测导致这种遗传性缺失的基因座。在这里,我们使用了一组由秀丽隐杆线虫组成的296个重组自交高级交配系和高通量适应性检测来检测基因座对16种不同毒素(包括重金属,化学治疗药,农药和神经药物)的潜在应答。使用连锁作图,我们确定了82个QTL,这些QTL是对这些毒素的反应变化的基础,并预测了各种生长参数之间加性基因座和遗传相互作用的相对贡献。此外,我们确定了三个基因组区域,这些区域影响对多种毒素的反应。这些QTL热点可能代表影响毒素反应的常见因素。我们进一步产生了近等基因系和染色体替代菌株,然后通过实验验证了这些QTL热点,暗示了毒素反应变异基础上的加性和相互作用位点。

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