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首页> 外文期刊>Genetics: A Periodical Record of Investigations Bearing on Heredity and Variation >Shared Genomic Regions Underlie Natural Variation in Diverse Toxin Responses
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Shared Genomic Regions Underlie Natural Variation in Diverse Toxin Responses

机译:共享基因组区域利用不同毒素反应的自然变化

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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种遗传基因座的贡献引起的,独立互动或行动。对酵母和拟南芥的研究经常发现,对表型的大部分自然变化是归因于独立添加剂定量性状基因座(QTL)。在这些生物中检测到的基因座解释了大多数估计的可遗传变异。相比之下,美观型模型中的许多遗传症潜在的表型变化仍未被发现。在可以解剖的添加剂和交互式方差分量对甲卓和交互式方差分量的相对冲击之前,可以解剖,需要高复制和精确的表型测量以获得足够的统计功率以检测有助于这种遗失性的基因座。在这里,我们使用了一组296个重组近亲高级间隙线的Caenorhabditis elegans和高通量健康测定,以检测到16种不同毒素的基因潜在的反应,包括重金属,化学治疗药物,农药和神经药物。使用连杆映射,我们确定了82 QTL对这些毒素的响应的变化,并预测了各种生长参数的附加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,影响对多种毒素的反应。这些QTL热点可以代表影响毒素反应的常见因素。我们进一步进一步生成近代源性线和染色体替代菌株,然后通过实验验证了这些QTL热点,暗示了毒素响应变异的添加剂和交互基因座。

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