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A New Advanced Backcross Tomato Population Enables High Resolution Leaf QTL Mapping and Gene Identification

机译:新的先进回交番茄种群能够实现高分辨率叶片QTL定位和基因鉴定

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摘要

Quantitative Trait Loci (QTL) mapping is a powerful technique for dissecting the genetic basis of traits and species differences. Established tomato mapping populations between domesticated tomato (Solanum lycopersicum) and its more distant interfertile relatives typically follow a near isogenic line (NIL) design, such as the S. pennellii Introgression Line (IL) population, with a single wild introgression per line in an otherwise domesticated genetic background. Here, we report on a new advanced backcross QTL mapping resource for tomato, derived from a cross between the M82 tomato cultivar and S. pennellii. This so-called Backcrossed Inbred Line (BIL) population is comprised of a mix of BC2 and BC3 lines, with domesticated tomato as the recurrent parent. The BIL population is complementary to the existing S. pennellii IL population, with which it shares parents. Using the BILs, we mapped traits for leaf complexity, leaflet shape, and flowering time. We demonstrate the utility of the BILs for fine-mapping QTL, particularly QTL initially mapped in the ILs, by fine-mapping several QTL to single or few candidate genes. Moreover, we confirm the value of a backcrossed population with multiple introgressions per line, such as the BILs, for epistatic QTL mapping. Our work was further enabled by the development of our own statistical inference and visualization tools, namely a heterogeneous hidden Markov model for genotyping the lines, and by using state-of-the-art sparse regression techniques for QTL mapping.
机译:数量性状位点(QTL)作图是一种用于分析性状和物种差异的遗传基础的强大技术。在驯养番茄(Solanum lycopersicum)及其较远的亲缘亲戚之间建立的番茄作图种群通常遵循近等基因系(NIL)设计,例如彭尼氏菌(S. pennellii)渐渗系(IL)种群,每行中的单条野生渐渗否则驯化的遗传背景。在这里,我们报道了一种新的先进的番茄回交QTL定位图资源,该资源来自M82番茄品种和彭氏葡萄球菌之间的杂交。这个所谓的回交自交系(BIL)种群由BC2和BC3系的混合物组成,其中驯化的番茄为轮回亲本。 BIL人口与现有的S. pennellii IL人口互补,并与父母共享。使用BIL,我们绘制了叶片复杂性,叶片形状和开花时间的性状。我们通过将几个QTL精细映射到单个或几个候选基因,证明了BIL用于精细映射QTL(特别是最初在IL中定位的QTL)的实用性。此外,我们确定了上交QTL作图时每行具有多个基因渗入的回交种群的价值,例如BIL。通过开发我们自己的统计推断和可视化工具(即用于对基因型进行分型的异构隐马尔可夫模型)以及使用最新的稀疏回归技术进行QTL映射,我们的工作得到了进一步的支持。

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