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Reconstruction of linkage maps in the distorted segregation populations of backcross, doubled haploid and recombinant inbred lines

机译：回交，双单倍体和重组自交系畸形分离群体中连锁图的重建

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摘要

Non-Mendelian segregation of markers, known as distorted segregation, is a common biological phenomenon. Although segregation distortion affects the estimation of map distances and the results of quantitative trait loci (QTL) mapping, the effects of distorted markers are often ignored in the construction of linkage maps and in QTL mapping. Recently, we have developed a multipoint method via a Hidden Markov chain method to reconstruct linkage maps in an F_2 population that corrects for bias of map distances between distorted markers. In this article, the method is extended to cover backcross, doubled haploid and recombinant inbred line (RIL) populations. The results from simulated experiments show that: (1) the degree that two linked segregation distortion loci (SDL) affect the estimation of map distances increases as SDL heritability and interval length between adjacent markers increase, whereas sample size has little effect on the bias; (2) two linked SDL result in the underestimation of linkage distances for most cases, overestimation for an additive model with opposite additive effects, and unbiased estimation for an epistatic model with negative additive-by-additive effects; (3) the proposed method can obtain the unbiased estimation of linkagedistance. This new method was applied to a rice RIL population with severely distorted segregation to reconstruct the linkage maps, and a bootstrap method was used to obtain 95% confidence intervals of map distances. The results from real data analysisfurther demonstrate the utility of our method, which provides a foundation for the inheritance analysis of quantitative and viability traits.
机译：标记物的非孟德尔分离，被称为扭曲分离，是一种常见的生物学现象。尽管隔离失真会影响地图距离的估计以及定量特征位点（QTL）映射的结果，但在链接图的构建和QTL映射中，失真标记的影响通常会被忽略。最近，我们通过隐马尔可夫链方法开发了一种多点方法，以重建F_2种群中的连锁图谱，该图谱可校正失真标记之间的图谱距离偏差。在本文中，该方法已扩展到涵盖回交，双单倍体和重组近交系（RIL）群体。模拟实验的结果表明：（1）随着SDL的遗传力和相邻标记之间的间隔长度的增加，两个连锁的分离畸变位点（SDL）对图谱距离估计的影响程度增加，而样本大小对偏差的影响很小; （2）在大多数情况下，两个链接的SDL导致链接距离的低估，具有相反加性效应的加性模型的过高估计，以及具有逐加性负面效果的上位性模型的无偏估计; （3）提出的方法可以获得联动距离的无偏估计。将该新方法应用于隔离度严重失真的水稻RIL群体以重建连锁图，并且使用自举法获得图距离的95％置信区间。真实数据分析的结果进一步证明了我们方法的实用性，为定量和生存性状的遗传分析提供了基础。

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