PDF(Pubmed)
Abstract：
Linkage maps are essential for genetic mapping of phenotypic traits, gene map-based cloning, and marker-assisted selection in breeding applications. Construction of a high-quality saturated map requires high-quality genotypic data on a large number of molecular markers. Errors in genotyping cannot be completely avoided, no matter what platform is used. When genotyping error reaches a threshold level, it will seriously affect the accuracy of the constructed map and the reliability of consequent genetic studies. In this study, repeated genotyping of two recombinant inbred line (RIL) populations derived from crosses Yangxiaomai × Zhongyou 9507 and Jingshuang 16 × Bainong 64 was used to investigate the effect of genotyping errors on linkage map construction. Inconsistent data points between the two replications were regarded as genotyping errors, which were classified into three types. Genotyping errors were treated as missing values, and therefore the non-erroneous data set was generated. Firstly, linkage maps were constructed using the two replicates as well as the non-erroneous data set. Secondly, error correction methods implemented in software packages QTL IciMapping (EC) and Genotype-Corrector (GC) were applied to the two replicates. Linkage maps were therefore constructed based on the corrected genotypes and then compared with those from the non-erroneous data set. Simulation study was performed by considering different levels of genotyping errors to investigate the impact of errors and the accuracy of error correction methods. Results indicated that map length and marker order differed among the two replicates and the non-erroneous data sets in both RIL populations. For both actual and simulated populations, map length was expanded as the increase in error rate, and the correlation coefficient between linkage and physical maps became lower. Map quality can be improved by repeated genotyping and error correction algorithm. When it is impossible to genotype the whole mapping population repeatedly, 30% would be recommended in repeated genotyping. The EC method had a much lower false positive rate than did the GC method under different error rates. This study systematically expounded the impact of genotyping errors on linkage analysis, providing potential guidelines for improving the accuracy of linkage maps in the presence of genotyping errors.
摘要：
连锁图谱对于表型性状的遗传作图至关重要，基因图谱克隆,和标记辅助选择在育种中的应用。高质量饱和图谱的构建需要大量分子标记的高质量基因型数据。基因分型错误不能完全避免，无论使用什么平台。当基因分型错误达到阈值水平时，这将严重影响所构建图谱的准确性和后续遗传研究的可靠性。在这项研究中,对扬小迈×中优9507和京双16×百农64杂交的两个重组自交系（RIL）种群进行重复基因分型，以研究基因分型错误对连锁图谱构建的影响。两次重复之间不一致的数据点被认为是基因分型错误，分为三种类型。基因分型错误被视为缺失值，因此产生了非错误的数据集。首先,使用两个重复以及非错误数据集构建了连锁图谱。其次,在软件包QTLIciMapping（EC）和基因型校正（GC）中实施的错误校正方法被应用于两个重复实验。因此，基于校正的基因型构建连锁图，然后将其与来自非错误数据集的连锁图进行比较。通过考虑不同水平的基因分型错误来进行模拟研究，以研究错误的影响和错误校正方法的准确性。结果表明，在两个RIL群体中，两个重复和非错误数据集之间的图谱长度和标记顺序不同。对于实际和模拟种群，地图长度随着错误率的增加而扩大，连锁与物理图谱的相关系数降低。通过重复基因分型和纠错算法可以提高地图质量。当不可能重复对整个作图群体进行基因型时，在重复基因分型中推荐30%。在不同错误率下，EC方法的假阳性率远低于GC方法。本研究系统地阐述了基因分型错误对连锁分析的影响,为在存在基因分型错误的情况下提高连锁图的准确性提供潜在的指导。
