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首页> 外文期刊>Australian Journal of Crop Science >QTL mapping of soybean cyst nematode race 9: a generalized linear modeling approach
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QTL mapping of soybean cyst nematode race 9: a generalized linear modeling approach

机译：大豆囊肿线虫种族的QTL定位9：广义线性建模方法

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The Female Index (FI) is a relative measure of host suitability of a soybean line for a particular nematode population and often shows a non-normal distribution. Moreover, most quantitative trait loci (QTL) mapping methods assume that the phenotype follows a normal distribution such as composite interval mapping (CIM). Therefore, a generalized linear modeling (GLM) approach was employed to map QTL for resistance to race 9 of the soybean cyst nematode (SCN) using a total of 83 simple sequence repeat markers (SSR). Two GLM models were tested: model 1, where the FI was treated as a continuous variable, assuming a Gamma distribution with a logarithmic link function; and model 2, where the FI was treated as a categorical trait in a five-item hierarchy, assuming a multinomial distribution with a cumulative logit link function. The FI data of 108 recombinant inbred lines (RIL) confirmed the non-normal distribution for race 9 of the SCN (Shapiro-Wilk’s w=0.86, P<0.0001, skewness=1.52 and kurtosis=2.93). Eight RIL were confirmed to be resistant (FI≤10), and 23 to be highly susceptible (FI≥100). Both GLM models identified one QTL for SCN on the molecular linkage group G, between the markers Satt275 and Satt038 at 48.4 centiMorgans (P=0.017 and 0.033, for models 1 and 2, respectively). Additionally, these results were compared with the CIM and Bayesian interval mapping (BIM) methods, assuming experimental data with a non-normal response, to determine the robustness and statistical power of these two methods. The results make clear that generalized linear modeling approach can be used as an efficient method to map QTLs in a continuous trait with a non-Gaussian distribution. CIM and BIM were robust enough for a reliable mapping of QTLs underlying nonnormally distributed data.
机译：女性指数（FI）是大豆品系对特定线虫种群的宿主适宜性的相对度量，通常显示出非正态分布。此外，大多数定量性状基因座（QTL）映射方法都假定表型遵循正态分布，例如复合区间映射（CIM）。因此，采用了通用线性建模（GLM）方法，使用总共83个简单序列重复标记（SSR）绘制了QTL对大豆孢囊线虫（SCN）第9种种族的抗性。测试了两个GLM模型：模型1，其中FI被视为连续变量，假设Gamma分布具有对数链接函数;模型2，其中FI被视为五项层次结构中的分类特征，假定具有累积对数链接函数的多项式分布。 108个重组自交系（RIL）的FI数据证实了SCN第9种的非正态分布（Shapiro-Wilk的w = 0.86，P <0.0001，偏度= 1.52，峰度= 2.93）。确认有8个RIL具有抗药性（FI≤10），有23个具有高度敏感性（FI≥100）。两种GLM模型都在分子连锁群G上的Satt275和Satt038标记之间在48.4厘摩处识别出一个SCN QTL（对于模型1和2，分别为P = 0.017和0.033）。此外，将这些结果与CIM和贝叶斯间隔映射（BIM）方法进行了比较（假设实验数据具有非正态响应），以确定这两种方法的鲁棒性和统计功效。结果表明，广义线性建模方法可作为一种有效的方法来绘制具有非高斯分布的连续性状中的QTL。 CIM和BIM具有足够的鲁棒性，可以可靠地映射基于非正态分布数据的QTL。

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