Bayesian multilocus association mapping on ordinal and censored traits and its application to the analysis of genetic variation among Oryza sativa L. germplasms
Bayesian multilocus association mapping on ordinal and censored traits and its application to the analysis of genetic variation among Oryza sativa L. germplasms
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DOI：
10.1007/s00122-008-0945-6
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发表时间：
2009-03-01
影响因子：
5.4
通讯作者：
Hayashi, Takeshi
中科院分区：
文献类型：
--
作者：
Iwata, Hiroyoshi;Ebana, Kaworu;Hayashi, Takeshi
关联作图是一种不需要自交试验就能检测数量性状基因座（QTL）的有力工具。我们以前提出了一个贝叶斯方法，同时映射多个QTL的回归方法，直接结合人口结构的估计。在本研究中，我们扩展了我们的方法来分析有序和删失性状，因为这两种类型的性状是常见的种质资源的评价。有序和删失性状分别采用Ordinal-probit和tobit模型进行分析。在这两个模型中，我们假设存在与可观察数据相关的潜在连续变量，并使用马尔科夫链蒙特卡罗算法对潜在变量进行采样并确定模型参数。我们评估了我们的方法的效率，通过使用模拟和真实性状分析的水稻种质资源收集。基于真实的标记数据的模拟分析表明，该模型可以将QTL检测的假阳性率和假阴性率降低到合理的水平。模拟分析的基础上高度多态性的标记数据，这是由合并模拟产生的，表明我们的模型可以应用于基因型数据的基础上高度多态性的标记系统，如简单的序列重复。对于真实的性状，我们将抽穗期作为删失性状，直链淀粉含量和精米粒形作为有序性状进行分析。我们发现了可能与先前报道的QTL相关的重要标记。该方法可用于水稻种质资源有序性状和删失性状的全基因组关联分析。
Association mapping can be a powerful tool for detecting quantitative trait loci (QTLs) without requiring line-crossing experiments. We previously proposed a Bayesian approach for simultaneously mapping multiple QTLs by a regression method that directly incorporates estimates of the population structure. In the present study, we extended our method to analyze ordinal and censored traits, since both types of traits are common in the evaluation of germplasm collections. Ordinal-probit and tobit models were employed to analyze ordinal and censored traits, respectively. In both models, we postulated the existence of a latent continuous variable associated with the observable data, and we used a Markov-chain Monte Carlo algorithm to sample the latent variable and determine the model parameters. We evaluated the efficiency of our approach by using simulated- and real-trait analyses of a rice germplasm collection. Simulation analyses based on real marker data showed that our models could reduce both false-positive and false-negative rates in detecting QTLs to reasonable levels. Simulation analyses based on highly polymorphic marker data, which were generated by coalescent simulations, showed that our models could be applied to genotype data based on highly polymorphic marker systems, like simple sequence repeats. For the real traits, we analyzed heading date as a censored trait and amylose content and the shape of milled rice grains as ordinal traits. We found significant markers that may be linked to previously reported QTLs. Our approach will be useful for whole-genome association mapping of ordinal and censored traits in rice germplasm collections.