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首页> 外文期刊>Heredity: An International Journal of Genetics >pKWmEB: integration of Kruskal-Wallis test with empirical Bayes under polygenic background control for multi-locus genome-wide association study
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pKWmEB: integration of Kruskal-Wallis test with empirical Bayes under polygenic background control for multi-locus genome-wide association study

机译：PKWMEB：在多基因座基因组 - 范围内研究的多基因背景控制下与经验贝叶斯的kruskal-wallis测试的整合

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摘要

Although nonparametric methods in genome-wide association studies (GWAS) are robust in quantitative trait nucleotide (QTN) detection, the absence of polygenic background control in single-marker association in genome-wide scans results in a high false positive rate. To overcome this issue, we proposed an integrated nonparametric method for multi-locus GWAS. First, a new model transformation was used to whiten the covariance matrix of polygenic matrix K and environmental noise. Using the transferred model, Kruskal-Wallis test along with least angle regression was then used to select all the markers that were potentially associated with the trait. Finally, all the selected markers were placed into multi-locus model, these effects were estimated by empirical Bayes, and all the nonzero effects were further identified by a likelihood ratio test for true QTN detection. This method, named pKWmEB, was validated by a series of Monte Carlo simulation studies. As a result, pKWmEB effectively controlled false positive rate, although a less stringent significance criterion was adopted. More importantly, pKWmEB retained the high power of Kruskal-Wallis test, and provided QTN effect estimates. To further validate pKWmEB, we re-analyzed four flowering time related traits in Arabidopsis thaliana, and detected some previously reported genes that were not identified by the other methods.
机译：尽管在基因组 - 宽协会研究（GWAs）中的非参数方法是在定量性状核苷酸（QTN）检测中的稳健性，但在基因组扫描中单标记关联中的多基因背景控制的缺失导致高误率。为了克服这个问题，我们提出了一种用于多基因座GWA的集成非参数方法。首先，使用新的模型转换来美白多种子基质k和环境噪声的协方差矩阵。使用转移模型，然后使用kruskal-wallis测试以及最小角度回归来选择可能与特征相关的所有标记。最后，将所有选定的标记放入多基因座模型中，这些效果由经验贝叶斯估算，并且通过对真正的QTN检测的似然比测试进一步识别所有非零效应。这种命名为PKWMEB的方法被一系列蒙特卡罗仿真研究验证。结果，PKWMEB有效地控制了假阳性率，尽管采用了较严格的显着性标准。更重要的是，PKWMEB保留了Kruskal-Wallis测试的大功率，并提供了QTN效应估计。为了进一步验证PKWMEB，我们在拟南芥中重新分析了四个开花时间相关性状，并检测了一些未被其他方法鉴定的先前报告的基因。

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    Ren Wen-Long; Wen Yang-Jun; Dunwell Jim M.; Zhang Yuan-Ming;

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    Nanjing Agr Univ State Key Lab Crop Genet &

    Germplasm Enhancement Nanjing 210095 Jiangsu;

    Nanjing Agr Univ State Key Lab Crop Genet &

    Germplasm Enhancement Nanjing 210095 Jiangsu;

    Univ Reading Sch Agr Policy &

    Dev Reading RG6 6AR Berks England;

    Nanjing Agr Univ State Key Lab Crop Genet &

    Germplasm Enhancement Nanjing 210095 Jiangsu;

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  • 正文语种 eng
  • 中图分类 遗传学;
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