利用不完整个表型数据进行多性状数量性状基因座定位
Multiple-trait quantitative trait locus mapping with incomplete phenotypic data.
作者信息
Guo Zhigang, Nelson James C
机构信息
Department of Plant Pathology, Kansas State University, Manhattan, Kansas 66506, USA.
出版信息
BMC Genet. 2008 Dec 5;9:82. doi: 10.1186/1471-2156-9-82.
BACKGROUND
Conventional multiple-trait quantitative trait locus (QTL) mapping methods must discard cases (individuals) with incomplete phenotypic data, thereby sacrificing other phenotypic and genotypic information contained in the discarded cases. Under standard assumptions about the missing-data mechanism, it is possible to exploit these cases.
RESULTS
We present an expectation-maximization (EM) algorithm, derived for recombinant inbred and F2 genetic models but extensible to any mating design, that supports conventional hypothesis tests for QTL main effect, pleiotropy, and QTL-by-environment interaction in multiple-trait analyses with missing phenotypic data. We evaluate its performance by simulations and illustrate with a real-data example.
CONCLUSION
The EM method affords improved QTL detection power and precision of QTL location and effect estimation in comparison with case deletion or imputation methods. It may be incorporated into any least-squares or likelihood-maximization QTL-mapping approach.
背景
传统的多性状数量性状基因座（QTL）定位方法必须舍弃具有不完整表型数据的样本（个体），从而牺牲了被舍弃样本中包含的其他表型和基因型信息。在关于缺失数据机制的标准假设下，可以利用这些样本。
结果
我们提出了一种期望最大化（EM）算法，该算法是针对重组自交系和F2遗传模型推导出来的，但可扩展到任何交配设计，它支持在存在缺失表型数据的多性状分析中对QTL主效应、多效性和QTL与环境互作进行传统的假设检验。我们通过模拟评估了其性能，并以一个实际数据示例进行了说明。
结论
与舍弃样本或插补方法相比，EM方法提高了QTL检测能力以及QTL定位和效应估计的精度。它可以纳入任何最小二乘法或似然最大化QTL定位方法中。
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