A note on inference of trait associations with SNP haplotypes and other attributes in generalized linear models
A note on inference of trait associations with SNP haplotypes and other attributes in generalized linear models
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DOI：
10.1159/000081447
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发表时间：
2004-01-01
期刊：
影响因子：
1.8
通讯作者：
Graham, J
中科院分区：
文献类型：
--
作者：
Burkett, K;McNeney, B;Graham, J
最近，Lake等人[Human Heredity 2003; 55：56-65]提出了一种基于EM算法的方法，用于广义线性模型中性状与单倍型和环境辅因子的关联的最大似然推断。在这个简短的报告中，我们描述了一个扩展，以适应缺失的SNP基因型信息。我们还讨论了他们的实施和我们自己的标准误差计算的差异。最后，我们提出的结果表明，推理是强大的单倍型和非遗传因素之间的依赖程度低，但有偏见的推理时，可能会导致中度到强烈的依赖。总体而言，该方法被认为是在我们考虑的模型中表现良好。版权所有（C）2004 S. Karger AG，巴塞尔。
Recently, Lake et al. [Human Heredity 2003; 55: 56-65] have proposed an approach based on the EM algorithm for maximum-likelihood inference of trait associations with haplotypes and environmental cofactors in generalized linear models. In this short report, we describe an extension to accommodate missing SNP genotype information. We also discuss differences in the calculation of standard errors between their implementation and our own. Finally, we present results indicating that inference is robust to low levels of dependence between haplotypes and nongenetic factors, but that biased inference can result when there is moderate to strong dependence. Overall, the method is found to perform well in the models we considered. Copyright (C) 2004 S. Karger AG, Basel.