Papachristou Charalampos, Ober Carole, Abney Mark
Department of Mathematics, Physics, and Statistics, University of the Sciences, 600 S. 43rd Street, Philadelphia, PA 19104 USA ; Department of Mathematics, Rowan University, 201 Mullica Hill Road, Glassboro, NJ 08028 USA.
Department of Human Genetics, University of Chicago, 920 E. 58th Street, CLSC 4th floor, Chicago, IL 60637 USA.
BMC Proc. 2016 Oct 18;10(Suppl 7):221-226. doi: 10.1186/s12919-016-0034-9. eCollection 2016.
We propose a novel LASSO (least absolute shrinkage and selection operator) penalized regression method used to analyze samples consisting of (potentially) related individuals. Developed in the context of linear mixed models, our method models the relatedness of individuals in the sample through a random effect whose covariance structure is a linear function of known matrices with elements combinations of the condensed coefficients of identity between the individuals in the sample. We implement our method to analyze the simulated family data provided by the 19th Genetic Analysis Workshop in an effort to identify loci regulating the simulated trait of systolic blood pressure. The analyses were performed with full knowledge of the simulation model. Our findings demonstrate that we can significantly reduce the rate of false positive signals by incorporating the relatedness of the study participants.
我们提出了一种新颖的LASSO（最小绝对收缩和选择算子）惩罚回归方法，用于分析由（可能）相关个体组成的样本。我们的方法是在线性混合模型的背景下开发的，它通过一个随机效应来对样本中个体的相关性进行建模，该随机效应的协方差结构是已知矩阵的线性函数，这些矩阵的元素是样本中个体之间恒等压缩系数的组合。我们运用该方法分析了第19届遗传分析研讨会提供的模拟家系数据，以努力识别调控收缩压模拟性状的基因座。这些分析是在完全了解模拟模型的情况下进行的。我们的研究结果表明，通过纳入研究参与者的相关性，我们可以显著降低假阳性信号的发生率。
