The Current and Future Use of Ridge Regression for Prediction in Quantitative Genetics.
The Current and Future Use of Ridge Regression for Prediction in Quantitative Genetics.
复制标题
DOI：
10.1155/2015/143712
复制
发表时间：
2015
影响因子：
--
通讯作者：
Groenen PJ
中科院分区：
文献类型：
--
作者：
de Vlaming R;Groenen PJ
近年来，在数量遗传学中使用正则化方法进行推理和预测已经有了相当多的研究。这些研究主要集中在标记的选择和效应的缩小上。在这篇综述文章中，使用岭回归预测在数量遗传学使用单核苷酸多态性数据进行了讨论。特别是，我们考虑（i）岭回归的理论基础，（ii）它与动物育种中常用方法的联系，（iii）计算可行性，以及（iv）构建具有非线性效应的预测模型的范围（例如，显性和上位性）。基于一项模拟研究，我们评估了岭回归在使用全基因组SNP数据预测人类性状方面的当前和未来潜力。我们的结论是，对于具有相对简单的遗传结构的结果，考虑到大多数队列的当前样本量（即，N <10，000）岭回归的预测准确性略高于重复简单回归的经典全基因组关联研究方法（即，每个SNP一次回归）。然而，这两种方法都只占遗传力的一小部分。然而，我们发现的证据表明，对于大规模的举措，如生物银行，样本量可以实现岭回归相比，经典的方法大大提高了预测精度。
In recent years, there has been a considerable amount of research on the use of regularization methods for inference and prediction in quantitative genetics. Such research mostly focuses on selection of markers and shrinkage of their effects. In this review paper, the use of ridge regression for prediction in quantitative genetics using single-nucleotide polymorphism data is discussed. In particular, we consider (i) the theoretical foundations of ridge regression, (ii) its link to commonly used methods in animal breeding, (iii) the computational feasibility, and (iv) the scope for constructing prediction models with nonlinear effects (e.g., dominance and epistasis). Based on a simulation study we gauge the current and future potential of ridge regression for prediction of human traits using genome-wide SNP data. We conclude that, for outcomes with a relatively simple genetic architecture, given current sample sizes in most cohorts (i.e., N < 10,000) the predictive accuracy of ridge regression is slightly higher than the classical genome-wide association study approach of repeated simple regression (i.e., one regression per SNP). However, both capture only a small proportion of the heritability. Nevertheless, we find evidence that for large-scale initiatives, such as biobanks, sample sizes can be achieved where ridge regression compared to the classical approach improves predictive accuracy substantially.