期刊
GENETICS
卷 185, 期 1, 页码 349-U529出版社
GENETICS SOCIETY AMERICA
DOI: 10.1534/genetics.110.114280
关键词
-
资金
- National Institute of Mental Health (NIMH) [1 P50 MH090338-01, 1 RC2 MH089951-01]
- National Institutes of Health (NIH) [GM70335, CA74015, GM074175-03]
Genomewide multiple-loci mapping can be viewed as a challenging variable selection problem where the major objective is to select genetic markers related to a trait of interest. It is challenging because the number of genetic markers is large (often much larger than the sample size) and there is often strong linkage or linkage disequilibrium between markers. In this article, we developed two methods for genomewide multiple loci mapping: the Bayesian adaptive Lasso and the iterative adaptive Lasso. Compared with eight existing methods, the proposed methods have improved variable selection performance in both simulation and real data studies. The advantages of our methods come from the assignment of adaptive weights to different genetic makers and the iterative updating of these adaptive weights. The iterative adaptive Lasso is also computationally much more efficient than the commonly used marginal regression and stepwise regression methods. Although our methods are motivated by multiple-loci mapping, they are general enough to be applied to other variable selection problems.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据