Detection of gene–gene interactions using multistage sparse and low-rank regression

Hung Hung, Yu-Ting Lin, Penweng Chen, Chen-Chien Wang, Su-Yun Huang and Jung-Ying Tzeng

Biometrics, 2016, vol. 72, issue 1, 85-94

Abstract: type="main" xml:lang="en">

Finding an efficient and computationally feasible approach to deal with the curse of high-dimensionality is a daunting challenge faced by modern biological science. The problem becomes even more severe when the interactions are the research focus. To improve the performance of statistical analyses, we propose a sparse and low-rank (SLR) screening based on the combination of a low-rank interaction model and the Lasso screening. SLR models the interaction effects using a low-rank matrix to achieve parsimonious parametrization. The low-rank model increases the efficiency of statistical inference and, hence, SLR screening is able to more accurately detect gene–gene interactions than conventional methods. Incorporation of SLR screening into the Screen-and-Clean approach (Wasserman and Roeder, 2009; Wu et al., 2010) is also discussed, which suffers less penalty from Boferroni correction, and is able to assign p-values for the identified variables in high-dimensional model. We apply the proposed screening procedure to the Warfarin dosage study and the CoLaus study. The results suggest that the new procedure can identify main and interaction effects that would have been omitted by conventional screening methods.

Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/ (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:biomet:v:72:y:2016:i:1:p:85-94

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0006-341X

Access Statistics for this article

More articles in Biometrics from The International Biometric Society
Bibliographic data for series maintained by Wiley Content Delivery ().