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

TitleDetection of gene-gene interactions using multistage sparse and low-rank regression.
Publication TypeJournal Article
Year of Publication2016
AuthorsHung, Hung, Yu-Ting Lin, Penweng Chen, Chen-Chien Wang, Su-Yun Huang, and Jung-Ying Tzeng
JournalBiometrics
Volume72
Issue1
Pagination85-94
Date Published2016 Mar
ISSN1541-0420
KeywordsAlgorithms, Computer Simulation, Data Interpretation, Statistical, High-Throughput Screening Assays, Models, Statistical, Pattern Recognition, Automated, Protein Interaction Mapping, Regression Analysis, Reproducibility of Results, Sensitivity and Specificity
Abstract

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.

DOI10.1111/biom.12374
Alternate JournalBiometrics
Original PublicationDetection of gene-gene interactions using multistage sparse and low-rank regression.
PubMed ID26288029
PubMed Central IDPMC4760921
Grant ListR01 MH084022 / MH / NIMH NIH HHS / United States
U01-HL-114494 / HL / NHLBI NIH HHS / United States
P01-CA-142538 / CA / NCI NIH HHS / United States
U01 HL114494 / HL / NHLBI NIH HHS / United States
R01-MH-084 022 / MH / NIMH NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
Project: