RT Journal Article
SR Electronic
T1 Principal component of explained variance: an efficient and optimal data dimension reduction framework for association studies
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 036566
DO 10.1101/036566
A1 Maxime Turgeon
A1 Karim Oualkacha
A1 Antonio Ciampi
A1 Golsa Dehghan
A1 Brent W. Zanke
A1 Andréa L. Benedet
A1 Pedro Rosa-Neto
A1 Celia MT. Greenwood
A1 Aurélie Labbe
A1 for the Alzheimer’s Disease Neuroimaging Initiative
YR 2016
UL http://biorxiv.org/content/early/2016/01/27/036566.abstract
AB The genomics era has led to an increase in the dimensionality of the data collected to investigate biological questions. In this context, dimension-reduction techniques can be used to summarize high-dimensional signals into low-dimensional ones, to further test for association with one or more covariates of interest. This paper revisits one such approach, previously known as Principal Component of Heritability and renamed here as Principal Component of Explained Variance (PCEV). As its name suggests, the PCEV seeks a linear combination of outcomes in an optimal manner, by maximising the proportion of variance explained by one or several covariates of interest. By construction, this method optimises power but limited by its computational complexity, it has unfortunately received little attention in the past. Here, we propose a general analytical PCEV framework that builds on the assets of the original method, i.e. conceptually simple and free of tuning parameters. Moreover, our framework extends the range of applications of the original procedure by providing a computationally simple strategy for high-dimensional outcomes, along with exact and asymptotic testing procedures that drastically reduce its computational cost. We investigate the merits of the PCEV using an extensive set of simulations. Furthermore, the use of the PCEV approach will be illustrated using three examples taken from the epigenetics and brain imaging areas.