  Recent technological advances have made it possible to collect
high-dimensional genomic data along with clinical data on a large number of
subjects. In the studies of chronic diseases such as cancer, it is of great
interest to integrate clinical and genomic data to build a comprehensive
understanding of the disease mechanisms. Despite extensive studies on
integrative analysis, it remains an ongoing challenge to model the interaction
effects between clinical and genomic variables, due to high-dimensionality of
the data and heterogeneity across data types. In this paper, we propose an
integrative approach that models interaction effects using a single-index
varying-coefficient model, where the effects of genomic features can be
modified by clinical variables. We propose a penalized approach for separate
selection of main and interaction effects. We demonstrate the advantages of the
proposed methods through extensive simulation studies and provide applications
to a motivating cancer genomic study.
