PT  - JOURNAL ARTICLE
AU  - Sahir R Bhatnagar
AU  - Amanda Lovato
AU  - Yi Yang
AU  - Celia MT Greenwood
TI  - Sparse Additive Interaction Learning
AID  - 10.1101/445304
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 445304
4099  - http://biorxiv.org/content/early/2018/10/16/445304.short
4100  - http://biorxiv.org/content/early/2018/10/16/445304.full
AB  - Diseases are now thought to be the result of changes in entire biological networks whose states are affected by a complex interaction of genetic and environmental factors. In general, power to estimate interactions is low, the number of possible interactions could be enormous and their effects may be non-linear. Existing approaches such as the lasso might keep an interaction but remove a main effect, which is problematic for interpretation. In this work, we introduce a sparse additive interaction learning model called sail for detecting non-linear interactions with a key environmental or exposure variable in high-dimensional settings. Our method can accommodate either the strong or weak heredity constraints. We develop a computationally efficient fitting algorithm with automatic tuning parameter selection, which scales to high-dimensional datasets. Through an extensive simulation study, we show that sail outperforms existing penalized regression methods in terms of prediction error, sensitivity and specificity when there are non-linear interactions with an exposure variable. We apply sail to the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data to select non-linear interactions between clinical diagnosis and Aβ protein in 96 brain regions on mini-mental state examination. Our algorithms are available in an R package (https://github.com/greenwoodlab).