Efficient implementation of cumulative probability models for association studies

Music City Center 

Association studies, such as genome-wide association studies (GWAS), for continuous outcomes are commonly conducted using standard linear regression models. However, these biological outcomes are frequently skewed, necessitating transformations (e.g., log transformations) that are often not known in advance. This dependency on transformations can lead to variability in results and interpretations. Recently, cumulative probability models (CPMs) have emerged as a semi-parametric alternative to linear models. CPMs treat continuous outcomes as ordered categories, assigning each unique value as a category, and utilize cumulative link models for estimation. While existing algorithms for association analyses with ordinal outcomes are efficient and scalable, they struggle to handle a significantly large number of outcome categories. To address this, we leverage the CPM's sparse Hessian structure to develop an efficient score test algorithm, making association studies for continuous outcomes with CPMs computationally feasible. We demonstrate the algorithm's effectiveness using a large-scale omics dataset.

ordinal regression

genome-wide association study

set-based testing

cumulative link models 

Section on Statistics in Genomics and Genetics