A0474
Title: Efficient implementation of cumulative probability models for association studies of continuous phenotypes
Authors: Eric Kawaguchi - University of Southern California (United States) [presenting]
Abstract: Association mapping for continuous traits, such as genome-wide association studies (GWAS), is 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 cumulative link models are utilized 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, the CPM's sparse Hessian structure is leveraged to develop an efficient score test algorithm, making association studies for continuous outcomes with CPMs computationally feasible. The capability of the algorithm is demonstrated on a large-scale omics dataset.
