Learning phenotype densities conditional on many interacting predictors.

Journal Article (Journal Article)


Estimating a phenotype distribution conditional on a set of discrete-valued predictors is a commonly encountered task. For example, interest may be in how the density of a quantitative trait varies with single nucleotide polymorphisms and patient characteristics. The subset of important predictors is not usually known in advance. This becomes more challenging with a high-dimensional predictor set when there is the possibility of interaction.


We demonstrate a novel non-parametric Bayes method based on a tensor factorization of predictor-dependent weights for Gaussian kernels. The method uses multistage predictor selection for dimension reduction, providing succinct models for the phenotype distribution. The resulting conditional density morphs flexibly with the selected predictors. In a simulation study and an application to molecular epidemiology data, we demonstrate advantages over commonly used methods.

Full Text

Duke Authors

Cited Authors

  • Kessler, DC; Taylor, JA; Dunson, DB

Published Date

  • June 2014

Published In

Volume / Issue

  • 30 / 11

Start / End Page

  • 1562 - 1568

PubMed ID

  • 24501099

Pubmed Central ID

  • PMC4029029

Electronic International Standard Serial Number (EISSN)

  • 1367-4811

International Standard Serial Number (ISSN)

  • 1367-4803

Digital Object Identifier (DOI)

  • 10.1093/bioinformatics/btu040


  • eng