Large sample size and nonlinear sparse models outline epistatic effects in inflammatory bowel disease.

ABSTRACT:

Background

Despite clear evidence of nonlinear interactions in the molecular architecture of polygenic diseases, linear models have so far appeared optimal in genotype-to-phenotype modeling. A key bottleneck for such modeling is that genetic data intrinsically suffers from underdetermination ([Formula: see text]). Millions of variants are present in each individual while the collection of large, homogeneous cohorts is hindered by phenotype incidence, sequencing cost, and batch effects.

Results

We demonstrate that when we provide enough training data and control the complexity of nonlinear models, a neural network outperforms additive approaches in whole exome sequencing-based inflammatory bowel disease case-control prediction. To do so, we propose a biologically meaningful s

SUBMITTER: Verplaetse N 

PROVIDER: S-EPMC10552306 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature