Large sample size and nonlinear sparse models outline epistatic effects in inflammatory bowel disease.
Genome Biol
; 24(1): 224, 2023 10 05.
Article
em En
| MEDLINE
| ID: mdl-37798735
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 sparsified neural network architecture, providing empirical evidence for positive and negative epistatic effects present in the inflammatory bowel disease pathogenesis.CONCLUSIONS:
In this paper, we show that underdetermination is likely a major driver for the apparent optimality of additive modeling in clinical genetics today.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Doenças Inflamatórias Intestinais
/
Dinâmica não Linear
Tipo de estudo:
Prognostic_studies
Limite:
Humans
Idioma:
En
Revista:
Genome Biol
Ano de publicação:
2023
Tipo de documento:
Article