Performance and robustness of penalized and unpenalized methods for genetic prediction of complex human disease.
Genet Epidemiol
; 37(2): 184-95, 2013 Feb.
Article
em En
| MEDLINE
| ID: mdl-23203348
ABSTRACT
A central goal of medical genetics is to accurately predict complex disease from genotypes. Here, we present a comprehensive analysis of simulated and real data using lasso and elastic-net penalized support-vector machine models, a mixed-effects linear model, a polygenic score, and unpenalized logistic regression. In simulation, the sparse penalized models achieved lower false-positive rates and higher precision than the other methods for detecting causal SNPs. The common practice of prefiltering SNP lists for subsequent penalized modeling was examined and shown to substantially reduce the ability to recover the causal SNPs. Using genome-wide SNP profiles across eight complex diseases within cross-validation, lasso and elastic-net models achieved substantially better predictive ability in celiac disease, type 1 diabetes, and Crohn's disease, and had equivalent predictive ability in the rest, with the results in celiac disease strongly replicating between independent datasets. We investigated the effect of linkage disequilibrium on the predictive models, showing that the penalized methods leverage this information to their advantage, compared with methods that assume SNP independence. Our findings show that sparse penalized approaches are robust across different disease architectures, producing as good as or better phenotype predictions and variance explained. This has fundamental ramifications for the selection and future development of methods to genetically predict human disease.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Doença
/
Herança Multifatorial
/
Polimorfismo de Nucleotídeo Único
/
Modelos Genéticos
Tipo de estudo:
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Genet Epidemiol
Assunto da revista:
EPIDEMIOLOGIA
/
GENETICA MEDICA
Ano de publicação:
2013
Tipo de documento:
Article
País de afiliação:
Austrália