A random forest approach to capture genetic effects in the presence of population structure.
Nat Commun
; 6: 7432, 2015 Jun 25.
Article
em En
| MEDLINE
| ID: mdl-26109276
ABSTRACT
The accurate mapping of causal variants in genome-wide association studies requires the consideration of both, confounding factors (for example, population structure) and nonlinear interactions between individual genetic variants. Here, we propose a method termed 'mixed random forest' that simultaneously accounts for population structure and captures nonlinear genetic effects. We test the model in simulation experiments and show that the mixed random forest approach improves detection power compared with established approaches. In an application to data from an outbred mouse population, we find that mixed random forest identifies associations that are more consistent with prior knowledge than competing methods. Further, our approach allows predicting phenotypes from genotypes with greater accuracy than any of the other methods that we tested. Our results show that approaches that simultaneously account for both, confounding due to population structure and epistatic interactions, are important to fully explain the heritable component of complex quantitative traits.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Simulação por Computador
/
Estudo de Associação Genômica Ampla
/
Modelos Genéticos
Tipo de estudo:
Prognostic_studies
Limite:
Animals
Idioma:
En
Revista:
Nat Commun
Assunto da revista:
BIOLOGIA
/
CIENCIA
Ano de publicação:
2015
Tipo de documento:
Article
País de afiliação:
Alemanha
País de publicação:
ENGLAND
/
ESCOCIA
/
GB
/
GREAT BRITAIN
/
INGLATERRA
/
REINO UNIDO
/
SCOTLAND
/
UK
/
UNITED KINGDOM