Efficient Strategy to Identify Gene-Gene Interactions and Its Application to Type 2 Diabetes
Genomics & Informatics
;
: 160-165, 2016.
Article
Dans Anglais
| WPRIM
| ID: wpr-172205
ABSTRACT
Over the past decade, the detection of gene-gene interactions has become more and more popular in the field of genome-wide association studies (GWASs). The goal of the GWAS is to identify genetic susceptibility to complex diseases by assaying and analyzing hundreds of thousands of single-nucleotide polymorphisms. However, such tests are computationally demanding and methodologically challenging. Recently, a simple but powerful method, named “BOolean Operation-based Screening and Testing” (BOOST), was proposed for genome-wide gene-gene interaction analyses. BOOST was designed with a Boolean representation of genotype data and is approximately equivalent to the log-linear model. It is extremely fast, and genome-wide gene-gene interaction analyses can be completed within a few hours. However, BOOST can not adjust for covariate effects, and its type-1 error control is not correct. Thus, we considered two-step approaches for gene-gene interaction analyses. First, we selected gene-gene interactions with BOOST and applied logistic regression with covariate adjustments to select gene-gene interactions. We applied the two-step approach to type 2 diabetes (T2D) in the Korea Association Resource (KARE) cohort and identified some promising pairs of single-nucleotide polymorphisms associated with T2D.
Texte intégral:
Disponible
Indice:
WPRIM (Pacifique occidental)
Sujet Principal:
Modèles linéaires
/
Modèles logistiques
/
Dépistage de masse
/
Études de cohortes
/
Prédisposition génétique à une maladie
/
Diabète de type 2
/
Étude d'association pangénomique
/
Génotype
/
Corée
/
Méthodes
Type d'étude:
Etude d'étiologie
/
Etude d'incidence
/
Étude observationnelle
/
Étude pronostique
/
Facteurs de risque
/
Étude de dépistage
Pays comme sujet:
Asie
langue:
Anglais
Texte intégral:
Genomics & Informatics
Année:
2016
Type:
Article
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