Efficient Strategy to Identify Gene-Gene Interactions and Its Application to Type 2 Diabetes
Genomics & Informatics
;
: 160-165, 2016.
Artigo
em Inglês
| 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.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Modelos Lineares
/
Modelos Logísticos
/
Programas de Rastreamento
/
Estudos de Coortes
/
Predisposição Genética para Doença
/
Diabetes Mellitus Tipo 2
/
Estudo de Associação Genômica Ampla
/
Genótipo
/
Coreia (Geográfico)
/
Métodos
Tipo de estudo:
Estudo de etiologia
/
Estudo de incidência
/
Estudo observacional
/
Estudo prognóstico
/
Fatores de risco
/
Estudo de rastreamento
País/Região como assunto:
Ásia
Idioma:
Inglês
Revista:
Genomics & Informatics
Ano de publicação:
2016
Tipo de documento:
Artigo
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