A new efficient method to detect genetic interactions for lung cancer GWAS.

ABSTRACT:

Background

Genome-wide association studies (GWAS) have proven successful in predicting genetic risk of disease using single-locus models; however, identifying single nucleotide polymorphism (SNP) interactions at the genome-wide scale is limited due to computational and statistical challenges. We addressed the computational burden encountered when detecting SNP interactions for survival analysis, such as age of disease-onset. To confront this problem, we developed a novel algorithm, called the Efficient Survival Multifactor Dimensionality Reduction (ES-MDR) method, which used Martingale Residuals as the outcome parameter to estimate survival outcomes, and implemented the Quantitative Multifactor Dimensionality Reduction method to identify significant interactions associated with age

SUBMITTER: Luyapan J 

PROVIDER: S-EPMC7596958 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature