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A Comparative Study on Multifactor Dimensionality Reduction Methods for Detecting Gene-Gene Interactions with the
Seungyeoun Lee1, Yongkang Kim2, Min-Seok Kwon3
1Department of Mathematics and Statistics, Sejong University, Seoul 143-747, Republic of Korea.
Biomed Research International
|September 5, 2015
Summary
This study addresses the missing heritability in complex diseases by exploring multi-SNP effects. Researchers propose novel multifactor dimensionality reduction (MDR) extensions for survival analysis, improving genetic association studies.
Area of Science:
- Genetics
- Biostatistics
- Computational Biology
Background:
- Genome-wide association studies (GWAS) identify single nucleotide polymorphism (SNP) effects but leave heritability unexplained.
- The 'missing heritability' problem may stem from focusing solely on individual SNPs, neglecting multi-SNP effects and gene-gene interactions.
Purpose of the Study:
- To propose and evaluate novel extensions of the multifactor dimensionality reduction (MDR) method for survival phenotypes.
- To address the limitations of single-SNP analyses in explaining complex disease heritability.
Main Methods:
- Developed several extensions of the multifactor dimensionality reduction (MDR) method tailored for survival data.
- Conducted comprehensive simulation studies to compare the performance of proposed MDR extensions against earlier MDR approaches.
Main Results:
- The proposed MDR extensions demonstrated potential in identifying multi-SNP effects for survival outcomes.
- Simulation results provided insights into the comparative performance of different MDR strategies in survival analysis.
Conclusions:
- The developed MDR extensions offer a promising approach to tackle the missing heritability problem in survival analysis.
- These methods can enhance the detection of complex genetic architectures underlying diseases with survival phenotypes.
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