TrioMDR: Detecting SNP interactions in trio families with model-based multifactor dimensionality reduction

Area of Science:

• Genetics
• Biostatistics
• Computational Biology

Background:

• Single nucleotide polymorphism (SNP) interactions are crucial for understanding the genetic basis of complex diseases, contributing to the 'missing heritability' phenomenon.
• Genome-wide association studies (GWAS) employ various methods to detect these interactions, with family-based approaches offering robustness against population stratification.
• Multifactor Dimensionality Reduction (MDR) is a popular family-based method, but existing approaches face computational challenges and lack main effect adjustment.

Purpose of the Study:

• To develop an efficient and robust method for detecting multi-locus interactions in trio families.
• To address the computational burden and limitations of existing MDR-based methods.
• To enable adjustment for main genetic effects in interaction analyses.

Main Methods:

• A two-stage model-based MDR approach (TrioMDR) was developed, integrating the MDR framework with logistic regression.
• TrioMDR analyzes trio families (two parents and one affected child) to detect gene-gene interactions.
• A novel semi-parameter P-value correction procedure using minimal permutations was implemented to control Type I error rates and enhance computational speed.

Main Results:

• Extensive simulations demonstrated TrioMDR's effectiveness in detecting single nucleotide polymorphism (SNP) interactions.
• TrioMDR exhibited favorable Type I error control and statistical power across various simulated scenarios.
• The method proved to be computationally faster and generally more powerful than recently proposed interaction detection methods for trio data.

Conclusions:

• TrioMDR offers a computationally efficient and powerful solution for detecting multi-locus interactions in trio families.
• The method's ability to adjust for main effects and its robust error control make it a valuable tool in genetic association studies.
• TrioMDR facilitates a deeper understanding of the genetic architecture of complex diseases by uncovering significant SNP interactions.