A Comparative Study of Joint-SNVs Analysis Methods and Detection of Susceptibility Genes for Gastric Cancer in Korean Population

TL;DR

This study systematically compares various joint-SNVs analysis methods, highlighting the superior detection power of the S-space BBT method through extensive simulations and real gastric cancer data in Koreans.

Contribution

It provides a comprehensive evaluation of joint-SNVs analysis methods and identifies S-space BBT as the most effective for detecting susceptibility genes.

Findings

S-space BBT outperforms other methods in detection power

Simulation results cover diverse practical scenarios

Real data analysis confirms S-space BBT's effectiveness

Abstract

Many joint-SNVs (single-nucleotide variants) analysis methods were proposed to tackle the "missing heritability" problem, which emphasizes that the joint genetic variants can explain more heritability of traits and diseases. However, there is still lack of a systematic comparison and investigation on the relative strengths and weaknesses of these methods. In this paper, we evaluated their performance on extensive simulated data generated by varying sample size, linkage disequilibrium (LD), odds ratios (OR), and minor allele frequency (MAF), which aims to cover almost all scenarios encountered in practical applications. Results indicated that a method called Statistics-space Boundary Based Test (S-space BBT) showed stronger detection power than other methods. Results on a real dataset of gastric cancer for Korean population also validate the effectiveness of the S-space BBT method.