  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.
