# The Iterative Exclusion of Compatible Samples Workflow for Multi-SNP Analysis in Complex Diseases
## 2 Materials and Methods
### Iterative Exclusion of Compatible Samples Workflow
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Configuration** & **Round 1** & **Round 2** & **Round 3** & **Round 4** \\ \hline \(-\)204 G \(>\) C & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) \\ IVS3 + 106 T \(>\) G & \(\bigcirc\) & & \(\bullet\) & \(\bigcirc\) \\ c\(\cdot\)401 A \(>\) G & & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) \\ IVS10-5 C \(>\) T & \(\bigcirc\) & \(\bigcirc\) & \(\bullet\) & \(\bigcirc\) \\ IVS15 + 144 T \(>\) A & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) & \(\bigcirc\) \\ IVS17-294 C \(>\) T & & \(\bullet\) & \(\bigcirc\) & \(\bigcirc\) \\ IVS22 + 36 C \(>\) A & \(\bullet\) & \(\bigcirc\) & \(\bigcirc\) & \(\bullet\) \\ 3′UTR159 C \(>\) T & \(\bullet\) & \(\bigcirc\) & \(\bullet\) & \(\bigcirc\) \\ Raw coverage & 9.73\% & 2.71\% & 1.96\% & 1.51\% \\ Consistency & 1 & 1 & 1 & 1 \\ \hline \hline \end{tabular} Black circles “\(\bullet\)” indicate mutation, while white circles “\(\bigcirc\)” indicate no mutation; the blank spaces indicate “don’t care”; four iterations are completed in the IECS workflow.

\end{table} 
Table 2: QCA results for Alzheimer’s disease data of four iterations. The relationships between SNPs and Alzheimer's disease obtained by IECS and the analysis of necessary conditions are shown in Figure 4. All results are supported by the literature. ## 4 Conclusions

The IECS workflow with QCA as the calculation engine was proposed and applied to analyze simulated datasets and the real dataset of Alzheimer's disease, and its performance was compared with that of the BOOST and MDR algorithms. The results revealed that IECS has greater power with relatively less computation cost. IECS has a relatively acceptable runtime and can compute high-dimensional pathogenic patterns with greater power. IECS could be applied to multi-SNP analysis in complex diseases as well as gene-gene and gene-environment interactions to explore the causes of complex diseases. In further research, we \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Condition** & **Consistency** & **Coverage** & \(p\)**-Value** \\ \hline \(\sim\)\(-\)204 G \(>\) C & 0.969 & 0.530 & 0.008 \\ \(\sim\).401 A \(>\) G & 0.961 & 0.510 & 0.130 \\ \(\sim\)VS10-5 C \(>\) T & 0.911 & 0.518 & 0.711 \\ \(\sim\)VS15 + 144 T \(>\) A & 0.926 & 0.528 & 0.082 \\ \hline \hline \end{tabular}

\end{table} 
Table 4: Results of analysis of necessary conditions for Alzheimer’s disease data. Figure 4: Relationships between SNPs and Alzheimer’s disease obtained by IECS and analysis of necessary conditions. R6 indicates \(-\)204 G \(>\) C, N4 indicates IVS3 + 106 T \(>\) G, N6 indicates C-401 A \(>\) G, N11 indicates I V S10 \(-\) 5 C \(>\) T, N15 indicates I V S15 + 144 T \(>\) A, N18 indicates I V S17 \(-\) 294 C \(>\) T, N22 indicates I V S22 + 36 C \(>\) A, N24 indicates 3’U T R159 C \(>\) T and asterisk “*” indicates simultaneous mutation. Simultaneous mutation of I V S22 + 36 C \(>\) A and 3’U T R159 C \(>\) T, I V S22 + 36 C \(>\) A mutation, and I V S17 \(-\) 294 C \(>\) T mutation cause Alzheimer’s disease; \(-\)204 G \(>\) C mutation prevents Alzheimer’s disease. * (21) [PERSON]; [PERSON]; [PERSON] Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. _Bioinformatics_**2003**, _19_, 376-382. [CrossRef] [PubMed]
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