This supplement provides detailed notes and methods for the study on rare copy-number variants (CNVs) as modulators of common disease susceptibility. The study analyzed CNVs in a large cohort of individuals, focusing on their association with various diseases. The CNVs were identified using microarray-based CNV calling and were encoded into PLINK binary files for genome-wide association studies (GWAS). The study applied multiple filters to select high-confidence CNVs and performed quality control to ensure the reliability of the data. The CNVs were then analyzed using different association models, including mirror, U-shape, duplication-only, and deletion-only models, to assess their impact on disease susceptibility. The study also included replication analyses using the Estonian Biobank (EstBB) data to validate the findings. The results showed significant associations between specific CNVs and various diseases, including ovarian cancer, ischemic heart disease, and other cardiovascular conditions. Additionally, the study explored the association between BRCA1 deletion and ovarian and other female cancers, as well as the association between LDLR deletion and ischemic heart disease. The study also examined the 16p12.2 and 22q11.2 CNV regions, finding associations with hypertension, cardiac conduction disorders, and other conditions. The findings highlight the role of CNVs in modulating common disease susceptibility and provide insights into the genetic basis of various diseases. The study used rigorous statistical methods and validation procedures to ensure the reliability of the results.This supplement provides detailed notes and methods for the study on rare copy-number variants (CNVs) as modulators of common disease susceptibility. The study analyzed CNVs in a large cohort of individuals, focusing on their association with various diseases. The CNVs were identified using microarray-based CNV calling and were encoded into PLINK binary files for genome-wide association studies (GWAS). The study applied multiple filters to select high-confidence CNVs and performed quality control to ensure the reliability of the data. The CNVs were then analyzed using different association models, including mirror, U-shape, duplication-only, and deletion-only models, to assess their impact on disease susceptibility. The study also included replication analyses using the Estonian Biobank (EstBB) data to validate the findings. The results showed significant associations between specific CNVs and various diseases, including ovarian cancer, ischemic heart disease, and other cardiovascular conditions. Additionally, the study explored the association between BRCA1 deletion and ovarian and other female cancers, as well as the association between LDLR deletion and ischemic heart disease. The study also examined the 16p12.2 and 22q11.2 CNV regions, finding associations with hypertension, cardiac conduction disorders, and other conditions. The findings highlight the role of CNVs in modulating common disease susceptibility and provide insights into the genetic basis of various diseases. The study used rigorous statistical methods and validation procedures to ensure the reliability of the results.