An Integrative Co-localization (INCO) Analysis for SNV and CNV Genomic Features With an Application to Taiwan Biobank Data - Yu, Qi-You; Lu, Tzu-Pin; Hsiao, Tzu-Hung; Lin, Ching-Heng; Wu, Chi-Yun; Tzeng, Jung-Ying; Hsiao, Chuhsing Kate (Frontiers in Genetics, 2021)
Genomic studies have been one major approach to elucidating the disease etiology and to exploring potential targets for treatments of many complex diseases. Statistical analyses in these studies often face the challenges of multiplicity, weak signals, and the dependence nature among genetic markers. This situation becomes even more complicated when multi-omics data are available. To integrate data from different platforms, various integrative analyses have been adopted, ranging from the direct union or intersection operation on sets derived from different single-platform analysis to complex hierarchical multi-level models. The former ignores the biological relationship between molecules while the latter can be hard to interpret. We propose in this study an integrative approach that combines both single nucleotide variants (SNV) and copy number variations (CNV) in the same genomic unit to co-localize the concurrent effect and to deal with the sparsity due to rare variants. This approach is illustrated with simulation studies to evaluate its performance and is applied to low-density of lipoprotein cholesterol and triglyceride measurements from Taiwan Biobank. The results show that the proposed method can more effectively detect the collective effect from both SNV and CNV compared to traditional methods. For the biobank analysis, the identified genetic regions including the gene VNN2 could be novel and deserve further investigations.