摘要：Although next-generation sequencing has demonstrated great potential for novel gene discovery, confirming disease-causing genes after initial discovery remains challenging. Here, we applied a network analysis approach to prioritize candidate genes identified from whole-exome sequencing analysis of 98 cutaneous melanoma patients from 27 families. Using a network propagation method, we ranked candidate genes by their similarity to known disease genes in protein–protein interaction networks and identified gene clusters with functional connectivity. Using this approach, we identified several new candidate susceptibility genes that warrant future investigations such as NGLY1, IL1RN, FABP2, PRKDC, and PROSER2. The propagated network analysis also allowed us to link families that did not have common underlying genes but that carried variants in genes that interact on protein–protein interaction networks. In conclusion, our study provided an analysis perspective for gene prioritization in the context of genetic heterogeneity across families and prioritized top potential candidate susceptibility genes in our dataset.
其他摘要：Abstract Although next-generation sequencing has demonstrated great potential for novel gene discovery, confirming disease-causing genes after initial discovery remains challenging. Here, we applied a network analysis approach to prioritize candidate genes identified from whole-exome sequencing analysis of 98 cutaneous melanoma patients from 27 families. Using a network propagation method, we ranked candidate genes by their similarity to known disease genes in protein–protein interaction networks and identified gene clusters with functional connectivity. Using this approach, we identified several new candidate susceptibility genes that warrant future investigations such as NGLY1, IL1RN, FABP2, PRKDC , and PROSER2 . The propagated network analysis also allowed us to link families that did not have common underlying genes but that carried variants in genes that interact on protein–protein interaction networks. In conclusion, our study provided an analysis perspective for gene prioritization in the context of genetic heterogeneity across families and prioritized top potential candidate susceptibility genes in our dataset.
