摘要
Background： One of the most important and challenging issues in biomedicine and genomics is how to identify disease related genes. Datasets from high-throughput biotechnologies have been widely used to overcome this issue from various perspectives, e.g., epigenomics, genomics, transcriptomics, proteomics, metabolomics. At the genomic level, copy number variations （CNVs） have been recognized as critical genetic variations, which contribute significantly to genomic diversity. They have been associated with both common and complex diseases, and thus have a large influence on a variety of Mendelian and somatic genetic disorders. Results： In this review, based on a variety of complex diseases, we give an overview about the critical role of using CNVs for identifying disease related genes, and discuss on details the different high-throughput and sequencing methods applied for CNV detection. Some limitations and challenges concerning CNV are also highlighted. Conclusions： Reliable detection of CNVs will not only allow discriminating driver mutations for various diseases, but also helps to develop personalized medicine when integrating it with other genomic features.
Background： One of the most important and challenging issues in biomedicine and genomics is how to identify disease related genes. Datasets from high-throughput biotechnologies have been widely used to overcome this issue from various perspectives, e.g., epigenomics, genomics, transcriptomics, proteomics, metabolomics. At the genomic level, copy number variations （CNVs） have been recognized as critical genetic variations, which contribute significantly to genomic diversity. They have been associated with both common and complex diseases, and thus have a large influence on a variety of Mendelian and somatic genetic disorders. Results： In this review, based on a variety of complex diseases, we give an overview about the critical role of using CNVs for identifying disease related genes, and discuss on details the different high-throughput and sequencing methods applied for CNV detection. Some limitations and challenges concerning CNV are also highlighted. Conclusions： Reliable detection of CNVs will not only allow discriminating driver mutations for various diseases, but also helps to develop personalized medicine when integrating it with other genomic features.
基金
This work was supported by the National Natural Science Foundation of China （Nos. 61602386 and 61332014）, the Natural Science Foundation of Shaanxi Province （No. 2017JQ6008）, and the top university visiting foundation for excellent youth scholars of Northwestern Polytechnical University.
