Copy number variation (CNV) identification, interpretation, and database from Brazilian patients.
Victória Cabral Silveira Monteiro de GodoyFernanda Teixeira BelluccoMileny ColovatiHélio Rodrigues de Oliveira-JuniorMariana Moysés-OliveiraMaria Isabel MelaragnoPublished in: Genetics and molecular biology (2020)
Copy number variations (CNVs) constitute an important class of variation in the human genome and the interpretation of their pathogenicity considering different frequencies across populations is still a challenge for geneticists. Since the CNV databases are predominantly composed of European and non-admixed individuals, and Brazilian genetic constitution is admixed and ethnically diverse, diagnostic screenings on Brazilian variants are greatly difficulted by the lack of populational references. We analyzed a clinical sample of 268 Brazilian individuals, including patients with neurodevelopment disorders and/or congenital malformations. The pathogenicity of CNVs was classified according to their gene content and overlap with known benign and pathogenic variants. A total of 1,504 autosomal CNVs (1,207 gains and 297 losses) were classified as benign (92.9%), likely benign (1.6%), VUS (2.6%), likely pathogenic (0.2%) and pathogenic (2.7%). Some of the CNVs were recurrent and with frequency increased in our sample, when compared to populational open resources of structural variants: 14q32.33, 22q11.22, 1q21.1, and 1p36.32 gains. Thus, these highly recurrent CNVs classified as likely benign or VUS were considered non-pathogenic in our Brazilian sample. This study shows the relevance of introducing CNV data from diverse cohorts to improve on the interpretation of clinical impact of genomic variations.
Keyphrases
- copy number
- mitochondrial dna
- genome wide
- dna methylation
- end stage renal disease
- endothelial cells
- newly diagnosed
- chronic kidney disease
- ejection fraction
- minimally invasive
- biofilm formation
- peritoneal dialysis
- electronic health record
- machine learning
- pseudomonas aeruginosa
- patient reported outcomes
- deep learning
- genetic diversity