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A statistical method for detection of copy number variation using next-generation sequencing data

Grant number: 19/12333-0
Support type:Scholarships abroad - Research Internship - Post-doctor
Effective date (Start): July 01, 2020
Effective date (End): June 30, 2021
Field of knowledge:Biological Sciences - Genetics
Principal Investigator:Iscia Teresinha Lopes Cendes
Grantee:Barbara Henning Silva
Supervisor abroad: Heather Christy Mefford
Home Institution: Faculdade de Ciências Médicas (FCM). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Local de pesquisa : University of Washington, United States  
Associated to the scholarship:16/19484-6 - Quantitative proteomic analysis of neuronal tissue obtained from patients and animal models of epilepsy, BP.PD


Epilepsy is a group of disorders characterised by recurrent seizures and is one of the most common neurological conditions. Genetic factors are believed to be the cause of the majority of epilepsy cases. Copy number variations (CNVs) are deletions and duplications of stretches of DNA that have particularly contributed to elucidating the aetiology in several patients with epilepsy. Traditionally, CNVs have been detected based on SNP array experiments. However, next-­generation sequencing (NGS) has evolved into a popular strategy for genotyping and it is desirable to exploit these data also to detect CNVs. Several methods have been proposed to detect CNVs from NGS data, each one with its advantages and limitations. Though there has been great progress, none of the currently available methods have resulted in a satisfactory comprehensively detection of CNVs. The main goal of this project is to develop a statistical method for CNV detection from whole exome sequencing (WES) and panel sequencing data using CNV calls from SNP arrays as gold­ standard. More specifically, we will propose estimators for LRR and BAF, adapted for data generated through WES/Panel experiments designed specifically for neurological disorders, to proceed with CNV calls. We have already generated data for a large group of subjects on different platform configurations: a) SNP array and panel sequencing; or b) SNP array and WES. We propose to use this combined data to fit a model for CNV calling using WES/gene panel data. We will focus our efforts on Brazilians, an admixed population, often underrepresented on genomic studies. This is the population on which we generated the aforementioned dataset, and this approach will allow us to investigate the association between copy number events and population­ specific factors.