Detecting copy number variation in next generation sequencing data from diagnostic gene panels.

Background: Detection of copy number variation (CNV) in genes associated with disease is important in genetic diagnostics, and next generation sequencing (NGS) technology provides data that can be used for CNV detection. However, CNV detection based on NGS data is in general not often used in diagnostic labs as the data analysis is challenging, especially with data from targeted gene panels. Wet lab methods like MLPA (MRC Holland) are widely used, but are expensive, time consuming and have gene-specific limitations.

Comprehensive mismatch repair gene panel identifies variants in patients with Lynch-like syndrome.

Background: Lynch-like syndrome (LLS) represents around 50% of the patients fulfilling the Amsterdam Criteria II/revised Bethesda Guidelines, characterized by a strong family history of Lynch Syndrome (LS) associated cancer, where a causative variant was not identified during genetic testing for LS.

Methods: Using data extracted from a larger gene panel, we have analyzed next-generation sequencing data from 22 mismatch repair (MMR) genes (MSH3, PMS1, MLH3, EXO1, POLD1, POLD3 RFC1, RFC2, RFC3, RFC4, RFC5, PCNA, LIG1, RPA1, RPA2, RPA3, POLD2, POLD4, MLH1, MSH2, MSH6, and PMS2) in 274 LLS patients. Detected variants were annotated and filtered using ANNOVAR and FILTUS software.