Study

Complete Genomics paired end sequencing; Ovarian cancer

Study ID Alternative Stable ID Type
EGAS00001000158 Whole Genome Sequencing

Study Description

We sequenced the genomes from a monozygotic twin discordant for schizophrenia and a tumor-normal pair of an ovarian cancer patient. Using whole-genome twin data to discriminate between correctly identified single nucleotide variants (SNVs) and errors a strategy for the accurate detection of SNVs was developed. By applying stringent sequencing quality measures, excluding error-prone regions and selecting SNVs identified by different mapping and variation calling algorithms, error rates were ~37-fold reduced. This enabled us to identify the first discordant SNVs in monozygotic twins using whole-genome sequencing. In addition, by showing that novel SNVs are highly enriched in errors, accurate estimates of the number of novel and rare SNVs occurring in unrelated Caucasian individuals were obtained. Finally, somatic mutations in coding and regulatory sequences were reliably identified in the highly rearranged ovarian tumor. Overall, our data demonstrate that strategies to reduce error rates in whole-genomes are required for disease gene discovery.

Study Datasets 3 datasets.

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Dataset ID Description Technology Samples
EGAD00001000139
Tumor sample of a serious ovarian carcinoma
Complete Genomics 1
EGAD00001000140
Blood sample of serious ovarian carcinoma patient
Complete Genomics 1
EGAD00010000220
Ovarian & matched normal (Genotypes)
Complete Genomics - CG Build 1.4.2.8 2

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