Outlier-Based Identification of Copy Number Variations Using Targeted Resequencing in a Small Cohort of Patients with Tetralogy of Fallot

Vikas Bansal,Sabine Klaassen,Cornelia Dorn,Felix Berger,Roland Hetzer,Silke R Sperling,Marcel Grunert,Chunyu Liu

doi:10.1371/journal.pone.0085375

Abstract

Copy number variations (CNVs) are one of the main sources of variability in the human genome. Many CNVs are associated with various diseases including cardiovascular disease. In addition to hybridization-based methods, next-generation sequencing (NGS) technologies are increasingly used for CNV discovery. However, respective computational methods applicable to NGS data are still limited. We developed a novel CNV calling method based on outlier detection applicable to small cohorts, which is of particular interest for the discovery of individual CNVs within families, de novo CNVs in trios and/or small cohorts of specific phenotypes like rare diseases. Approximately 7,000 rare diseases are currently known, which collectively affect ∼6% of the population. For our method, we applied the Dixon’s Q test to detect outliers and used a Hidden Markov Model for their assessment. The method can be used for data obtained by exome and targeted resequencing. We evaluated our outlier- based method in comparison to the CNV calling tool CoNIFER using eight HapMap exome samples and subsequently applied both methods to targeted resequencing data of patients with Tetralogy of Fallot (TOF), the most common cyanotic congenital heart disease. In both the HapMap samples and the TOF cases, our method is superior to CoNIFER, such that it identifies more true positive CNVs. Called CNVs in TOF cases were validated by qPCR and HapMap CNVs were confirmed with available array-CGH data. In the TOF patients, we found four copy number gains affecting three genes, of which two are important regulators of heart development (NOTCH1, ISL1) and one is located in a region associated with cardiac malformations (PRODH at 22q11). In summary, we present a novel CNV calling method based on outlier detection, which will be of particular interest for the analysis of de novo or individual CNVs in trios or cohorts up to 30 individuals, respectively.

Highlights

Many genomic studies have revealed a high variability of the human genome, ranging from single nucleotide variations and short insertions or deletions to larger structural variations and aneuploidies
Ethics Statement Studies on Tetralogy of Fallot (TOF) patients were performed according to institutional guidelines of the German Heart Institute in Berlin, with approval of the ethics committee of the Charite Medical Faculty and informed written consent of patients and/or parents, kin, caretakers, or guardians on the behalf of the minors/children participants involved in our study
In addition to our method, we used the two publicly available tools ExomeDepth and CoNIFER [23,27]. Other tools such as CONTRA, FishingCNV, CoNVEX and ExomeCNV could not be applied to this dataset since they need either matched or non-matched controls

Summary

Introduction

Many genomic studies have revealed a high variability of the human genome, ranging from single nucleotide variations and short insertions or deletions to larger structural variations and aneuploidies. Structural variations include copy number variations (CNVs), which cause gains (duplications) or losses (deletions) of genomic sequence. Congenital heart disease (CHD) are the most common birth defect in human with an incidence of around 1% in all live births [7,8]. They comprise a heterogeneous group of cardiac malformations that arise during heart development. Deletions at the 22q11 locus account for up to 16% of TOF cases [12] and copy number changes at other loci were identified in several syndromic TOF patients [13,14,15]. Observing the overlap between these studies with hundreds of cases revealed only one locus (1q21.1) affected in 11 patients (Figure 1), which underlines the heterogeneous genetic background of non-syndromic TOF

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