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Detecting large copy number variants using exome genotyping arrays in a large Swedish schizophrenia sample.

ABSTRACT: Although copy number variants (CNVs) are important in genomic medicine, CNVs have not been systematically assessed for many complex traits. Several large rare CNVs increase risk for schizophrenia (SCZ) and autism and often demonstrate pleiotropic effects; however, their frequencies in the general population and other complex traits are unknown. Genotyping large numbers of samples is essential for progress. Large cohorts from many different diseases are being genotyped using exome-focused arrays designed to detect uncommon or rare protein-altering sequence variation. Although these arrays were not designed for CNV detection, the hybridization intensity data generated in each experiment could, in principle, be used for gene-focused CNV analysis. Our goal was to evaluate the extent to which CNVs can be detected using data from one particular exome array (the Illumina Human Exome Bead Chip). We genotyped 9100 Swedish subjects (3962 cases with SCZ and 5138 controls) using both standard genome-wide association study (GWAS) and exome arrays. In comparison with CNVs detected using GWAS arrays, we observed high sensitivity and specificity for detecting genic CNVs ?400?kb including known pathogenic CNVs along with replicating the literature finding that cases with SCZ had greater enrichment for genic CNVs. Our data confirm the association of SCZ with 16p11.2 duplications and 22q11.2 deletions, and suggest a novel association with deletions at 11q12.2. Our results suggest the utility of exome-focused arrays in surveying large genic CNVs in very large samples; and thereby open the door for new opportunities such as conducting well-powered CNV assessment and comparisons between different diseases. The use of a single platform also minimizes potential confounding factors that could impact accurate detection.

SUBMITTER: Szatkiewicz JP

PROVIDER: S-EPMC3966073 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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Detecting large copy number variants using exome genotyping arrays in a large Swedish schizophrenia sample.

Szatkiewicz J P JP Neale B M BM O'Dushlaine C C Fromer M M Goldstein J I JI Moran J L JL Chambert K K Kähler A A Magnusson P K E PK Hultman C M CM Sklar P P Purcell S S McCarroll S A SA Sullivan P F PF

Molecular psychiatry 20130813 11

Although copy number variants (CNVs) are important in genomic medicine, CNVs have not been systematically assessed for many complex traits. Several large rare CNVs increase risk for schizophrenia (SCZ) and autism and often demonstrate pleiotropic effects; however, their frequencies in the general population and other complex traits are unknown. Genotyping large numbers of samples is essential for progress. Large cohorts from many different diseases are being genotyped using exome-focused arrays ...[more]

PMID: 23938935

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Project description:BackgroundGenotypes obtained with commercial SNP arrays have been extensively used in many large case-control or population-based cohorts for SNP-based genome-wide association studies for a multitude of traits. Yet, these genotypes capture only a small fraction of the variance of the studied traits. Genomic structural variants (GSV) such as Copy Number Variation (CNV) may account for part of the missing heritability, but their comprehensive detection requires either next-generation arrays or sequencing. Sophisticated algorithms that infer CNVs by combining the intensities from SNP-probes for the two alleles can already be used to extract a partial view of such GSV from existing data sets.ResultsHere we present several advances to facilitate the latter approach. First, we introduce a novel CNV detection method based on a Gaussian Mixture Model. Second, we propose a new algorithm, PCA merge, for combining copy-number profiles from many individuals into consensus regions. We applied both our new methods as well as existing ones to data from 5612 individuals from the CoLaus study who were genotyped on Affymetrix 500K arrays. We developed a number of procedures in order to evaluate the performance of the different methods. This includes comparison with previously published CNVs as well as using a replication sample of 239 individuals, genotyped with Illumina 550K arrays. We also established a new evaluation procedure that employs the fact that related individuals are expected to share their CNVs more frequently than randomly selected individuals. The ability to detect both rare and common CNVs provides a valuable resource that will facilitate association studies exploring potential phenotypic associations with CNVs.ConclusionOur new methodologies for CNV detection and their evaluation will help in extracting additional information from the large amount of SNP-genotyping data on various cohorts and use this to explore structural variants and their impact on complex traits.

Dataset Information

Detecting large copy number variants using exome genotyping arrays in a large Swedish schizophrenia sample.

Publications

Detecting large copy number variants using exome genotyping arrays in a large Swedish schizophrenia sample.

Similar Datasets

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