Microarray-Based Maps of Copy-Number Variant Regions in European and Sub-Saharan Populations

Christian Vogler,Iris-Tatjana Kolassa,Leo Gschwind,Andreas Papassotiropoulos,Philippe Demougin,Benno Röthlisberger,Thomas Elbert,Isabel Filges,Attila Stetak,Andreas Huber,Dominique J.-F De Quervain,Bianca Auschra,Vanja Vukojevic,Peter Miny

doi:10.1371/journal.pone.0015246

Abstract

The genetic basis of phenotypic variation can be partially explained by the presence of copy-number variations (CNVs). Currently available methods for CNV assessment include high-density single-nucleotide polymorphism (SNP) microarrays that have become an indispensable tool in genome-wide association studies (GWAS). However, insufficient concordance rates between different CNV assessment methods call for cautious interpretation of results from CNV-based genetic association studies. Here we provide a cross-population, microarray-based map of copy-number variant regions (CNVRs) to enable reliable interpretation of CNV association findings. We used the Affymetrix Genome-Wide Human SNP Array 6.0 to scan the genomes of 1167 individuals from two ethnically distinct populations (Europe, N = 717; Rwanda, N = 450). Three different CNV-finding algorithms were tested and compared for sensitivity, specificity, and feasibility. Two algorithms were subsequently used to construct CNVR maps, which were also validated by processing subsamples with additional microarray platforms (Illumina 1M-Duo BeadChip, Nimblegen 385K aCGH array) and by comparing our data with publicly available information. Both algorithms detected a total of 42669 CNVs, 74% of which clustered in 385 CNVRs of a cross-population map. These CNVRs overlap with 862 annotated genes and account for approximately 3.3% of the haploid human genome.We created comprehensive cross-populational CNVR-maps. They represent an extendable framework that can leverage the detection of common CNVs and additionally assist in interpreting CNV-based association studies.

Highlights

Copy Number Variations (CNVs) have been recently receiving growing attention as a steadily increasing number of CNVs in the human genome has been identified [1,2] and successfully linked to a variety of medical conditions [3,4,5,6]
We found a total of 141 copy-number variant regions (CNVRs) that overlapped when comparing the Chinese map to the SW-map and 124 overlapping events when compared to the RW-map
We note that all tested algorithms were able to detect largescale genomic aberrations ranging from a 14 Mb deletion to a whole chromosome triplication

Summary

Introduction

Copy Number Variations (CNVs) have been recently receiving growing attention as a steadily increasing number of CNVs in the human genome has been identified [1,2] and successfully linked to a variety of medical conditions [3,4,5,6]. In order to improve detection methods for Copy Number Events, standard maps providing information on genomic regions that are prone to structural variation are needed. Such maps containing information about hotspots for CNV-formation can provide prior knowledge in Bayesian terms about genomic localization and frequency of occurrence of CNVs. Such maps containing information about hotspots for CNV-formation can provide prior knowledge in Bayesian terms about genomic localization and frequency of occurrence of CNVs Incorporation of these priors leads to a considerably reduced marker set that either facilitates faster detection of common CNVs or allows for a more precise CNV-analysis

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Conclusion