Manuel Mattheisen

Copy number variants in german patients with schizophrenia

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

  • Lutz Priebe
  • ,
  • Franziska Degenhardt
  • ,
  • Jana Strohmaier
  • ,
  • René Breuer
  • ,
  • Stefan Herms
  • ,
  • Stephanie H Witt
  • ,
  • Per Hoffmann
  • ,
  • Rebecca Kulbida
  • ,
  • Manuel Mattheisen
  • Susanne Moebus
  • ,
  • Andreas Meyer-Lindenberg
  • ,
  • Henrik Walter
  • ,
  • Rainald Mössner
  • ,
  • Igor Nenadic
  • ,
  • Heinrich Sauer
  • ,
  • Dan Rujescu
  • ,
  • Wolfgang Maier
  • ,
  • Marcella Rietschel
  • ,
  • Markus M Nöthen
  • ,
  • Sven Cichon
Large rare copy number variants (CNVs) have been recognized as significant genetic risk factors for the development of schizophrenia (SCZ). However, due to their low frequency (1∶150 to 1∶1000) among patients, large sample sizes are needed to detect an association between specific CNVs and SCZ. So far, the majority of genome-wide CNV analyses have focused on reporting only CNVs that reached a significant P-value within the study cohort and merely confirmed the frequency of already-established risk-carrying CNVs. As a result, CNVs with a very low frequency that might be relevant for SCZ susceptibility are lost for secondary analyses. In this study, we provide a concise collection of high-quality CNVs in a large German sample consisting of 1,637 patients with SCZ or schizoaffective disorder and 1,627 controls. All individuals were genotyped on Illumina's BeadChips and putative CNVs were identified using QuantiSNP and PennCNV. Only those CNVs that were detected by both programs and spanned ≥30 consecutive SNPs were included in the data collection and downstream analyses (2,366 CNVs, 0.73 CNVs per individual). The genome-wide analysis did not reveal a specific association between a previously unknown CNV and SCZ. However, the group of CNVs previously reported to be associated with SCZ was more frequent in our patients than in the controls. The publication of our dataset will serve as a unique, easily accessible, high-quality CNV data collection for other research groups. The dataset could be useful for the identification of new disease-relevant CNVs that are currently overlooked due to their very low frequency and lack of power for their detection in individual studies.
Original languageEnglish
JournalP L o S One
Volume8
Issue7
Pages (from-to)e64035
ISSN1932-6203
DOIs
Publication statusPublished - 2013

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