title: Copy number variation genotyping using family information.
abstract: BACKGROUND
  In recent years there has been a growing interest in the role of copy number variations (CNV) in genetic diseases. Though there has been rapid development of technologies and statistical methods devoted to detection in CNVs from array data, the inherent challenges in data quality associated with most hybridization techniques remains a challenging problem in CNV association studies.
  
  
  RESULTS
  To help address these data quality issues in the context of family-based association studies, we introduce a statistical framework for the intensity-based array data that takes into account the family information for copy-number assignment. The method is an adaptation of traditional methods for modeling SNP genotype data that assume Gaussian mixture model, whereby CNV calling is performed for all family members simultaneously and leveraging within family-data to reduce CNV calls that are incompatible with Mendelian inheritance while still allowing de-novo CNVs. Applying this method to simulation studies and a genome-wide association study in asthma, we find that our approach significantly improves CNV calls accuracy, and reduces the Mendelian inconsistency rates and false positive genotype calls. The results were validated using qPCR experiments.
  
  
  CONCLUSIONS
  In conclusion, we have demonstrated that the use of family information can improve the quality of CNV calling and hopefully give more powerful association test of CNVs.
authors: Channing Division of Network Medicine, Brigham and Women's Hospital, ,MA, USA. stjhc@channing.harvard.edu
authors: Jen-Hwa
authors: JH
authors: Chu
authors: Angela
authors: A
authors: Rogers
authors: Iuliana
authors: I
authors: Ionita-Laza
authors: Katayoon
authors: K
authors: Darvishi
authors: Ryan E
authors: RE
authors: Mills
authors: Charles
authors: C
authors: Lee
authors: Benjamin A
authors: BA
authors: Raby
mesh_terms: D001249:Asthma / Q000235:genetics; D056915:DNA Copy Number Variations; D005190:Family; D055106:Genome-Wide Association Study; D060005:Genotyping Techniques; D006801:Humans; D016133:Polymerase Chain Reaction
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