title: Probabilistic method for detecting copy number variation in a fetal genome using maternal plasma sequencing.
abstract: BACKGROUND
  The past several years have seen the development of methodologies to identify genomic variation within a fetus through the non-invasive sequencing of maternal blood plasma. These methods are based on the observation that maternal plasma contains a fraction of DNA (typically 5-15%) originating from the fetus, and such methodologies have already been used for the detection of whole-chromosome events (aneuploidies), and to a more limited extent for smaller (typically several megabases long) copy number variants (CNVs).
  
  
  RESULTS
  Here we present a probabilistic method for non-invasive analysis of de novo CNVs in fetal genome based on maternal plasma sequencing. Our novel method combines three types of information within a unified Hidden Markov Model: the imbalance of allelic ratios at SNP positions, the use of parental genotypes to phase nearby SNPs and depth of coverage to better differentiate between various types of CNVs and improve precision. Our simulation results, based on in silico introduction of novel CNVs into plasma samples with 13% fetal DNA concentration, demonstrate a sensitivity of 90% for CNVs >400 kb (with 13 calls in an unaffected genome), and 40% for 50-400 kb CNVs (with 108 calls in an unaffected genome).
  
  
  METHODS
  Implementation of our model and data simulation method is available at http://github.com/compbio-UofT/fCNV.
authors.affiliation: Department of Computer Science, University of Toronto, Toronto M5S 2E4, Centre for Computational Medicine and Genetics and Genome Biology, Hospital for Sick Children, Toronto M5G 1L7, Canada.
authors.forename: Ladislav
authors.initials: L
authors.lastname: Rampášek
authors.affiliation: Department of Computer Science, University of Toronto, Toronto M5S 2E4, Centre for Computational Medicine and Genetics and Genome Biology, Hospital for Sick Children, Toronto M5G 1L7, Canada.
authors.forename: Aryan
authors.initials: A
authors.lastname: Arbabi
authors.affiliation: Department of Computer Science, University of Toronto, Toronto M5S 2E4, Centre for Computational Medicine and Genetics and Genome Biology, Hospital for Sick Children, Toronto M5G 1L7, CanadaDepartment of Computer Science, University of Toronto, Toronto M5S 2E4, Centre for Computational Medicine and Genetics and Genome Biology, Hospital for Sick Children, Toronto M5G 1L7, CanadaDepartment of Computer Science, University of Toronto, Toronto M5S 2E4, Centre for Computational Medicine and Genetics and Genome Biology, Hospital for Sick Children, Toronto M5G 1L7, Canada.
authors.forename: Michael
authors.initials: M
authors.lastname: Brudno
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004247:DNA / Q000097:blood; D056915:DNA Copy Number Variations; D005260:Female; D005333:Fetus; D005820:Genetic Testing / Q000379:methods*; D015894:Genome, Human; D005838:Genotype; D006801:Humans; D008297:Male; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D011247:Pregnancy; D011296:Prenatal Diagnosis / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*
chemical_list: D004247:DNA