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Discovery and statistical genotyping of copy-number variation from whole-exome sequencing depth

Fromer, Menachem, Moran, Jennifer L., Chambert, Kimberly, Banks, Eric, Bergen, Sarah E., Ruderfer, Douglas M., Handsaker, Robert E., McCarroll, Steven A., O'Donovan, Michael Conlon, Owen, Michael John, Kirov, George, Sullivan, Patrick F., Hultman, Christina M., Sklar, Pamela and Purcell, Shaun M. 2012. Discovery and statistical genotyping of copy-number variation from whole-exome sequencing depth. American Journal of Human Genetics 91 (4) , pp. 597-607. 10.1016/j.ajhg.2012.08.005

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Abstract

Sequencing of gene-coding regions (the exome) is increasingly used for studying human disease, for which copy-number variants (CNVs) are a critical genetic component. However, detecting copynumber from exomesequencing is challenging because of the noncontiguous nature of the captured exons. This is compounded by the complex relationship between read depth and copynumber; this results from biases in targeted genomic hybridization, sequence factors such as GC content, and batching of samples during collection and sequencing. We present a statistical tool (exome hidden Markov model [XHMM]) that uses principal-component analysis (PCA) to normalize exome read depth and a hidden Markov model (HMM) to discover exon-resolution CNV and genotype variation across samples. We evaluate performance on 90 schizophrenia trios and 1,017 case-control samples. XHMM detects a median of two rare (<1%) CNVs per individual (one deletion and one duplication) and has 79% sensitivity to similarly rare CNVs overlapping three or more exons discovered with microarrays. With sensitivity similar to state-of-the-art methods, XHMM achieves higher specificity by assigning quality metrics to the CNV calls to filter out bad ones, as well as to statistically genotype the discovered CNV in all individuals, yielding a trio call set with Mendelian-inheritance properties highly consistent with expectation. We also show that XHMM breakpoint quality scores enable researchers to explicitly search for novel classes of structural variation. For example, we apply XHMM to extract those CNVs that are highly likely to disrupt (delete or duplicate) only a portion of a gene.

Item Type: Article
Date Type: Publication
Status: Published
Schools: MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG)
Medicine
Neuroscience and Mental Health Research Institute (NMHRI)
Subjects: Q Science > QH Natural history > QH426 Genetics
Publisher: Elsevier
ISSN: 0002-9297
Last Modified: 04 Jun 2017 04:30
URI: http://orca.cf.ac.uk/id/eprint/40076

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