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Am J Hum Genet DOI:10.1016/j.ajhg.2012.08.005

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

Publication TypeJournal Article
Year of Publication2012
AuthorsFromer, M, Moran, JL, Chambert, K, Banks, E, Bergen, SE, Ruderfer, DM, Handsaker, RE, McCarroll, SA, O'Donovan, MC, Owen, MJ, Kirov, G, Sullivan, PF, Hultman, CM, Sklar, P, Purcell, SM
JournalAm J Hum Genet
Date Published2012 Oct 05
KeywordsCase-Control Studies, DNA Copy Number Variations, Exome, Exons, Genome-Wide Association Study, Genotype, Genotyping Techniques, High-Throughput Nucleotide Sequencing, Humans, Models, Genetic, Nucleic Acid Hybridization, Oligonucleotide Array Sequence Analysis

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 copy number from exome sequencing is challenging because of the noncontiguous nature of the captured exons. This is compounded by the complex relationship between read depth and copy number; 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 (


Alternate JournalAm. J. Hum. Genet.
PubMed ID23040492
PubMed Central IDPMC3484655
Grant ListR01 MH095034 / MH / NIMH NIH HHS / United States
RC2 MH089905 / MH / NIMH NIH HHS / United States
RC2MH089905 / MH / NIMH NIH HHS / United States
R01HG005827 / HG / NHGRI NIH HHS / United States
R01 HG005827 / HG / NHGRI NIH HHS / United States