Systematic Inference of Copy-Number Genotypes from Personal Genome Sequencing Data Reveals Extensive Olfactory Receptor Gene Content Diversity

Abstract

Copy-number variations (CNVs) are widespread in the human genome, but comprehensive assignments of integer locus copy-numbers ( i.e., copy-number genotypes) that, for example, enable discrimination of homozygous from heterozygous CNVs, have remained challenging. Here we present CopySeq, a novel computational approach with an underlying statistical framework that analyzes the depth-of-coverage of high-throughput DNA sequencing reads, and can incorporate paired-end and breakpoint junction analysis based CNV-analysis approaches, to infer locus copy-number genotypes. We benchmarked CopySeq by genotyping 500 chromosome 1 CNV regions in 150 personal genomes sequenced at low-coverage. The assessed copy-number genotypes were highly concordant with our performed qPCR experiments (Pearson correlation coefficient 0.94), and with the published results of two microarray platforms (95–99% concordance). We further demonstrated the utility of CopySeq for analyzing gene regions enriched for segmental duplications by comprehensively inferring copy-number genotypes in the CNV-enriched >800 olfactory receptor (OR) human gene and pseudogene loci. CopySeq revealed that OR loci display an extensive range of locus copy-numbers across individuals, with zero to two copies in some OR loci, and two to nine copies in others. Among genetic variants affecting OR loci we identified deleterious variants including CNVs and SNPs affecting ∼15% and ∼20% of the human OR gene repertoire, respectively, implying that genetic variants with a possible impact on smell perception are widespread. Finally, we found that for several OR loci the reference genome appears to represent a minor-frequency variant, implying a necessary revision of the OR repertoire for future functional studies. CopySeq can ascertain genomic structural variation in specific gene families as well as at a genome-wide scale, where it may enable the quantitative evaluation of CNVs in genome-wide association studies involving high-throughput sequencing.

Author Summary

Human individual genome sequencing has recently become affordable, enabling highly detailed genetic sequence comparisons. While the identification and genotyping of single-nucleotide polymorphisms has already been successfully established for different sequencing platforms, the detection, quantification and genotyping of large-scale copy-number variants (CNVs), i.e., losses or gains of long genomic segments, has remained challenging. We present a computational approach that enables detecting CNVs in sequencing data and accurately identifies the actual copy-number at which DNA segments of interest occur in an individual genome. This approach enabled us to obtain novel insights into the largest human gene family – the olfactory receptors (ORs) – involved in smell perception. While previous studies reported an abundance of CNVs in ORs, our approach enabled us to globally identify absolute differences in OR gene counts that exist between humans. While several OR genes have very high gene counts, other ORs are found only once or are missing entirely in some individuals. The latter have a particularly high probability of influencing individual differences in the perception of smell, a question that future experimental efforts can now address. Furthermore, we observed differences in OR gene counts between populations, pointing at ORs that might contribute to population-specific differences in smell.

Contributors

: Role: Editor

Journal

Journal ID (nlm-ta): PLoS Comput Biol

Journal ID (publisher-id): plos

Journal ID (pmc): ploscomp

Title: PLoS Computational Biology

Publisher: Public Library of Science (San Francisco, USA )

ISSN (Print): 1553-734X

ISSN (Electronic): 1553-7358

Publication date Collection: November 2010

Publication date (Print): November 2010

Publication date (Electronic): 11 November 2010

Volume: 6

Issue: 11

Affiliations

[1 ]Department of Molecular Genetics, Crown Human Genome Center, Weizmann Institute of Science, Rehovot, Israel

[2 ]Department of Biotechnology and Bioinformatics, Weihenstephan-Triesdorf University of Applied Sciences, Freising, Germany

[3 ]Genome Biology Research Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany

[4 ]European Bioinformatics Institute, EMBL-EBI, Hinxton, United Kingdom

University of British Columbia, Canada

Author notes

* E-mail: jan.korbel@ 123456embl.de

Conceived and designed the experiments: SMW DL JOK. Performed the experiments: YH MK AMS. Analyzed the data: SMW YH TZ TO IK AS JOK. Wrote the paper: SMW YH DL JOK. Designed and implemented the CopySeq software: SMW.

Article

Publisher ID: 10-PLCB-RA-2163R2

DOI: 10.1371/journal.pcbi.1000988

PMC ID: 2978733

PubMed ID: 21085617

Copyright statement: Waszak et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Page count

Pages: 20

Categories

Subject: Research Article

Subject: Computational Biology/Genomics

Subject: Genetics and Genomics/Genomics