Extensive copy-number variation of the human olfactory receptor gene family

As much as a quarter of the human genome has been reported to vary in copy number between individuals, including regions containing about half of the members of the olfactory receptor (OR) gene family. We have undertaken a detailed study of copy-number variation of ORs to elucidate the selective and mechanistic forces acting on this gene family and the true impact of copy-number variation on human OR repertoires. We argue that the properties of copy-number variants (CNVs) and other sets of large genomic regions violate the assumptions of statistical methods that are commonly used in the assessment of gene enrichment. Using more appropriate methods, we provide evidence that OR enrichment in CNVs is not due to positive selection but is because of OR preponderance in segmentally duplicated regions, which are known to be frequently copy-number variable, and because purifying selection against CNVs is lower in OR-containing regions than in regions containing essential genes. We also combine multiplex ligation-dependent probe amplification (MLPA) and PCR to assay the copy numbers of 37 candidate CNV ORs in a panel of ~50 human individuals. We confirm copy-number variation of 18 ORs but find no variation in this human-diversity panel for 16 other ORs, highlighting the caveat that reported intervals often overrepresent true CNVs. The copy-number variation we describe is likely to underpin significant variation in olfactory abilities among human individuals. Finally, we show that both homology-based and homology-independent processes have played a recent role in remodeling the OR family.     

Extensive Copy-Number Variation of Young Genes across Stickleback Populations

Duplicate genes emerge as copy-number variations (CNVs) at the population level, and remain copy-number polymorphic until they are fixed or lost. The successful establishment of such structural polymorphisms in the genome plays an important role in evolution by promoting genetic diversity, complexity and innovation. To characterize the early evolutionary stages of duplicate genes and their potential adaptive benefits, we combine comparative genomics with population genomics analyses to evaluate the distribution and impact of CNVs across natural populations of an eco-genomic model, the three-spined stickleback. With whole genome sequences of 66 individuals from populations inhabiting three distinct habitats, we find that CNVs generally occur at low frequencies and are often only found in one of the 11 populations surveyed. A subset of CNVs, however, displays copy-number differentiation between populations, showing elevated within-population frequencies consistent with local adaptation. By comparing teleost genomes to identify lineage-specific genes and duplications in sticklebacks, we highlight rampant gene content differences among individuals in which over 30% of young duplicate genes are CNVs. These CNV genes are evolving rapidly at the molecular level and are enriched with functional categories associated with environmental interactions, depicting the dynamic early copy-number polymorphic stage of genes during population differentiation.     

Genomic rearrangements and gene copy-number alterations as a cause of nervous system disorders

Genomic disorders are a group of human genetic diseases caused by genomic rearrangements resulting in copy-number variation (CNV) affecting a dosage-sensitive gene or genes critical for normal development or maintenance. These disorders represent a wide range of clinically distinct entities but include many diseases affecting nervous system function. Herein, we review selected neurodevelopmental, neurodegenerative, and psychiatric disorders either known or suggested to be caused by genomic rearrangement and CNV. Further, we emphasize the cause-and-effect relationship between gene CNV and complex disease traits. We also discuss the prevalence and heritability of CNV, the correlation between CNV and higher-order genome architecture, and the heritability of personality, behavioral, and psychiatric traits. We speculate that CNV could underlie a significant proportion of normal human variation including differences in cognitive, behavioral, and psychological features.     

Dosage Sensitivity Shapes the Evolution of Copy-Number Varied Regions

Dosage sensitivity is an important evolutionary force which impacts on gene dispensability and duplicability. The newly available data on human copy-number variation (CNV) allow an analysis of the most recent and ongoing evolution. Provided that heterozygous gene deletions and duplications actually change gene dosage, we expect to observe negative selection against CNVs encompassing dosage sensitive genes. In this study, we make use of several sources of population genetic data to identify selection on structural variations of dosage sensitive genes. We show that CNVs can directly affect expression levels of contained genes. We find that genes encoding members of protein complexes exhibit limited expression variation and overlap significantly with a manually derived set of dosage sensitive genes. We show that complexes and other dosage sensitive genes are underrepresented in CNV regions, with a particular bias against frequent variations and duplications. These results suggest that dosage sensitivity is a significant force of negative selection on regions of copy-number variation.     

Bias of selection on human copy-number variants

Although large-scale copy-number variation is an important contributor to conspecific genomic diversity, whether these variants frequently contribute to human phenotype differences remains unknown. If they have few functional consequences, then copy-number variants (CNVs) might be expected both to be distributed uniformly throughout the human genome and to encode genes that are characteristic of the genome as a whole. We find that human CNVs are significantly overrepresented close to telomeres and centromeres and in simple tandem repeat sequences. Additionally, human CNVs were observed to be unusually enriched in those protein-coding genes that have experienced significantly elevated synonymous and nonsynonymous nucleotide substitution rates, estimated between single human and mouse orthologues. CNV genes encode disproportionately large numbers of secreted, olfactory, and immunity proteins, although they contain fewer than expected genes associated with Mendelian disease. Despite mouse CNVs also exhibiting a significant elevation in synonymous substitution rates, in most other respects they do not differ significantly from the genomic background. Nevertheless, they encode proteins that are depleted in olfactory function, and they exhibit significantly decreased amino acid sequence divergence. Natural selection appears to have acted discriminately among human CNV genes. The significant overabundance, within human CNVs, of genes associated with olfaction, immunity, protein secretion, and elevated coding sequence divergence, indicates that a subset may have been retained in the human population due to the adaptive benefit of increased gene dosage. By contrast, the functional characteristics of mouse CNVs either suggest that advantageous gene copies have been depleted during recent selective breeding of laboratory mouse strains or suggest that they were preferentially fixed as a consequence of the larger effective population size of wild mice. It thus appears that CNV differences among mouse strains do not provide an appropriate model for large-scale sequence variations in the human population.     

Next-Generation Sequencing of Duplication CNVs Reveals that Most Are Tandem and Some Create Fusion Genes at Breakpoints

Interpreting the genomic and phenotypic consequences of copy-number variation (CNV) is essential to understanding the etiology of genetic disorders. Whereas deletion CNVs lead obviously to haploinsufficiency, duplications might cause disease through triplosensitivity, gene disruption, or gene fusion at breakpoints. The mutational spectrum of duplications has been studied at certain loci, and in some cases these copy-number gains are complex chromosome rearrangements involving triplications and/or inversions. However, the organization of clinically relevant duplications throughout the genome has yet to be investigated on a large scale. Here we fine-mapped 184 germline duplications (14.7 kb–25.3 Mb; median 532 kb) ascertained from individuals referred for diagnostic cytogenetics testing. We performed next-generation sequencing (NGS) and whole-genome sequencing (WGS) to sequence 130 breakpoints from 112 subjects with 119 CNVs and found that most (83%) were tandem duplications in direct orientation. The remainder were triplications embedded within duplications (8.4%), adjacent duplications (4.2%), insertional translocations (2.5%), or other complex rearrangements (1.7%). Moreover, we predicted six in-frame fusion genes at sequenced duplication breakpoints; four gene fusions were formed by tandem duplications, one by two interconnected duplications, and one by duplication inserted at another locus. These unique fusion genes could be related to clinical phenotypes and warrant further study. Although most duplications are positioned head-to-tail adjacent to the original locus, those that are inverted, triplicated, or inserted can disrupt or fuse genes in a manner that might not be predicted by conventional copy-number assays. Therefore, interpreting the genetic consequences of duplication CNVs requires breakpoint-level analysis.     

An algorithm for inferring complex haplotypes in a region of copy-number variation

Recent studies have extensively examined the large-scale genetic variants in the human genome known as copy-number variations (CNVs), and the universality of CNVs in normal individuals, along with their functional importance, has been increasingly recognized. However, the absence of a method to accurately infer alleles or haplotypes within a CNV region from high-throughput experimental data hampers the finer analyses of CNV properties and applications to disease-association studies. Here we developed an algorithm to infer complex haplotypes within a CNV region by using data obtained from high-throughput experimental platforms. We applied this algorithm to experimental data and estimated the population frequencies of haplotypes that can yield information on both sequences and numbers of DNA copies. These results suggested that the analysis of such complex haplotypes is essential for accurately detecting genetic differences within a CNV region between population groups.     

Segmental copy-number gain within the region of isopentenyl diphosphate isomerase genes in sporadic amyotrophic lateral sclerosis

Aims: Sporadic amyotrophic lateral sclerosis (SALS) seems to be a multifactorial disease, the pathogenesis of which may involve both genetic and environmental factors. The present study aims at identifying a possible genetic change that confers risk for SALS. Methods: We performed whole-genome screening of a copy-number variation (CNV) using a CNV beadchip, followed by real-time quantitative polymerase chain reaction (qPCR) and region-targeted high-density oligonucleotide tiling microarray. Results: Within the 40-kb region on 10p15.3 subtelomere, which harbours two genes encoding isopentenyl diphosphate isomerase 1 (IDI1) and IDI2, we found a segmental copy-number gain in a large proportion of SALS patients. qPCR analysis demonstrated the copy-number gain in 46 out of 83 SALS patients, as compared with 10 out of 99 controls (p = 4.86 × 10⁻¹¹, Odds Ratio 10.8); subsequent tiling microarray validated qPCR results and elucidated the fine structure of segmental gains. Conclusions: A segmental copy-number gain in the IDI1/IDI2 gene region may play a significant role in the pathogenesis of SALS.     

Improving detection of copy-number variation by simultaneous bias correction and read-depth segmentation

Structural variation is an important class of genetic variation in mammals. High-throughput sequencing (HTS) technologies promise to revolutionize copy-number variation (CNV) detection but present substantial analytic challenges. Converging evidence suggests that multiple types of CNV-informative data (e.g. read-depth, read-pair, split-read) need be considered, and that sophisticated methods are needed for more accurate CNV detection. We observed that various sources of experimental biases in HTS confound read-depth estimation, and note that bias correction has not been adequately addressed by existing methods. We present a novel read-depth–based method, GENSENG, which uses a hidden Markov model and negative binomial regression framework to identify regions of discrete copy-number changes while simultaneously accounting for the effects of multiple confounders. Based on extensive calibration using multiple HTS data sets, we conclude that our method outperforms existing read-depth–based CNV detection algorithms. The concept of simultaneous bias correction and CNV detection can serve as a basis for combining read-depth with other types of information such as read-pair or split-read in a single analysis. A user-friendly and computationally efficient implementation of our method is freely available.     

Systematic inference of copy-number genotypes from personal genome sequencing data reveals extensive olfactory receptor gene content diversity

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.     

High-resolution copy-number variation map reflects human olfactory receptor diversity and evolution

Olfactory receptors (ORs), which are involved in odorant recognition, form the largest mammalian protein superfamily. The genomic content of OR genes is considerably reduced in humans, as reflected by the relatively small repertoire size and the high fraction ( approximately 55%) of human pseudogenes. Since several recent low-resolution surveys suggested that OR genomic loci are frequently affected by copy-number variants (CNVs), we hypothesized that CNVs may play an important role in the evolution of the human olfactory repertoire. We used high-resolution oligonucleotide tiling microarrays to detect CNVs across 851 OR gene and pseudogene loci. Examining genomic DNA from 25 individuals with ancestry from three populations, we identified 93 OR gene loci and 151 pseudogene loci affected by CNVs, generating a mosaic of OR dosages across persons. Our data suggest that approximately 50% of the CNVs involve more than one OR, with the largest CNV spanning 11 loci. In contrast to earlier reports, we observe that CNVs are more frequent among OR pseudogenes than among intact genes, presumably due to both selective constraints and CNV formation biases. Furthermore, our results show an enrichment of CNVs among ORs with a close human paralog or lacking a one-to-one ortholog in chimpanzee. Interestingly, among the latter we observed an enrichment in CNV losses over gains, a finding potentially related to the known diminution of the human OR repertoire. Quantitative PCR experiments performed for 122 sampled ORs agreed well with the microarray results and uncovered 23 additional CNVs. Importantly, these experiments allowed us to uncover nine common deletion alleles that affect 15 OR genes and five pseudogenes. Comparison to the chimpanzee reference genome revealed that all of the deletion alleles are human derived, therefore indicating a profound effect of human-specific deletions on the individual OR gene content. Furthermore, these deletion alleles may be used in future genetic association studies of olfactory inter-individual differences.     

Genetic association analysis of copy-number variation (CNV) in human disease pathogenesis

Structural genetic variation, including copy-number variation (CNV), constitutes a substantial fraction of total genetic variability and the importance of structural genetic variants in modulating human disease is increasingly being recognized. Early successes in identifying disease-associated CNVs via a candidate gene approach mandate that future disease association studies need to include structural genetic variation. Such analyses should not rely on previously developed methodologies that were designed to evaluate single nucleotide polymorphisms (SNPs). Instead, development of novel technical, statistical, and epidemiologic methods will be necessary to optimally capture this newly-appreciated form of genetic variation in a meaningful manner.     

The application of gene co-expression network reconstruction based on CNVs and gene expression microarray data in breast cancer

Copy number variations (CNVs) are one type of the human genetic variations and are pervasive in the human genome. It has been confirmed that they can play a causal role in complex diseases. Previous studies of CNVs focused more on identifying the disease-specific CNV regions or candidate genes on these CNV regions, but less on the synergistic actions between genes on CNV regions and other genes. Our research combined the CNVs with related gene co-expression to reconstruct gene co-expression network by using single nucleotide polymorphism microarray datasets and gene microarray datasets of breast cancer, and then extracted the modules which connected densely inside and analyzed the functions of modules. Interestingly, all of these modules’ functions were related to breast cancer according to our enrichment analysis, and most of the genes in these modules have been reported to be involved in breast cancer. Our findings suggested that integrating CNVs and gene co-expressed relations was an available way to analyze the roles of CNV genes and their synergistic genes in breast cancer, and provided a novel insight into the pathological mechanism of breast cancer.     

Discovery and Statistical Genotyping of Copy-Number Variation from Whole-Exome Sequencing Depth

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 (<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.     

Origins and Long-Term Patterns of Copy-Number Variation in Rhesus Macaques

Mutations play a key role in the development of disease in an individual and the evolution of traits within species. Recent work in humans and other primates has clarified the origins and patterns of single-nucleotide variants, showing that most arise in the father’s germline during spermatogenesis. It remains unknown whether larger mutations, such as deletions and duplications of hundreds or thousands of nucleotides, follow similar patterns. Such mutations lead to copy-number variation (CNV) within and between species, and can have profound effects by deleting or duplicating genes. Here, we analyze patterns of CNV mutations in 32 rhesus macaque individuals from 14 parent–offspring trios. We find the rate of CNV mutations per generation is low (less than one per genome) and we observe no correlation between parental age and the number of CNVs that are passed on to offspring. We also examine segregating CNVs within the rhesus macaque sample and compare them to a similar data set from humans, finding that both species have far more segregating deletions than duplications. We contrast this with long-term patterns of gene copy-number evolution between 17 mammals, where the proportion of deletions that become fixed along the macaque lineage is much smaller than the proportion of segregating deletions. These results suggest purifying selection acting on deletions, such that the majority of them are removed from the population over time. Rhesus macaques are an important biomedical model organism, so these results will aid in our understanding of this species and the disease models it supports.