Human Molecular Genetics 2006 15(Review Issue 1):R57-R66; doi:10.1093/hmg/ddl057
© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Structural variants: changing the landscape of chromosomes and design of disease studies
Lars Feuk,
Christian R. Marshall,
Richard F. Wintle and
Stephen W. Scherer*
The Centre for Applied Genomics and Program in Genetics and Genomic Biology, The Hospital for Sick Children, Department of Molecular and Medical Genetics, University of Toronto, Ontario, Canada
* To whom correspondence should be addressed at: The Centre for Applied Genomics, The Hospital for Sick Children, MaRS Centre—East Tower, 101 College Street, Room 14-701, Toronto, Ontario, Canada M5G 1L7. Tel: +1 4168137613; Fax: +1 4168138319; Email: swscherer{at}sickkids.ca
Received February 23, 2006; Accepted March 9, 2006
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ABSTRACT
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The near completeness of human chromosome sequences is facilitating
accurate characterization and assessment of all classes of genomic
variation. Particularly, using the DNA reference sequence as
a guide, genome scanning technologies, such as microarray-based
comparative genomic hybridization (array CGH) and genome-wide
single nucleotide polymorphism (SNP) platforms, have now enabled
the detection of a previously unrecognized degree of larger-sized
(non-SNP) variability in all genomes. This heterogeneity can
include copy number variations (CNVs), inversions, insertions,
deletions and other complex rearrangements, most of which are
not detected by standard cytogenetics or DNA sequencing. Although
these genomic alterations (collectively termed structural variants
or polymorphisms) have been described previously, mainly through
locus-specific studies, they are now known to be more global
in occurrence. Moreover, as just one example, CNVs can contain
entire genes and their number can correlate with the level of
gene expression. It is also plausible that structural variants
may commonly influence nearby genes through chromosomal positional
or domain effects. Here, we discuss what is known of the prevalence
of structural variants in the human genome and how they might
influence phenotype, including the continuum of etiologic events
underlying monogenic to complex diseases. Particularly, we highlight
the newest studies and some classic examples of how structural
variants might have adverse genetic consequences. We also discuss
why analysis of structural variants should become a vital step
in any genetic study going forward. All these progresses have
set the stage for a golden era of combined microscopic and sub-microscopic
(cytogenomic)-based research of chromosomes leading to a more
complete understanding of the human genome.
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INTRODUCTION
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In the past few years, several studies have identified a previously
uncharacterized prevalence of structural variants of DNA along
chromosomes in the size range of 1 kb or greater, adding
to the catalog of variants in the human genome (Table
1).
Namely, sub-microscopic (usually less than
3 Mb) copy number
variations (CNVs) and inversions have been found to occur in
every genome studied at high frequencies when compared with
the equivalent classes of cytogenetically detectable rearrangements
(1
–8
). Similar findings are also now being made more readily
in disease gene studies.
These discoveries have come somewhat later than the description
(and generation of comprehensive maps) of single nucleotide
polymorphisms (SNPs) (9
,10
), microsatellites (11
,12
) and minisatellites
(13
), as well as catalogs of cytogenetically detectable heteromorphisms
and rearrangements, because of limits of resolution in the technology
at that time. However, new developments in genome-wide scanning
methodologies using genomic clone and oligonucleotide-based
arrays occurring in parallel with the availability of a reference
human genome sequence now provide opportunity to generate advanced
maps of structural variation in worldwide populations. Moreover,
next generation sequencing technologies and computational comparisons
of sequences from different sources will yield a vast number
of variants primarily in the <1 kb size range that have
not been described previously. Comprehensive reviews describing
the discovery and properties of, in particular, CNVs, but also
other structural variants, have been published recently (14
–19
).
Here, we highlight the latest findings, with a particular emphasis
on the new sub-microscopic variants being increasingly described
in the
1 kb to
3 Mb size range and how they may influence
phenotype or be involved in disease.
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STRUCTURAL VARIATION INFLUENCING PHENOTYPE
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Changes in DNA that affect gene function (often through affecting
dosage) can have a deleterious effect on the reproductive fitness
of an organism, and in some cases represent lethal mutations.
In these circumstances, the variants would eventually be destined
to disappear, but they can often exist in a heterozygous form
for many generations. In between these extremes of selectively
neutral variants and lethal mutations lie variants that can
influence physiological, biochemical, morphological and pathological
variation in the human population. Recent descriptions of numerous
‘gene-sized’ (the average size of a gene being
70 kb)
sub-microscopic structural variants in all genomes have generated
significant excitement in the field (20
–25
), because (i)
it was presumed they probably should exist for the same reasons
as SNPs and microscopic variants; (ii) their sheer size (often
affecting hundreds to thousands of nucleotides of DNA) increases
the likelihood that the alteration is, in fact, a genomic lesion
explaining disease outcomes; (iii) as such, some will also be
shown to predispose to disease either directly or in combination
with other variants and factors and (iv) some will provide substrate
for evolutionary change. The description of all variants will
be important for many wide-ranging reasons, better resolving
a more completely annotated reference genome sequence to understanding
implications in pharmacogenomics and clinical diagnostic testing.
It has been well established in many classic (26–33) as well as in more recent studies of monogenic disease (34), oligogenetic disease (35–41), and most recently in complex disease that the study of such chromosome rearrangements can be the most rapid approach to identify candidate susceptibility loci and genes (that then need to be confirmed in other samples). For complex diseases (note that in some cases, these were the Mendelian sub-forms of complex disease demonstrating the same phenotypic endpoint), examples include: in autism, X-chromosome deletions led to the identification of the neuroligin NLGN3 and NLGN4 genes (42); in schizophrenia, a familial chromosome 1 translocation led to the discovery of the DISC1 and DISC2 genes (43); in dyslexia, distinct chromosomes 3 and 15 translocations led to the discovery of ROBO1 and DYXC1, respectively (44,45); in severe speech and language disorder, a chromosome 7q31 translocation pinpointed the FOXP2 gene (46); in Tourette syndrome, a de novo inversion led to SLITRK1 involvement (47); in severe expressive language delay, microduplication of the Williams–Beuren syndrome locus on chromosome 7q11.23 (48) and in early onset Parkinson and Alzheimer's disease, duplications of SNCA and APP on chromosomes 4 and 21, respectively, have been shown to be causative (49,50).
Indeed, the primary message of this review is to increase the awareness of the necessity for including steps for screening for structural variants in genetic experiments. This was exquisitely demonstrated in a recent study showing that copy number polymorphism in the FCGR3 gene predisposes to glomerulonephritis in humans and rats (51). Preliminary data suggest that dozens of CNVs alone will be found in a given genome when assessed using comprehensive scanning methodologies. The sub-microscopic variants will be intermediate in size and frequency in comparison to occurrence of cytogenetically detectable and smaller (<1 kb) polymorphisms. These aberrations can be hundreds of kilobases long, having important implications for the potential effect they may exert on genes and transcriptional regulation (Fig. 1). Moreover, a large number of CNVs have been shown to contain one or more entire coding transcripts (1,2,52,53). In studies where these genes have been characterized, there seems to be a direct correlation between increases in gene copy number and increased levels of mRNA (53–56). Polymorphic deletions containing entire genes have also been described, where a fraction of the population are homozygous for the deletion allele and, therefore, do not have the gene present in their genome (6,8,57). Most of the genes in this category belong to gene families or are recently duplicated in evolutionary history, and this may increase the tolerance for null alleles.
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Figure 1. CNV influencing gene dosage and expression and disease. There are a number of mechanisms by which CNVs potentially could have an effect on gene expression and phenotypic traits. Well-documented examples involve regions with multiple genes deleted in microdeletion syndromes and microduplication syndromes (A), where there is a direct correlation between genotype and phenotype. Copy number polymorphisms where a gene is located entirely within a region that varies in copy number (B) have also been described to show a direct correlation between gene copy number and gene expression. Another mechanism by which CNVs may have an influence on disease phenotype is if the remaining copy harbors a risk allele that becomes apparent only in the hemizygous state (C). The opposite scenario may also occur, with an increased number of copies harboring a risk allele, causing a concurrent increase in disease susceptibility. It can also be hypothesized that CNVs may affect gene expression without directly changing the gene copy number. Gains or losses affecting the regulatory elements or promoter regions can also be important contributors to differences in gene expression. This could involve either loss of an element of transcriptional regulation (D and F) or a loss/gain changing the structural properties of DNA inhibiting enhancer interaction, chromatin structure or access of transcription factors to their binding sites (E). Interaction and additive models with any of these scenarios combined or in combination with any type of variation at other loci can be expected to be the cause of more complex genetic traits.
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Specific categories of genes seem over-represented in CNVs including
those important for interaction with the surrounding environment,
such as olfaction and response to external stimuli (19
,58
).
Examples of such polymorphic genes include glutathione
S-transferase
genes (59
,60
), cytochrome P450 genes (61
–65
) and the complement
component C4 (66
). In each case, changes to gene copy number
have been shown to give rise to concomitant changes in the level
of enzyme activity, with phenotypic consequences. Another example
is the
CCL3L1 gene, where the increased copy number has been
shown to be protective against HIV infection (56
).
Inversions represent another class of structural variation (Table 1), but knowledge of their prevalence in the human genome is more limited. This is partly due to a lack of technologies for robust and inexpensive discovery of such balanced rearrangements. In addition, preliminary data indicate that inversion variants are less abundant than CNVs in the human genome (4). However, there are a number of well-documented cases where inversion variants can be associated with disease predisposition, primarily in microdeletion syndromes. In these instances, the inversion variant need not be a direct cause of the disease, but instead it can act as a risk factor for microdeletion to occur in the offspring, as appears to be the case in Williams–Beuren (67), Angelman (68) and Sotos syndromes (69).
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POTENTIAL LONG-RANGE (POSITION) EFFECTS OF STRUCTURAL VARIANTS ON GENES
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As discussed, structural variants can affect dosage by directly
interrupting genes, but it is important to appreciate that they
can have an equivalent effect at a distance (in an indirect
manner) (Fig.
1). Although genes only represent a small
portion (<3%) of the human genome and there are hundreds
of putative ‘gene deserts’, sometimes millions of
base pairs in size (70
–72
), there is now substantial evidence
that regulatory elements of genes can reside up to a million
base pairs or more away (Fig.
1; Table
2) (Supplementary
Material, Table S1). Thus, structural variants cannot be
presumed to be selectively neutral because they encompass only
non-coding segments, but instead a careful assessment of nearby
genes that may be affected via a ‘position effect’
mechanism also needs to be considered.
Position effect refers to the alteration of a gene's expression
pattern as a result of a change in its genomic location or chromatin
environment. This phenomenon has been most extensively studied
in
Drosophila (73
) and yeast (74
), but an increasing number
of examples in humans have been reported, including a variety
of developmental disorders such as aniridia (75
–78
), holoprosencephaly
(79
–81
), campomelic dysplasia (82
–90
), thalassemias
(91
–94
), X–Y sex reversal (95
,96
) and others (Supplementary
Material, Table S1). Position effects can be caused by
a variety of mechanisms. These include translocation of a gene
into a heterochromatic region resulting in the methylation of
promoter regions and consequent down-regulation of expression,
chromosome breakage (translocations, inversions, deletions and
duplications) that separates a gene from some or all of its
transcriptional control elements or otherwise alters gene expression
(94
), or genomic rearrangements that bring a gene into close
proximity to a positive regulatory element (97
). In recent reviews
(80
,97
), several additional examples of congenital abnormalities
resulting in either obvious or postulated position effects in
humans have been reported. Here, we call attention to some studies
from the past few years, highlighting how the structural variants
can be involved in disease through different mechanisms of action
(Table
2) (additional historical studies are summarized
in Supplementary Material, Table S1).
In most cases, the effect of genomic rearrangement on gene expression has been inferred, rather than observed directly. This is often due to the unavailability of appropriate tissue or developmental timing of expression that would render gene expression analysis impossible. As an example, a translocation that disrupts the HDAC9 gene at 7p21.1 has its reciprocal breakpoint on chromosome 1, 500 kb from the TGFB2 gene. The patient carrying this translocation has Peter's anomaly, a defect of the anterior chamber of the eye, and as Tgfb2 null mice have very similar developmental eye defects, and therefore, the authors consider a position effect at TGFB2, rather than HDAC9 disruption, to be the most likely underlying pathology (98). A more complex example is a 23–25 kb deletion and 340 kb insertion at the deletion point, 67 kb 3' to the SOX3 gene, found in a patient with X-linked recessive hypoparathyroidism (99). It is presumed that down-regulation of SOX3 results in the phenotype, as SOX3 has been observed to be expressed in the developing parathyroid of mouse embryos.
In other recent examples, Wakui et al. (100) report a patient with a large deletion located just 3' of the ALX4 gene having atypical manifestation of Potocki–Shaffer syndrome. Beysen et al. (101) describe a number of patients with Blepharophimosis syndrome (one of the syndromes most frequently reported to be associated with position effects in humans), each having a microdeletion near the FOXL2 gene. Lee et al. (102) describe a patient with an atypical phenotype (spastic paraplegia type II with axonal neuropathy) because of a duplication near the PLP1 gene, deletions and duplications of which usually result in Pelizaeus–Merzbacher syndrome (103). Mild forms of campomelic dysplasia, a skeletal malformation syndrome, have also been reported, as a result of balanced translocations near the SOX9 gene in three different patients [two with simple reciprocal translocations and one with a complex translocation (82,83)]. Ellison et al. (104) report a patient with a ring (X) chromosome that is presumed to cause a down-regulation of the SHOX gene, resulting in short stature in that patient, although a significant amount of Xp and Xq material is also deleted, including several other genes. Finally, in an extreme case carrying a de novo t(6;7) (p21.1;q36) reciprocal translocation exhibiting both holoprosencephaly and cleidocranial dysplasia, there are two apparent position effect mutations in the same individual: the 7q36 breakpoint mapping 15 kb telomeric to the 5' end of Sonic Hedgehog causes holoprosencephaly and the 6p21.1 breakpoint mapping 800 kb upstream of CBFA1 (RUNX2) causes cleidocranial dysplasia (80).
It is striking that a majority of genes reported to be affected by apparent position effects in humans are involved in developmental syndromes. This could be due to ascertainment bias, as phenotypes in these patients tend to be either atypical or unusually mild. Alternatively, it could be that other classes of genes, e.g. those encoding enzymes, are much more tolerant to positional silencing or down-regulation and that individuals with such rearrangements thus escape clinical notice. It has also been suggested that large ‘gene deserts’ often found around the developmental genes (71) may serve as enhanced targets for chromosomal rearrangements (105). Notwithstanding, the take home message from these studies and others is that the structural variant need not only affect what we usually define as the classical gene unit to have an effect; proximal and distal genes also need to be considered.
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MEDICAL AND BIOLOGICAL SIGNIFICANCE
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With the ability to recognize dozens of sub-microscopic variants
in all genomes, a hierarchical paradigm of how to differentiate
the manifestation of disease- (or phenotype-) associated changes
needs to be considered (Fig.
2). For example, in some cases,
a structural variant correlates directly with the disease, such
as the case in dosage-related microdeletions and duplications
that cause genomic disorders and in other cases described earlier
(106
). Family-based studies can demonstrate whether a change
is
de novo or has been inherited and, in the latter case, whether
there are likely to be associated phenotypic consequences. However,
there are numerous examples of lack of penetrance or variable
expression of phenotype in inherited chromosomal rearrangements
(107
), requiring analysis be extended to a larger population
of controls. Other factors such as the genomic context (e.g.
types of genes and likelihood to be affected by position effect),
the heritable stability of the variant (it could predispose
to other mitotic or meiotic rearrangements), influence of other
variants and possible parent-of-origin effects (e.g. imprinted
regions) all need to be considered when evaluating the effect
at the genic level.
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Figure 2. Genetic and genomic considerations of assessing the influence of structural variants on gene expression and phenotype/disease manifestation.
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Databases cataloging large-scale genotype and phenotype correlations
(17
,70
,108
) will be increasingly important to help discern how
these changes might cause phenotypic or functional outcomes
(Table
3 provides web links). For example, the genome-wide
initiatives including the Database of Chromosomal Imbalance
and Phenotypes in Humans using Ensembl Resources (DECIPHER)
and the Developmental Genome Anatomy Project (DGAP) promise
to bridge the gap from DNA sequence to medical genetic outcomes,
but the databases are still sparsely populated. Other efforts,
including the Mendelian Cytogenetics Network Online Database,
the Chromosome Abnormality Database, the European Collection
of Cell Cultures, Coriell Cell Repositories, NIGMS Human Genetic
Cell Repository and the Mitelman Database of chromosome aberrations
in cancer, among others, provide catalogs of samples with karyotypes
and phenotypes. The Database of Genomic Variants and the Human
Structural Variation Database house information on structural
variants that are generally not known to cause disease. It is
also worth noting that there are large data sets of SNPs and
microsatellites from disease mapping studies (Supplementary
Material, Tables S2 and S3) that could be analyzed for
their putative CNV content in a manner similar to what has been
done in autism (109
) and HapMap samples (6
,8
).
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CONCLUSIONS AND FUTURE STUDIES
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The complexity of variation in the human genome continues to
be unraveled, providing opportunity to explain genetic contributions
to disease in a more comprehensive manner. Going forward into
the next few years, studies examining the role of sub-microscopic
structural variation will become a predominant theme because
of significant advances in technology allowing for the scanning
of genomes at relatively high resolution. In fact, on the basis
of the numbers of discoveries and impact alone in the past 2
years, it could be argued that we have entered a ‘cytogenomic’
era for discovery in human genetics. In large-scale population-based
whole genome association studies (Supplementary Material, Table S3),
and in any disease gene study, a component of assessing structural
variation content should be incorporated. However, comprehending
the contribution of these variants will require the understanding
of wide-ranging data from simple presence or absence (in cases
and controls) to the position and context in the genome (Fig.
2).
It will be important to determine the new mutation rate (25
,110
)
of these variants across the genome, including the heterochromatic
regions. The next frontier will be to fully catalog all the
structural variants in the
1 kb to 3 Mb size range
discussed here, but also all other variations in the 1 bp
to 1 kb size range (Table
1), which will probably
be best discerned through personalized genome (re-)sequencing
(111
). Coupling all of this information to large cohorts of
meticulously phenotyped sample collections and corresponding
databases would provide insight toward understanding the etiology
of many unresolved diseases.
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SUPPLEMENTARY MATERIAL
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Supplementary Material is available at HMG Online.
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ACKNOWLEDGEMENTS
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The work was supported by funds from The Centre for Applied
Genomics, Hospital for Sick Children, Genome Canada/Ontario
Genomics Institute, the Canadian Institutes of Health Research
(CIHR) and the McLaughlin Centre for Molecular Medicine. L.F.
is supported by the Swedish Medical Research Council, C.R.M.
by The Hospital for Sick Children Research Training Centre and
S.W.S. is an Investigator of CIHR and International Scholar
of the Howard Hughes Medical Institute.
Conflict of Interest statement. None declared.
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ABSTRACT
INTRODUCTION