Circulation. 2007;115:3130-3138
doi: 10.1161/CIRCULATIONAHA.106.677591
(Circulation. 2007;115:3130-3138.)
© 2007 American Heart Association, Inc.
Contemporary Reviews in Cardiovascular Medicine |
Copy Number Variation in the Human Genome and Its Implications for Cardiovascular Disease
Rebecca L. Pollex, MSc;
Robert A. Hegele, MD, FRCPC
From the Robarts Research Institute and University of Western Ontario, London, Ontario, Canada.
Correspondence to Robert A. Hegele, MD, FRCPC, Blackburn Cardiovascular Genetics Laboratory, Robarts Research Institute, 100 Perth Dr, Room 406, London, Ontario, Canada, N6A 5K8. E-mail hegele{at}robarts.ca
Key Words: DNA • genetics • genomics • myocardial infarction • risk factors • stroke
 |
Introduction
|
|---|
Unlocking the information contained within the human genome
will likely advance our understanding of cardiovascular (CV)
health and disease by leading to discovery of new molecules,
pathways, and networks. A central strategy in genetic studies
of CV disease has been to correlate human genomic DNA variation
with clinical phenotypes, such as myocardial infarction, heart
failure, stroke, and their risk factors, with a range of experimental
designs and analytical procedures. The ability to detect genomic
differences between individuals is the foundation of this research.
Human genomic variation exists in many forms, each of which
has unique qualitative and quantitative features. Each form
of human genomic variation is composed of many individual variants
that occur across the genome. The population frequency of individual
variants can range from rare to common. The effect of a specific
genomic variant can range from beneficial to neutral to deleterious.
To rapidly translate genomic knowledge into diagnosis and treatment
of CV disease, it is logical to search for common genomic variants
that have a non-neutral impact. In the recent past, one form
of genomic variation, the single-nucleotide variant, has dominated
the experimental landscape: It is the currency of present genetic
CV disease studies. However, recent developments indicate that
the focus on single-nucleotide polymorphisms (SNPs) alone will
not capture the full range of meaningful human genomic variation,
such as a newly characterized and annotated form called copy
number variation (CNV).
|
Main Varieties of Human Genomic Variation
|
|---|
The main forms of human genomic variation are shown in
Figure 1.
These include SNPs, which are qualitative in nature and involve
only a single nucleotide, and a family of genomic changes collectively
called structural variations, which are quantitative in nature
because they affect the dosage or copy number of a particular
genomic region. Structural variant types include deletions,
duplications, inversions, and rearrangements of "chunks" of
the genome, which range from small insertions and deletions
that involve 1 to 50 base pairs through to very large cytogenetic
changes that involve entire chromosomes. It is estimated that
roughly 5% of the human genome is structurally variable.
1 The
recently characterized CNVs comprise structural variants of
intermediate size that range from 1000 to 5
x10
6 bases of DNA.
Furthermore, the types of CNV changes (ie, deletions, duplications,
and inversions) are analogous to large cytogenetic changes that
were previously visualized through microscopy.
View larger version (19K):
[in this window]
[in a new window]
|
Figure 1. The spectrum of variation in the human genome. A logarithmic x-axis measures the number of nucleotides, from 1 bp to  100 Mb. Above the axis, types of genetic variation are shown, with their size range depicted below by a double-headed arrow. Size ranges are not definitive. SNP indicates single-nucleotide polymorphism; indels, insertions and deletions; STR, short tandem repeat; bp, base pair; kb, kilobase; Mb, megabase; and CNV, copy number variation.
|
|
Other variable regions include short repetitive sequences, 1 to 5 base pairs in length, termed short tandem repeat sequences or microsatellite repeats, such as (A)n, (CA)n, or (AAG)n, where n is variable. These repeat units, being neutral and widely dispersed, have been used as markers to "tag" segments of the genome that can then be tracked through families in linkage studies. A few short tandem repeats are functional and give rise to human diseases [eg, neurological disorders resulting from expansion of (CAG)n repeats].2 Minisatellites or "variable number tandem repeats," which are 5 to 64 base pairs in length and extend over several thousand base pairs, are less evenly distributed but highly informative.2 Transposons and transposon-like repetitive elements, such as the ubiquitous 
300–base pair Alu repeat sequence, also contribute to human genomic variation.3 Whereas this review will focus on the relationship of SNPs and CNVs to the CV system, a more detailed description of all forms of human genomic variation, such as tandem repeats and various interspersed genomic elements, can be found in a recent comprehensive review.1
|
Mutations Versus Polymorphisms
|
|---|
When genomic variants are discussed, both "mutation" and "polymorphism"
are used, sometimes interchangeably. By convention in human
genetic research, any genomic variant with population frequency
<1% is termed a "mutation," whereas a variant with population
frequency >1% is termed a "polymorphism,"
4 a convention that
will be followed in this review. The distinction is based on
population frequency of the variant rather than the type of
variant or its possible functional impact. However, as a rule,
rare mutations tend to have a functional impact that deviates
from the "wild type" (or most common form), yielding a higher
signal-to-noise ratio for detection of a genetic influence on
a trait. In contrast, polymorphisms tend to connote less functionally
deviant genomic variants. However, some rare genomic mutations
have been found to be functionally neutral, whereas some common
polymorphisms also have a major functional impact.
|
Technologies to Visualize Human Genomic Mutations
|
|---|
Technology is an important determinant of the type of genomic
mutation detected. For instance, in the pre–polymerase
chain reaction (PCR) era (the 1970s and early 1980s) the most
prevalent mutation types in disease databases were large-scale
genomic DNA changes, such as large insertions and deletions.
One reason for this was that most genomic mutations in this
era were detected by the Southern blot method. Southern blots
were ideal for detection of large-scale rearrangements that
involved >200 bases of target genomic DNA, which was the
method’s resolution limit. However, detection of small
mutations was beyond the Southern blot’s resolving capacity,
unless a mutation altered a recognition site for a restriction
endonuclease. Another key methodology to pinpoint variants involved
cloning of human genomic DNA coupled with manual DNA sequence
analysis, a combination of technologies that were comparatively
inefficient and required high levels of skill, labor, and exposure
to radioactivity.
These now almost forgotten methods were superseded in the 1980s by PCR and automated DNA sequence analysis. With the invention of PCR, it became possible to rapidly amplify discrete regions of genomic DNA (up to 2000 base pairs) in sufficient quantity and with sufficient quality to allow for high-capacity, high-resolution automated DNA sequence analysis, which in turn enabled rapid ordering of the 4 letters of the genomic alphabet (A, C, G, T) into a continuous data string. Since the late 1980s, the complementary fundamental technologies of gene amplification by PCR and automated genomic DNA sequencing, together with advances in DNA cloning, drove genomic research and ultimately enabled the determination of the entire sequence of the human genome.5,6 These methods were ideally suited to detect small qualitative changes in the genomic sequence of an individual: 1 or a few genomic DNA nucleotide bases.
|
SNPs in the Human Genome
|
|---|
The result of the domination of these methods in human genetic
research has been that most genomic variation detected over
the past 20 years has been small and qualitative—mainly
single-nucleotide changes. Their potential pathogenicity is
summarized in
Figure 2. The 2001 draft of the human genome sequence
provided, in effect, an initial but imperfect reference standard
for subsequent annotation of genomic variation. Every examination
of partial or complete genomic DNA sequence since then has essentially
been a "resequencing" experiment that has built on the first
draft. The cumulative results of automated DNA sequence analysis
over the past 6 years have helped identify millions of common
differences between people at the level of single letters of
DNA code. These differences, called SNPs, occur with a frequency
of
1 of every 400 bases of DNA sequence. The extent of SNP variation
in populations was exquisitely defined by the International
HapMap project
7: If one considers only SNPs that are present
in >5% of specific population samples, SNPs may involve up
to 10 million nucleotide bases of DNA, or
0.3% of the total
genome. Once SNPs have been defined, various dedicated technologies
to assay individual nucleotides can be used to screen human
samples. A popular current platform is the high-density SNP
microarray, which permits simultaneous assay of >500 000
and soon >1 000 000 SNPs from a human genomic DNA sample.
View larger version (17K):
[in this window]
[in a new window]
|
Figure 2. Single-nucleotide genomic changes. The "swap" of a single nucleotide with another, such as the replacement of the wild-type guanine (allele 1) with adenine (allele 2), is referred to either as a mutation, if present in <1% of the general population, or as SNP, if present at a frequency >1%. SNPs are common and span the human genome. Most single-nucleotide changes are found outside coding regions (noncoding) and have no impact on the biological function of a protein (silent), though they may affect gene expression or splicing. However, variants found within the coding region may code for functional changes in amino acid structure (missense) or predict premature protein truncation (nonsense) and thus may have a possible direct association with disease.
|
|
Until recently, SNPs were considered to be the major source of genomic variation in the phenotypic differences between people, which include physiologically neutral features like eye color as well as medically relevant phenotypes such as disease susceptibility and differential response to medications. However, individuals with a hammer seem to see nails everywhere; by analogy, investigators with technologies designed to assess single nucleotides soon regard these as the principal form of genetic variability. The publication of SNP-based studies that attempt to identify the genetic basis of disease traits has become commonplace in the CV field. As important as current SNP-based methods are, however, it is important to recall that large-scale, cytogenetic, chromosomal changes rather than single-nucleotide variants have long been recognized to cause certain CV disorders, even before recent developments that culminated in publication of the CNV map of the human genome.
|
Historical Snapshot: CV Disorders Caused by Cytogenetic Chromosomal Changes
|
|---|
Since the 1960s, traditional microscopy-based karyotype analysis
and, more recently, higher-resolution fluorescent dye–based
visualization with microscopy would occasionally detect patients
whose nuclei harbored large-scale rearrangements that affected
whole chromosomes or sizable fragments of chromosomes. The changes
that were cytogenetically visible included deletion (loss of
one or both copies), duplication (gain of one or more copies),
inversion (flipped orientation of a chromosomal segment), and
translocation (transfer of a piece of one chromosome to another).
A single instance of such a dramatic chromosomal rearrangement
would encompass millions to hundreds of millions of DNA nucleotides.
The major types of chromosomal alterations are shown in
Figure 3.
View larger version (24K):
[in this window]
[in a new window]
|
Figure 3. Large-scale genomic DNA CNV. The left side of the figure shows some of the types of CNVs; the right side of the figure shows some of the consequences of CNVs at the level of the gene. Segment A represents the normal structure of a region of one of a homologous pair of chromosomes. The type of genomic alterations of the normal structure that lead to deletion, duplication, and inversion are shown at the top. Segment B represents the normal structure of a chromosomal locus that contains a cluster of 3 genes (1, 2, and 3). Segment C shows the detailed structure of gene 2 and includes key functional elements, such as the 5' regulatory region, with promoter, enhancer, and silencer elements; the 3' untranslated regions that can regulate message stability; and the intron–exon structure of the gene. Any of these structural and functional elements can become involved in a CNV, with a range of functional consequences that depends on the size and nature of the variant and the affected functional domains. Adapted from Hegele8 with permission from NRC Research Press. Copyright 2006.
|
|
These major rearrangements were considered to be rare events and were frequently associated with clinical syndromes: The most familiar example would be the duplication of one copy of chromosome 21 entirely or in part, which is known as Down syndrome. Several disorders that result from large cytogenetic changes involved the CV system and some of these are listed in Table 1.9–18 At the same time, some dramatic, large-scale, cytogenetic variants were detected incidentally without apparent clinical impact.19 The standard method for detection of large-scale chromosomal variants has been GTG-banded karyotyping, which has a resolution of 3 to 5 Mb. Finer-resolution methods, collectively called fluorescent in situ hybridization (FISH), are based on hybridization of fluorescent probes onto chromosomes that have been captured in metaphase or interphase. Interestingly, improvements in cytogenetic technology have allowed for detection of smaller structural variants (or CNVs) approached from the low-resolution side of the methodology spectrum, whereas microarray-based platforms have permitted the detection of structural changes from the higher-resolution side of the spectrum. The net result has been that a growing number of common, smaller, quantitative genomic variants are being independently discovered by various technologies. In contrast to the large, uncommon, and frequently pathogenic cytogenetic changes, the much smaller-sized CNVs appeared to be prevalent in the healthy control population. Some genetics researchers clearly foresaw that these genomic structural variants would be a ubiquitous source of variation and likely disease mechanism,20,21 but most geneticists and nongeneticists have only recently begun to appreciate the potential relevance of structural variants. Selected technologies to detect specific forms and sizes of genomic variants are shown in Table 2.
|
Brief Overview of New Genomic Technologies
|
|---|
With variation characterized at the single-nucleotide level
and also the microscopic level, present-day strategies and tools
now examine the variation between these 2 extremes: examination
of submicroscopic structural variants on the scale of
1 kb to
3 Mb (
Table 2). When screening is performed on a genome-wide
scale, the main approach is array-based analysis, which was
used in the first studies that described the global presence
and distribution of CNVs in the human genome.
22,23 The driving
force behind this breakthrough technology was the development
of microarrays.
24 Composed of thousands of microscopic DNA probes
spotted onto a solid surface such as a glass slide, microarrays
allow for much greater resolution. In array-based comparative
genome hybridization, the whole genome is fragmented, labeled,
and then competitively hybridized to arrays spotted with one
of several DNA sources, such as BACs (clone based) or PCR fragments.
25,26 Representational oligonucleotide microarray analysis, is a variation
of array-based comparative genome hybridization. It includes
an additional preparative step to reduce the complexity of the
input DNA.
27 High-density SNP arrays can also be used, such
as those used in the development of the most recent CNV map
of the human genome.
28
In the future, it is likely that with the possible development of inexpensive and reliable whole-genome sequencing, computational approaches (eg, sequence-assembly comparison) will become the most popular choice for identification of structural variants. Here, the advantage is that all types of variants, such as balanced inversions, can be easily detected, and the resolution will be down to the nucleotide level. A recent in silico strategy mapped over 1.1 million paired-end sequences from a high-density fosmid library against a reference assembly and discovered numerous CNVs that had not been identified previously, the majority below the expected resolution of other array platforms.29 Also, SNP genotypes that are obtained from high-density microarrays will need to be further assessed for the possibility that they overlap with CNVs. This could be resolved bioinformatically or perhaps will require a complete overhaul of high-density SNP microarray design.
Newer methods have also been developed to detect structural changes in targeted regions in a more cost-effective and higher-throughput fashion than the traditional fluorescent in situ hybridization (FISH) and Southern blot methods. Such alternative methods include quantitative multiplex PCR of short fluorescent fragments,30 multiplex amplifiable probe hybridization,31,32 and multiplex ligation-dependent probe amplification.33,34 Such methods allow for the scoring of up to 50 independent regions in one experiment and can detect deletions or insertions that involve whole exons that would otherwise be overlooked by traditional exon-by-exon sequence analysis.
|
CNVs in the Human Genome
|
|---|
The meteoric ascent of CNVs into contemporary genetic discourse
began with 2 seminal 2004 publications.
22,23 Each research effort
used distinct but complementary technologies designed to detect
dosage differences of genes and chromosomal regions compared
with the standard 2 copies (maternal and paternal) that are
expected in each genome. Both teams saw numerous submicroscopic
chromosomal alterations in the genomes of small samples of healthy
control subjects. These quantitative genomic variants, eventually
called CNVs, were analogous to the chromosomal changes detected
by classical cytogenetic methods described above. Initially,
larger variants were found, which ranged between 10 kb and 500
kb in size and numbered in the tens to hundreds per genome.
22,23 Recently, much smaller variants, mainly deletions that ranged
from 500 bp to 10 kb in size have been found; they number in
the hundreds and perhaps the thousands in the genome.
35,36 CNVs
can also include variants that primarily affect qualitative
genomic attributes of large chromosomal segments, such as through
inversion of genomic regions with no change in copy number per
se. The potential mechanisms that underlie the generation of
CNVs may have broader implications as a source of variation
between species and a means to generate new genes with new functions.
37
Surprising features of CNVs included their ubiquity in the genome, high population frequency, and frequent lack of association with disease phenotypes. The genomes of any 2 individuals might differ from each other by hundreds to thousands of CNV events. This high prevalence in the genomes of apparently healthy individuals motivated efforts to create a unified CNV map in control samples and to integrate these with SNP maps by study of samples that had already been mapped for SNPs. In late 2006, Redon and colleagues published the CNV map of the genome,28 followed by a similar map published by Wong and colleagues in early 2007.38
Redon and colleagues defined a CNV to encompass any submicroscopic chromosomal change that affected >1000 (and up to half a million or more) nucleotides of genomic DNA. These authors used both SNP microarrays and comparative hybridization to identify a total of 1447 CNVs in the genomes of 270 healthy individuals from 4 different geographical ancestries.28 The extent of the variation was breathtaking: These relatively common CNVs cumulatively affected 360 million nucleotides, or 12% of the human genome (one of a homologous pair of chromosomes was often 1 million nucleotides and 20 genes shorter than the other). The map subsequently generated by Wong and colleagues38 was based on study of the genomes of 105 individuals with the use of a whole-genome comparative hybridization assay and reported 800 CNVs that had a frequency of >3%; about two thirds of these CNVs overlapped with known genes.
|
How Do CNVs Cause Disease or Influence Phenotypes?
|
|---|
CNVs can affect phenotypes by alteration of levels of genes
and gene products both at the level of transcription of genomic
DNA to the RNA message and presumably at translation of the
RNA message to the protein product.
4 For instance, deletion
of one copy of a dosage-sensitive gene results in deficient
function that cannot be rescued. Also, genomic deletions in
apparently normal individuals might not directly cause a simple
monogenic disease, but in the presence of additional genetic
or environmental factors may contribute to development of complex
polygenic CV diseases with late onset. Similarly, gene-dosage
increases are already known to cause a few diseases in humans,
but the ubiquity of CNVs means this could be a much more important
and general disease mechanism. Furthermore, CNVs may have a
role in common diseases if only for the simple reason that certain
CNVs span regions that contain many genes. Therefore, the study
of SNPs alone when genomic variation is correlated with disease
is now inadequate in the context of knowledge of CNVs. A focus
on SNPs will literally "miss the forest for the trees." For
instance, we recently showed that testing for both SNPs and
CNVs expands the molecular diagnosis of familial hypercholesterolemia.
39
Like SNPs, smaller CNVs will affect only single genes and thus contribute, together with SNPs, to single-gene disorders. However, unlike SNPs, larger CNVs can affect 2 or more contiguous genes and thus contribute to syndromic or complex disorders caused by defects in multiple genes. Finally, some CNVs involve gene-poor regions and may either be functionally neutral or may still have an impact on disease susceptibility through their effects on nontranscribed domains that regulate gene expression at a distance.4 Like any genetic variant, any deleterious effect must be considered in the context of redundancy of other related genes and gene products that might rescue a deficiency that results from the CNV.
An estimation of the relative impact of SNPs and CNVs on gene expression phenotypes was recently reported by Stranger and collegues.40 With the use of lymphoblastoid cell lines of all 210 unrelated individuals from the International HapMap project, association analyses compared the expression levels of 14 000 genes with SNPs and CNVs. Of the 1061 genes found to be associated, 83.6% were associated with SNPs, 17.7% were associated with comparative genome hybridization clones, and only 1.3% were associated with both types of genetic variation, which clearly indicated that exploration of only one source of variation may not be enough to explore the genetic causes of disease.
|
CNVs Associated With Known Genetic Cardiovascular Diseases
|
|---|
Table 3 shows a list of selected CV diseases, each of which
has an established and strong genetic component, often single-gene
or "monogenic" mendelian disorders, for which CNVs overlap the
chromosomal region that harbors the disease gene.
28 These represent
7% of the total of
300 disease genes that are contained within
CNVs from the map by Redon and colleagues. Most CNVs that overlap
with CV monogenic disorders are present at a frequency between
1% and 5%, which seems reasonable when the fact that these disorders
are rare in the general population is considered. One exception
that shows a high degree of common variability is
LPA: The region
that harbors
LPA on chromosome 6 was remarkably variable and
commonly polymorphic in both the Redon and Wong CNV maps.
28,38 The findings are consistent with the known biology of
LPA, which
encodes the atherogenic apolipoprotein(a). This protein has
long been known to have marked size heterogeneity as a result
of variability in the number of tandem repeats of genomic DNA
sequence that encodes a critical expressed functional domain.
With regard to other genes in
Table 3, the original study samples
were relatively small, so it remains important to replicate
these findings in larger independent samples to demonstrate
that these diseases are associated with CNVs. If replicated,
it may be worthwhile to consider these CNVs in studies of association
or linkage with specific CV disease traits. If these variants
actually occur with such frequency in the general population,
then perhaps subtle or later-onset forms of the monogenic phenotypes
might be more prevalent than has been generally recognized.
|
Curbing Enthusiasm: Time Needed to Characterize, Validate, and Associate
|
|---|
Although completion of genome-wide CNV maps has been a great
accomplishment, it is important to recognize certain limitations.
For instance, genomic DNA from cell cultures was used almost
exclusively, which, although it provided numerous practical
advantages, has the potential limitation that cells in culture
might more readily acquire genomic CNVs that were not present
in the starting material. Expansion of the databases to contain
CNV information from multiple platforms on thousands of individuals
analyzed according to established standards is essential if
CNV mapping is to become a more routinely used technology in
research and diagnosis.
On the assumption that technical issues are dealt with, assay performance is optimized, and consensus standards are applied to CNV mapping, the next logical step would be to determine the potential role of CNVs in rare or common diseases. Large-scale genome-wide association and case–control studies will have to incorporate CNV analyses into their designs. Future comprehensive studies of CV disease will require reliable, complementary, and harmonized technologies that account for several forms of genomic variation simultaneously. For any particular CNV, increased confidence of its validity would derive from confirmation of the same CNV in the same individual by different technologies and from observation of the same CNV in multiple individuals by multiple methodologies.
Because CNVs are so prevalent and because certain chromosomal regions that harbor CNVs recur across multiple normal samples, it is important to curb the inclination to provide clinical advice on the basis of the presence of CNVs until CNVs have been even more completely mapped in a much wider range of normal healthy subjects, and their association with phenotypes, particularly congenital syndromes, single-gene disorders, and complex diseases of later life, has been more fully characterized.
|
Implications of CNV Knowledge to Cardiovascular Health and Disease
|
|---|
There is considerable excitement in the recognition of the research
opportunities created by awareness of CNVs as an entire new
family of genomic DNA variants. In the clinical arena, will
the benefits of new technologies with improved resolution be
offset by problems that arise from the detection of large numbers
of heretofore-unseen genomic abnormalities? The probability
of finding normal genomic variants in screened samples is now
markedly increased, given the sensitivity of the new methods
and the ubiquity of CNVs >1 kb in the human genome. In a
clinical research setting, how will the management and counseling
of a patient and his/her family unfold with and without taking
CNVs into account? Will ethical issues that arise from analysis
of CNVs simply mirror past issues encountered when cytogenetic
methods are used? Could past medical genetic diagnosis be revised
in light of new knowledge afforded by CNVs? Should archived
specimens be reevaluated in light of the CNV map? These questions
and others
41 will require attention soon.
Past clinical experience with cytogenetic abnormalities may provide important direction for new CNV information. For instance, in postnatal diagnosis of a child with a CV developmental or morphological abnormality, it has been generally accepted that any genomic CNV inherited from a phenotypically normal parent is probably less clinically significant than a variant that has arisen de novo.42 When a CNV has been detected in the genome of a child with a clinical abnormality, it would thus be essential to exclude a de novo chromosomal change in the parents, only after nonpaternity is excluded. The same CNV in a healthy parent suggests it might be a normal variant. If the CNV is not present in either parent, it could then be searched against the database of known CNVs; potential pathogenicity could be inferred through homology of the CNV with known non–disease-related CNVs. However, creation of an extensive and authoritative archive that relates CNVs to disease will require time, resources, and cooperation between research and clinical communities. A current Web site that contains integrated data and includes CNVs is found at http://projects.tcag.ca/variation.
|
Conclusions
|
|---|
Thus, the CNV maps in healthy "normal" individuals add a new
dimension to the study of the human genome. These new comprehensive
genomic maps and analyses have initiated a paradigm shift that
might profoundly affect CV biology and medicine. They have altered
the notion that SNPs are the main source of interindividual
genomic variation. The size and ubiquity of CNVs suggest a potential
role for susceptibility to common complex, polygenic CV diseases.
Although it is promising as a hypothesis for human genetic research,
the ultimate proof of the involvement of CNVs in CV disease
phenotypes will require large-scale studies that comprise well-phenotyped
cohorts and comprehensive, robust methods to classify individuals
according to their CNV status. In any event, future genomic
mapping experiments and genome-wide association analyses, and
their respective detection technologies, will need to account
for the presence of CNVs. Current platforms to study the genome
may need to be redesigned either to optimize detection of CNVs
or minimize their interference with detection of other forms
of genomic variation. As the "personal genome" moves closer
to reality, it will be important to interpret the biological
meaning of all forms of genomic variation (SNPs and CNVs) for
any individual. Finally, CNVs are now part of the contemporary
discourse on genomic variation studies and their biological,
health, and societal implications. More research is required
to fully understand the implications and potential applications
of human genomic CNVs.
|
Acknowledgments
|
|---|
Dr Hegele is a Career Investigator of the Heart and Stroke Foundation
of Ontario and holds the Edith Schulich Vinet Canada Research
Chair in Human Genetics and the Jacob J. Wolfe Distinguished
Medical Research Chair.
Sources of Funding
This work was supported by operating grants from the Canadian Institutes of Health Research, the Heart and Stroke Foundation of Ontario, and Genome Canada through the Ontario Genomics Institute.
Disclosures
Dr Hegele has received support from the Structural and Functional Annotation of the Human Genome (Genome Canada). R. Pollex reports no conflicts.
|
References
|
|---|
- Sharp AJ, Cheng Z, Eichler EE. Structural variation of the human genome. Annu Rev Genomics Hum Genet. 2006; 7: 407–442.[CrossRef][Medline]
[Order article via Infotrieve]
- Wright AF. Genetic variation: polymorphisms and mutations. In: Nature Encyclopedia of the Human Genome. London: Nature Publishing Group; 2003: 959–968.
- Bennett EA, Coleman LE, Tsui C, Pittard WS, Devine SE. Natural genetic variation caused by transposable elements in humans. Genetics. 2004; 168: 933–951.[Abstract/Free Full Text]
- Feuk L, Carson AR, Scherer SW. Structural variation in the human genome. Nat Rev Genet. 2006; 7: 85–97.[Medline]
[Order article via Infotrieve]
- Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, Devon K, Dewar K, Doyle M, FitzHugh W, Funke R, Gage D, Harris K, Heaford A, Howland J, Kann L, Lehoczky J, LeVine R, McEwan P, McKernan K, Meldrim J, Mesirov JP, Miranda C, Morris W, Naylor J, Raymond C, Rosetti M, Santos R, Sheridan A, Sougnez C, Stange-Thomann N, Stojanovic N, Subramanian A, Wyman D, Rogers J, Sulston J, Ainscough R, Beck S, Bentley D, Burton J, Clee C, Carter N, Coulson A, Deadman R, Deloukas P, Dunham A, Dunham I, Durbin R, French L, Grafham D, Gregory S, Hubbard T, Humphray S, Hunt A, Jones M, Lloyd C, McMurray A, Matthews L, Mercer S, Milne S, Mullikin JC, Mungall A, Plumb R, Ross M, Shownkeen R, Sims S, Waterston RH, Wilson RK, Hillier LW, McPherson JD, Marra MA, Mardis ER, Fulton LA, Chinwalla AT, Pepin KH, Gish WR, Chissoe SL, Wendl MC, Delehaunty KD, Miner TL, Delehaunty A, Kramer JB, Cook LL, Fulton RS, Johnson DL, Minx PJ, Clifton SW, Hawkins T, Branscomb E, Predki P, Richardson P, Wenning S, Slezak T, Doggett N, Cheng JF, Olsen A, Lucas S, Elkin C, Uberbacher E, Frazier M, Gibbs RA, Muzny DM, Scherer SE, Bouck JB, Sodergren EJ, Worley KC, Rives CM, Gorrell JH, Metzker ML, Naylor SL, Kucherlapati RS, Nelson DL, Weinstock GM, Sakaki Y, Fujiyama A, Hattori M, Yada T, Toyoda A, Itoh T, Kawagoe C, Watanabe H, Totoki Y, Taylor T, Weissenbach J, Heilig R, Saurin W, Artiguenave F, Brottier P, Bruls T, Pelletier E, Robert C, Wincker P, Smith DR, Doucette-Stamm L, Rubenfield M, Weinstock K, Lee HM, Dubois J, Rosenthal A, Platzer M, Nyakatura G, Taudien S, Rump A, Yang H, Yu J, Wang J, Huang G, Gu J, Hood L, Rowen L, Madan A, Qin S, Davis RW, Federspiel NA, Abola AP, Proctor MJ, Myers RM, Schmutz J, Dickson M, Grimwood J, Cox DR, Olson MV, Kaul R, Raymond C, Shimizu N, Kawasaki K, Minoshima S, Evans GA, Athanasiou M, Schultz R, Roe BA, Chen F, Pan H, Ramser J, Lehrach H, Reinhardt R, McCombie WR, de la Bastide M, Dedhia N, Blocker H, Hornischer K, Nordsiek G, Agarwala R, Aravind L, Bailey JA, Bateman A, Batzoglou S, Birney E, Bork P, Brown DG, Burge CB, Cerutti L, Chen HC, Church D, Clamp M, Copley RR, Doerks T, Eddy SR, Eichler EE, Furey TS, Galagan J, Gilbert JG, Harmon C, Hayashizaki Y, Haussler D, Hermjakob H, Hokamp K, Jang W, Johnson LS, Jones TA, Kasif S, Kaspryzk A, Kennedy S, Kent WJ, Kitts P, Koonin EV, Korf I, Kulp D, Lancet D, Lowe TM, McLysaght A, Mikkelsen T, Moran JV, Mulder N, Pollara VJ, Ponting CP, Schuler G, Schultz J, Slater G, Smit AF, Stupka E, Szustakowski J, Thierry-Mieg D, Thierry-Mieg J, Wagner L, Wallis J, Wheeler R, Williams A, Wolf YI, Wolfe KH, Yang SP, Yeh RF, Collins F, Guyer MS, Peterson J, Felsenfeld A, Wetterstrand KA, Patrinos A, Morgan MJ, de Jong P, Catanese JJ, Osoegawa K, Shizuya H, Choi S, Chen YJ. Initial sequencing and analysis of the human genome. Nature. 2001; 409: 860–921.[CrossRef][Medline]
[Order article via Infotrieve]
- Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, Smith HO, Yandell M, Evans CA, Holt RA, Gocayne JD, Amanatides P, Ballew RM, Huson DH, Wortman JR, Zhang Q, Kodira CD, Zheng XH, Chen L, Skupski M, Subramanian G, Thomas PD, Zhang J, Gabor Miklos GL, Nelson C, Broder S, Clark AG, Nadeau J, McKusick VA, Zinder N, Levine AJ, Roberts RJ, Simon M, Slayman C, Hunkapiller M, Bolanos R, Delcher A, Dew I, Fasulo D, Flanigan M, Florea L, Halpern A, Hannenhalli S, Kravitz S, Levy S, Mobarry C, Reinert K, Remington K, Abu-Threideh J, Beasley E, Biddick K, Bonazzi V, Brandon R, Cargill M, Chandramouliswaran I, Charlab R, Chaturvedi K, Deng Z, Di Francesco V, Dunn P, Eilbeck K, Evangelista C, Gabrielian AE, Gan W, Ge W, Gong F, Gu Z, Guan P, Heiman TJ, Higgins ME, Ji RR, Ke Z, Ketchum KA, Lai Z, Lei Y, Li Z, Li J, Liang Y, Lin X, Lu F, Merkulov GV, Milshina N, Moore HM, Naik AK, Narayan VA, Neelam B, Nusskern D, Rusch DB, Salzberg S, Shao W, Shue B, Sun J, Wang Z, Wang A, Wang X, Wang J, Wei M, Wides R, Xiao C, Yan C, Yao A, Ye J, Zhan M, Zhang W, Zhang H, Zhao Q, Zheng L, Zhong F, Zhong W, Zhu S, Zhao S, Gilbert D, Baumhueter S, Spier G, Carter C, Cravchik A, Woodage T, Ali F, An H, Awe A, Baldwin D, Baden H, Barnstead M, Barrow I, Beeson K, Busam D, Carver A, Center A, Cheng ML, Curry L, Danaher S, Davenport L, Desilets R, Dietz S, Dodson K, Doup L, Ferriera S, Garg N, Gluecksmann A, Hart B, Haynes J, Haynes C, Heiner C, Hladun S, Hostin D, Houck J, Howland T, Ibegwam C, Johnson J, Kalush F, Kline L, Koduru S, Love A, Mann F, May D, McCawley S, McIntosh T, McMullen I, Moy M, Moy L, Murphy B, Nelson K, Pfannkoch C, Pratts E, Puri V, Qureshi H, Reardon M, Rodriguez R, Rogers YH, Romblad D, Ruhfel B, Scott R, Sitter C, Smallwood M, Stewart E, Strong R, Suh E, Thomas R, Tint NN, Tse S, Vech C, Wang G, Wetter J, Williams S, Williams M, Windsor S, Winn-Deen E, Wolfe K, Zaveri J, Zaveri K, Abril JF, Guigo R, Campbell MJ, Sjolander KV, Karlak B, Kejariwal A, Mi H, Lazareva B, Hatton T, Narechania A, Diemer K, Muruganujan A, Guo N, Sato S, Bafna V, Istrail S, Lippert R, Schwartz R, Walenz B, Yooseph S, Allen D, Basu A, Baxendale J, Blick L, Caminha M, Carnes-Stine J, Caulk P, Chiang YH, Coyne M, Dahlke C, Mays A, Dombroski M, Donnelly M, Ely D, Esparham S, Fosler C, Gire H, Glanowski S, Glasser K, Glodek A, Gorokhov M, Graham K, Gropman B, Harris M, Heil J, Henderson S, Hoover J, Jennings D, Jordan C, Jordan J, Kasha J, Kagan L, Kraft C, Levitsky A, Lewis M, Liu X, Lopez J, Ma D, Majoros W, McDaniel J, Murphy S, Newman M, Nguyen T, Nguyen N, Nodell M, Pan S, Peck J, Peterson M, Rowe W, Sanders R, Scott J, Simpson M, Smith T, Sprague A, Stockwell T, Turner R, Venter E, Wang M, Wen M, Wu D, Wu M, Xia A, Zandieh A, Zhu X. The sequence of the human genome. Science. 2001; 291: 1304–1351.[Abstract/Free Full Text]
- The International HapMap Project. Nature. 2003; 426: 789–796.[CrossRef][Medline]
[Order article via Infotrieve]
- Hegele RA. Genetic susceptibility to heart disease in Canada: lessons from patients with familial hypercholesterolemia. Genome. 2006; 49: 1343–1350.[Medline]
[Order article via Infotrieve]
- Byrne JL, Harrod MJ, Friedman JM, Howard-Peebles PN. del(20p) with manifestations of arteriohepatic dysplasia. Am J Med Genet. 1986; 24: 673–678.[CrossRef][Medline]
[Order article via Infotrieve]
- Greenberg F, Elder FF, Haffner P, Northrup H, Ledbetter DH. Cytogenetic findings in a prospective series of patients with DiGeorge anomaly. Am J Hum Genet. 1988; 43: 605–611.[Medline]
[Order article via Infotrieve]
- Lindsay EA, Goldberg R, Jurecic V, Morrow B, Carlson C, Kucherlapati RS, Shprintzen RJ, Baldini A. Velo-cardio-facial syndrome: frequency and extent of 22q11 deletions. Am J Med Genet. 1995; 57: 514–522.[CrossRef][Medline]
[Order article via Infotrieve]
- Catovic A, Kendic S. Cytogenetic findings at Down syndrome and their correlation with clinical findings. Bosn J Basic Med Sci. 2005; 5: 61–67.[Medline]
[Order article via Infotrieve]
- Huang T, Lin AE, Cox GF, Golden WL, Feldman GL, Ute M, Schrander-Stumpel C, Kamisago M, Vermeulen SJ. Cardiac phenotypes in chromosome 4q-syndrome with and without a deletion of the dHAND gene. Genet Med. 2002; 4: 464–467.[Medline]
[Order article via Infotrieve]
- Pauli RM, Scheib-Wixted S, Cripe L, Izumo S, Sekhon GS. Ventricular noncompaction and distal chromosome 5q deletion. Am J Med Genet. 1999; 85: 419–423.[CrossRef][Medline]
[Order article via Infotrieve]
- Hutchinson R, Wilson M, Voullaire L. Distal 8p deletion (8p23.1–8pter): a common deletion? J Med Genet. 1992; 29: 407–411.[Abstract]
- Matsuoka R, Misugi K, Goto A, Gilbert EF, Ando M. Congenital heart anomalies in the trisomy 18 syndrome, with reference to congenital polyvalvular disease. Am J Med Genet. 1983; 14: 657–668.[CrossRef][Medline]
[Order article via Infotrieve]
- Van Praagh S, Truman T, Firpo A, Bano-Rodrigo A, Fried R, McManus B, Engle MA, Van Praagh R. Cardiac malformations in trisomy-18: a study of 41 postmortem cases. J Am Coll Cardiol. 1989; 13: 1586–1597.[Abstract]
- Gantt PA, Byrd JR, Greenblatt RB, McDonough PG. A clinical and cytogenetic study of fifteen patients with 45,X/46XY gonadal dysgenesis. Fertil Steril. 1980; 34: 216–221.[Medline]
[Order article via Infotrieve]
- de la Chapelle A, Schroder J, Stenstrand K, Fellman J, Herva R, Saarni M, Anttolainen I, Tallila I, Tervila L, Husa L, Tallqvist G, Robson EB, Cook PJ, Sanger R. Pericentric inversions of human chromosomes 9 and 10. Am J Hum Genet. 1974; 26: 746–766.[Medline]
[Order article via Infotrieve]
- Lupski JR. Genomic disorders: structural features of the genome can lead to DNA rearrangements and human disease traits. Trends Genet. 1998; 14: 417–422.[CrossRef][Medline]
[Order article via Infotrieve]
- Stankiewicz P, Lupski JR. Genome architecture, rearrangements and genomic disorders. Trends Genet. 2002; 18: 74–82.[CrossRef][Medline]
[Order article via Infotrieve]
- Iafrate AJ, Feuk L, Rivera MN, Listewnik ML, Donahoe PK, Qi Y, Scherer SW, Lee C. Detection of large-scale variation in the human genome. Nat Genet. 2004; 36: 949–951.[CrossRef][Medline]
[Order article via Infotrieve]
- Sebat J, Lakshmi B, Troge J, Alexander J, Young J, Lundin P, Maner S, Massa H, Walker M, Chi M, Navin N, Lucito R, Healy J, Hicks J, Ye K, Reiner A, Gilliam TC, Trask B, Patterson N, Zetterberg A, Wigler M. Large-scale copy number polymorphism in the human genome. Science. 2004; 305: 525–528.[Abstract/Free Full Text]
- Pinkel D, Albertson DG. Comparative genomic hybridization. Annu Rev Genomics Hum Genet. 2005; 6: 331–354.[Medline]
[Order article via Infotrieve]
- Pinkel D, Segraves R, Sudar D, Clark S, Poole I, Kowbel D, Collins C, Kuo WL, Chen C, Zhai Y, Dairkee SH, Ljung BM, Gray JW, Albertson DG. High-resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays. Nat Genet. 1998; 20: 207–211.[CrossRef][Medline]
[Order article via Infotrieve]
- Solinas-Toldo S, Lampel S, Stilgenbauer S, Nickolenko J, Benner A, Dohner H, Cremer T, Lichter P. Matrix-based comparative genomic hybridization: biochips to screen for genomic imbalances. Genes Chromosomes Cancer. 1997; 20: 399–407.[CrossRef][Medline]
[Order article via Infotrieve]
- Lucito R, Healy J, Alexander J, Reiner A, Esposito D, Chi M, Rodgers L, Brady A, Sebat J, Troge J, West JA, Rostan S, Nguyen KC, Powers S, Ye KQ, Olshen A, Venkatraman E, Norton L, Wigler M. Representational oligonucleotide microarray analysis: a high-resolution method to detect genome copy number variation. Genome Res. 2003; 13: 2291–2305.[Abstract/Free Full Text]
- Redon R, Ishikawa S, Fitch KR, Feuk L, Perry GH, Andrews TD, Fiegler H, Shapero MH, Carson AR, Chen W, Cho EK, Dallaire S, Freeman JL, Gonzalez JR, Gratacos M, Huang J, Kalaitzopoulos D, Komura D, MacDonald JR, Marshall CR, Mei R, Montgomery L, Nishimura K, Okamura K, Shen F, Somerville MJ, Tchinda J, Valsesia A, Woodwark C, Yang F, Zhang J, Zerjal T, Zhang J, Armengol L, Conrad DF, Estivill X, Tyler-Smith C, Carter NP, Aburatani H, Lee C, Jones KW, Scherer SW, Hurles ME. Global variation in copy number in the human genome. Nature. 2006; 444: 444–454.[CrossRef][Medline]
[Order article via Infotrieve]
- Tuzun E, Sharp AJ, Bailey JA, Kaul R, Morrison VA, Pertz LM, Haugen E, Hayden H, Albertson D, Pinkel D, Olson MV, Eichler EE. Fine-scale structural variation of the human genome. Nat Genet. 2005; 37: 727–732.[CrossRef][Medline]
[Order article via Infotrieve]
- Charbonnier F, Raux G, Wang Q, Drouot N, Cordier F, Limacher JM, Saurin JC, Puisieux A, Olschwang S, Frebourg T. Detection of exon deletions and duplications of the mismatch repair genes in hereditary nonpolyposis colorectal cancer families using multiplex polymerase chain reaction of short fluorescent fragments. Cancer Res. 2000; 60: 2760–2763.[Abstract/Free Full Text]
- Armour JA, Sismani C, Patsalis PC, Cross G. Measurement of locus copy number by hybridisation with amplifiable probes. Nucleic Acids Res. 2000; 28: 605–609.[Abstract/Free Full Text]
- Hollox EJ, Akrami SM, Armour JA. DNA copy number analysis by MAPH: molecular diagnostic applications. Expert Rev Mol Diagn. 2002; 2: 370–378.[CrossRef][Medline]
[Order article via Infotrieve]
- Schouten JP, McElgunn CJ, Waaijer R, Zwijnenburg D, Diepvens F, Pals G. Relative quantification of 40 nucleic acid sequences by multiplex ligation-dependent probe amplification. Nucleic Acids Res. 2002; 30: e57.[Abstract/Free Full Text]
- Sellner LN, Taylor GR. MLPA and MAPH: new techniques for detection of gene deletions. Hum Mutat. 2004; 23: 413–419.[CrossRef][Medline]
[Order article via Infotrieve]
- Hinds DA, Kloek AP, Jen M, Chen X, Frazer KA. Common deletions and SNPs are in linkage disequilibrium in the human genome. Nat Genet. 2006; 38: 82–85.[Medline]
[Order article via Infotrieve]
Top
Introduction
Main Varieties of Human...