Overview of Structural Variation

  1. Introduction
  2. dbVar/DGVa Data Model and Data Exchange Policy
  3. How NCBI Displays Variant Data
  4. SV detection technology
  5. References

I. Introduction

Structural variation (SV) is generally defined as a region of DNA approximately 1 kb and larger in size [1] and can include inversions and balanced translocations or genomic imbalances (insertions and deletions), commonly referred to as copy number variants (CNVs). These CNVs often overlap with segmental duplications, regions of DNA >1 kb present more than once in the genome, copies of which are >90% identical [2]. If present at >1% in a population a CNV may be referred to as copy number polymorphism (CNP).

In 1991, Charcot-Marie Tooth (CMT) disease was the first autosomal dominant disease associated with a gene dosage effect due to an inherited DNA rearrangement. Most cases of CMT1A are associated with a 1.5-Mb tandem duplication in 17p11.2-p12, mediated by flanking segmental duplications, that encompasses the PMP22 gene (see Figure 1). The disease phenotype results from having three copies of the normal gene. The reciprocal product of the recombination, a single copy of the PMP22 gene, results in the clinically distinct hereditary neuropathy with liability to pressure palsies (HNPP) [3].

Figure 1: Charcot-Marie Tooth (CMT) disease

Figure 1: Charcot-Marie Tooth (CMT) disease.  Unequal crossing over between two highly homologous repeats on chromosome 17p12 can result in (A) 3 copies of the PMP22 gene with the CMT1A phenotype or the reciprocal (B) and 1 copy of the PMP22 gene with the HNPP phenotype.


It is now widely accepted that CNVs account for a number of genomic disorders including DiGeorge/velocardiofacial, Smith-Margenis, Williams-Beuren and Prader-Willi syndromes and, with increased genotype-phenotype correlations, an increasing number of new genomic disorders such as the 17q21.31 microdeletion in learning disability [4] [5] and most recently with the 16p11.2 microdeletion in autism [6].

Copy number variation among genes is not restricted to a disease phenotype. Many genes that are found to be CNV (both in humans and in mouse) are involved in environmental response, for example sensory perception (olfactory receptors) and immunity (defensins) [7-13]. However, evidence for copy number variation in disease resistance and susceptibility in humans is accumulating with publications on CCL3L1 and susceptibility to HIV/AIDS [14], FCGR3B and risk of systemic lupus erythematosus [15] and several independent studies correlating copy number of the beta defensin genes with predisposition to Crohn’s disease [16], risk of psoriasis [17] and sporadic prostate cancer [18].

Although single nucleotide polymorphisms (SNPs) were initially thought to contribute the majority of human genomic variation [19-20] it is now recognized that structural variation represents a significant, and at present poorly understood, contribution to an individual’s genetic makeup. It is only within the past 5 years, aided by the development of technologies such as high-throughput sequencing and array comparative genome hybridization (aCGH), that the extent of structural variation in phenotypically normal individuals has been investigated.

Estimates for the extent of CNV in the phenotypically normal human genome vary. The Database of Genomic Variants (DGV) has annotated 18.8% of the euchromatic human genome as copy number variable. However, most recently Perry et al. suggest that previous reports are overestimates and that the actual CNV content of the human genome, while still covering Mb of DNA, will be less than 12% [21].


II. dbVar/DGVa Data Model and Data Exchange Policy

Data Model

dbVar accessions three specific objects:

  1. Studies (std): All variant regions and variant instances that are submitted as a group are part of a study. Each study typically represents a coherent set of methods and analyses that were performed at around the same time, by the same authors, in the same laboratory (or laboratories). Because these parameters determine to a large extent the variability that exists between datasets, all data in dbVar is organized by study. Typically, a study will correspond to a single publication or community resource.  Study ids are prefixed with ‘nstd’ if the data were accessioned at NCBI and ‘estd’ if they were accessioned at EBI.

  2. Variant regions (sv): Variant regions are regions of the genome that a submitter has defined as containing structural variation. Very little meta-data is contained on these objects, as they are meant to provide a mark on the genome to define regions containing variation. Variant regions point to sets of exemplar variant instances which support the assertion that the region contains variation. Important: Key to understanding dbVar's data model is an awareness that variant regions do not represent reference variants, nor are they idealized representations of individual structural variant events. Rather, they are simply markers on the genome to denote regions within which structural variation has been observed. It may be helpful to think of variant regions as similar to ss-IDs used in dbSNP - they are submitters' assertions concerning the location of variation. Variant region ids are prefixed with ‘nsv’ if the data were accessioned at NCBI and ‘esv’ if t were accessioned at EBI.

  3. Variant calls (ssv): Variant calls are the individual instances of structural variation observed in a study and are based on the output of raw data analyses. dbVar accepts the following Variant call types:

Variant Call and RegionTypes

Variant Call Type Sequence Ontology ID Variant Region Type

copy number gain

SO:0001742  A sequence alteration whereby the copy number of a given region is greater than the reference sequence.

copy number variation
copy number loss

SO:0001743  A sequence alteration whereby the copy number of a given region is less than the reference sequence.

copy number variation
duplication

SO:0001742 (copy number gain)  A sequence alteration whereby the copy number of a given region is greater than the reference sequence.

copy number variation
deletion

SO:0000159  The point at which one or more contiguous nucleotides were excised.

copy number variation
insertion

SO:0000667   The sequence of one or more nucleotides added between two adjacent nucleotides in the sequence.

insertion
mobile element insertion

SO:0001837   A kind of insertion where the inserted sequence is a mobile element.

mobile element insertion
novel sequence insertion

SO:0001838   An insertion the sequence of which cannot be mapped to the reference genome.

novel sequence insertion
tandem duplication

SO:1000173   A duplication consisting of 2 identical adjacent regions.

tandem duplication
inversion

SO:1000036   A continuous nucleotide sequence is inverted in the same position.

inversion
intrachromosomal breakpoint

SO:0001874   A rearrangement breakpoint within the same chromosome.

translocation or complex chromosomal mutation
interchromosomal breakpoint

SO:0001873   A rearrangement breakpoint between two different chromosomes.

translocation or complex chromosomal mutation
translocation

SO:0000199   A region of nucleotide sequence that has translocated to a new position.

translocation
complex

SO:0001784   A structural sequence alteration or rearrangement encompassing one or more genome fragments.

complex
sequence alteration

SO:0001059   A sequence_alteration is a sequence_feature whose extent is the deviation from another sequence.

sequence alteration

A variant call is associated with only one analysis. Variant calls at or near the same location may represent data from different individuals, or they can represent variant calls derived from the same sample but as a result of different analyses. Variant call ids are prefixed with ‘nssv’ if the data were accessioned at NCBI and ‘essv’ if they were accessioned at EBI. For all studies except curated studies, it is expected that all variant regions (SV) should have at least 1 variant call (SSV).

Figure 2: SV-SSV Relationships


Figure 2: SV-SSV Relationships. Two examples illustrating possible relationships between SVs and SSVs. SSVs (blue bars = gains, red bars = losses) are individual, experimentally detected structural variant calls. SVs (black bars) represent the authors’ assertions of variant regions, based on the merging of SSVs. Arrows at the ends of variants represent breakpoint ambiguity (click here for more information on breakpoint ambiguity). Decisions about merging SSV data to create SVs will be based on the level of resolution of both the method and the analysis used to define variants.

A Short Glossary:

Variant region (SV) – a location on a genome assembly, marked by start and stop coordinates, representing a submitter's assertion of a region containing observed structural variation.

Variant call (SSV) – a variant call, produced by experimental methods and a subsequent analysis, denoting the location, type, and size of a detected structural variant event.

Reference variants – Reference variants (analogous to rs identifiers in dbSNP - e.g., rs1872633) do not currently exist for structural variants. Because the systematic detection of structural variation is still a nascent field, it is not possible to define reference variants based on current data. As detection technology and variant-calling algorithms improve, it may become possible to detect precise breakpoints both reliably and unambiguously, making it feasible to establish a reference structural variant set.

Data Exchange Policy

dbVar operates in close cooperation with the Database of Genomic Variants Archive (DGVa), a sister database at the European Bioinformatics Institute (EBI). dbVar and DGVa both accept data submissions, and use similar data models and submission templates. After regular monthly syncing, dbVar and DGVa contain the same data. After the data has been made public at dbVar and DGVa, it may also be imported by the Database of Genomic Variants (DGV) at the Center for Applied Genomics in Toronto.


III. How NCBI Displays Variant Data

Capturing Variant Information

Structural Variation (SV) can be complex to represent. Current technologies rarely provide base pair resolution for variant breakpoints. However, there is a core set of data that captures all the necessary information on a variant, including the degree of uncertainty present in the location of breakpoints. This data set includes:

start-stop coordinates: used to define events where breakpoints are known to basepair resolution.
inner start-stop coordinates: used to define regions that are known to be affected by a variant, but do not define the actual breakpoints. The breakpoints lie outside of the defined region.
outer start-stop coordinates: used to define the absolute outer boundary of a variation event but do not define the actual breakpoints. The breakpoints lie inside of the defined region.
allele length: the length of the affected variant. For example, paired-end mapping may identify a 5-kb deletion that is known to reside within a defined 40-kb interval, but its breakpoints are not known. Allele length (in this case, 5 kb) does not have to be exact - approximations are acceptable, depending on the method.

Visual representation of Variants

Displaying uncertainty of breakpoint locations

Displaying the uncertainty in defining the regions depends on the combination of coordinates that are associated with the variant:

Start and stop only: This implies that we have breakpoint resolution and is represented simply:

Example of a rendered variant - breakpoint resolution

Inner/outer start/stop: Typical of a probe-based method, but could occur with other methods as well. Inner start/stop define region known to be involved with the event. Outer start/stop define region where breakpoint is likely to occur.

Example of a rendered variant - inners and outers

Inner start/stop only: May occur in probe studies, curated studies or historical studies.

Example of a rendered variant - inners only

Outer start/stop only: Likely to occur with mapping studies, but could show up in other studies as well. Note inward-pointing grey arrows, which indicate that the inner boundaries are not known.

Example of a rendered variant - outers only

dbVar Variant rendering

Table showing variant type rendering

Figure 3: Variant rendering. Variants are visually represented in several places: the Genome View tab of variant pages; the dbVar Genome Browser; and NCBI's Sequence Viewer. Colors distinguish types of structural variant (copy number gain/loss, insertion, inversion, etc.) while breakpoint ambiguity is represented by translucency and/or arrows at variant ends.


IV. SV Detection Technology

The first whole genome reports of the extent of structural variation in phenotypically normal humans included those from BAC aCGH [22], representational oligonucleotide microarray analysis (ROMA) [23] and fosmid paired-end mapping (PEM) [24] . Newer technologies include oligo aCGH [7] [25] [26-29], the analysis of SNP genotyping data [7] [12-13] [29-34] , and PEM using next-generation 454 sequencing [27] .

BAC aCGH [35-37], oligo aCGH [37,38] and SNP genotyping analysis [39-41] have also been used to investigate clinical cohorts with unexplained mental retardation and autism.

A number of studies have also used aCGH (see Figure 2) to investigate the copy number of genes in other species, such as mouse [9] [41] , rat [42] and macaque [43] , and between human and other primate species including chimpanzee, bonobo, gorilla, orangutan and macaque in an attempt to define lineage-specific genes that may aid in understanding genome evolution [21] [45-48] .

Figure 2: Array Comparative Genomic Hybridization (aCGH)

Figure 4: ArrayComparative Genomic Hybridization (aCGH). A test and reference DNA sample are differentially labeled with fluorochromes, commonly Cy3 (green) and Cy5(red), and hybridized to a microarray which can comprise known BAC or oligonucleotide sequences. The ratio of intensities of the two fluorochromes is then analyzed to infer the relative copy numbers of each BAC or oligonucleotide sequence present in each DNA sample.

 

BAC clone (~150 kb) arrays used for CNV detection include tiling arrays, where overlapping clones cover the majority of the human genome assembly [25][13] [35] [48] , and custom arrays with clones targeted to structural variant regions of the genome that are often not comprehensively represented by the current genome assembly [2] [36] [49] . The major limitation of BAC aCGH is the low resolution (~1 Mb), so the boundaries of the CNVs can not accurately be delineated, the identified region may contain several smaller CNVs and the extent of the CNV is overestimated.

Oligonucleotide (45-85 bp) arrays offer higher resolution than BAC aCGH, and those used for CNV detection include Agilent commercial [21] [50] [26] [28] and CNV-specific custom [21] [26] or targeted to regions of gaps in the human genome assembly [50] , NimbleGen commercial [28-29] and CNV-specific custom[25] [36] , and in house CNV-specific custom arrays [7] . The probe spacing is in the region of 1 probe every 5-6 kb for whole genome arrays and greater than 1 probe every 50 bp for custom arrays.

ROMA aims to reduce the complexity of the genome by PCR of a restriction digest before hybridization and has been used for CNV detection on a NimbleGen custom oligo arrays in human [23] and mouse [51] .

Oligonucleotide SNP arrays used include those from Affymetrix [13] [29-30] [33] [39-41] and Illumina [28] [30-31] [52] with an average probe spacing of 1 probe every 2-6 kb.

Dense SNP genotypes, such as those available for the HapMap samples, can be used to discover segregating deletion variants evident from patterns of null genotypes, Mendelian inconsistencies and Hardy-Weinberg disequilibrium [7] [33] . It is important to note that insertions are not identified by this method. Also, the detection is limited to areas of the genome containing SNPs, while many regions of known structural variation are sparsely covered by SNPs. This is being improved by some of the new microarrays such as Illumina's Human1M–DuoHuman660W-Quad and HumanOmni1-Quad and Affymetrix's Genome Wide Human SNP Array 6.0.

Paired-end mapping (PEM) uses the end sequences of BACs, fosmids, and most recently 3kb DNA fragments from the new 454 sequencing technology [25] , to compare to the reference genome (See Figure 3). The advantage of this methodology is that it not only allows identification of insertions and deletions, as seen in aCGH, but also allows detection of balanced translocations and inversions which are not amenable to aCGH, highlighting the importance such sequencing technologies will play in identifying structural variation and facilitating the completion of comprehensive genomes.

Figure 3: Paired-end mapping (PEM)

Figure 5: Paired-end mapping (PEM).

A library of known insert size e.g., 40kb fosmid sequences or 3kb DNA fragments is end sequenced and aligned to a genomic assembly.
(A) Ends that map at a similar distance and orientation to the genomic assembly are concordant and do not indicate any structural variation.
(B) Ends that map at a distance significantly less than the insert size on the genomic assembly indicate an insertion in the insert relative to the assembly.
(C) Ends that map at a distance significantly more than the insert size on the genomic assembly indicate an deletion in the insert relative to the assembly.
(D) Ends that map in the same orientation on the genomic assembly indicate an inversion relative to the assembly.

V. References

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Last updated: 2013-10-22T14:28:02-04:00