Detection of
Structural Variants (SVs)
and
Copy Number Variations
(CNVs)
on
NGS Data
SVs and CNVs
• They are often confused…
• SVs: regions contain insertions and deletions
(indels) or inversions.
• CNVs: regions appearing a different number of
times in different individuals.
• They are closely related phenomena.
• SVs are operationally defined as genomic events
involving >50bp. They include CNVs as well as
rearrangements such as inversions and
translocations.
Why studying SVs and CNVs
• It has been acknowledged only very recently that human
genomes differ more as a consequence of SVs (including
CNVs) than of single-base differences. [first "hypothesis" in
2004-2005 not taken seriously, and evidence only in 2010
with NGS].
• In particular, many studies observed CNVs and did
genotyping with them: they are the easiest SVs to detect.
• I am not aware of tools for detecting inversions and
translocations of DNA durectly on NGS data
• There are some tools for detecting CNVs: from now on we
discuss them only.
Copy Number Variations
• The challenge is to discover effects of CNVs on
human diseases, complex traits (combination of
genetic and environmental effects), and evolution.
• Genotyping of human CNVs is far from being a
routine procedure. This is a limit to personalized
medicine, for which CNVs detection is a crucial
step.
• No standard method (many and very recent tools,
not yet a fair/sharp comparison) exists.
Detecting CNV
• Since late nineties and until very recently
(and still) CNVs were/are detected using
aCGH: Array Comparative Hybridization.
• The array platform are not as rapid and
cheap as NGS, and their data cannot be reused once processed.
• The aGCH Array has inherent limits on the
size and frequency of detectable CNVs.
• NGS opened a new era in CNV-detection!
CNV calling
• There are three approaches for CNV calling:
– Based on read count (RC), or read alignment
coverage (Breakdancer, CNVnator, CNV-seq, and
tools of [Campbell et al, 2008], [Alkan et al, 2009],
[Sudmant et al, 2010], [Yoon et al, 2009]).
– Based on paired end reads (PEMer, CNVer,
VariationHunter, MoDIL, Breakdancer, tools of
[Sindi et al, 2009], [Quinlan et al, 2010]).
– Based on split-read alignments (Pindel, Mosaik,
tool of [Mills et al, 2006]).
Read Count Approaches
• Measuring the amount of reads mapped
to a location in the reference genome.
• Identify CNV regions
• Estimating Copy Number
• Some use a sliding window.
• Problems when coverage is not uniform
within a CNV…
Paired End Reads Approaches
• Mate/Paired End Reads are mapped on
the reference genome.
Split Read Approach
• A read is not mapped in a single
locations because of possible
structural variation.
• All un-aligned reads are split and
then mapping is sought again.
• Iterate to find the actual
breakpoint.