Detection Of Copy Number Variation Between Two Groups Using Whole-Genome Ngs Paired Reads

Question

[David M]

I have access to the whole-genome sequencing data for 10 individuals (and a reference), half of whom are resistant to a certain treatment. As part of the effort to determine potential genetic causes of this resistance, I want to look at Copy Number Variation. I've come across a number of methods for identifying CNVs between a reference and a single individual (by read hybridization/alignment or read fragment length analysis), but I'd like to take advantage of the large dataset I have access to.

Can anyone point me in the direction of any statistical methods or programs which could compare two groups of multiple genomes, looking for read-depth differences?

Thanks!

-David

Answer 1

[Charles Warden]

CoNIFER is probably my favorite method for exon-capture CNV calling (although it would have to be modified for whole genome analysis, and I would typically use more than 10 samples):

http://conifer.sourceforge.net/

VarScan 2.0 provides CNV calls (I haven't tried it yet, but I like using VarScan for SNP calling):

http://varscan.sourceforge.net/

You might also want to look into the DNAcopy Bioconductor package:

http://www.bioconductor.org/packages/release/bioc/html/DNAcopy.html

GATK probably also provides some copy number functionality, but I haven't really been keeping track after it was announced that they would switch to a commercial license system.

I've tried using Pindel and BreakDancer for calling structural variants, but I haven't really liked the results that I have seen so far (and they aren't based upon read-depth differences, like CoNIFER and VarScan).

Answer 2

[Chris Whelan]

They don't solely work using read depth (they use discordant paired-read signals as well), but you might want to look into Genome STRiP:

http://www.broadinstitute.org/software/genomestrip/genome-strip

And CommonLAW:

http://www.ncbi.nlm.nih.gov/pubmed/?term=Simultaneous+structural+variation+discovery+among+multiple+paired-end+sequenced+genomes

Which are both designed to analyze multiple samples simultaneously.