Pratique du Shadowing: CNV analysis with Daniel Howrigan - Apprendre l'anglais à l'oral avec la vidéo
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Hello.
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My name is Daniel Harrigan, and today I'll be talking about how to run copy number variation analysis.
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Today's talk, I'll address a couple of questions, namely, what is a copy number variant and how do we detect it with genotype data?
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What does the CNV file format look like?
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And what does CNV analysis output look like?
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Finally, how do I use this to run CNV burden and association tests?
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So, what is a copy number variant?
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Well, I define it here as a subset of structural variation involving a gain
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or duplication or a loss slash deletion of genomic sequence.
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Now, I call it a subset of structural variation because structural variation
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can encompass basically any change in the length of a genomic sequence
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that can be as small as a single base pair insertion
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or deletion now most snips that we we think of are just substitutions
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so the actual size of the genome isn't changed with a single nucleotide polymorphism
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but a structural variant can encompass anything from very small all the way to entire chromosomes.
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What we call CNVs, while it being kind of a general name,
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falls into the category of roughly at least defined here at minimum one kilobase to submicroscopic.
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With array data, we generally say something around 10 to 100 kilobases, given our sensitivity to detect these CNVs,
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whereas larger CNVs usually are greater than 500 kilobases up to multiple megabases.
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And then we get into much larger events.
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Now, I'm not going to get into the details of the mechanisms that cause it.
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I've listed a few here.
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You can look them up.
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But there are different hotspots in the genome
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that are more prone to these copy number variants due to repeat regions
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or often ways in the machinery that can make mistakes and lead to these gains or losses of genomic sequence.
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How do we detect it using genotype data?
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So when we run across the genome collecting a bunch of SNPs, I show here kind of the basic mechanism
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or at least a figure showing how we take light intensity from different experiments looking at capturing either
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you know allele a i have aa as allele a
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or the b allele being tt here
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and then a heterozygous you can see a mix of both red
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and green now we can leverage these allele frequencies
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and what we call the b allele frequency
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or at least the frequency of of the t in this case now normally
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when there's no copy number variant these will be roughly equal
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and so will be around 50 percent
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when we see something like a large deletion or any sort of deletion we should see a loss of heterozygosity,
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and therefore we will see a gap in these B alleles,
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at least at any site that you would normally be heterozygote at.
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With the duplication, these would be a little bit off, so it would be more like two -thirds,
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one -third, and you would see maybe a movement kind of akin to where these pink lines may be going,
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even though they don't quite to find that you would see in a duplication
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that the b allele frequencies move off 50 but not to one
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and zero now that's looking at the b alleles the other thing
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we use to detect it is the log r ratio
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and this is basically measuring the light intensity and when you see a drop here
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where we would see maybe in in all of these calls you should see a drop of say 50 percent
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when we have a deletion and you will see subsequently a rise of about 33 percent
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for a duplication now i'm not going to get into the details of the colors
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that are used to detect these copy number variants suffice to say this is kind of the foundation that it's built upon
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What I'm going to talk about next is given
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that you've run copy number variant callers here are the kind of QC calls that you're moving into the analysis stage.
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Now, I use Plink to analyze the CNV data so you can get a bunch of different file formats,
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copy number variants from different callers.
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But you would let, you know, if you want to use Plink, you would convert this into this particular file format, which is the .cnv file.
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And it's basically individual identifiers, the chromosome, the start and end position of this CNV,
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the type, one being a deletion, three being a duplication.
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and then also there's a few other fields here the score
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and sites field in this example i use the score being say the number of copy number variant callers
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that agreed upon this given call
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and it could range up to six sites here being the number of snips that are used to call the cnv
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Now, I note here that score and sites are not forced into a particular convention.
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You could say replace score with the number of genes overlapping the CNV,
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or the sites being some other variable that you would be interested in measuring.
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Now along with this file, plink creates a .cnv .map file,
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and this is basically breaking down the break points of each
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cnv into akin to a map file similar to what we have for snip data
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and you can see here i note
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that every different position is mapped even the the end position
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but also maybe a single position after that end
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because you may want to do a test an additional final
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test after the end of a CNB to see how things have changed.
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Note that the CNB file format commands are not available in Plink 1 .9, but the initial version granted the speed ups
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that you get using the newer version of Plink aren't all
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that applicable here because we're generally dealing with rare variation and
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so these file sizes are generally not too large and the sorts of computing that you use is not too heavy.
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So what does CNV analysis output look like?
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Usually whenever you run a command in Flink looking at your CNV files, you get out .cnv .indiv file.
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So this is per sample file where you say the number of cnv segments
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that this individual has the number of kilobases that these segments cover
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and the average kilobases covered per segment you also get a cnv .summary file
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this is akin to the map file that is summarizing the number of affected
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and unaffected individuals at any given break point or start, end, and plus one of a CMB.
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And this is basically, you know, the output here, while it seems quite simple,
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I'll show how with these files you can do quite sophisticated analysis by using a lot of different filters.
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So a lot of the magic in Plink is all the flags that you can use to subset your list of CNVs.
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So what I have here is a kind of verbose command just to show the optionality available in Plink.
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And for each of these, I describe what that function is doing.
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So with Plink, you have dash dash C file.
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This reads in the CNV and CNV map file.
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I want to select only deletions.
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I want to select CNVs that are at least 100 kilobases in length.
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I want CNVs with a score of four or higher and at least 50 sites.
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I want to exclude CNVs that overlap a particular region.
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So I can insert a different text file with a list of chromosome and start -end positions here.
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And I want to make sure in this exclusion that the CNVs must overlap by at least 50 % to be excluded.
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I can also look at frequency where at least 10 CNVs overlap.
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I would like to exclude those because maybe I'm more interested in very rare.
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uh cnvs i can also write out the frequencies of of these of these cnvs as well just
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so i can guarantee which cnvs are being dropped
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which cnvs are being kept
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and then i can run a basic burden test using a permutation model
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and here i just set the number of permutations these 10 000
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so you can see there's a lot of different flags here and it's manipulating a lot of these flags
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that can give you just what you would like in terms of your analysis now granted
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using the burden tests in plank doesn't handle covariates it basically just looks at something like case control status
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and so what i recommend is taking the output particularly the cnd cnv .indiv file
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and reading that into say python or r i prefer r to run more sophisticated models
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and so you can see when you put more filters here
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if i go back the number of segments will change depending on what filters,
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and obviously, subsequently, the number of kilobases covered by these segments will change as a function.
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And it's kind of iteratively reading in these files at different filtering steps that can produce a wide range of tests.
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So I've shown a couple figures here that we published when looking at CNV burden in the PGC schizophrenia.
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So I took in those .CNV .indiv files into R.
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I ran a logistic regression predicting schizophrenia status and adding a number of covariates,
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such as principal components, genotype platforms, and then basically as I iteratively ran different
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uh commands in plank to look at kv cnv counts lengths frequencies whether
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or not they're in
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or not in particular regions you can build up a number of tests of overall burn
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so in this example here this would be all cnvs deletions
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and duplications stratified by different genotyping platforms and then all together and then b here i'm stratifying by different frequencies
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and say previously implicated cnvs as a region
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i want to say in the blue bars we're looking at enrichment here in the green
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and blue bars but you can see the big deviation here there's a lot of enrichment
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when we talk about cnvs at this size or at least this frequency i mean
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and they go away because most of these have been implicated
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so i've excluded these regions rerun the burden test
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and you can see that we've captured much of the signal already with previously implicated cnvs
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so how do we use this to run cnv association tests at individual cnv loci or at individual break points of CNVs.
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Basically, I would run a very similar command.
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I could use all the same filters, basically get rid of a number of commands.
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In particular, you're getting rid of this CNV indiv .perm step, but you still run a permutation test,
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and what you'll get out is a .cnv .summary .m perm file
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and at each base position you can run an association test here using permutation
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this would be the point wise permutation value
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but there's also a family wise permutation p -value
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that corrects for all the tests here
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so this association is run at all possible start
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and in n plus one positions and one of the things you can do
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if you like to include covariates in your data at least
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what i've done in the past is maybe run your logistic
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regression model with a lot of your covariates pull out the residuals
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and then use this as a quantitative trait and you can run association mapping in Plink to get p -values that way.
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So what does this look like?
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I think having a figure kind of is instructive here.
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So I plotted
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through a browser i'll break this down this is our signal at the norexin1 gene
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so in red we have our deletions uh light red deletions in our schizophrenia cases
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dark red deletions in our schizophrenia controls i also have duplications in blue
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which don't make up much of the signal here
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but as you can see i've also plotted the log negative log 10 p -value.
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And if you kind of look closely, you can see each little breakpoint, you can see a different test being run.
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And you can see at, you know, a spot like this where there's many different breakpoints,
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a lot of granularity, you can run many different tests and kind of get a shape of the association around this gene.
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Now you can also collapse.
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Another test that we've done is collapsing across say all the exons of this gene
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and so this would be akin more to like a gene burden test where you collapse this region
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and then you test for overlap at that region run a similar model
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and then you can kind of aggregate all the cnvs and cases and controls to report say a gene -based p -value.
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So that's a very quick overview of how to run burden and association with CNV data.
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And a few considerations is that, you know, one of the things that you don't have access to
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when looking at cnvs is imputation and so it is a consideration to think about you know
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there isn't kind of a reference uh you know reference haplotypes
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or a larger data set to do additional qc
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so there can be additional challenges particularly with subpar data
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that they can't be rescued in the way
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that imputation uh can rescue uh snip genotypes and on
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that note you know genotyping chip does matter very much
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because you can't impute a bunch of different new sites the
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variability in terms of the number of snips particularly for smaller cnvs is a very kind of important consideration
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and so you know you can think of in a particular genotyping chip
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if you don't have a good case control balance there you may not have the sensitivity to properly detect CNVs.
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And so there's a lot of work
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that goes into determining at what length of CNVs or at least at what case control balances given your genotyping chip,
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do you have the right amount of power and sensitivity to do a proper case control association
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Another thing too, ancestry PCs,
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most of the CNV hotspots and associated CNV that we see in psychiatric disease aren't very impacted,
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and mainly that's because these are recurrent de novo CNV areas where there's a higher mutation rate,
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but this is not really ancestrally defined in terms of the fact
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that there's not a large difference in the allele frequency of the CNV across different ancestries, but it is still useful to include,
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particularly as you get to higher frequencies and you get more inherited CNVs. And finally,
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multiple testing correction biopermutation is one of the more robust ways to account for the multiple tests
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because the nature of cnv data is such
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that given the type of genotyping chip
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that you use the size of your data set no no
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particular study is going to be very similar to similar to another study and
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that kind of leveraging the correlation structure within your own data set given
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you know your ability to detect CNVs of various sizes
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and frequencies using permutation is usually the best way to go about properly testing for association.
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So if you have any questions, feel free to email me.
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I've also put down some sites.
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You could search here for the PGC CNV paper that we did in 2017.
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And also, I think the write -up in PLINK on how to do this is really good
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and very descriptive of all its functionality.
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Thank you.
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Vocabulaire et conseils d’expression pour cette leçon
Cette leçon d’expression orale s’appuie sur la vidéo « CNV analysis with Daniel Howrigan ». Les mots qui reviennent le plus souvent : cnv, file, test, deletion, plink. Cette vidéo contient 196 phrases et 2593 mots à répéter en shadowing. La partie parlée dure 19:53. Le locuteur parle à un rythme régulier d’environ 130 mots par minute, confortable pour le shadowing. Seuls 77 % des mots font partie des 3 000 mots les plus courants en anglais, le vocabulaire est donc exigeant.
Vocabulaire clé de cette vidéo
15 mots de la vidéo à retenir, avec leur prononciation et leur sens :
| Mot | Prononciation | Sens |
|---|---|---|
| frequency nom | /ˈfɹiːkwənsi/ | fréquence |
| variant nom | /ˈvæɹi.ənt/ | variante |
| burden nom | /ˈbɝdn̩/ | charge, fardeau |
| detect verbe | /dɪˈtɛkt/ | déceler, détecter |
| gene nom | /d͡ʒiːn/ | gène |
| filter nom | /ˈfɪltɚ/ | filtre |
| segment nom | /ˈsɛɡmɛnt/ | segment |
| variation nom | /ˌvɛəɹiˈeɪʃn̩/ | variation |
| overlap verbe | /ˌəʊvəˈlæp/ | chevaucher |
| format nom | /ˈfɔːɹ.mæt/ | format |
| output nom | /ˈaʊtpʊt/ | sortie |
| chip nom | /t͡ʃɪp/ | brisure, fragment |
| structural adjectif | /ˈstɹʌk(t͡)ʃəɹəl/ | structurel, structural |
| exclude verbe | /ɪksˈkluːd/ | exclure |
| akin adjectif | /ʌˈkɪn/ | apparenté |
Les verbes à particule que vous entendrez
| Mot | Prononciation | Sens |
|---|---|---|
| get out verbe | sortir | |
| break down verbe | tomber en panne | |
| build up verbe | s'accumuler, s'entasser | |
| go away verbe | partir, s'en aller | |
| go back verbe | rentrer, retourner | |
| make up verbe | /ˌmeɪk ˈʌp/ | constituer, composer |
| pull out verbe | se retirer | |
| put down verbe | médire |
Des phrases à répéter
Des phrases courtes et complètes de la vidéo, à réutiliser dans la conversation de tous les jours :
- I've listed a few here.
- How do we detect it using genotype data?
- I've also put down some sites.
La grammaire de cette vidéo
Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :
| Structure | Dans la vidéo |
|---|---|
| Present perfect have/has + participe passé — une action passée qui compte encore maintenant | I've listed · you've run · have changed |
| Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait | isn't changed · is given · are not forced |
Prononciation à surveiller
Le locuteur utilise 35 contractions et formes réduites, comme I've, I'm, don't. Prononcez-les sous leur forme courte, telles que vous les entendez.
- Les sons « sh » et « zh »: variation /ˌvɛəɹiˈeɪʃn̩/, structural /ˈstɹʌk(t͡)ʃəɹəl/, consideration /kənˌsɪd.əˈɹeɪ.ʃən/, capture /ˈkæp.(t)ʃɚ/, dash /ˈdæʃ/
- Mots longs — placez bien l’accent: schizophrenia /ˌskɪtsəˈfɹiniə/, consideration /kənˌsɪd.əˈɹeɪ.ʃən/, sensitivity /ˌsɛn.səˈtɪv.ə.ti/, mechanism /ˈmɛk.ə.nɪ.zəm/, subsequently /ˈsʌb.sɪ.kwənt.li/
Les sons difficiles pour les francophones :
- /h/ — il se souffle, il n’est pas muet: whereas /(h)wɛɹˈæz/, inherit /ɪnˈhɛɹɪt/, haplotype /ˈhap.lə(ʊ).taɪp/
- /tʃ/ et /dʒ/ — à ne pas adoucir en « ch » et « j »: gene /d͡ʒiːn/, chip /t͡ʃɪp/, genome /ˈd͡ʒiː.noʊm/, leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/, enrichment /ɪnˈɹɪt͡ʃ.mənt/
- /r/ anglais — langue recourbée, sans frotter la gorge: frequency /ˈfɹiːkwənsi/, variant /ˈvæɹi.ənt/, variation /ˌvɛəɹiˈeɪʃn̩/, format /ˈfɔːɹ.mæt/, structural /ˈstɹʌk(t͡)ʃəɹəl/
Comment s’entraîner avec cette vidéo
- Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
- Répétez phrase par phrase à vitesse normale, en reprenant chacune jusqu’à ce que votre rythme corresponde à celui du locuteur.
- Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme frequency, variant, burden.
Qu'est-ce que la technique du Shadowing ?
Le Shadowing est une technique d'apprentissage des langues fondée sur la science, développée à l'origine pour la formation des interprètes professionnels. Le principe est simple mais puissant : vous écoutez de l'anglais natif et le répétez immédiatement à voix haute — comme une ombre suivant le locuteur avec un décalage de 1 à 2 secondes. Les recherches montrent une amélioration significative de la précision de la prononciation, de l'intonation, du rythme, des liaisons, de la compréhension orale et de la fluidité.