쉐도잉 연습: CNV analysis with Daniel Howrigan - 영상으로 영어 말하기 배우기

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Hello.

My name is Daniel Harrigan, and today I'll be talking about how to run copy number variation analysis.

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?

What does the CNV file format look like?

And what does CNV analysis output look like?

Finally, how do I use this to run CNV burden and association tests?

So, what is a copy number variant?

Well, I define it here as a subset of structural variation involving a gain

or duplication or a loss slash deletion of genomic sequence.

Now, I call it a subset of structural variation because structural variation

can encompass basically any change in the length of a genomic sequence

that can be as small as a single base pair insertion

or deletion now most snips that we we think of are just substitutions

so the actual size of the genome isn't changed with a single nucleotide polymorphism

but a structural variant can encompass anything from very small all the way to entire chromosomes.

What we call CNVs, while it being kind of a general name,

falls into the category of roughly at least defined here at minimum one kilobase to submicroscopic.

With array data, we generally say something around 10 to 100 kilobases, given our sensitivity to detect these CNVs,

whereas larger CNVs usually are greater than 500 kilobases up to multiple megabases.

And then we get into much larger events.

Now, I'm not going to get into the details of the mechanisms that cause it.

I've listed a few here.

You can look them up.

But there are different hotspots in the genome

that are more prone to these copy number variants due to repeat regions

or often ways in the machinery that can make mistakes and lead to these gains or losses of genomic sequence.

How do we detect it using genotype data?

So when we run across the genome collecting a bunch of SNPs, I show here kind of the basic mechanism

or at least a figure showing how we take light intensity from different experiments looking at capturing either

you know allele a i have aa as allele a

or the b allele being tt here

and then a heterozygous you can see a mix of both red

and green now we can leverage these allele frequencies

and what we call the b allele frequency

or at least the frequency of of the t in this case now normally

when there's no copy number variant these will be roughly equal

and so will be around 50 percent

when we see something like a large deletion or any sort of deletion we should see a loss of heterozygosity,

and therefore we will see a gap in these B alleles,

at least at any site that you would normally be heterozygote at.

With the duplication, these would be a little bit off, so it would be more like two -thirds,

one -third, and you would see maybe a movement kind of akin to where these pink lines may be going,

even though they don't quite to find that you would see in a duplication

that the b allele frequencies move off 50 but not to one

and zero now that's looking at the b alleles the other thing

we use to detect it is the log r ratio

and this is basically measuring the light intensity and when you see a drop here

where we would see maybe in in all of these calls you should see a drop of say 50 percent

when we have a deletion and you will see subsequently a rise of about 33 percent

for a duplication now i'm not going to get into the details of the colors

that are used to detect these copy number variants suffice to say this is kind of the foundation that it's built upon

What I'm going to talk about next is given

that you've run copy number variant callers here are the kind of QC calls that you're moving into the analysis stage.

Now, I use Plink to analyze the CNV data so you can get a bunch of different file formats,

copy number variants from different callers.

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.

And it's basically individual identifiers, the chromosome, the start and end position of this CNV,

the type, one being a deletion, three being a duplication.

and then also there's a few other fields here the score

and sites field in this example i use the score being say the number of copy number variant callers

that agreed upon this given call

and it could range up to six sites here being the number of snips that are used to call the cnv

Now, I note here that score and sites are not forced into a particular convention.

You could say replace score with the number of genes overlapping the CNV,

or the sites being some other variable that you would be interested in measuring.

Now along with this file, plink creates a .cnv .map file,

and this is basically breaking down the break points of each

cnv into akin to a map file similar to what we have for snip data

and you can see here i note

that every different position is mapped even the the end position

but also maybe a single position after that end

because you may want to do a test an additional final

test after the end of a CNB to see how things have changed.

Note that the CNB file format commands are not available in Plink 1 .9, but the initial version granted the speed ups

that you get using the newer version of Plink aren't all

that applicable here because we're generally dealing with rare variation and

so these file sizes are generally not too large and the sorts of computing that you use is not too heavy.

So what does CNV analysis output look like?

Usually whenever you run a command in Flink looking at your CNV files, you get out .cnv .indiv file.

So this is per sample file where you say the number of cnv segments

that this individual has the number of kilobases that these segments cover

and the average kilobases covered per segment you also get a cnv .summary file

this is akin to the map file that is summarizing the number of affected

and unaffected individuals at any given break point or start, end, and plus one of a CMB.

And this is basically, you know, the output here, while it seems quite simple,

I'll show how with these files you can do quite sophisticated analysis by using a lot of different filters.

So a lot of the magic in Plink is all the flags that you can use to subset your list of CNVs.

So what I have here is a kind of verbose command just to show the optionality available in Plink.

And for each of these, I describe what that function is doing.

So with Plink, you have dash dash C file.

This reads in the CNV and CNV map file.

I want to select only deletions.

I want to select CNVs that are at least 100 kilobases in length.

I want CNVs with a score of four or higher and at least 50 sites.

I want to exclude CNVs that overlap a particular region.

So I can insert a different text file with a list of chromosome and start -end positions here.

And I want to make sure in this exclusion that the CNVs must overlap by at least 50 % to be excluded.

I can also look at frequency where at least 10 CNVs overlap.

I would like to exclude those because maybe I'm more interested in very rare.

100

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

175

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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이 레슨의 어휘와 말하기 포인트

이 말하기 레슨은 영상 “CNV analysis with Daniel Howrigan”을(를) 바탕으로 합니다. 가장 자주 반복되는 단어는 다음과 같습니다: cnv, file, test, deletion, plink. 이 영상에는 섀도잉할 문장 196개와 단어 2593개가 있습니다. 말하는 구간의 길이는 19:53입니다. 화자는 분당 약 130단어의 일정한 속도로 말해서 섀도잉하기에 편한 속도입니다. 영어에서 가장 많이 쓰이는 3,000단어에 속하는 단어가 77%뿐이라 어휘가 어려운 편입니다.

이 영상의 핵심 어휘

영상에 나오는 익혀 둘 만한 단어 15개를 발음, 뜻과 함께 정리했습니다.

단어	발음	뜻
frequency 명사	/ˈfɹiːkwənsi/	빈도
variant 명사	/ˈvæɹi.ənt/	변체, 변종
burden 명사	/ˈbɝdn̩/	짐, 바리
detect 동사	/dɪˈtɛkt/	찾다, 탐지하다
gene 명사	/d͡ʒiːn/	유전자
filter 명사	/ˈfɪltɚ/	여과기, 필터
variation 명사	/ˌvɛəɹiˈeɪʃn̩/	변화
format 명사	/ˈfɔːɹ.mæt/	포맷
output 명사	/ˈaʊtpʊt/	출력
chip 명사	/t͡ʃɪp/	칩
schizophrenia 명사	/ˌskɪtsəˈfɹiniə/	조현병, 정신분열증
sequence 명사	/ˈsiː.kwəns/	순서
sensitivity 명사	/ˌsɛn.səˈtɪv.ə.ti/	민감
genome 명사	/ˈd͡ʒiː.noʊm/	게놈
subset 명사	/ˈsʌbˌsɛt/	부분집합

영상에 나오는 구동사

단어	뜻
get out 동사	나오다, 나가다
go away 동사	떠나다, 떠나가다
go back 동사	돌아가다
pull out 동사	회수(回收)하다
put down 동사	내려놓다

따라 말해 볼 만한 문장

영상에 나오는 짧고 완결된 문장으로, 일상 대화에서 그대로 쓸 수 있습니다:

I've listed a few here.
How do we detect it using genotype data?
I've also put down some sites.

이 영상의 문법

화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.

문형	영상 속 표현
현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때	I've listed · you've run · have changed
수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점	isn't changed · is given · are not forced

주의할 발음

화자는 I've, I'm, don't 같은 축약형과 약화된 형태를 35번 사용합니다. 들리는 대로 짧게 발음하세요.

“sh”와 “zh” 소리: variation /ˌvɛəɹiˈeɪʃn̩/, structural /ˈstɹʌk(t͡)ʃəɹəl/, consideration /kənˌsɪd.əˈɹeɪ.ʃən/, capture /ˈkæp.(t)ʃɚ/, dash /ˈdæʃ/
긴 단어 — 강세 위치에 주의: schizophrenia /ˌskɪtsəˈfɹiniə/, consideration /kənˌsɪd.əˈɹeɪ.ʃən/, sensitivity /ˌsɛn.səˈtɪv.ə.ti/, mechanism /ˈmɛk.ə.nɪ.zəm/, subsequently /ˈsʌb.sɪ.kwənt.li/

한국어 화자가 어려워하는 소리:

/f/ — ㅍ(/p/)으로 바꾸지 말고 윗니를 아랫입술에 대기: frequency /ˈfɹiːkwənsi/, filter /ˈfɪltɚ/, format /ˈfɔːɹ.mæt/, schizophrenia /ˌskɪtsəˈfɹiniə/, define /dɪˈfaɪn/
/v/ — ㅂ(/b/)과 구별하기: variant /ˈvæɹi.ənt/, variation /ˌvɛəɹiˈeɪʃn̩/, overlap /ˌəʊvəˈlæp/, sensitivity /ˌsɛn.səˈtɪv.ə.ti/, leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/
/z/ — ㅈ이 아니라 성대를 울리는 /s/: mechanism /ˈmɛk.ə.nɪ.zəm/, whereas /(h)wɛɹˈæz/, browser /ˈbɹaʊ.zəɹ/, analyze /ˈæn.əˌlaɪz/, residual /ɹɪˈzɪd͡ʒ.u.əl/

이 영상으로 연습하는 방법

먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
보통 속도로 한 문장씩 섀도잉하고, 화자의 리듬과 맞을 때까지 반복합니다.
자신의 목소리를 녹음해 원본과 비교하고, frequency, variant, burden 같은 단어에 특히 주의합니다.

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쉐도잉 연습: CNV analysis with Daniel Howrigan - 영상으로 영어 말하기 배우기

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