تدريب Shadowing: CNV analysis with Daniel Howrigan - تعلم التحدث بالإنجليزية عبر الفيديو

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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

101

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

106

that can give you just what you would like in terms of your analysis now granted

107

using the burden tests in plank doesn't handle covariates it basically just looks at something like case control status

108

and so what i recommend is taking the output particularly the cnd cnv .indiv file

109

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

111

if i go back the number of segments will change depending on what filters,

112

and obviously, subsequently, the number of kilobases covered by these segments will change as a function.

113

And it's kind of iteratively reading in these files at different filtering steps that can produce a wide range of tests.

114

So I've shown a couple figures here that we published when looking at CNV burden in the PGC schizophrenia.

115

So I took in those .CNV .indiv files into R.

116

I ran a logistic regression predicting schizophrenia status and adding a number of covariates,

117

such as principal components, genotype platforms, and then basically as I iteratively ran different

118

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.

132

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?

146

I think having a figure kind of is instructive here.

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So I plotted

148

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.

156

Now you can also collapse.

157

Another test that we've done is collapsing across say all the exons of this gene

158

and so this would be akin more to like a gene burden test where you collapse this region

159

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.

162

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

167

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

172

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.

174

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,

176

do you have the right amount of power and sensitivity to do a proper case control association

177

Another thing too, ancestry PCs,

178

most of the CNV hotspots and associated CNV that we see in psychiatric disease aren't very impacted,

179

and mainly that's because these are recurrent de novo CNV areas where there's a higher mutation rate,

180

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,

182

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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المفردات وملاحظات التحدث لهذا الدرس

هذا الدرس في المحادثة مبني على الفيديو “CNV analysis with Daniel Howrigan”. أكثر الكلمات تكرارًا في الفيديو: cnv, file, test, deletion, plink. يحتوي هذا الفيديو على 196 جملة و2593 كلمة للتدرب على الترديد. مدة الكلام 19:53. يتكلم المتحدث بسرعة ثابتة، نحو 130 كلمة في الدقيقة، وهي سرعة مريحة للترديد. 77% فقط من الكلمات ضمن أكثر 3,000 كلمة شيوعًا في الإنجليزية، فالمفردات صعبة نسبيًا.

أهم المفردات في هذا الفيديو

15 كلمة من الفيديو تستحق التعلّم، مع النطق والمعنى:

الكلمة	النطق	المعنى
frequency اسم	/ˈfɹiːkwənsi/	تَرَدُّد
variant اسم	/ˈvæɹi.ənt/	اِخْتِلَاف
burden اسم	/ˈbɝdn̩/	حِمْل, عِبْء
detect فعل	/dɪˈtɛkt/	كَشَفَ
gene اسم	/d͡ʒiːn/	مُوَرِّثَة, جِين
filter اسم	/ˈfɪltɚ/	مِصْفَايَة
variation اسم	/ˌvɛəɹiˈeɪʃn̩/	تَرَاوُح, اِخْتِلَاف
output اسم	/ˈaʊtpʊt/	مُخرَج
schizophrenia اسم	/ˌskɪtsəˈfɹiniə/	فُصَام, الْفِصَام الذُّهَانِيّ
collapse فعل	/kəˈlæps/	اِنْهَار
genome اسم	/ˈd͡ʒiː.noʊm/	مَجِين, جِينُوم
rescue فعل	/ˈɹɛs.kjuː/	أَنْقَذَ, خَلَّصَ
select فعل	/sɪˈlɛkt/	اِخْتَارَ
capture فعل	/ˈkæp.(t)ʃɚ/	أَسَرَ
summary اسم	/ˈsʌm.ə.ɹi/	خُلَاصَة, مُلَخَّص

الأفعال المركبة التي ستسمعها

الكلمة	المعنى
go away فعل	اِبْتَعَدَ

جمل تستحق التكرار

جمل قصيرة وكاملة من الفيديو يمكنك استخدامها في الحديث اليومي:

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

القواعد في هذا الفيديو

أكثر التراكيب التي يستخدمها المتحدث، مع العبارات نفسها من الفيديو:

التركيب	في الفيديو
المضارع التام have/has + اسم المفعول — حدث ماضٍ ما زال له أثر الآن	I've listed · you've run · have changed
المبني للمجهول be + اسم المفعول — التركيز على ما يحدث لا على من يفعله	isn't changed · is given · are not forced

نطق يحتاج إلى انتباه

يستخدم المتحدث 35 من الصيغ المختصرة، مثل I've, I'm, don't. انطقها بصيغتها القصيرة كما تسمعها.

أصوات “sh” و“zh”: variation /ˌvɛəɹiˈeɪʃn̩/, structural /ˈstɹʌk(t͡)ʃəɹəl/, consideration /kənˌsɪd.əˈɹeɪ.ʃən/, capture /ˈkæp.(t)ʃɚ/, dash /ˈdæʃ/
الكلمات الطويلة — انتبه لموضع النبر: schizophrenia /ˌskɪtsəˈfɹiniə/, consideration /kənˌsɪd.əˈɹeɪ.ʃən/, sensitivity /ˌsɛn.səˈtɪv.ə.ti/, mechanism /ˈmɛk.ə.nɪ.zəm/, subsequently /ˈsʌb.sɪ.kwənt.li/

أصوات يجدها متحدثو العربية صعبة:

/p/ — ليس /b/: انطقه مع نفخة هواء: overlap /ˌəʊvəˈlæp/, output /ˈaʊtpʊt/, chip /t͡ʃɪp/, collapse /kəˈlæps/, capture /ˈkæp.(t)ʃɚ/
/v/ — ليس /f/: مع اهتزاز الأوتار الصوتية: variant /ˈvæɹi.ənt/, variation /ˌvɛəɹiˈeɪʃn̩/, overlap /ˌəʊvəˈlæp/, sensitivity /ˌsɛn.səˈtɪv.ə.ti/, leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/
تتابع الصوامت — لا تضف حركة بينها: frequency /ˈfɹiːkwənsi/, variant /ˈvæɹi.ənt/, detect /dɪˈtɛkt/, segment /ˈsɛɡmɛnt/, structural /ˈstɹʌk(t͡)ʃəɹəl/

كيف تتدرب بهذا الفيديو

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ردّد جملة جملة بالسرعة العادية، وكرّر كل جملة حتى يتطابق إيقاعك مع المتحدث.
سجّل صوتك وقارنه بالأصل، مع الانتباه إلى كلمات مثل frequency, variant, burden.

ما هي تقنية التظليل الصوتي؟

التظليل الصوتي (Shadowing) تقنية تعلم لغة مدعومة علمياً، طُورت أصلاً لتدريب المترجمين الفوريين المحترفين. الطريقة بسيطة لكنها قوية: تستمع لصوت إنجليزي أصلي وتكرره فوراً بصوت عالٍ — كظل يتبع المتحدث بتأخير 1-2 ثانية. تُظهر الأبحاث تحسناً كبيراً في دقة النطق والتنغيم والإيقاع وربط الأصوات والاستماع والطلاقة.

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تدريب Shadowing: CNV analysis with Daniel Howrigan - تعلم التحدث بالإنجليزية عبر الفيديو

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الأفعال المركبة التي ستسمعها

جمل تستحق التكرار

القواعد في هذا الفيديو

نطق يحتاج إلى انتباه

كيف تتدرب بهذا الفيديو

ما هي تقنية التظليل الصوتي؟