Shadowing Practice: CNV analysis with Daniel Howrigan - Learn English Speaking with Video
Choose a practice mode
Each mode opens a full-screen practice room.
196 sentences
If sentences are too short or too long, click Edit to adjust them.
1
Hello.
2
My name is Daniel Harrigan, and today I'll be talking about how to run copy number variation analysis.
3
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?
4
What does the CNV file format look like?
5
And what does CNV analysis output look like?
6
Finally, how do I use this to run CNV burden and association tests?
7
So, what is a copy number variant?
8
Well, I define it here as a subset of structural variation involving a gain
9
or duplication or a loss slash deletion of genomic sequence.
10
Now, I call it a subset of structural variation because structural variation
11
can encompass basically any change in the length of a genomic sequence
12
that can be as small as a single base pair insertion
13
or deletion now most snips that we we think of are just substitutions
14
so the actual size of the genome isn't changed with a single nucleotide polymorphism
15
but a structural variant can encompass anything from very small all the way to entire chromosomes.
16
What we call CNVs, while it being kind of a general name,
17
falls into the category of roughly at least defined here at minimum one kilobase to submicroscopic.
18
With array data, we generally say something around 10 to 100 kilobases, given our sensitivity to detect these CNVs,
19
whereas larger CNVs usually are greater than 500 kilobases up to multiple megabases.
20
And then we get into much larger events.
21
Now, I'm not going to get into the details of the mechanisms that cause it.
22
I've listed a few here.
23
You can look them up.
24
But there are different hotspots in the genome
25
that are more prone to these copy number variants due to repeat regions
26
or often ways in the machinery that can make mistakes and lead to these gains or losses of genomic sequence.
27
How do we detect it using genotype data?
28
So when we run across the genome collecting a bunch of SNPs, I show here kind of the basic mechanism
29
or at least a figure showing how we take light intensity from different experiments looking at capturing either
30
you know allele a i have aa as allele a
31
or the b allele being tt here
32
and then a heterozygous you can see a mix of both red
33
and green now we can leverage these allele frequencies
34
and what we call the b allele frequency
35
or at least the frequency of of the t in this case now normally
36
when there's no copy number variant these will be roughly equal
37
and so will be around 50 percent
38
when we see something like a large deletion or any sort of deletion we should see a loss of heterozygosity,
39
and therefore we will see a gap in these B alleles,
40
at least at any site that you would normally be heterozygote at.
41
With the duplication, these would be a little bit off, so it would be more like two -thirds,
42
one -third, and you would see maybe a movement kind of akin to where these pink lines may be going,
43
even though they don't quite to find that you would see in a duplication
44
that the b allele frequencies move off 50 but not to one
45
and zero now that's looking at the b alleles the other thing
46
we use to detect it is the log r ratio
47
and this is basically measuring the light intensity and when you see a drop here
48
where we would see maybe in in all of these calls you should see a drop of say 50 percent
49
when we have a deletion and you will see subsequently a rise of about 33 percent
50
for a duplication now i'm not going to get into the details of the colors
51
that are used to detect these copy number variants suffice to say this is kind of the foundation that it's built upon
52
What I'm going to talk about next is given
53
that you've run copy number variant callers here are the kind of QC calls that you're moving into the analysis stage.
54
Now, I use Plink to analyze the CNV data so you can get a bunch of different file formats,
55
copy number variants from different callers.
56
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.
57
And it's basically individual identifiers, the chromosome, the start and end position of this CNV,
58
the type, one being a deletion, three being a duplication.
59
and then also there's a few other fields here the score
60
and sites field in this example i use the score being say the number of copy number variant callers
61
that agreed upon this given call
62
and it could range up to six sites here being the number of snips that are used to call the cnv
63
Now, I note here that score and sites are not forced into a particular convention.
64
You could say replace score with the number of genes overlapping the CNV,
65
or the sites being some other variable that you would be interested in measuring.
66
Now along with this file, plink creates a .cnv .map file,
67
and this is basically breaking down the break points of each
68
cnv into akin to a map file similar to what we have for snip data
69
and you can see here i note
70
that every different position is mapped even the the end position
71
but also maybe a single position after that end
72
because you may want to do a test an additional final
73
test after the end of a CNB to see how things have changed.
74
Note that the CNB file format commands are not available in Plink 1 .9, but the initial version granted the speed ups
75
that you get using the newer version of Plink aren't all
76
that applicable here because we're generally dealing with rare variation and
77
so these file sizes are generally not too large and the sorts of computing that you use is not too heavy.
78
So what does CNV analysis output look like?
79
Usually whenever you run a command in Flink looking at your CNV files, you get out .cnv .indiv file.
80
So this is per sample file where you say the number of cnv segments
81
that this individual has the number of kilobases that these segments cover
82
and the average kilobases covered per segment you also get a cnv .summary file
83
this is akin to the map file that is summarizing the number of affected
84
and unaffected individuals at any given break point or start, end, and plus one of a CMB.
85
And this is basically, you know, the output here, while it seems quite simple,
86
I'll show how with these files you can do quite sophisticated analysis by using a lot of different filters.
87
So a lot of the magic in Plink is all the flags that you can use to subset your list of CNVs.
88
So what I have here is a kind of verbose command just to show the optionality available in Plink.
89
And for each of these, I describe what that function is doing.
90
So with Plink, you have dash dash C file.
91
This reads in the CNV and CNV map file.
92
I want to select only deletions.
93
I want to select CNVs that are at least 100 kilobases in length.
94
I want CNVs with a score of four or higher and at least 50 sites.
95
I want to exclude CNVs that overlap a particular region.
96
So I can insert a different text file with a list of chromosome and start -end positions here.
97
And I want to make sure in this exclusion that the CNVs must overlap by at least 50 % to be excluded.
98
I can also look at frequency where at least 10 CNVs overlap.
99
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
102
which cnvs are being kept
103
and then i can run a basic burden test using a permutation model
104
and here i just set the number of permutations these 10 000
105
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
110
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
119
or not they're in
120
or not in particular regions you can build up a number of tests of overall burn
121
so in this example here this would be all cnvs deletions
122
and duplications stratified by different genotyping platforms and then all together and then b here i'm stratifying by different frequencies
123
and say previously implicated cnvs as a region
124
i want to say in the blue bars we're looking at enrichment here in the green
125
and blue bars but you can see the big deviation here there's a lot of enrichment
126
when we talk about cnvs at this size or at least this frequency i mean
127
and they go away because most of these have been implicated
128
so i've excluded these regions rerun the burden test
129
and you can see that we've captured much of the signal already with previously implicated cnvs
130
so how do we use this to run cnv association tests at individual cnv loci or at individual break points of CNVs.
131
Basically, I would run a very similar command.
132
I could use all the same filters, basically get rid of a number of commands.
133
In particular, you're getting rid of this CNV indiv .perm step, but you still run a permutation test,
134
and what you'll get out is a .cnv .summary .m perm file
135
and at each base position you can run an association test here using permutation
136
this would be the point wise permutation value
137
but there's also a family wise permutation p -value
138
that corrects for all the tests here
139
so this association is run at all possible start
140
and in n plus one positions and one of the things you can do
141
if you like to include covariates in your data at least
142
what i've done in the past is maybe run your logistic
143
regression model with a lot of your covariates pull out the residuals
144
and then use this as a quantitative trait and you can run association mapping in Plink to get p -values that way.
145
So what does this look like?
146
I think having a figure kind of is instructive here.
147
So I plotted
148
through a browser i'll break this down this is our signal at the norexin1 gene
149
so in red we have our deletions uh light red deletions in our schizophrenia cases
150
dark red deletions in our schizophrenia controls i also have duplications in blue
151
which don't make up much of the signal here
152
but as you can see i've also plotted the log negative log 10 p -value.
153
And if you kind of look closely, you can see each little breakpoint, you can see a different test being run.
154
And you can see at, you know, a spot like this where there's many different breakpoints,
155
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
160
and then you can kind of aggregate all the cnvs and cases and controls to report say a gene -based p -value.
161
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
163
when looking at cnvs is imputation and so it is a consideration to think about you know
164
there isn't kind of a reference uh you know reference haplotypes
165
or a larger data set to do additional qc
166
so there can be additional challenges particularly with subpar data
167
that they can't be rescued in the way
168
that imputation uh can rescue uh snip genotypes and on
169
that note you know genotyping chip does matter very much
170
because you can't impute a bunch of different new sites the
171
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
173
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
181
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,
183
multiple testing correction biopermutation is one of the more robust ways to account for the multiple tests
184
because the nature of cnv data is such
185
that given the type of genotyping chip
186
that you use the size of your data set no no
187
particular study is going to be very similar to similar to another study and
188
that kind of leveraging the correlation structure within your own data set given
189
you know your ability to detect CNVs of various sizes
190
and frequencies using permutation is usually the best way to go about properly testing for association.
191
So if you have any questions, feel free to email me.
192
I've also put down some sites.
193
You could search here for the PGC CNV paper that we did in 2017.
194
And also, I think the write -up in PLINK on how to do this is really good
195
and very descriptive of all its functionality.
196
Thank you.
Download App
Everything you need to speak fluently
AI PronunciationScore every sentence
IPA PracticeMaster every sound
VocabularyBuild your word bank
Vocab GameLearn while playing
FOR YOU
Recommend Video
C11
239 sentences
4 How to use PCR and qPCR
Daily ConversationUniversity Students
C1192 sentences
Other Corneal Disorders - CRASH! Medical Review Series
Daily ConversationUniversity Students
C12
64 sentences
We can cure almost all human diseases. Here’s how. | Albert-László Barabási
Daily ConversationUniversity Students
![SANA-Video: Efficient Video Generation with Block Linear Diffusion Transformer, [ICLR 2026, Oral]](https://img.youtube.com/vi/bHHL691_IPo/mqdefault.jpg)
C11
125 sentences
SANA-Video: Efficient Video Generation with Block Linear Diffusion Transformer, [ICLR 2026, Oral]
Daily ConversationUniversity Students
C11
154 sentences
Sequential, Multiple Assignment, Randomized Trial Designs With Dr Kidwell
Daily ConversationUniversity StudentsWorking Adults
C12
32 sentences
RE2 Unit 9B
Daily ConversationUniversity Students
C17
107 sentences
Gaussian Naive Bayes, Clearly Explained!!!
Daily ConversationUniversity Students
C12
223 sentences
9 Insane Claude Mods I Already Can't live Without
Daily ConversationUniversity StudentsWorking Adults
C11
511 sentences
Interview with Dr. Peter Schwartz | SADS Live Episode 23 | July 31, 2020
Daily ConversationUniversity StudentsWorking Adults
C15
29 sentences
SVINutes: VAN Score
Daily ConversationUniversity Students
C11
195 sentences
VETERINARY BASICS: How to read a blood test! CBC & Haematology
Daily ConversationUniversity Students
C11
150 sentences
What it's like to have schizophrenia
Daily ConversationUniversity Students
Vocabulary and speaking notes for this lesson
This speaking lesson is built on the video “CNV analysis with Daniel Howrigan”. The speaker keeps coming back to these words: cnv, file, test, deletion, plink. This video has 196 sentences and 2593 words to shadow. The speech runs for 19:53. The speaker talks at a steady 130 words per minute, a comfortable pace for shadowing. Only 77% of the words are among the 3,000 most common in English, so the vocabulary is demanding.
Key vocabulary in this video
15 words from the video worth learning, with pronunciation and meaning:
| Word | Pronunciation | Meaning |
|---|---|---|
| frequency noun | /ˈfɹiːkwənsi/ | The rate of occurrence of anything; the relationship between incidence and time period. |
| variant noun | /ˈvæɹi.ənt/ | Something that is slightly different from a type or norm. |
| burden noun | /ˈbɝdn̩/ | A heavy load. |
| detect verb | /dɪˈtɛkt/ | To discover or find by careful search, examination, or probing. |
| gene noun | /d͡ʒiːn/ | A theoretical unit of heredity of living organisms which may take several values and (in principle) predetermines a precise trait of an organism's form… |
| filter noun | /ˈfɪltɚ/ | A device which separates a suspended, dissolved, or particulate matter from a fluid, solution, or other substance; any device that separates one substance from… |
| segment noun | /ˈsɛɡmɛnt/ | A length of some object. |
| variation noun | /ˌvɛəɹiˈeɪʃn̩/ | The act of varying; a partial change in the form, position, state, or qualities of a thing. |
| overlap verb | /ˌəʊvəˈlæp/ | To extend over and partly cover something. |
| format noun | /ˈfɔːɹ.mæt/ | The layout of a publication or document. |
| output noun | /ˈaʊtpʊt/ | Production; quantity produced, created, or completed. |
| chip noun | /t͡ʃɪp/ | A small piece broken from a larger piece of solid material. |
| structural adjective | /ˈstɹʌk(t͡)ʃəɹəl/ | Of, relating to, or having structure. |
| exclude verb | /ɪksˈkluːd/ | To bar (someone or something) from entering; to keep out. |
| akin adjective | /ʌˈkɪn/ | Of persons, of the same kin; related by blood. |
Phrasal verbs you will hear
| Word | Pronunciation | Meaning |
|---|---|---|
| get out verb | To leave or escape. | |
| break down verb | To stop functioning. | |
| build up verb | To erect; to construct. | |
| go away verb | To depart or leave a place. | |
| go back verb | To return to a place or state after having been there at a previous time. | |
| make up verb | /ˌmeɪk ˈʌp/ | To constitute, to compose. |
| move off verb | To start moving. | |
| pull out verb | To withdraw; especially of military forces; to retreat. |
Sentences worth repeating
Short, complete lines from the video that you can reuse in everyday conversation:
- I've listed a few here.
- How do we detect it using genotype data?
- I've also put down some sites.
Grammar in this video
The structures the speaker uses most, with the exact words from the video:
| Structure | In the video |
|---|---|
| Present perfect have/has + past participle — a past action that still matters now | I've listed · you've run · have changed |
| Passive voice be + past participle — the focus is on what happens, not who does it | isn't changed · is given · are not forced |
Pronunciation to watch
The speaker uses 35 contractions and reduced forms, such as I've, I'm, don't. Say them the short way, as you hear them.
- The “sh” and “zh” sounds: variation /ˌvɛəɹiˈeɪʃn̩/, structural /ˈstɹʌk(t͡)ʃəɹəl/, consideration /kənˌsɪd.əˈɹeɪ.ʃən/, capture /ˈkæp.(t)ʃɚ/, dash /ˈdæʃ/
- Long words — get the stress right: schizophrenia /ˌskɪtsəˈfɹiniə/, consideration /kənˌsɪd.əˈɹeɪ.ʃən/, sensitivity /ˌsɛn.səˈtɪv.ə.ti/, mechanism /ˈmɛk.ə.nɪ.zəm/, subsequently /ˈsʌb.sɪ.kwənt.li/
How to practise with this video
- Listen to the whole video once without speaking and note the words you do not know.
- Shadow it sentence by sentence at normal speed, repeating each one until your rhythm matches the speaker.
- Record yourself and compare with the original, paying attention to words like frequency, variant, burden.
What is the Shadowing Technique?
Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.