>> Okay. Welcome back. I hope everybody is properly caffeinated and
snacked. We will take it to the next step. So I am going to present sort of the phase
3, you heard from Jim Mullikin, some of the nuts and bolts how the DNA goes into the instruments,
how data, raw data are generated and sort of early analyzed. You heard from Jamie about intermediate analysis. And some user tools that people can use to
begin to manipulate the data. So what I want to present next are some general
considerations.
I come at this from the perspective of a practicing
medical clinical human geneticist. And I think about what kind of projects ought
to go into this pipeline and how I'm going to look at the data. So I'll give you some examples of how those
things work. What every is asking themselves, what can
I do with these tools that are so widely available and becoming so inexpensive, which of my patient
should I bring to be sequenced using this technology and which of the samples that I
have previously col
lected might be appropriate for that. And there's lots to choose from and a lot
of accordances that you need to take into account to decide which projects you want
to use and how you will analyze them to answer the questions that you want to approach. there's good news and bad here here as always. Exomes are not magic. They can work wonders and do thing absolutely
not possible before. You can do things with small families that
you could not do with traditional clones in the past. That's certainl
y an incredibly powerful option
with exomes. You can if you will ununstick stuck cloning
projectsch basically in the old days if you land in a huge regeneral and you western in
the mood to sequence 3 or 400 genes using a capillary strum you can approach that with
exomes. De novo dominant disorders were completely
non-tractable using positional cloning because there was no mapping to be done. And other circumstances I'll talk about. I this summer attended the Gordon conference
of genetics and gen
omics and there was discussion about how often does exome work for Mendelian
disease projects. The consensus from a number of the speakers
an presenters that the meeting which is a bunch of people doing good work in this area
is it works about 30 to 40% of the time. So it's not magic. There are reasons why your project may not
work. So hopefully this course and my talk will
help you figure out which ones are more likely to work so we can increase that. We're subject to seeing publication bias. N
ot too many people want to publish unsuccessful
exome gene identification projects. So we don't get to see those, don't get to
see what can go wrong but I'll try to give you some background to think about that. Lots of reasons for that to happen. Another thing to think about too, it is not
necessarily enough days to do a gene identification by exome sequencing to get yourself a good
publication out of your efforts. So coupling your exome disease gene identification
to additional genetic and func
tional data should help that a lot. I'll give you some examples of that. So whey I'm going to talk about in this talk
is from a perspective of a practicing physician who wants to understand what's going on in
a patient, what is exome and what is not because you have to think about that and exome is
not the whole genome and what are those differences. The differences of exome sequencing when compared
to positional cloning they each have strengths and weaknesses. And then some sort of how toes, I'
m going
to give examples of five projects, some from our lab, some from other people's labs, to
give a flavor of different types of projects, recessive dominant sporadic de novo and mosaic
case. Okay. So what is a whole exome sequence? This is somewhat of an unformat label because
it is not all that whole. What it includes are the, it's supposed to
include the definition of an exome is the sequence of all exons in the genome. There's problems here. First is that we don't currently know what
all
the genes are in the genome. The second is we do not necessarily know of
all the axons of the genes we recognize to be there. Non-coding exons are not consistently targeted. Some kits and approaches target those, some
do not. Not all the axons that you do target are effectively
captured. If you do target it, not all the sequences
that you generate can be aligned and accurate bases can be called from them. And that leaves you with a fraction of the
exome that you're actually interrogating. Things
missing from the exome sequence, some
genes are just not there at all. You'll see an example how that tripped up
some investigators in the study example I'm going to give later. Some parts of some genes may not be there. Almost all the non-genic control elements
that control gene regulation are going to be missing from an exome sequence. Non-canoncal or deep intronnic splicing he
willn'ts that are important areas for mutation in genes maybe missing. Structural DNA assessment, small inversions
o
r movements of pieces of DNA within your genome are very poorly assessed by exome sequence
so if your disease is caused by a mutation of that nature, you're in trouble. Copy number variation is not tractable using
exome sequence. Mitochondrial DNA is not most mostly targeted
but some information on that can be derived from an exome. Some microRNA and other small RNA molecules
are included in exome sequence, some are not F you think your disease or trait can be caused
by one of those DNA elements
you have to think hard whether exome is your right approach
or whether you shall go to whole genome as Jim discussed in the first talk. To set up the pros ab cons an strengths and
weaknesses of exon sequencing versus positional cloning. In the second slide with exome sequencing
you can get away, of using very small families, and sometimes single individuals to identify
potentially causative mutations. Those of you know who practice positional
cloning it is essential, it is nearly essential to h
ave large families if you want to do myiotic
mapping which is the first step of a positional cloning project. For exome sequencening general what you want
is locust homogeneity, it's going to dramatically increase the sampleious need to sequence to
find variants that you want to find because in most cases you're going to be looking across
sample for variation in gene shared among the different affected persons or samples. The more loci you have that cause your trait,
the harder it will be to do
that. Locust heterogeneity is not --
when you're doing your myiotic maping you are localizing genes an can tell right away
where your low -- if your family that you're analyzing is at a given locust or not using
linkage inclusion or exclusion. So this is an issue that is tricky for exome
sequence, it can cause difficulties in positional cloning but easily sorted out. Allelic heterogeneity in an exome project
is a great thing. Because the overall sensitivity has been discussed
shown by Jim Mulli
kin, if you have a 90% sensitivity for decking any given part of the exonly,
were it to be that your trait is caused by a single mutation, in a single gene, there
is a 10% chance that no matter how you flog that exome the variant will not be there. If you have locust allelic heterogeneity that
spreads your risk across multiple sequencing targets and increases the chance that you
are going to see it. And for positional cloning allelic heterogeneity
not a big issue. The burden of one in the hand o
ne in the bush
is tricky to parse doing exome sequencingch you're going to be faced by a list of variants
that are the potential cause of the traits that you're studying. Your task at that juncture is to make a decision
whether to pursue a subset of variants that -- those variants identified verses pursuing
any more depth, other variants that that may not have been initially detected or maybe
problematic to detect in your samples. That's a trade-off and a critical decision
juncture you have to c
onsider moving through the process of gene ide ifcation in the pipeline. Positional cloning is not an issue. These are gene candidates most likely all
over the genome. Positional cloning you have a region of the
gene, region of the genome nailed down and you know you need to interrogate just about
everything that's in that region to be thorough which you can't do in an exome sequence. Phenocopies are not a big you shall shoe in
exome sequencing as you'll see in examples that you'll show, patient
whose have a appear
to have the phenotype but don't have it for the underlying genetic reason that you think
is the cause. Phenocopy can be a huge issue in mapping because
it can allow you to exclude or include mappings in your region. There are pros an cons and you knee to think
ab your project, phenotype or trait and see how it it fits in here and how to approach
it. A lot of talk also at the -- Gordon conference
about type 1 error. The dispair Rajing term thrown around at the
meeting is exom
e sequencing or whole genome sequencing as the potential of generating
what are called just so stories. This is based on the Kipling book where fanciful
stories that are circular reasoning that mean nothing can be beautifully drawn up and become
pleatly wrong. That's really a manifestation of type 1 error. When you're interrogating 20,000 candidate
genes for a trait the potential that any one variant that you find of being the cause,
the prior probability of that variant being the cause is small
. And can be led down a guarded path if you
don't do studies to but tress and validate the findings and carry them forward. Without myiotic mapping which decreases your
type 1 statistical error problem without myiotic mapping you're going to need additional sources
of evidence for causation. In fact I'll show how you can fold mapping
in linkage data later to support and reduce the probably of that error. First example of the five traits I'm going
to talk about today. First because it's easiest t
o do because it's
the smallest amount of the genome to interrogate, an X linked disorder, doesn't so much matter
what the trait is but the general nature is important because the character again of your
trait will determine what filters you want to apply and how you think about the results
of that filtering that is the result of that sequencing project. This is an X linked trait that causes severe
congenital anomalies, 1 % lethality which helped a lot in the filtering. Other than fact it was ult
ra rare and only
2 known families to be affected with this trait allows you to do good powerful things
with your filtering with controls as Jamie described. In this particular case you have to go to
sample availability quality an quantity of the DNA that you have and turned out we didn't
have use DNA on the boys for exome so we sequenced the characters. What do these numbers look like? For X here are raw starting number of what
the initial data onslaught will look lake thisyou need to deal with.
So the exome capture in quote because we have
a hard time from reviewers who thug thought we shouldn't call it an exome at all because
it's the exon of sang chromosome. All the exons on X that are not in the pseudoautosomal
regions of the X chromosome and genes are defined as the UCSC annotated coding axons
so right there again you see a decrement of interrogation from everything on X, starting
to notch our way downwards. The sequences or two samples were about 20
million reads off the aLumina
instrument. Two third or three quarters of a billion base
pairs of sequence. Of that sequence, just under half aligned
back to the intended sequencing target of the experiment. Low but good because the coding region is
a small fraction of the DNA that went into that experiment so incredibly strong enrichment
so half aligned to those targeted exons with high coverage and as Jim mentioned with that
wide dynamic range of coverage you see in exome sequencing you need to go deep to ensure
you have lo
t of bases covered by 10X more sequencing because of the tail on the left
side. We had about two million base pairs of 10X
coverage sequence which is in that case 77%. I think the hit rate would be higher now with
new versions of this kit. This is old technology, 18 months old. Thing change so fast here. Just as a note, I mentioned yes sequencing
carrier females so you can use an autosomal base caller for calling the variant but if
you do a X link project for males you need a difference caller b
ecause the algorithm
is important for carrying variants in heterozygous males so the filler look like, it was base
odden a number of criteria, the biology an geneticses of the trait. since sequencing carrier females we said it
should be heterozygous. We knew that the trait was severe so we could
set a relatively high filtering threshold for the kinds of predictedded changes in the
genome. We didn't mild missense variation so we looked
for non-synonymous intels which frame shift the open reading
frame nonsense an those types
of variants an splice should be on here also that's a typo. Again because this trait was so rare we could
implement relatively stringent filters for presence of the punitive variant in controls. And we started out here with two samples,
with 350 substitutions and this is already excludes a number of changes and thousands
of variants have already been excluded by implementing some of these filters. And the heterozygous non-synonymous absent
in DB SNP as yeah any ment
ioned is a dangerous thing to do, you have to be careful about
that. More to say about that in a few slides. We sequence concurrently patients who had
other disorders. We excluded variants in those samples as well. What you can see here is that this worked
exceedingly well. So this was an easy project. Family one had no substitution variants that
net all the filters and it had one variant that was a frame shifting intel that did lead
to filters. Family 2 had a single variant that had no
variants
that were frame shifting in the exons on the X chromosome. Turned out this hit and this hit were in the
same gene. So that's a nice tidy result. This was an example of what I was talking
about earlier with a stuck project. This was a project that had fat in the laboratory
-- sat in the laboratory for five or six years because it was mapped to a 25 to 30 megabase
region of the X chromosome, that included 2 or 300 candidate genes. We just didn't have the wherewithal to sequence
those using PCR an
d 3130 sequencing instrument. So with that, until the X exome came and we
applied that tool and this worked. Now, this was a fortunate occurrence because
this simple stringent filter that we implemented took us right to varying in a single gene. You could say what if that wasn't the case
and we western quite so lucky what could you do? This is an example of what I was referring
to earlier, you can use myiotic or linkage or haplotype data to exclude regions of the
genome to reduce the number of v
ariants you have to consider. If you use mapping data from a family like
this, that took us to a region of the X chromosome that eliminated between 60 and 80% of the
variants from consideration because they were excluded by linkage alone. Important thing here, it does not require
those high thresholds that we used to apply to ourselves for ABANICIO linkage mapping,
a log score of 3.0 for autosomal locust or 2.0 for X locust. Whatever linkage you can extract from the
family you have will help eli
minate variants. Any amount of cranking that down if first
filters done give you a hint can be very, very helpful for you in pushing variant out
of consideration. Again, but you have to be careful and the
old cry Teer we used to use in positional cloning as to whether you should allow a single
recombination to exclude a region or two, still apply because you can erroneously push
things in and out of consideration. Again, if your disorder has fee know copies
or ambiguous affection assignment of t
he patients you're studying. This particular case because this was a gene
of known function with no available the only thing we could look for for supportive evidence
was expression so we took this to some colleagues for mouse expression analysis and cloned out
the mouse gene hybridize that to showed -- embryos and showed that wasn't expressed in the tissues
one predict based on phenotype and humans which was nice, not the strongest supporting
evidence but if you're on the X chromosome, the prob
ability of a type 1 error is much
less likely so not quite as robust of an evidence that is needed. Next to autosomal recessive. This is a case we worked on with a colleague,
chuck VENDETI in our institute. This is a severe childhood onset metabolic
acidOSIS disorder. Chuck's group did work to he can Chrud other
loci that may have been other known loci that cause a phenotype. This was a cohort of patients all known causes
had been excluded and this was an exome project where we sequenced only th
ree samples in affected
child and his two parents. So this single trio, led us to variants. This is autosomal traits now looking across
the genome and this gives you the kind of numbers that Jim approximate Jamie both started
with, 100,000 to 150,000 range for variants. And then we started implementing filters to
crank the number down because obviously this is completely intractable so we could exclude
a number of variants based on the most probably genotype based calling that Jim described. The
n we use the zygosity status of the samples
to exclude variants, the parents should have at least two. And the type of mutation that we want, this
again, is something that you will need to play with with your projects. And generally what we recommend is starting
on a relatively stringent level to see what you can fine and if you don't fine what you're
looking for you start to relax criteria one at a time backing down your stringent to let
more variants come through to see if that shows what you'
re looking for. We did a risky thing at the outset excluded
variants that were indeed SNP an excluded as well variants homozygous an controls and
that's in our set of adults which are presumably normal for this trait and you can see how
much trouble in a few moneys that can get you into. Or we exclued allele frequency greater than
10% reasoning that the carrier rate couldn't be near that high if it's a rare recessive
disorder. That led us with a dozen genes that the affectedded
that two variant
this is that gene and those were present one in each parent. Here is the list of genes. Then Jennifer Johnson in the group looked
manually at this list and curated those an discovered that ACSF-3 was a gene predicted
to have a mitochondrial leader sequence and was a good candidate for that reason. I want to mention here, we also had to but
tress the genetic data, in this case but had seven unrelated affecteds, four general tick
confirmation of variance in the gene. It's always a question when yo
u start the
outset of this project how many samples go into the exome pipeline versus how many issues
hold back for manual genotyping PCR 3130 or capillary sequencing post hock. That's a decision that you can make based
on cost accordances because Eomes are expensive but don't forget technician an post doc time
is also a very expensive commodity as well so balancing that and the right number of
exomes versus follow-up work is an important accordance. Whether and how to use DB SNP. As Jamie said,
DB SNP a helpful potentially
dangerous tool to use, it is a repository of genetic variation that is completely independent
of any relationship with a variant to any particular disease. Individual variants in DB SNP can be pathologic
and some variants are going into DB SNP from disease studies an even from clinical pathology
labs. The cohort are in those studies maybe very,
very difference from what you think they might be. We had a situation where we were annotating
genomes for cardiac conducti
on defects and found over 2% of a sample set had the same
variant in their samples. When we dug deep into the data it became clear
that was a sample side of patients recruited for a pharmaceutical trial for cardiac conduction
defects. Be careful what ewe use and can be tedious
to dig down to to that level so we rely on our own internal control sets because owe
know them better. So your cognitive DB SNP and filtering is
it ra active so you can try some -- iterative so you can trial DB SNP early o
n but don't
hesitate the back off on that filtering early if you done find what you want and use cut
offs that are conservative. With generally shoot 5 to 10 times the estimated
frequency of the disorder as a flesh hold saying if the frequency of any single variant
is above that it's unlikely to be causative. Remember cystic fibrosis, a good example,
cystic fibrosis has a allelic -- peculiar allelic distribution, 70% of alleles are a
single allele. So you have to that population carefully so
you
wouldn't exclude a varyian based on commonality because it might have caused the variant. Okay. So we used our twin seek control set to me
the carrier frequency identified in the study and you can see here these are the raw data,
this is exported from my's program into a excel spreadsheet. You can see homozygous reference, heterozygous
an hetero zy non-reference. You can notice in the control set we had a
patient pop up who was homozygous for a variant. this was a variant found and thought to b
e
pathologic in the patients with the methodologic acid disorder. We looked at this patient carefully, turns
outs she was symptomatic and when we evaluated her biochemically she has this disease. So you have to be very careful, your controls,
your control set may have the disease, this is one of the powers of exome and genome studies. You can find phenotypes that you didn't know
you ought to be looking for in who is affected and who is not could be challenged by these
data. So chuck's group went
to do functional studies,
enzymeATIC studies, localization studies that was the cause of this disorder. Next dominant. I gave an example of two papers that appeared
recently on a rare disorder with skeletal dysplasia and other anomalies, thought to
be a great Joe project because it has associated with it severe osteoporosis and it was hopeful
that they were hopeful it would be generalizable to more common forms of osteoporosis. This is ultra rare. And autosomal dominant and institute relatively
general filters, and they had several cases that were de novos. So this is another group that you essentially
the same processes as concluding adjuvant capture and aLumina sequencing.
3 to 4 giga bases per sample was generated. They use one simplex case, only one affected
in the family and one multi-plex family probe band so three samples went in the sequencing
instrument. The filtering criteria relatively similar,
non-synonymous nonsense slice an insertion deletions and they again instituted a
relatively
stringent DB SNP filter. As well as thousand genomes an controls. Their disorder was so rare it should appear
and it was dominant, should appear zero times in any of these control samples. They came out with all three cases, the only
gene that was mutated all three cases was not too. They went to but that with genetic data and
Saenger sequenced them and 11 of 12 had same mutation for the genes. Seven that were simplex cases and they had
two parents confirmed at de novo using annual s
equencing, an interestingly enough this paper
appeared in nature genetics and was published with biofunctional data because of its high
priority as a skeletal phenotype. Sporadic de novo disorders, this is a genetic
pattern essentially completely intrackable with positional cloning. The first one done was KABUKI syndrome or
makeup syndrome, some people call this disorder. Again, Dismore if I can syndrome, skeletal
phenotypes an intellectual disability, quite rare, most are simplex or apparently
de novo. Lightly different approach on (indiscernible). As well they did a strategy that you might
not think, they did not exploit the de novo aspect of this disorder. What they did instead is sequence across a
number of affected probands to see if they can find a varyian in that way. Reading between the the lines I'm guessing
they're concerned about the error rate of the platform leading to too many false positives
for potential demo s. Selection platform which works quite well
in addition to o
ther ones you have heard today, was a -- on array selection approach followed
by a sequencing relatively lower depths that we use, only to 40X coverage map of the regions,
this is a good example, I applaud the author, they publish a filtering strategy that they
use that did not work. I thought it was illustrative. What they ask from their ten pro bands, any
N number of those ten, how many of them shared variants in genes with the given criteria,
here is non-synonymous, slicing and nonsense varia
nts, DB SNP in genomes absence in their
own controls and absent in both data sets. So for single gene variants you can see they
have 7,000 across those specimens. Present in two or more, variants in the same
gene in two or more individual, narrowed it down and successfully 3, 4, 5, 6, 7, 8, 9. As they went across an down, the numbers go
down because it's more and more fillerring. And that was a single variant and this variant
was not the cause of that disease. So a good example of how filtering
can leave
you at a dead end, you have to then take a step back and say what's an alternative strategy,
what are the potential explanations for not finding the gene and how should I do it next. They did another strategy, they went back
and did really careful phenotyping and rank ordered their patients using clinical experts
to rank the patients from the most severe to the least severe reasoning that they should
be filtering based on the best strongest most clear cases. They also implemented here
you notice on the
previous slides no assessment of the functional consequence of the predicted change in genome. And they manual review of those data an rank-ordered
the patients. They identified nonsense variants in this
gene MLL-2, in four of four highest ranked i.e., the most severe cases, then I had a
couple of hits in the lower cases so you can see why they failed with the first fillerring
strategy, which is we're trying to be too inclusive, they had two problem, one, there's
probably -- th
ere is locust heterogeneity for this so if you filter across the variants
you're going to stumble on that. Number 2, if you do not have great coverage
of the genome in the original capture so they had overall 96% coverage but missing a significant
number of exons in this particular gene and when they went back and did manual capillary
sequencing, they found mutations in two of the cases that were missed by the next GEN
sequencing. Then they when and did manual testing on that
candidate gene in 4
3 cases, found mutations in more than half of them and then went back
and did the de novo check for cases of DNA samples of both parents and in 12 of 12 cases
both parents were negative for the mutation confirming that it was de novo. Very let regenius here, your friend is allelic
heterogeneity. There was some locust heterogeneity, one thing
that tripped them up in the earlier filtering. Here is an example, two groups working on
this at the same time. A European group making public the fact they
did not find the causative gene because the exome targeting kit didn't include MLL manufacture
2. If your targeting kit doesn't include your
gene, spend all you want on sequencing and you won't fine a mutation. No functional data were included here. I'll go through briefly a mosaic disorder,
highlight sampling issuesch this is a disorder of asymmetric overgrowth an includes pig men
tear lesions vascular malformations, it's's never familial and has been described in discordant
monozygotic twinsc
h this was matic, in the a germ line disorder which poses some challenges
that explains the clinical observations but then for the sequencing tough take a difference
approach. Almost all is done using peripheral blood
mononuclear DNA samples from affected patients here one not necessary hi want to do that. If a disorder is mosaic you might not want
to sequence any part of the patient. You might sequence an prepare what's affected
with what's unaffected. For the Natalie for us as well there were
a pair of discordant monozygotic twins we had access to and the unaffected twin was
a great control. We sequenced DNA from skin biopsies and surgical
specimens and it was from clinically affected and unaffected areas based on clinical judgment
of those taking care of patients as well as tissues harvested in the operating room with
clinician standing at the surgeon side designated specific tissues being affected or unaffected
and no blood DNA was used in the sequencing study because there was no
hematoty phenotype
known so the affection status of blood couldn't be determined. Pretty similar to the briefius ones though
what tissues were compared again was quite difference so we have the same set, non-synonymous
nonsense splice an intels absent in DB SNP though Jamie showed how that could trip us
up because when TCGA sequenced tumors they found the same variant we did in cancers an
deposited that in DB SNP so that leads to a false negative result if you filter that
out. Most affected unaf
fected intrapatient pairs
we have between 100 an 300 sequences in the exome data, those have to be validated manually,
one in fact persistent. You can see here the distribution of the mutations
in blue, the non-mutation in white, all the patients have the same variant at different
frequencies and different tissues. Look across hundreds of specimens you see
a bias an affected tissues higher mutation Hahn the unaffected and the blood was born
out to be not a fruitful play place to plan lack for th
is mutation. So following up with the patients, 29 of 31
had the same mutation. The mutation was more often found in the affected
tissues than in the unaffected tissues, peripheral blood was not positive and it was absent in
control. It was absent as a call in the thousand genomes
project. You can dig deeper in the thousand genomes
data and ask how many sequences include the variant you're interred in. We did that, we found a single sequence read
of 30,000 at that position in the thousand genome
s in fact had this. But of course that base wasn't called because
that was below the base calling stringency. And functional data to show it was functioning
as predicted by the mutation. Where are we in the big picture? You can see from these five examples for all
different genetic models of Mendelian traits in humans, you can use exome sequencing to
delineate the molecular etiology, it doesn't always work but it can work a lot of the time. And it can be a fruitful way to get projects
moving aga
in. Mall formation syndromes in humans, there's
2 1/2 thousand clinical entities in this textk book, this is an nonmized database for the
syndrome, 4 1/2 thousand entities, few of these have known genetic etiology. Or little known at the natural history how
to diagnose patients so it's a huge challenge in the clinical and basic science perspective
to push through these, find the etiology an sure all of you have other classes of phenotypes
that are hundreds if not thousands more projects that nee
d to be undertaken along these lines. I think as well taking beyond ha we do here
in a research environment, the exome and genome is going to become a clinical diagnostic tool. So I hope as all of you start to work with
these data you think creatively about how to solve these problems and answer these questions
and put these tools out there so that these things can be transformed not just ass sew
tearic research tools but into the clinical so we can improve clinical diagnosis going
forward. So i
n thinking how you do a project you have
to think about the entire picture. All the way from what is the disorder I'm
study, genetic aspects the clinical attributes, phenotypic attributes that allow me to determine
what samples I should sequence. What filters I should apply to the data that
come out of the instrument. The genetic data I need to buttress the results
of the sequence, the nextGEN sequencing instrument and coupling that to functional assays downstream,
that nail down the cause an ef
fect relationship so that we can have robust associations of
genotype in the diseases we're studying and not write a bunch of just so stories. Thank you very much for your time. We have one more talk then we'll take some
questions.
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