¿Qué es la enfermedad de los riñones? (What is kidney disease?) - Mapping the Epigenetic Basis of Kidney Disease - Katalin Susztak

¿Qué es la enfermedad de los riñones? (What is kidney disease?)




Narradora: Un médico responde
a la pregunta de un paciente.
¿Qué es la enfermedad crónica
de los riñones, doctora?
La enfermedad crónica
de los riñones
es una enfermedad
que es silenciosa.
Quiere decir que los pacientes
no se sienten diferente,
no tiene usted forma de decir:
"Ah, me están fallando
los riñones".
La enfermedad crónica
de los riñones se diagnostica
a través de pruebas de la sangre
y de la orina,
pero usted tiene que ir a su
médico para hacer esas pruebas.
Cuando los riñones
no funcionan bien,
esto puede suceder
por varias razones.
Si su presión arterial
no está bien controlada
usted puede tener enfermedad
crónica de los riñones.
Si usted tiene diabetes y no
está bien controlada su diabetes
también puede usted desarrollar
enfermedad crónica
de los riñones.
Pero en realidad
no importa la causa,
el resultado de esta enfermedad
crónica de los riñones
es que sus riñones
dejan de filtrar
o limpiar su sangre
de los desechos
y dejan de hacer
las funciones importantes,
y usted se va a sentir mal.
Pero algo que debo comentarle
es que aunque es silenciosa
y aunque no tiene cura,
se puede detener, o se puede diagnosticar
temprano
y le podemos ayudar.

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Mapping the Epigenetic Basis of Kidney Disease - Katalin Susztak




Katalin Susztak:
Thank you. So, thank the organizers for inviting
me, and I am fully aware of the fact that
my talk which is going to be half-an-hour
exactly is between you and getting lunch,
and I know it’s been a long session, so
I want to tell a little bit about the work
that we have been doing over the last five
years. My lab was part of the Roadmap Project,
so if you’ve been working on this just a
way of an introduction. I’m actually a physician
scientist, just like the first speaker, so
I am a nephrologist. So, what I do on a daily
basis, I have patients who are on dialysis,
so we have about half a million patients in
the United States. And they spend an excessive
amount of time over there, so it’s four
hours three times a week, and it’s not the
best to have it.
So, in general just a way of introduction
to the kidney, the kidney is basically a,
you know, a [unintelligible] organ, right,
and on a microscopic basis it consists of
-- I think you can see this -- so it consists
of the structure which is called glomerulus,
where you just basically filter your blood.
It’s actually you filter a lot, so it’s
about 100 cc per minute. So, you filter about
one coffee every two minutes, and then you
-- as you probably noticed you don’t pee
out 10 buckets, actually 18 buckets of water
every day, so that’s because you have these
long and convoluted and different parts of
a tubal system which basically reabsorbs the
water and electrolytes, and then there’s
some form of a secretion, so the function
of the kidney is measured by the filtering
function of this glomerulus, and this is a
fibroid kidney, so in kidney disease that
we study and of course advanced adrenal disease
is basically you get this scarring of the
organ where you lose the epithelial cells
and then the glomerulus as well. So, the function
is measured how much you filter and nephrologists
have really simple people so you filter 100
cc per minute. You know we like that round
number 100. That’s having measured it, so
I know you -- many of the people have this
notion that, well why to care about kidney
disease? You have dialysis and transportation.
Indeed we do have it, but I just want to tell
you that if you have end-stage kidney disease
and you are on dialysis, you have about 20,
25 percent chance of living through five years,
and that’s just a little bit better than
getting lung cancer of AML and then it’s
actually largely worse than many of the common
cancers, and just a way of putting renal cancer
on this bar -- on this graph as well, so actually
the survivor of [unintelligible] cancer is
slightly better than being on dialysis, so
it’s not a trivial problem, and also it
costs about $30 billion a year which is actually
10 percent of the Medicare budget despite
these patients actually I think consists of
only 1 percent of the total population of
it. So, it’s quite costly. You do better
if you get a transplant, but very few people
are able to get a transplant.
So, why do people develop kidney disease and
how can we solve it, and that’s what my
lab is trying to understand. So, as Nancy
Cox kind of introduced us. It’s a complex
trait. We have a contribution -- a genetic
contribution and then we have these numbers
for hereditability and you can see that this
reaches .3 to .7. Right now, we believe the
hereditability of GFR amount in Europeans
is somewhere around .3 to .7 comes in for
African Americans for end-stage kidney disease,
and I’m going to show an example of what
could explain that actually very high hereditability,
and then a bunch of environmental factors,
aging. That’s why most people -- it contributes
very strongly for kidney disease development,
diabetes and smoking. And then, here you are
with kidney disease.
So, how to understand the genetics of kidney
disease; we have GWAS and I think people have
kind of talked about this quite extensively.
This is the data for the most updated GWAS
paper from CKDGen that my lab collaborates
quite significantly and there is a new one
in the pipeline. This one has about 67,000
participants in it, and the new one is going
to have about -- more than 100,000 cases of
European descent and what you see here that
some of the low side of these come out to
be significance and then we were able to increase
the significance and I actually don’t know
how many on this part -- this graph, but right
now we have about 67 curated loci that we
work on that has shown reproducible association
in people, the European descent in chronic
kidney disease development.
I will talk a little bit about this top locus
over here on chromosome 16, and as you know,
you know we all love geneticists -- we already
gave a name, so we don’t have nothing to
do with -- after that they know what the genes
that cause kidney disease indeed as it was
explained in the very beginning. We really
don’t know whether these are the actual
genes that underlie their association or causes
related to disease development. So, as for
many other traits, for kidney disease also
these SNPs are in the non-coding area of the
genome, so 80 percent are non-coding and then
we have the questions that have been discussed
before that; how do these SNPs actually lead
to kidney disease development? So we would
just like to know which one is the causal
SNP, which one is the target cell type. Really
because I’m a cell biologist mostly so we
really would like to know the target genes,
and then maybe the mode of this regulation
would not be as bad as well.
So, what my lab -- so this is the framework,
the way I -- we think about it and then I
think many of the people in the [unintelligible]
thinks about this of how we could understand
and make sense of these GWAS. So, we think
that this causal variance somehow localized
the regulatory region and disease relevance
cell type. I’m going to give data and there
are papers from John Stem and Brad Bernstein
also looking at the kidney associated traits
that we believe that actually these cell types
somewhere localized in the kidney. It’s
not really an [unintelligible] phenotype.
That’s what we talked about already as well.
So, the variant should alter the target gene,
especially in this disease relevant cell type
via most likely altering transcription factor
binding although we could maybe accept other
mechanisms.
What we add to this is that we believe that
the target expression -- the target should
be expressed in the kidney, and then we also
think that the target expression should change
in disease states, and then we would like
to have a correlation how the genotype and
the disease states changes the target expression,
so if the risk allele increases the target
expression, we hope that we find the same
kind of correlation if you look at samples
from patients with chronic kidney disease.
And obviously the target expression should
somehow cause kidney disease and therefore
should be functional, so I will go through
a couple of examples, so the first one is
that this should be localized in the regulatory
region in the kidney.
So, to understand that, my lab physically
started to develop this fairly large kidney
bank, so we have more than 1,000 samples at
the moment, 1,200 on the last count and then
what we have here is slightly similar for
other GWAS data, so this is actually updated
with clinical data in real time, so mostly
these are collected for unaffected part of
tumor nephrectomies and those patients disease
-- kidney disease incidences fairly high,
20 percent of them and since the common condition
is called kidney disease is diabetes, hypertension
so these are actually quite highly prevalent
conditions in people who are getting nephrectomies,
who are, you know, the usual 58 year old males
or females, and what we have built in is this
data is updated itself, so we have not just
the static clinical update, but it updates
over the years as -- so we have information
for functional decline.
We have done a fairly detailed histopathological
examination, which is not just like whether
you have a disease or you don’t have disease,
but we use many parameters that are -- we
hope to use as maybe as endophenotypes as
we score different things that people under
the microscopes can score off of the differentiation
of epithelial cells, the scarring, the inflammatory
cells, and so on just by visually looking,
so we have large efforts to do transcriptome
analysis, and I think we are about 500 samples
that we have done already. And because I’ve
told you that there are two different segments
in the kidney, one is this glomerulus which
is the filter and the tubules that kind of
process the filtrate, so these -- we micro-dissect
all sample to glomeruli and tubules. We have
epigenome analysis, mostly methylation, and
we are working on what I will show later to
isolate different cell types out of the kidney
and make ChIP-seq base chromatin annotation
for them and then we have genotype all the
samples that we have processed using biobank
because it is much cheaper and then obviously
we tried to integrate all of that together
to figure out what’s causing kidney disease,
so the causal variance should be somewhere
in the kidney, so to do that we get this kind
of organ transplant of kidneys where we use
just the kidney cortex itself or we separate
different cell types out of it, and using
the end-code based chromatin -- I mean ChIP-seq
marks, the H3K27 acetylation and then K4 monomethylation
as an enhancer marks and K4 trimethylation
as promoters and K36 for methylation is as
transcribed regions of two annotate regions
in different cell types.
So, now if you look at the SNPs, so we could
look at in the kidney, so this is just a so-called
adult kidney of what you find is -- what we
find is -- and that’s fairly similar. What’s
published is that a large percentage of the
SNPs of the six or seven of the locus I’d
actually localized the enhancers, so this
actually -- there are several ways to do this
-- this is mapping just the leading SNP that
is published in the paper, and then we can
kind of enhance this to about 65 percent if
you take all the tagging SNPs in the LD block
and then you accept that if one of the LDs
actually in an enhancer, then you call it
as an enhancer, but not more than that for
the kidney, and that’s -- there is a significant
enrichment if you compare it to like a H1
stem cell and the fibroblast and this is actually
ENCODE data, and then we looked at multiple
ENCODE cell type, so indicating that kidney
disease associated polymorphisms are localized
to enhance the region in the kidney.
So now we can do a little bit better than
that because we have now these multiple cell
types that we make out of the kidney, and
then we make the maps for these cell types
as well, and then we can also say that this
is actually not just somewhere in the kidney,
but maybe in some enrichment. Although, I
would take this with a grain of salt, but
you see an enrichment that it is somewhere
in the tubule epithelial from all the places
when we compare it to other cell types that’s
in the kidney of glomerular epithelial cells
and epithelial fibroblasts in mesangial cells,
that seems to be the cell type where we see
kind of more clustering of these CKD associated
polymorphisms. So, that’s very nice, but
that computational, and then obviously my
lab is very interested in the mechanism, so
we have to actually do the hard work so we
have to screen through these enhancers and
then show that they are actually localized
and then to act as a regulatory region in
the kidney, so to do that, we actually use
the zebra fish system and this very nice reporter
system where you have an mCherry flying by
two Tol2 sites, and then you can do large-scale
cloning into it which we got via [unintelligible]
Fisher who has helped us quite a bit. So we
clone all these, so computative [sic] enhancers
over here and then we use a fish where we
have -- it’s a transgenic fish where we
labeled the tubule, so the zebra fish has
actually just one filter by two little tubes
on the side, so we label this with green and
therefore if the clone in the mCherry, we
could see that whether it’s in the -- you
could screen very efficiently whether you
see that.
So, here it is in real life, so this is the
tube which is green and this is the mCherry
of this -- this is actually that chromosome
16 locus which we are working on dissecting
which had the highest peak on the GWAS and
then we are dissecting into multiple regions,
and you see that that actually localizes again
to the tubules, so the histone-based attestation
and now a validation coincides that both of
them -- this region, somewhere in this region
is able to drive expression to the kidney,
so it’s a kidney specific regulatory element.
So, that’s very nice. The question is obviously
which -- because we are somewhat biology based
is, what are the target genes of these variants,
so this is nice that it’s in regulatory
region, but you know, what are the target
transcripts? And to do that we toyed a little
bit with in vitro transfectional luciferase
[unintelligible] looking at them that many
of these genes actually are putative targets
and not expressed in these cell lines that
we can easily transfect, so we mostly use,
looking at -- working through -- using eQTLs
which have been introduced before, so basically
you’re looking at the genetic variations
and the transcript expression, and then so
we have -- because we have a lot of kidneys
that are genotype and we have transcript level
data, then we can use now a kidney specific
data to annotate the variance, so depending
on the genotype, you see variation in gene
expression.
So, this is a result -- so this is 100 of
the kidneys that we have because this is more
of a homogeneous CU decent. We feel that’s
important and then you find, you know, large
number of so called E genes that are genes
that are SNPs that are associated with transcript
level changes in the kidney, so just to probably
-- I should have introduced that, that some
had the kidneys left out of all these big
efforts, so GTEx is not very good at collecting
kidneys and that big science paper that just
came out, they had three kidneys. Although,
I have to say that they made a major conclusion
out of it that I’m not 100 percent sure,
and I think kidney is being transplanted,
so it’s hard to collect them, so I think
it’s a quite useful, unique resource, and
also in Roadmap John and Brad Bernstein had
some kidney data here and there, but it really
was not well represented even in the Roadmap
data and it’s not really part of really
ENCODE, so maybe in a way of advertising should
be included and so I feel that these efforts
are actually quite important.
So, we have a number of E genes which is quite
consistent of what GTEx is finding and that
many of them are -- seem to be quote, quote,
shared genes, but one-third of these is shared
what’s not published in GTEx, so this is
the CCQ2 [unintelligible], so with 100 samples
we cannot really do trans, so this is the
SNP location, this is the transcript location
and each spot is represented here if that
SNP is significantly regulate the target gene
expression, and in real life it looks like
this. This is I think one of the best eQTL-plus
that we have, so this particular variant which
could be C/C, or C/T, and T/T, and then you
see that this solid carriers, you know, the
tubules are mainly -- you know, express high
number of salt carriers, because that’s
what it’s function; it has to reabsorb salt
and water, and you see that this variance
has a very nice strong effect on the transcript
level of this particular salt carrier.
And then this is another one. I showed this
because this being proposed by the CKDGen
consortium. And they did functional studies
indicating that this variance actually influences
the level of this gene. They did not have
eQTL data in the paper; what they did is they
did a morpholino-based knock-down of this
gene and that showed a phenotype, but indeed
looking at the eQTL now, this affect is not
as great as this one. I guarantee you, but
there is an association between the genotype
of this and the target gene of this, and that
seems to validate what is inside there.
So doing this obviously you can see very small
fraction of overlap with the CKD GWAS hits
and what you could do you could obviously
you can just look at the GWAS SNPs whether
you can find an association for any type of
target gene. So to be very transparent, right
now I think we have three or four where we
have good statistical significance and then
hopefully we will have more maybe by dissection,
or other matters that we are doing. Just in
a way of introducing, indeed these E SNPs
are enriched and they are more an enhancer
and specifically this is an overlap of the
tubal cell line H3K4 monomethylation and the
E SNP location and this is -- E SNP is out
control SNPs, and you see an enrichment, and
that is not there if you use other type of
regulatory marks and then actually this is
also not there if you are looking at other
cell types, and thinking so this glomerulus
epithelial cells and mesangial cells, so again
somehow indicating that the tubal epithelial
cells may be the important cell type for the
kidney and [unintelligible] development.
So, I’m going to show you an example of
that. So, this is that [unintelligible] chromosome
16 and what you see is this is the SNPs that
are showing the highest significance and then
these are the genes under here similarly that
have been shown previously by the other speakers,
and well, you probably saw the first plot
on the disk. It is something called UMOD.
UMOD has a urinary gene, has a name urine
in it. So, it has something to do with the
kidney, so that’s why this spot is actually
-- was labeled with a big sign UMOD in the
kidney and that’s believed to be -- this
SNP is actually -- seems to increase the expression
of this gene by some studies, and what we
know that the gene expression actually decreases
in disease development. So, the SNP should
increase the expression of this gene, but
in disease the gene expression goes down.
So, if we look at this locus again because
now we have eQTL data, but you see it is actually
quite broader, so there are couple of other
genes around it as well.
So, this is the locus again, so these are
the SNPs here. This is that UMOD. These are
the other genes over here, and then here is
how the eQTL looks. So, this is the transcript
expression of the UMOD genes. There is a little
trend for increased expression, what has been
described in the literature, but it didn’t
reach statistical significance in our data.
Then you’re looking at the next gene over
here, which is actually a gene family, ACSM,
something to do with acyl-CoA medium-chain.
I really -- it’s not really well annotated
in the literature, but there are five of them,
and they are right here together. And this
one did not show a change, but this one if
you look at it, there is a very nice change
between the genotype and an expression of
this gene and actually there are -- PKM values
for this gene is fairly decent showing as
an E gene. This one did not, and this one
again shows some association and here is not
as nice as for this one, and expression of
this gene is actually much lower, so indicating
that for us when we look at this SNP, it was
associated that this gene as a target gene,
now, maybe one gene away is where we find
the significant effect on gene expression.
So, we included two additional cordelia that
the target should be expressed in the disease-relevant
tissue in the kidney so this is actually an
Illumina body data RNA-seq data, and what
you see is the expression of these genes of
that area in the kidney. What you see is this
gene UMOD that’s proposed to be -- is highly
expressed, but our target is also fairly nicely
expressed in the kidney. Maybe some expression
in the liver, but it’s indeed it is very
nicely expressed, and then if you look at
the protein expression, indeed, again, it’s
fairly nicely expressed in the kidney as well.
Now, we also added that target expression
should change in kidney disease development.
So, because we have a 1,000 samples, we can
actually look at the correlation of the gene
and kidney function because that’s a kidney
function [unintelligible] changes, so going
from 100 to zero, you still see that there
is quite nice R square and correlation, and
then that’s not just RNA expression, but
you can pick random samples from the top and
on the bottom and then the protein expression
correlate with disease development as well.
So, alteration of the target can cause kidney
disease, so the target should be functional
in the kidney. So, for this again we use the
zebra fish system, and the morpholino knock-down.
So, as I discussed the function of the kidney
is to get rid of salt and water. If the kidney
doesn’t function, you don’t get rid of
salt and water and that’s represented in
the fish as having an edema, so they puff
up and then they have a lot of -- it’s probably
called [unintelligible], so they have salt
and water in excess. And, that’s what you
see if you knock down the orthologue of this
Acsm gene in zebra fish. So, in kind of -- and
that’s kind of the proposed function of
this Acsm is something to do with acyl-CoA
and fatty acid metabolism, somewhere not much
known in the literature.
So, in conclusion, so we have this Roadmap
to understand GWAS associated hit. I think
human tissue samples and especially large
number of human tissue samples are really
critical to get to this; we used the epigenome
maps to identify regulatory regions, model
organisms to validate the causal variance,
eQTL maps for target gene identification,
and then we look at -- in addition to that
we also look at the correlation of the genes,
the kidney function because we feel that should
also be present, and then use model organisms,
and the zebra fish seems to be a fairly quick
screening tool to figure this out, and then
I showed you this out of the three that we
have as a hit, but mainly this is limited
by the eQTLs because right now these identify,
I think just very few variance with significant
affect because our sample size is small. And
a couple of other issues that’s -- so that
-- and the gene; maybe that has to do something
with fatty acid metabolism.
I don’t know how I am about time, but I
have a few other things that I wanted to share,
so I will go through that quickly. So, you
know that the SNPs actually explain 2 percent
of the hereditability and then we have about
30 to 70 percent, so what about the others?
So these variants you know explain very little.
So, where is the missing hereditability, and
then there are several things to think about
this: more samples, deeper sequencing, ethnic
groups, and epigenetics. I will show you an
example for two of these. One is I think is
absolutely tangential to the meeting, but
I think it’s a beautiful example of genetics,
so I cannot skip that, so -- and that’s
about different ethnic groups. So, the first
slide that I showed you GWAS was Europeans
and then you have the 67 regions, each of
them adding together maybe explaining 2 percent
of hereditability. Now, if you do the same,
a mixture study in a black population for
kidney disease, you get this one and only
beautiful, big hit on chromosome 22, one hit,
and that turns out to be a variant, a coding
region variance in a gene called ApoL1, so
that’s very, very rare for any kind of complex
trait, and that turns out to be that there
was, as evolutionary pressure to maintain
that coding region variant because that variant
protects people from trypanosomiasis, which
is the African sleeping sickness.
So, I guess shows similarities to malaria
and sickle cell, so this is the same exact
story. The heterozygote form of this variant
protects you from trypanosome and then this
is the lysis of the trypanosome by this G1
variant, but if you have two copies of this
variant, you get kidney disease and then [unintelligible]
ratios for kidney disease is not insignificant,
go from two to 100x and if you actually get
HIV on top of getting this variance, it’s
almost like sure to develop this disease with
this two alleles. So, just in a way of that,
so we -- my lab contributed to this by making
a mouse model for the variant, and indeed
if we produce variants into specific cell
type in the kidney which is these glomerulus
epithelial cells, you get disease development.
So, indicating that indeed this coding region
variance is disease causing, so that’s one
way of finding those rare variants with large
affect size going into a different population,
but as part of the roadmap for five years
we were looking at whether epigenetic differences
could explain this missing heritability. So,
this is actually -- just this part of my talk
is pretty much published so if we looked at
samples of 100 micro-dissected human patient
samples, kidney samples with different conditions
of kidney disease, and then this is what [unintelligible]
dissected, and then we looked at changes in
this tubular epithelial cells that we micro-dissected
from patients samples of 100 kidneys, and
with the genome via methylation analysis using
a method -- I would say it’s a -- something
like an MRE-Chi like a methylation-sensitive
-- [unintelligible] digestion was developed
by John Greally at Einstein, and of course
this Illumina 40 to 50 arrays. And what we
find is that indeed you can identify this
epigenetic changes in healthy and disease
kidneys that are able to cluster normal and
disease samples quite nicely and separately,
and if you look at validation cohort, again,
you’ll see that these methylation differences
cluster and different in control samples and
disease samples, but I just would like to
show some of the other things.
So, we got fantastic P values with even fairly
small samples, but what you see is the difference
in methylation differences in absolute values
scale is small, so what you see in kidney
disease, and I think I see that in multiple
other disease conditions. There are changes,
there are very consistent changes; we can
replicate it in different samples the same
changes, but the absolute difference in methylation
level is fairly small, unlike in cancer when
you can see a difference going from zero methylation
to 100 percent methylation, these methylation
differences are small and of course the future
should tell whether they are actually significant
going through that route. We looked at whether
these methylation differences are randomly
distributed to the genome or they are maybe
on promoters. There is a lot of data on promoter
methylation differences influencing gene expression,
but when we looked at by [unintelligible]
mapping, these differentiated mapping regions
were depleted on promoter regions. We could
hardly find any [unintelligible] difference
in a promoter, and when we looked at by [unintelligible]
mapping, these differentially methylated regions
were depleted on promoter regions. We could
hardly find any methylation difference in
a promoter, and when we looked at -- by ChIP-seq
base annotation where they are, they were
actually on enhancers, and they were on kidney
specific enhancers when we were able to -- we
looked at the nine ENCODE cell lines again.
So, these are small differences on enhancers;
therefore, we could look at with that they
could potentially influence transcription
factor binding, so we looked at the same computational
analysis, and we find that they influence
several transcription factors. One of them
was for example, SIX2, and we found a bunch
of others, and then I -- very few of them
are nephrologists, probably, in the audience,
but this is actually a very important kidney
development or transcription factor, so is
these two others. So, it seems that there
was some sort of an enrichment on these enhancers
that they can computationally bind kidney
specific developmental transcription factors
over here.
Now, looking at the other way of whether these
differential methylation is actually functional,
we looked at gene expression by mapping them
to the nearby genes, and indeed we find correlation
between differential methylations and transcript
level differences, so maybe these differential
methylations actually drive gene expression,
and if they drive gene expression maybe there
are of course important in disease development,
so we have some of like -- about 40 percent
of them were correlating with gene expression
and this is going to be my last slide. And,
they were also again enriched for developmental
processes. The same you find it when you do
enhancers for H3K4 monomethylation; again,
they are in enriched for developmental processes.
So, that correlates with some of the data
and the literature that kidney disease maybe
developmentally programmed. This is a slide
I borrowed from Francine Einstein from Einstein,
so if you feed rats on a controlled diets
and look at the pups, versus if you feed rats
in a calorie restricted diet, then you look
at these pups and you see is that these pup
with a calorie restricted diet developed one
measure of kidney disease which [unintelligible]
in there and that correlates the differences
in their epigenome and cytosine methylation
levels, indicating that maybe indeed they
are programmed somewhere early on.
So, this second set of conclusion is that
you find small, but highly consistent cytosine
methylation changes in kidney disease. Tubal
samples they’re isolated, the methylation
changes are enriched on kidney specific enhancers,
and then they are enriched on fibrosis and
developmental genes are affected more commonly
and maybe that’s consistent that somehow
this kidney disease has some sort of developmental
origin which is being proposed in the literature
in the past, and I would like to say that
most of the work has been done by really talented
graduate students, Yi-An Ko; she will be here
tomorrow, and Huigang Yi, who is an informatics
person in the lab, and the second half of
the project is published and that was part
of this Roadmap epigenomics project and we
have lots of collaborators who helped us with
the GWAS studies or eQTL analysis and many
of the other work we have been doing. Thanks
so much.
[applause]
[end of transcript]

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