Dan Roden: I thank Teri and Geoff for the opportunity
of being the spokesperson for eMERGE.
I'm also one of the eMERGE PIs.
I guess I should have introduced myself that way.
Fourteen minutes, now where's my --
[laughter]
So this is what I'm going to talk about.
I gave myself -- I gave myself this title here.
So, this is the way the Vanderbilt Medical Record room looked like 20 years ago, and
I think if we're going to deliver genomic medicine, it's almost inconceivable that we
can do it in a paper environment.
So, although electronic medical records have -- are controversial, including a story in
today's New York Times, I really deeply believe that creating electronic medical records helps
manage individual patient care, and is enabling for genomic medicine, both in the discovery
realm, as well as in the implementation realm.
Eric showed you these slides, so what I'm going to talk about is eMERGE, and eMERGE
has two domains.
One is the -- listed here, and the second is this more implementation side.
I'm going to talk a little bit about the discovery side, and then a little bit more about the
implementation side of eMERGE.
eMERGE was created in the spring of 2007, the first phase had five centers; they're
listed here.
And our mission really was to assess the utility of DNA collections coupled to electronic medical
records.
And the carrot for us to participate was that each site got to identify a phenotype of interest
in around 3,000 subjects and conduct a GWAS.
We also had a major focus on privacy and de-identification, which I'm not going to talk about; a major
focus on ethical, legal, and social issues, which I'm also not going to touch -- I'm touch
on a little bit later.
So, one of the first things that each site did was identify their subjects, do their
genotyping, do a GWAS, and discover that they had no signal.
They had no signal because they didn't have enough patients.
So one of the things that we did was we went through a very elaborate, laborious process
in order to share data.
And I think that this is an important lesson for all of us who are trying to do genome
science in the discovery or in the implementation phases.
This is what the data-sharing memorandum, I'm understanding, the elements that are listed;
so, each site managed to go to other sites, get other samples that had been genotyped
for cataract, but were useful for diabetes, and expand their sample sets, publish a genome-wide
association study.
We also managed to create datasets across the network that are now quite large.
The way we go about phenotyping -- I thought I'd show this very briefly -- is that we develop
a phenotype, identify a phenotype, we develop algorithms, and manually assess them in a
couple hundred patients.
If the positive predictive value is okay, we say, "Fine, we have an algorithm."
If it's not, we iteratively fix the algorithm.
Once the algorithm is done, we deploy at the site that developed the algorithm, and deploy
other sites, and then do the genome-wide association study.
The important part of showing this is that the algorithms that we develop at one site
that have to be identified or validated at other sites, we've done that.
And this is one of the studies that we did in hypothyroidism.
And you can see that the case and the control positive predictive values are really pretty
acceptable.
I really don't know what was going on at Mayo, but their PPV was a little lower than the
rest.
But this is an algorithm developed at one site, implemented in others.
So I think that's an important lesson as well.
So that resulted in a GWAS with a signal.
And this is a GWAS that was done with no extra genotyping.
Each one of these samples had been genotyped for some other indication to start with.
So we had readily-available datasets that we could use for this application.
It turns out the signal is next to FOXE1, which is a transcription factor that's been
implicated in hypothyroidism, or in thyroid cancer.
So, this made biological sense, and it was replicated and published.
Now I want to take a detour to tell you about a technology that we, in eMERGE, have been
developing that I think is uniquely suited to electronic medical records.
So GWAS starts with a target phenotype and then displays the data in the way that you're
all familiar with.
The phenome-wide association study turns the algorithm on its head.
It says, we're going to start with a target genotype.
Suppose the genotype is that SNP associated with hypothyroidism.
And then ask the question, which -- across electronic medical records with multiple diagnoses,
with what phenotype does that SNP associate?
If the reference -- are they reference or are they variant?
What is the chi squared look like?
And we display the data exactly the same way.
So the requirement to do that experiment is that you have to have a large number of patients
with many diagnoses and lots of genotypes.
So that's uniquely situated or uniquely suited to the eMERGE environment.
So this is what the PheWAS looks like for FOXE1 SNP, and we actually replicated the
hypothyroid and some signal.
And also identified other signals related to thyroiditis, related to pernicious anemia,
which may be clinically related, and related to atrial arrhythmias, which are also part
of the spectrum.
So that's an interesting validation.
We've done that now for other SNPs.
This is one of our best examples, IRF4 SNP that's associated with hair and eye color;
clearly associated with skin cancers and other kinds of skin diseases.
And we've now done this for all the SNPs in the GWAS catalog that Francis showed you,
and it provides a replication set.
It also identifies potential new associations, and all the data are publically displayed
because that's the philosophy that we have.
We generate the data, and then you can go to this website and look up your favorite
gene, your favorite SNP, your favorite odds ratio, and find what we have found.
And along the same lines, the phenotypes that we identify are also just publically displayed
in a knowledge base called the Phenome Knowledge Base, the algorithms that are there for anyone
to try to use.
So, eMERGE then moved into eMERGE Phase II in 2011.
And we expanded the number of sites to include Geisinger and Mount Sinai, and then expanded
to pediatric centers that are shown here.
And our mission really was to expand the electronic phenotyping efforts, but also to start implementation
at site-specific projects and across the network, and then other kinds of initiatives which
I'm not going to discuss in detail.
So, one of the things that is going on right now, following up on the Common Rule issue
that Dr. Collins alluded to, is this issue around consent for biobanking.
And we are now developing questionnaires and developing evidence sets that will enable
a very, very large survey, 100,000 patients across eMERGE institutions to identify policy
issues, really, around biobanking, with input from the actual consumer.
So that's an important network-wide initiative.
Another network-wide initiative -- another example of a network-wide initiative is in
the area of hemochromatosis.
It turns out this is a relatively common variant, and homozygotes are common; these are the
variants that cause hemochromatosis.
And we have a total of about 1,500 of those across the eMERGE sites now, and the questions
we're asking are how many of these patients actually carry the clinical diagnosis?
And among the ones who don't carry the clinical diagnosis, do they have phenotypes that make
it so that they should carry the clinical diagnosis?
We actually have examples at our own center of patients with this diagnosis who have been
transfused, which is really the wrong treatment.
So, we're interested in knowing whether this should be implemented in electronic medical
records more generally.
Each -- there are site-specific genomic medicine implementation projects.
I've divided them up into sort of the broad focus areas: one is developing genetic risk
scores and evaluating their potential clinical impact.
Another set of projects is to genotype specific variants and evaluate the impact on physicians
and patients of returning those results, and you can see the specific projects listed here.
The Geisinger group has a whole genome sequencing effort for undiagnosed mainly neurological
disease.
And then there's a number of sites that have a pharmacogenomics focus, including a focus
on hepatitis C, a focus on CYP2D6 genotyping, which turns out to be the most important enzyme
for drug disposition, and also one of the most problematic in terms of just assay issues.
A project around asthma.
And then at Vanderbilt we've had for a couple of years a large multiplexed preemptive pharmacogenomic
testing project that got some support through this mechanism, and that morphed into a project
that I'm going to talk about right now.
So we, at Vanderbilt, are part of both eMERGE and the Pharmacogenomics Research Network,
another large NIGMS-funded network.
And so this project that I'm going to outline for you is really a collaboration between
the two -- the two networks; we call it eMERGE PGx.
So the things that the Pharmacogenomics Research Network brings to the table are an effort
led by Mary Relling, who's in the audience, around the issue of, if you have genetic data
available to you, what should you do with it?
We don't, in CPIC, the Clinical Pharmacogenetics Implementation Consortium, we don't ask the
question, should people be genotyped?
What we ask is, if you have the genotype information already, because someone has had whole genome
sequencing, or exome sequencing, or targeting genotyping, what should you do with that information?
Those are published guidelines, and there's a very active working group around that.
The Pharmacogenomics Research Network also developed a resequencing platform for 84 what
we called very important pharmacogenes, including CYP2D6 and other CYPs, transporters, and drug
target genes.
And then we've spent a lot of time thinking about how to implement those in a clinical
environment using CLIA and acute quality control standards.
That's what PGRN has focused on.
Obviously, the electronic medical records and genomics network, eMERGE has focused on
issues of privacy or around electronic phenotyping, around generating large populations, and the
issues around decision support.
So, in the eMERGE-PGx project, we have three aims.
The first aim is to identify patients who are going to deploy this PGRN platform.
And we also have an aim of developing a list of actionable variants, and as those of you
who have thought about this problem will recognize, that list is pretty small; might -- may be
less than a dozen.
And then resequence those VIP 84, VIP genes, identify the actionable variants.
The target is to do this in 9,000 subjects across the network.
And then -- and the things that we're focused on mostly in the network are CYP2C19 and Clopidogrel.
Warfarin is a focus, although there's recent data that make people a little nervous about
implementing Warfarin in a routine clinical environment.
There's Simvastatin implementation project, and then there are others that I'll mention
if somebody asks.
To identify target patients, there are two ways to do it.
One way is to recruit new patients; another way is to look at patients who are already
in the biobank.
But we're basically using predictive algorithms that say, what is the likelihood that this
patient, over the next three years or five years, will be exposed to a statin drug?
Or will be exposed to an anticoagulant or anticoagulant drug.
And there are informatics approaches that allow us to identify so-called high-risk patients
and engage them in this eMERGE-PGx project.
So there's newly-recruited subjects, as well as with existing subjects within the biobank.
The second aim, then, is to take the ones who have actionable variants, deposit them
in the electronic medical record, display the data, deploy decision support, and then
track outcomes.
And I'll tell you what the outcomes are in a moment.
But basically there are two kinds of outcomes.
Number one is what we call process outcomes, and that is, how well does this work, how
good is the genotyping, how many times does the decision support fire?
And then the other outcome, of course, is how does that effect peoples' health care?
And then the third aim is to create a repository for all the other variants that we're going
to discover, common and rare, that are not clearly linked to a drug -- a variable drug
response.
And that repository -- the process of being created and has a name, and has a logo, and
that's the most important first step to developing the utility.
[laughter]
So it's SPHINX, and I wish I could remember what SPHINX stands for, but I'm pretty sure
the P stands for Pharmacogenetics.
So the outcomes, I'm not going to list them, but again, they're process outcomes.
How many patients do you recruit?
What does the sequencing look like?
Most centers are actually validating the results of sequencing, of the orthogonal platform,
and what does that look like?
And the others that are listed here.
And then healthcare outcomes, we're thinking about these kinds of outcomes with respect
to specific drugs, as well, among the 84 genes, there are six that have a special designation
from the American College of Medical Genetics as, quote, "actionable," unquote.
So if we find a variant in one of those genes, there is a thought that we should be delivering
those results to patients and physicians, and we're going to have a project around how
to do that and what kind of variants we find.
So this is, I think, my last slide.
This is what eMERGE looks like right now.
And these are the numbers.
And the total is about 350,000 subjects.
There are -- there's genome-wide association data at each of these sites.
And there is an imputed set, and this is a huge project that goes on in the genomics
working group of about 51,000 imputed genomes across these sites.
So we think this is going to be an incredibly rich resource for mining, for discovery.
And I thought I would close with a personal opinion, and that is that if we're going to
personalize medicine, if we're going to treat an individual patient differently from some
mean approach, then we have to have evidence.
And in order to have evidence, you have to have very large numbers.
So I call this the "paradox of personalized medicine."
If you have 100 patients and you want to treat one differently, there's no evidence that
you could ever develop.
If you have 100,000 patients, and you want to treat 1,000 of them differently, then you
can start to develop evidence.
So in order to personalize medicine, in order to individualize therapy for individual patients,
we have to have very large datasets to draw from.
And one of the lessons I think we're learning across eMERGE is that you have to have very
large datasets from multiple ancestries.
And we have some ancestries represented in eMERGE, but not all, so one of the really
great appeals of this particular meeting for me is the notion of creating these kinds of
resources worldwide in order to understand genetic diversity, and in order to personalize
medicine across the world.
So, thank you very much.