Eric Green: Well, good afternoon everyone.
Let me start off by welcoming you to the Francis Collins genomics reunion.
[laughter]
Actually it's wonderful to -- Francis' eyes lit up when he walked in the door, seeing
all these colleagues that he's been working with for so many years.
But more seriously, I wanted to give a little more specificity on what we're here to do
at this trans-NIH workshop, which has been affectionately referred to as the data aggregation
workshop, but has a more formal title that's shown here and on the materials that you were
given in advance.
And really, Francis crafted this with a series of issues and questions, but really the challenges
are the ones that I'm going to at least initially focus on.
I think the major challenge at hand relates to data access and in so many ways it's because
we're victims of our own success.
What we been so successful at?
Well, we've become remarkably successful at data production, in particular DNA sequencing,
genome sequencing.
But I think we've also been highly successful at one of our, basically achieving one of
our core values of open data release and data access.
But that's a tough combination.
If you're generating tons of data and you want everybody to access it and you want everybody
releasing it, that then creates a situation where, if you don't have the systems infrastructure
in place for everybody to be able to do something with all of that, then you have a mismatch.
And the mismatch that I hear about all the time, Francis hears about all the time, is
how we are in a situation of being data rich but analysis poor.
And so what we have heard about in various venues, whether we'd go out and travel and
talk to investigators, we go to meetings and hear this, or we hear this from advisory groups
and so forth, is that there is great opportunities here but the current systems just are cumbersome
and we're achieving little.
We are just too successful at a point but not having a streamlined process and system
in place that are going to allow us to achieve the full ability of having data available
for lots of people to be able to analyze in the most effective and efficient way possible.
So, ideas that have come up and ideas that will be discussed in the next day and a half
or so, of course, range from things as building a research commons, where individuals with
appropriate approvals or certification will access all the datasets all at once.
That's one possibility.
Other possibilities would be centralized servers or data servers that would provide results
and summaries to individual investigators but not necessarily the underlying data.
And these different models will be discussed and debated and have various advantages and
disadvantages.
But the major challenge I think we're here to tackle has to do with data access.
I think there are other challenges we want to contemplate, obviously there's issues that
-- approaches to variant calling, and clearly if we want to have pre-analyzed data available,
we're going to have to harmonize our thinking around what are the approaches we are going
to use for variant calling.
Speaking of harmonization, there's going to be lots of discussion here, we expect, about
harmonizing phenotypic and environmental data across different studies.
This is going to be a huge set of issues of great complexity.
We're going to have figure out what we're going to be able to do realistically and practically,
and the kinds of initiatives that one might launch coming out of a workshop like this.
And of course we're going to have major computing discussions.
Just computing on very large datasets.
This is just one component of sort of the classic big data problem that's being discussed
in various ways, in various settings, in and around and outside of NIH.
So, these sets of challenges and yet the exciting science that can be done that Francis framed
are what led us to this workshop.
So, let me pause for a minute and just say a few things about this workshop.
The first thing I want to point out is that this is not an NHGRI workshop.
This is a trans-NIH workshop.
We were asked to organize it, perhaps for obvious reasons, but I don't want to think
for a minute that we should be taking all the credit for it; by any means we shouldn't.
The reason Francis is here and the reason we were asked to do this is because there
was a crying need for this across the NIH because, as Francis mentioned, virtually every
institute and center generating data of this type, or else our supporting researchers who
desperately want to utilize data of this type.
And that is the reason why Francis provided funds for us to have this and also asked NHGRI
along with an organizing committee to put it together.
Specifically, Mike Boehnke and Wylie Burke are the co-chairs and we thank them for their
willingness to co-chair this workshop, but David Altshuler and Paul Flicek had a very
important input to this and we thank them as well.
There are 10 institutes and centers that have sent staff and are involved in this.
Lisa Brooks and Adam Felsenfeld have done the micro-organizing in many ways necessary
for this workshop.
Teri Manolio dealt some early planning that led to this idea of having this workshop and
then handed much of this over to Lisa and Adam for actually planning the actual workshop.
But then here I list various other people from NIH institutes, including NHGRI and others,
that have been involved in this and we're very appreciative.
But you can quickly see this is not just an NHGRI problem; it is truly a trans-NIH problem.
And also let me just quickly thank, in terms of logistics, Nicholas Clemm from the NHGRI
staff, and also Saundra Bromberg from Capital Consulting, who helped deal with all the logistics
associated with it.
This workshop involves all of you, it's an all-star cast.
I'm very appreciative and impressed with the people that we've got to come here, in some
cases from far distances and long trips, Ewan tells me since 4:00 this morning U.K. time
to make it.
There is about 47 non-NIH people, about 40 NIH people who'll be popping in.
And we deliberately aimed to get diverse expertise, so among the kind of expertise that we wanted
to include, ELSI and policy; and obviously genomics and functional data; disease studies,
GWAS, and cohorts; database, computation, data analysis.
But also relevant to this workshop, that a meeting we were at last week in Boston, drug
development, individuals with that expertise.
And we've even included scientific publication; in other words, journal editors are here to
help us because they play a role in some of this as well.
So, thanks again to all the participants and all of these individuals.
What are the goals?
What do we expect to try to accomplish?
We want to discuss the scientific questions that analyses across these datasets could
address.
Francis gave us a few examples; there'll be others.
Discuss the challenges to obtaining and analyzing across many datasets.
We can see the motivation to do that.
Discuss the options for dealing with these challenges, including costs and trade-offs.
We want practical ideas.
We want some sense of scale of what we might be contemplating doing.
This is not a thought experiment, I mean it could partially be a thought experiment -- so
this is actually something very practical.
We could envision putting together a trans-NIH program to make this happen, this to be defined,
we're going to have to have some sense of scale of what that's going to look like, because
the practical reality is this funding will have to be identified to make this a reality.
And then, of course, we want you to recommend steps to address these challenges, again,
including some practical realities of what the scale might look like, what the phasing
might look like, and therefore, we'll figure out how we might go about getting the money
to make it a reality.
What are the questions we are asking you to consider?
How should we deal with data already collected, whether it's retrospective data, and then
how should we maybe going forward, deal with data that is to be collected, prospective,
and what I'd imagine we do something different if we know how it's going to feed into some
centralized system or some way of aggregating the data going forward.
What options are the highest priorities?
We can't do everything at once; we are going to have to prioritize.
What are the cost-benefit trade-offs?
Again, we're going to have to think about this.
Money will be limiting but we also want you to think audaciously to some extent so that
we can think big and then we have the scale it back as needed.
What can be done now and what would require changes in policies or other work to be able
to implement?
So again, that's where the phasing and staging will come in.
And we also want to emphasize that we can't necessarily expect a 100 percent perfect solution
for all of these very difficult problems, Even 90 percent solutions would be progress
and would really open up the field in ways that Francis described and I think others
of you can imagine.
So again we're looking for realistic practical solutions, they don't have to be the perfect
permanent solution, but while a lot certainly could be done to make those tens of thousands
of genome sequences more readily accessible and analyzable to a larger fraction of the
research community.
So with that, I just want to end by offering you the ability to ask any questions either
of Francis, on behalf of NIH, or me specifically either about what NHGRI is doing in this or
about this workshop before we start really getting into the data at hand which I'll ask
Adam Felsenfeld to come up and discuss.
Lincoln.
Lincoln Stein: This is -- the answer I'm sure is embedded
in the title but I just want to confirm that we're not going to spin off into proteomic
and metabolomic, other bolin [spelled phonetically] scale sets in this resource, or is that -- are
other bolin scale sets on the table?
Eric Green: So, I'll answer this first.
I think the initial focus, an initial focus should be around genome sequencing data and
associated data for genetic studies that would include potentially phenotypic data and so
forth.
I wouldn't necessarily -- I could imagine that could be the first foundation for going
beyond that.
I think -- my personal opinion is that if you try to bite all that off at once, we would
break under our own weight.
My own personal opinion.
Lincoln Stein: What about epigenetics?
Eric Green: I think -- well --
Lincoln Stein: [inaudible]
Eric Green: Epigenetics --
Lincoln Stein: Haplocytes?
Francis Collins: To the extent that you want to broaden to
phenotypes and environmental exposures, and you happen to have that kind of data on the
same individuals, where you have whole-genome and whole-exome sequence and clinical information,
it seems like it would be unfortunate not to figure out how to link it up, but I think
the primary driver here is DNA.
Eric Green: And epigenetics would be included in that.
Francis Collins: If, well --
Eric Green: Genetic marks --
Francis Collins: [unintelligible]
Eric Green: Okay.
Francis Collins: Sure.
[laughter]
But, you know, pretty soon you've got a lot of data there.
If you're not just talking about methylation but all those histone marks and transcription
factor binding sites, and -- whew [spelled phonetically].
Eric Green: Other questions?
Debbie [spelled phonetically].
Female Speaker: If you layered the DNA down, which is what
you're suggesting, and then resources were provided over time at the NIH level, with
RFAs, et cetera, to put more and more data down and in the database -- I mean, starting
with the DNA like you're saying is good, but thinking at the end perhaps of the group,
how to get more datasets integrated into the data that would have full access would be
the way to, I think, look at that question.
Francis Collins: I like that way of putting it, because clearly
we don't want to encourage establishment of the database that is all, sort of, self-enclosed
and cannot be layered on top of with additional information as it comes forth, because this
ought to be a growing entity.
Eric Green.
The staging and phasing part was I was saying.
Anyone else?
Okay, yeah, Aravinda.
Aravinda Chakravarti: It's not so much, you know, a question but
I wanted both of you to sort of emphasize the following: you said at the end that 90
percent of going there would be great.
Since I envision many of the problems that we'll have to solve, technical things aside,
will be cultural, about people really being comfortable about sharing data and other matters,
I would think even getting half the way would be great.
Eric Green: Yeah.
Aravinda Chakravarti: And so if we could use that as an operating
principle of what we can get done by the end of whatever year we count, say in one year's
time, two years' time, three years' time, I think that could be tremendously helpful
in people getting confidence that this thing is working towards this goal.
Eric Green: What I would -- I mean, I could have just
as easily said less than perfect solutions would be wonderful, I mean, that was what
the less than 90 percent -- I mean, because you're right.
It could even be a 70 percent solution for some of these issues would be wonderful.
Francis Collins: But 1 percent would not be too good.
Eric Green [laughs]
Male Speaker: [inaudible]
[laughter]
Eric Green: Yes, Debbie.
Female Speaker: I just think that focusing on what would be
of the greatest utility to the NIH community as a whole, for example: I get approached
every day for controls for exome sequencing, right?
If we could focus down on sets that we could -- I don't want to put a percentage on this,
but if we could focus down on sets that would be eminently usable by every member of the
community, including myself, it would be great and I think that it should be a goal.
Eric Green: And I completely agree with you.
I think that Adam will start to paint that picture to some extent, maybe not, but I think
he will.
Only because, when you start to hear about data that is absolutely being generated and,
you know, individuals like you and lots of individuals say, "Wow, if all that was in
the same place it could be a controlled dataset for the things I'm doing," and so forth.
So, absolutely.
Female speaker: Well, people approach me all the time and
expect that I can provide these controls and that they're out there to be got, but putting
together many different studies where they've generated control datasets that could be used
by other people as controls because they have phenotypes was the kind of thing I was thinking
about.
Eric Green: And what I can tell you is, in the past year
and a half, NHGRI has had multiple workshops on multiple topics where that same request
comes up, and it could some of the most basic science topics or the most clinically applied
-- they all say the same thing: Where are these controls?
Where are they centralized?
Can we access them?
Why is it not all in one place?
Yes, David.
David Cox: So, Eric, one of the things that you put on
your slide that I thought was really important was making a distinction between retrospective
and going forward.
You know, five years from now isn't that far away and that we're in a very different world
than we were even five years before.
So, I would just make the plea that we don't confuse those two things because that we may
be quite limited in some of the things that we have for retrospectively but that -- not
to -- in five years, if we have those same limitations, it's our fault.
Eric Green: No, I completely agree and the two numbers
that, to be keeping in mind, I mean, there's a lot of data that has been generated and
we'll spend some time figuring out how do we get our arms around it and put it back
in.
But that has different connotations then the, all the data that's going to be generated
in the next year or two based on the surveys we've done of institutes and grants and so
forth, and maybe those are going to have slightly different solutions in the near term.
Okay.
Well, thank you all, and I will turn this over to Adam.