[Dr. Daniel Levy] Let me begin, if I can,
by asking Bob a question.
You mentioned in your presentation that good
quality science is made up of several components,
and one that you emphasized, actually three times,
was replication, replication, replication.
As we go from five to 10 to 20 to 50 to 100 genome-wide
association studies, and as the numbers
of individuals and cases and controls and populations
studied gets larger and larger and larger,
it really -- it would be wonderful to have the
tools and the opportunity to conduct in silico replication.
Are we near the point where we can have such tools,
and implement them effectively to allow
for an expensive replication?
[Dr. Robert Hoover] Well, I -- I think that David addressed that a little bit
in his presentation -- and actually Jim's is
relevant here, too, that the -- in fact,
two of the recent publications on prostate cancer used the NCI
results as a -- as another replication, since they were
on the Web.
So I think there -- as more and more
of the actual results of these get posted,
you -- you have an ability to --
to replicate instantaneously and in your findings.
[Dr. Daniel Levy] What about for more global tools,
to allow for replication across trades?
I imagine dgGaP might give some of that capability?
[Dr. Jim Ostell] Well, we'd love to, but we're not sure
how to do it.
We -- we do assume that people will be doing
in silico replications, particularly when there's
access to things like large cohort studies,
like Framingham, because there, there is a large number
of phenotype measures that are available that might be
appropriate to lots of these studies, because they do
physical exams on people.
And so even though the exact measure may not be exactly
the same, since they're typed on large number of SNPs,
since you can impute across platforms,
you could probably certainly find out if you were consistent
or not.
[Dr. Daniel Levy] We have questions from Teri Manolio
and Debbie Nickerson.
[Dr. Teri Manolio] One thing that I think people would find very
useful in these large databases is a -- is a very simple,
very, you know, obvious way to cite them.
When we're, you know, trying to -- trying
to look at these, and then acknowledge
the use of them in papers and other places,
it would be just grand to say, you know,
"To cite this, and you should if you're using the data
at all, here's what you put in your reference list."
Is that available on the CGEMS Web site?
[Dr. Robert Hoover] I mean, people have -- they did, in fact,
reference where they -- where they got the replication,
and they -- they referenced the Web site.
[Dr. Jim Ostell] In -- also to answer that on the dgGaP one,
in the authorized access download, you agree to data
use conditions.
And typically, it's things like, "I'll only study eye
disease," so it's consistent with the consent,
or "I promise not to try to re-identify individuals."
But also usually there's a few sentences in there
which say, "And I will cite this study, not by citing the
database, but by citing the study --
the original PIs."
A different type of citation is when you publish,
having analyzed a lot of this sort of stuff.
Those accession numbers are really for that.
They don't really credit the database,
but it's for tracking the data.
[Dr. Daniel Levy] Debbie?
[Dr. Debbie Nickerson] So I'm curious, Jim, when you take the
phenotype data, will you take only normalized phenotypes,
or will you get raw as well?
Because a lot of these large studies are going
to be a consortium of 10 epidemiological studies
that are out there, looking across maybe
20 to 30,000 people at a time.
The one thing I could think of is CARe.
But those phenotypes will be normalized,
and it's kind of like the difference between a raw
genotype and -- you know, having raw versus processed --
[Dr. Jim Ostell] Well, just like the genotypes, we'd like to have both.
And so, we encourage our submitters to submit both.
When they try to submit only one or the other,
we get back to them and encourage them.
But of course, this is voluntary,
so we can't really -- if they really only
want to submit the derived variable and won't reveal
the -- the raw data, we can't really force it.
I would say, for the most part, most studies are willing
to share it all.
[Dr. Debbie Nickerson] I guess I was just wondering if it's kind of like,
"I'll give you what the sequence is,
but I won't give you the trace."
[Dr. Jim Ostell] Yes.
[Dr. Debbie Nickerson] So you won't know if it's really junky, and whether
the base calls are really true.
[Dr. Jim Ostell] That's exactly right.
So -- that's the problem.
So we have had extensive discussion with some
submitters who didn't want to submit the background
information, and like I said, in most cases it resolved that
they found that they could.
In a few cases, they found they couldn't.
[Dr. Laura Scott] So, I have a follow-up on Debbie's question,
which is there's a lot of accumulated knowledge
that happens when people do a study, right?
And when you go into a new study,
it can sometimes take, I don't know,
a year to really get -- if you're that quick,
to get up to speed on all the nuances.
So is there a place in dbGaP where -- where someone can
say, "These are the things you need to watch out for,"
or how do you help people avoid making mistakes that someone
more familiar with the data might not have made?
[Dr. Jim Ostell] Well, that's a very good question.
The original submitter can supply whatever documentation
or comments they're able or willing to make.
And as you know, though, a lot of these things
are sort of, "Well, something's funny happening here,"
you talked to the PI, they say, "Oh yeah,
well, five years ago we changed exactly how we were doing that,
and now we're doing something else."
And this just has those same problems.
I mean, if it's really still just sitting in the head
of the PI, that's where it is, and -- however, you know,
these are not anonymous, so, you know, the name
of the group that supplied it and the people working on the
project are there, and if someone --
our assumption is that if someone really starts
to get into one of these and finds interesting things,
they're very likely to want to contact the PI
and collaborate, and that seems like a good thing.
In fact, I would guess that the PIs may find themselves with
more collaborators than they want.
As opposed to losing their data, it's going to be the opposite.
[Dr. Laura Scott] Yeah, I think that may be true.
Just another comment into the comments about the
candidate -- the candidate gene SNPs, and the seeming lack
of replication.
And I guess I went into doing this genome-wide association,
and having done candidate gene studies before,
and thinking, you know, the genome-wide is just going
to find so much more.
We're going to learn that this candidate gene approach
really -- really didn't work very well.
And what's amazing is that, especially for traits
like lipids, the things that are coming up at the top are
the known candidate genes,
and that's true also for diabetes.
So I guess I would see almost the glass more
full for candidate genes, maybe not in taking the
whole spectrum, but in terms of actually have been able
to pick out of the whole genome things that were associated was
pretty -- pretty amazing.
[Dr. Robert Hoover] I agree with you, in terms of the --
the diabetes area, and lipids that did
a much better job than the cancer people did.
I'm talking my own view of the cancer perspective.
We've done an absolutely terrible job, including
myself, with candidate genes, and so that's why I'm so
pleased with GWAS results, because we can actually
now start to look at gene environment interactions
for genes that look to be important.
[Dr. Daniel Levy] Bob, if I could return to the theme of replication,
early today, and actually several times today,
we heard about the Wellcome Trust Case Control Consortium,
and it was a real tour de force:
14,000 cases divided among seven diseases,
3,000 controls.
This was a paper that was published
in a top-tier journal.
But I don't remember seeing replication in there.
Does it surprise you that a paper was published
in a top-tier journal using genome-wide association
without replication?
[Dr. Robert Hoover] David, do you have any more -- since -- member of the group.
[Dr. David Hunter] [Inaudible] paper [inaudible] impress --
they published studies already, so this was supposed to be
their [inaudible], and you can look forward to a whole series
of [inaudible] replication --
[Dr. Daniel Levy] Actually, my question wasn't directed at the Wellcome Trust
Consortium, it was a question about journals and criteria
for publication in the absence of replication.
Debbie, you had a comment on it?
[Dr. Debbie Nickerson] Well, just that most of the things that were in that
paper were replicated prior to -- by prior publications.
So it is true that there were some things in there
that were not replicated, but for the most part,
most of the things that were reported had been
reported before.
[Dr. Daniel Levy] Thank you.
Raju Govindaraju has been waiting patiently.
[Dr. Raju Govindaraju] Well, this is for Jim Ostell.
I believe you mentioned that the dbGaP,
what I see from this stage it is geared
for individual -- primarily individual investigators,
so they could apply.
What if, on the other hand, you know, no individual
investigators -- probably except a few -- is clever
enough to look at the multiple aspects of the data,
and then what if multiple investigators get together
as a -- as a group, and then supposing
they want to take this data else --
to different places, is there a mechanism
built in for -- to allow this group to apply,
and then get the data, and then take the data elsewhere,
and then again come back, and then provide the results?
Is there any?
[Dr. Jim Ostell] Well, this is a -- this has been a hard
thing for the NIH policy people to deal with,
in terms of, you know, this broader sharing of --
of individual data like this.
And so the compromise position right now
is even though there's a lower bar for getting at this data
than traditionally was before, the -- each application has
to come from one institution, because what they're looking
for is the validation of an accepted -- it's actually
the institution that's taking responsibility for the
behavior of the investigator, the same as with a grant.
And so a group of collaborate -- you can list collaborators
on the application, but only collaborators
from the same institution, because the signing official
is who is signing off on this.
And if you're going to be collaborating with people
from another institution, they need to also apply,
so that their institution also signs off,
as a separate application.
One of the things you agree to is to not redistribute
the data.
And that essentially -- you're agreeing that
you will hold it, you'll protect it,
you're not going to put it somewhere where people
can take it, or Google can index it.
So it is a little clumsy if you're going to want
to collaborate very flexibly, but this is a big step
forward, and that's sort of the compromise position
for now.
[Dr. Raju Govindaraju] Thank you.
[Dr. Teri Manolio] Just a --
[Dr. Jim Ostell] Teri?
[Dr. Teri Manoli] A quick question, because it is 2:00
and we'll need to move on, but in terms of the --
the replication requirements that initially had been put
in the literature -- and journal editors were really quite,
you know, strong in requiring this -- it became kind of
obvious that sometimes people were sort of splitting their
samples, and doing -- doing other things that may not
have been as scientifically productive and useful.
And so replication -- while it's very important,
there may be other reasons not to,
and that's somewhat covered in the Nature paper that
came out, just the page before the -- the Wellcome Trust Case
Control Consortium.
And there, there really was a group of diseases that had
many things in common that the editors felt was --
was much more important to get published than waiting
for the replications.
Thank you.
[low audio]
[Female Speaker] One more.
[Male Speaker] Quick.
[Female Speaker] Yeah, I just want to say this is all very exciting,
but there is still a bit of trepidation I think several
people feel.
So it's exciting, but it's also scary,
and it -- it has to do with just maintaining the confidentiality
of our study participants,
and, you know, it is conceivable that there
are some situations where a genotype could be used
to infer identity, even if name, and you know,
the traditional identification factors aren't in there.
So I just want to kind of get it out there,
that there still is issue, and -- and I think part
of the issue is as well that there is confusion
about what exactly is going to be required,
as both a data provider and a data user.
So there's still -- I think NIH is coming
up with a policy of some sort, so I wonder if there's any
hint that we could get on kind of what's the guideline
for when data needs to be contributed, and what kind
of data?
Is it everything on the questionnaire?
I mean, you could really go on and on,
what kind of clinical data, so is there any information
we could get on that?
[Dr. Jim Ostell] Well, I hear it's covered in the next two talks,
and I can also say that I -- that policy is being
finalized now, I understand, and they're talking about
releasing it in August, I believe now.
So I don't want to jump the gun,
because a lot -- a lot of those decisions are being made
by people who aren't me --
[laughter]
-- who are lawyers.