Lawrence Brody: All right, concept clearance.
My brother will be on in a second.
So I don't know if Rudy mentioned this when I was preparing this, I think it was Jeff
who pointed out, you're actually all deputized, and this is going to be a concept clearance
that is not just for NHGRI, but it's for 12 other institutes.
And so I wrote this thinking that we're speaking to 12 instituters here.
As you'll see in a second, one of the main reasons for that is because they're providing
almost all the funding.
And so I also have done several contract concept clearances before, and they are very focused
really on the conceptual level, so I tried to capture the three concepts that we're essentially
looking at here.
I would have maybe added apple pie in mine at the bottom, but you see this is the concepts
that we're talking about, and this is a renewal of an existing program, but this is what the
contract -- are the concepts that underline the contract.
So, should we have a large central facility that can do things very efficiently and for
many different projects?
Should the facility be structured in a way that it can retain ultimate flexibility and
change as the technology changes?
And is it also a good idea to have flexibility that if we don't need it, we don't put the
money into it?
And so those are the key elements of how this contract is set up.
I just want to highlight what came up earlier today, that I’m a big fan of intra-institute
programs, I think it's because of all these advantages, shared expertise, governance resources,
you can pool research projects, and you really get economies of scale.
It's not something that NIH does a lot of, it's doing more of it now than when I first
got here.
So we're not 27 little islands as we used to be.
This is an example of an island that has 13 different members pooling together.
Another way to think of it is that we're kind of running a co-op.
And for those of you who are more Midwest oriented, you recognize what a grain co-op
does, it allows people to pull their truck up there full of the raw material, and dump
it in, and the grain co-op actually manufactures it into the refined flour with defined characteristics,
defined QC, and ready to go into products that we all eat.
And in some ways this is what this program does, it fills that middle ground instead
of having investigators learn how to do pheonotyping, learn how to get samples of genotyping, or
sequencing, or data analysis, and then figure out what the answer is, we're filling that
middle layer for them.
Just moving into some of the historical data, on the center for the program.
So it was founded in 1996, Bob had a major hand along with Frances in setting this up,
it morphed a few times in the early years, but the model that I'm going to be talking
about now is the one that has been operating probably since late 1996.
It's currently supported by 13 different institutes.
As I mentioned in the written material we lost one institute, we gained one institute
relatively recently.
And it's based in -- the current program is based in a building in Baltimore, an NIH building
that's been leased on the Bay View campus.
This is what the program currently offers, and I'm talking about this historically because
what we're voting on today is what'll be going on in the future and that's a little bit more
fuzzy.
Currently, they offer SNP genotyping, including custom genotyping, there's a bunch of focus
content panels -- I didn’t put them all on this list that are available.
A lot of genome one association studies [unintelligible] have passed through this program, and also
next gen sequencing right now they services offered are targeted exome whole genome -- both
deep whole genome and low pass whole genome.
And we also have a part of a program that offers statistical analysis, mostly data cleaning,
data preparation for depositing into public databases, not that much final analysis as
to, “Here's what your data told you.”
That's usually what the investigators do with the data once they get it.
This program has a lot of oversight.
There's a board of governors, representatives from each of the institutions has a member
on the board, three of them are institute directors now I think.
So the institute directors themselves are part of the board of governors, there's a
CIDR access committee, which is peer review panel that looks over projects before they
come into CIDR, that meets six times a year.
I meet with the laboratory staff every other week and lots of phone calls at odd hours
when things happened as well as a contracting officer’s representative.
This is my better half who does all the paperwork.
Because this is a contract, there's a lot of paperwork, so the two of us actually oversee
that area.
The NIH budget office plays a role in moving the money around and we actually are administratively
overseen by the contracting office that's run by NHLBI, and they make sure that we do
everything legal.
Or at least right at the edge of that -- shouldn't say that in public.
Here are some trends over just the last couple of years.
The number of projects that have passed through this center and, Bob I don't remember, what
year are you?
Was it --
Robert Nussbaum: 2006.
Lawrence Brody: 2006.
So this is the post-Bob era, I guess this is my era, [laughs] where projects has gone
up.
The cost per genotype has gone way down, as most of you know.
I put this in parenthesis because I really think that the cost per genotype should be
going down more than it is, and that's because there's not enough competition in the GWAS
setting.
We really should have a $70 GWAS chip that's universally available instead of a couple
hundred dollar GWAS chip.
We potentially, in the next incarnation, of our institute can play a little bit of a role
in maybe pushing that.
Size of the projects have gone up and up as I'll show you in a second.
Full-time staff has actually gone down, so this program is able to do more with less,
fewer people.
The administrative costs for a brief while were going up until we screamed and kicked
and said, you can't do this this way, and then they went down.
And sorry, that's the NIH administrative costs, not the cost of the program contractor.
Just to give you an idea of some metrics for the program, this is the samples completed
per fiscal year going back to 1998.
And for those of you who can't see it, the top of this scale is 180,000.
Relevant here is in the last two years, completed samples mean that the data is done and has
been released through the investigator.
There's been a couple hundred thousand samples go out the door.
Sites.
I pulled last night I pulled a list of where all the projects have come from different
investigators.
And Amy, I know people from Texas are sensitive about this, Texas has one, for some reason
it, the program didn't put the Dot on Texas.
So I just did --
[inaudible commentary]
[laughter]
Lawrence Brody: I didn't do all the international sites, except
for Toronto, because it happened to be on this map.
But there are international sites as well.
Many of these projects are contortion projects, or multicenter projects.
We have projects where the samples involved three or four primary investigators, but came
from 25 different sites.
All to Baltimore.
Because one come from a hundred sites, so that one actually really scared me and it's
been going really smoothly so far.
Number of projects that have been posted in dbGAP.
So I just showed you, this is by institute at the bottom, the big institutes have more,
NEI was an early adopter of GWAS studies, they have a fair bit.
I highlight this, and it's in the written material just to point out that about a third
of all GWAS studies in dbGAP came through this program.
And probably more than a third of all the sequencing studies in dbGAP at this point
probably came through here, but that's because that hasn't really hit critical mass yet,
and that will be coming in from elsewhere.
This is to highlight that what the program does is it creates an automatic pipeline for
data sharing.
And it takes the burden for the most part, except for some institutional certifications,
off the investigator lots of times.
They can actually as part of their project say, “Please share my data.”
The program interacts directly with NCBI and we have a data cleaning center, cleans the
data so at least it is immediately in there and lets the investigator focus on analyzing
their data and not doing all the paperwork.
Just to give you a snapshot, just over the summer there -- active projects in the lab,
there's about 100,000 sample sitting in the lab right now.
Over the summer, about 30,000 of those were in data production.
That means that at any given time people could have been touching up to 30,000 samples.
That probably represents about seven or eight different projects, so as you might guess
this is a program that relies on automation, computer tracking, limb systems, and all the
sophisticated ways to make sure that everything is working well.
Going forward, I think you might guess correctly that we expect array-based analysis will go
down.
They are not gone down nearly as fast as I had thought, and we're still cranking out
lots of arrays, customer arrays, and GWAS study arrays.
And of course the expectation is that sequence-based projects as the sequencing prices come down
to where it's feasible to do FADMlogic [spelled phonetically] sized studies that the number
of sample per year that go through this program that are sequenced will go up quite a bit.
So just starting, we're probably at this part of the ramp right now.
I want to end with some parameters of the program as we see it going forward you will
see that they are frustratingly broad and vague, and that's because this is not a lump
sum payment, this is -- you're voting on a concept to set up a program that has capacity
and the more capacity is used, the more funding will go into the program, if it's not used
it won't go into the program.
So it will be a research development contract.
It's what we call in the business an indefinite delivery-indefinite quantity type structure,
which means the contract doesn't say we're going to buy X amount for the next five years,
what we're buying is capacity.
The term is not yet decided, but the last couple have been five to seven years.
The funds, if no one uses it, could be as little at 10 million over five to eight years,
or even less, or if lots of people use it the capacity could handle about 150 million
over that particular interval.
If you're wondering what it costs NIHGRI, it's essentially nothing.
In the checkbook writing stage there's some personnel time, mine and John Garvy [spelled
phonetically], but really we managed this and we catalyze it for the rest of the institutes
that want to join in.
We're anticipating that it's an open competition.
We'll have contact solicitation, we'll set up a review, and then it makes sense for something
that's going to be highly efficient and centralized that there's going to be one award.
I didn't mention in all this how the [unintelligible], I know that some of you are familiars, I know
some of you have had projects that have passed through from CIDR, the investigator who has
the project has the project reviewed and then very much like Phil mentioned earlier, they're
given a credit.
They're not given cash, they're given access, and then the institute pays directly into
the contract.
So if you're at an institution and you want to use CIDR and you get approval to do so,
it doesn't cost the investigator anything directly.
And so that's how this mechanisms works.
So it was interesting to hear Phil talk about that same model for data analysis.
That's all.
I know that we've been a long day, and my brother has to get in and talk, so that’s
all I have.
I'm happy to open it up for as much discussion as we need, or additional comments.
[inaudible commentary]
Howard McLeod: Could you comment on comparing what's done
here with putting this out to the companies that are offering this -- how comparable is
our -- for, obviously that's changed a lot in the last five years.
Lawrence Brody: Yeah.
I can tell you because we have to justify that according to competition for every single
task order.
And so I can tell you on just pricing we're about comparable.
Where we blow the commercial things away is in quality, and in charging, in that we don't
charge, this program, I say we imbedded in this.
PRIM does not charge for samples that don't produce data.
So if you really don't, if you send us a hundred samples, and you get 98 that have data, you
only pay for 98.
So we have economies of scale that way.
There's also all these other ancillary services like the data cleaning and the data QC, I
didn't mention, there's a lot of details I didn't mention today, a sample comes in the
door it's DNA finger printed, so when it goes out the back end we know it's the right sample.
Those kinds of layers of quality control you don't get with the commercial companies.
So we really do push -- and I really push the quality and the economy at the same time.
We are not competitive, and I’ll just -- in whole genome sequencing at this point because
the sequencing capacity is smallish at this stage.
So...
Eric Boerwinkle: So your mom and apple pie slide, I think it's
still valid today, so I really think there is a need for such a resource.
Particularly for what you called smaller institutes, that's a good thing.
But my question is more technical.
In 2015-16, is it really worth several hundred dollars for a GWAS kit and not getting an
exome on the sample set?
And when is that pendulum going to shift that you, you being the science, is better off
spending a little bit more and getting rid of the rare variance spectrum in those specialized
sample sets as opposed to another GWAS array chip that is really SNP discovery on somebody
else's sample?
Lawrence Brody: Right.
And we, so I'll give you the short answer is that we do both.
And there are projects that combine both.
The longer answer is, has to do with what you just heard for a lot of these other concepts,
which are advancing technology and advancing the state of the science, this program is
a little bit behind what we call the bleeding edge, and so those are informative, and what's
going on in the field is informative for what we'll offer in the future, but the real kicker
point would be if arrays stay where they are, then they'll just go away, and we'll just
do more sequencing.
If we can drive arrays down to be really, really cheap as a first pass, there may be
ways to combine arrays and sequencing in an economically feasible way.
I don't think we're there yet, because sequencing is still too expensive, and our arrays are
still not where they should be.
Eric Boerwinkle: But I'm sort of surprised on you menu of things
to do driving sequencing costs down further doesn’t seem to be coming up, it just seems
like the institute could do a lot more to help pressure us, the community and the vendors,
to drive sequencing costs down.
Lawrence Brody: We have the ability to do that via volume
for established things.
Where we don't have the ability to do it is push, within the program.
The rest of this institute has the ability to push it and the technology, you're just
talking competitively.
Yeah.
Rudy Pozzatti: No further discussion?
Can I have a motion to prove concept?
Second?
All in favor?
Any opposed?
And abstentions?
Okay.
Thank you.
To you, Larry.
[end of transcript]