[Dr. Teri Manolio] So while people are sort of digesting their coffee,
and that, one of the challenges, Debbie,
that you alluded to, and Wendy as well,
is that these do require very large collaborations,
and publication is sort of the coin of the realm here.
And there is great angst in terms of, you know,
how one gets appropriate credit when you're 103rd author
in a 200 author paper.
And, Debbie, you were involved in several of those from the
HapMap, and can you give some comment as to how the people
who were involved in that, how that has affected their
careers, and --
[Dr. Deborah Nickerson] It hasn't really affected their careers that much because
when they've participated they've been full participants.
So, 100 people worked with them on this,
and they could ask any one of 100 individuals
for a reference letter for promotion, or any other thing,
and they could -- so I think that -- and people know exactly
what they contributed, because it's out in the open,
and it's clear.
Also, people can come in from the outside and say,
"Hey, I have a great idea. I want to try this."
And there's a feeling of, "Oh, this sounds really good."
And people inside say, "Well, let's have this person
come in and collaborate with us, too."
They don't even have to do that.
They could just do it on the outside.
The other thing is that the data goes in the database and
anybody can touch it before a publication.
What I want to say is that all the journals have also
been respectful.
You may not know, but there were many papers submitted on
the human genome sequence before the people who sequenced
the human genome actually had a paper.
But they were summarily rejected from the journals.
So this does happen, and by forming large consortiums,
the major journals are all aware that this is an issue
and they make sure that they quality-control what comes
into them from a particular group.
So I think that people should think about that.
Did you want to comment on --?
[Dr. Wendy Post] Well I think that the promotions committees are
hopefully understanding the changes in the way
first authorship works, and understanding that
these are large collaborative studies that many people have
contributed significantly to, and that now with NIH allowing
multiple PIs on a grant, we're also seeing more
and more manuscripts with multiple first authors
with the asterisk saying "these two or three authors
contributed equally."
So I think we're just evolving in that arena,
and I'm optimistic that it will continue to evolve
as we continue to have these large collaborative efforts.
[Vasrum] Vasrum [spelled phonetically] from Framingham.
Just to add to the discussion, I think one of the strata
we need to protect is the young investigators,
because they are evaluated on the basis of authorship,
first authorship, or whatever.
So the consortium model works very well
for the senior people.
So, in parallel with the progress, I think some of the
evaluation criteria for the young investigators
also need to be changed so that they are not just based on
the number of first author publications they have,
otherwise they will be left out of this important field,
which is moving ahead.
And some of our fellows have raised this issue, that when
we participate in this, what do we get out of this?
Would we be the first author?
And that really is not very practical when you are talking
about a consortium model.
So it'd be nice if there are strategies to protect the
career of young investigators who are interested in genomics
and population epidemiology.
[Dr. Wendy Post] One other comment is that we also realize there are going
to be so many things to write about, because there is
so much data that there should be something for everyone
to be able to have a first author paper on,
even if it's not the major finding that comes out of
a collaborative work, so we find that in these,
like in MESA [spelled phonetically],
we have hundreds of paper proposals,
and then we count the number of papers that come in.
So, you know, there's a wealth of potential opportunities
and so, although we all sort of worry about it,
in reality there's more than enough for everybody to do.
[Dr. Deborah Nickerson] I just want to reiterate that last.
There are so many paper opportunities in these
types of -- there's no limitation.
And many of the junior people in genomics have as many first
author publications as they can write.
And they do participate at a level, as junior investigators,
that frankly many people would kill to be a part of.
So I think there are many advantages
to young investigators.
[Dr. Teri Manolio] Yes, from the floor please.
[Female Speaker] Hello.
Dr. Nickerson talked quite a bit about non-coding
sequences that may regulate transcription and translation,
and I was wondering, in understanding these better,
what are new techniques that people need to think about
to start understanding the importance of those regions?
[Dr. Deborah Nickerson] Well, I think we need to tackle some of those regions.
Unfortunately, we tend to tackle those regions that
have a gene, first.
We often can predict, and don't really care about,
mechanism, even though we know mechanism is important
for treatment.
For example, in the CAB link that everybody is talking
about, chromosome nine, there's a microRNA and a
couple genes, but it's mostly a non-coding sequence.
We don't even have a complete picture of the sequence
variation in those regions.
For example, we've never sequenced the complement H
factor in multiple individuals with macular degeneration
with complement H.
So we need to go in and look at the variation in this region,
get a more complete spectrum and then begin to apply
some of the tools that we know, like transcription profiling
across it, but we can't get all the tissues,
predictions of transcription profiling,
multispecies comparison, and begin to actually do
animal model studies on some of these, if we can.
And those are very costly studies, so we need to know
a lot more before we do that.
But, I think with projects like ENCODE,
we go across and apply everything that we know
in a very large-scale way -- it won't be perfect,
like the HapMap, but it will capture a lot of what we know.
But we need to know more about even the variation
that we have inside the genes and regions to explore that.
[Dr. Teri Manolio] Debbie, could you comment a little bit on how population
studies might be useful in this kind of work?
For instance, in sequencing you talked about looking at
the tails of the distribution.
But are there other ways?
People who have one variant, who then you kind of consider
them to be almost a knockout, and look for, you know,
a human knockout, and look for other
sequence variations in them?
[Dr. Deborah Nickerson] Right. I mean -- so, it's unclear actually how to
do the sequencing right now, even when you have a hit.
Do you just take your affected allele from
multiple individuals and compare it to what you
consider the wild type?
And what is the wild type?
I'm not sure I know what a wild type human is.
When I look around the room, boy, we're wild, I'll say that.
[laughter]
Me, too, and so I think that knowing what to choose --
there are differences in -- if there are things --
there is something called -- there are ways,
using multiple populations.
If you have a phenotype that's present in multiple
populations, for example, we know that the association
between SNPs in different populations, for example,
individuals of African ancestry,
have less correlations among SNPs than individuals
of isolates from that, like Europe or Asia.
And so, you could think about looking across populations
to narrow regions.
You can think about looking -- but I think, early on,
we should sequence as many people as we can afford.
And I think that's one of the areas that people are getting
all these hits, but they're not really thinking about how to
actually go in and look at these regions.
We're just going to start piling these things up and
not really have a clue in terms of what the actual
functional alleles are.
And I think people will believe that the functional
allele is the SNP that was on that chip.
And most likely, it's not.
[Dr. Teri Manolio] That's an excellent point, and I think, too,
we ought to be thinking about how we can use population
studies to do -- in addition to sequencing,
are there ways to do some transcriptional profiling?
And I confess, I don't know what samples
you need to even do that.
So are those just DNA samples you could get,
or do you need to stimulate someone, or is it RNA,
or no, no, no --
[Dr. Deborah Nickerson] Most of the time what we have is B cell lines, sometimes,
or blood.
And that's just one type of tissue.
We don't often have cardiac biopsies of the same people
that we're studying for cardiovascular disease,
for example.
It'd be great if we did.
I'm not sure we'd get too many donors,
but we do need to think about making repositories of some
of these tissues over time, so that we could, for example,
even if we had 100 individuals that we had a complete
genome scan on, we could think about
expression profiling in those hundred,
and that might contribute more to the knowledge database,
using the best technology of its time.
So these could be independent studies that we could develop
over time.
We don't know what the appropriate tissue is,
because we're not really sure, when we get our hit,
where it's expressed, or if it's even expressed
in the genome, although we know expression is very pervasive.
Almost every region is expressed.
We have no idea if that's meaningful.
Yes?
[Female Speaker] That actually brings up my question, which is,
what about the role of abnormal methylation in these
epigenetic kinds of mechanisms?
[Dr. Deborah Nickerson] And this is something that the ENCODE project is getting at
and profiling on a large scale.
They have the patterns of methylation.
They have patterns -- they have almost every pattern,
on one percent of the genome.
But what they did when they were doing that was get a way
that they could -- and they're now thinking
about how they could put all this across the genome --
now methylation also is very tissue-specific.
I mean, there are many things that we need to get at
in tissues that we're not getting at right now because
we don't have the archives of the tissues.
But I think if we do even have a small semblance
of these tissues, and could develop it over time,
it would be extremely important.
I think you'll see, eventually, some time --
I wouldn't be surprised if there was something that
allowed people to donate their bodies to science for,
like a genome anatomy project or something like this.
Yes, please?
[Female Speaker] Can you comment on the portability of the HapMap
for the population I represent?
[Dr. Deborah Nickerson] Yes.
So there's a couple things I should say about the HapMap.
The HapMap gives very good coverage of many different
populations, and people have looked at coverage going to
different populations.
Is it as effective for other populations?
It drops in -- a little bit in its transability,
maybe by 10 percent.
But what's important to know is the chips are not actually
mirror images of the HapMap.
They also have a drop in how much information
that they capture.
So you get, obviously, drops in the amounts,
like 10 percent, 20 percent.
The coverage is not as good for all populations.
The drops are about the same, though, from where you're
starting from.
But obviously we have significant power to detect.
If we have a scan on individuals, we can only --
we can add to that and get better coverage as we
understand more about the human genome.
The HapMap is just a starting point.
It's not the end point.
We can just continue to add to our datasets.
But people have looked at portability;
it's extremely portable.
[Dr. Teri Manolio] And we might note that the original three HapMap
populations, which were the CEPH -- actually,
the CEU population.
I've forgotten what it stood for.
But anyway, European Caucasian population,
the Yoruba in Nigeria, and the Han Chinese
and the Japanese were the original three populations.
But those have been recently expanded by seven other
population samples, again of -- I believe those were all
trios, if I'm not mistaken.
[Dr. Deborah Nickerson] They could be, but it was to get at
this issue of transability.
Many people, though, have tested this
independently in their populations and have seen
that it's quite transferable, and I believe it will be
for most of those populations.
But we will have actual tests, which is really important.
[Dr. Teri Manolio] And keep in mind, too, that the HapMap was looking
at common variation, and that's probably why
it's so transportable.
And it may be that some of the rare variants
might not be as --
[Dr. Deborah Nickerson] I just want to bring up something, if I can,
on the CEPHs.
The CEPHs, actually, for those of you who are interested,
were the original families that were used to build
the linkage maps of the human genome.
So they became carried over into building the association
maps of the human genome because we had cell lines
from these individuals and unlimited DNA resources.
And the individuals had mostly consented to do the types of
studies that we had already planned to do.
So it seems like an unusual population,
but it was the population that was used for the linkage maps
that were developed.
[Dr. Teri Manolio] Yes, please?
[Female Speaker] What about the centromeres and telomeres?
Is ENCODE going to try and tackle those at all?
[Dr. Deborah Nickerson] The Structural Variation Project may get at that,
because there's a lot of structural variation there.
Those are probably the most difficult regions for us
to tackle, and they won't be great regions for these
whole-genome sequencing strategies that we're coming
up with, because they're the most repeated.
Those regions are extremely important.
We're getting more and more information.
But those are the tough regions.
What you should know about genomics is we do whatever
we can do in the quickest way possible,
and getting 80 percent of the data is always easy.
It's the last 20 percent that takes the next 20 years.
It's like we have -- Jim could probably tell you.
What percentage of the genome do we actually have
in the database now?
It's not every single nucleotide, but --
[Jim] [Inaudible]
[Dr. Deborah Nickerson] Yeah, right.
[laughter]
So, because we're missing some sequences around these
telomeres and centromeres, we know genes exist that are
not actually on the reference sequence that we have.
Where are those genes?
In the assemblies.
We know that it's not perfect.
So over time, though, we go after more and more
of those regions, and actually those regions
are the starts of some great genomic careers.
I can think of a young person who started out working on
these regions of structural variation who actually was
an assistant professor in the genome, Evan Eichler,
and now is the leader of the Structural Variation Project,
because that was the area --
[Dr. Teri Manolio] That's how he started?
[Dr. Deborah Nickerson] What?
[Dr. Teri Manolio] That's how he started?
[Dr. Deborah Nickerson] Yeah, that's how he started, and because he was looking
at these regions of the genome that no one else wanted,
segmental duplications, that everybody wanted
to stay away from.
And he made his career around that.
So, you know, there are lots of exciting and interesting
findings that will lead to brand new projects in areas.
And I think it's the same way for epidemiology.
I think that these datasets provide such an opportunity
for the young people to really find an area that can
be their own, and that they can take forward.
[Dr. Teri Manolio] Now, a degree -- and maybe even come back to a comment
you made at the beginning of the discussion session, Debbie,
when you said that you felt participating in the HapMap
had no effect on people's careers; I assume you meant
had no adverse effect on their careers,
[laughter]
because I can think of several very young folks
-- Mark Daly, Paul de Bakker, Itsik Pe'er, and that --
who basically, you know, had questions that they wanted --
sort of wanted to play with the data, and late at night,
or whenever else, came at it and looked at it
in a very different way from anyone else,
and really came up with some wonderful insights
and perspectives.
And you don't have to, you know, even come up with
fantastically brilliant things to be able to contribute
dramatically to just looking at associations and controlling
for confounding and other things that epidemiologists
do extraordinarily well, and that we have the patience
to do, is, you know, the substance of many,
many careers.
And so I think that rather than being concerned about
our young investigators, we should find ways
to fund them to do these kinds of analyses and encourage them
to put their data into databases so that others
behind can do the same kind of work.