[music playing]
Lisa Brooks: 1000 Genomes Project is looking at the genomes
at 2,500 people to find human genetic variation. This is places in the DNA where
people differ from each other, and the goal of this is -
- is the data from this project will be used to find genes that affect disease.
Steven Sherry: I frequently think of 1000 Genomes as a Lewis
and Clark expedition to the interior of the continent. We knew the outlines and the contours.
We understood where the major rivers and the bays were, but what we�re doing now
is kind of systematically kind of walking though the
interior into uncharted territory of rare variance.
Gilean McVean: The idea behind a 1000 Genomes project is
to build a map of human genetic variation, down to
frequencies of about a percent in most of the populations around the world.
Lisa Brooks: Some genetic variation is very common. Things
like ADO blood type, the variants that contribute to that are in every population.
If you look at any one population or any population, you�ll find those genes. But some variants
are much rarer or rare in families and specific populations, so in order to get a good picture
of human genetic variation, you have to look at a
lot of people.
David Altshuler: So the project actually oversaw the collection
of what is now a collection of 2,500 individuals from 26 different population groups around
the world, all with informed consent that allows this
open sharing of data. And then the project has put together these sequencing methods
and technologies with these DNA samples and is
producing an unmatched catalogue, at least to date,
of genetic variation in human beings around the world.
Steven Sherry: We are building that catalogue of what normal
looks like and, and so it�s available for a lot of
disease studies to use immediately.
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Lisa Brooks: The main findings were, first off, the amount
of variation. There�s 40 million variants in that
paper, which is a very large number of variants across these 1,092 people.
Gilean McVean: The things I found most surprising were to
do with the number of apparently bad mutations that
we all carry. So there are these catalogues out there, which try to list mutations that
have been though clinical labs is being identified as
pathogenic. We�re all walking around with between
two and five of these mutations, but most of us are apparently healthy.
Lisa Brooks: The other thing that was found -- and this
is not unexpected, but this actually gave the details of
that -- is common variants, variants that are frequent in any one population are frequent
in all populations. Again, this reflects our common,
shared ancestry from Africa.
Gilean McVean: I think another really important finding is
that these rarer variants -- and these are the ones that,
you know, we might think of as being more likely to cause more severe diseases -- are
very structured geographically, which means that
the rare variants that you find in the U.K. are
different from what you find in Italy or Germany or the U.S. So there�s quite strong
geographical restriction of very low frequency, possibly quite bad for you mutations, so that
when you�re looking at an individual who�s got the disease, you really need to take into
context their local genetic background, the geographic
ancestry essentially of these individuals.
Lisa Brooks: And that has real implications for how you
design disease studies, because a lot of people want
to say -- to be efficient with money, and of course NIH appreciates that; a lot of people
want to use a shared control group, and then look
at their disease study, their autism, the Type 2 diabetes,
arthritis against a common disease group. But it turns out, because of this differentiation
in rare variants, and because a lot of people nowadays
are interested in understanding how rare variants contribute to disease, you can�t take one
population and study diabetes in it, and a different
population and use it as your control group, because those rare variants will result in
a lot of false positives. You�ll get wrong results if you
do that. So that means you have to be very careful
how you design your studies so that you match your control group very carefully with your
case group.
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Gilean McVean: But actually genetic variation in its own,
on its own, doesn�t make a huge amount of sense; it�s
not wildly interesting. It only makes sense in the light of the phenotypes that we can
collect. What people look like, what diseases they�re
going to get, how they respond to different drugs.
Lisa Brooks: So there�s a couple ways in which 1000 Genomes
data are being used.
David Altshuler: The data can be used to support studies of
the genetic variance found in the 1000 Genomes Project for their role in disease.
Lisa Brooks: So you look through the whole genome, you
look for regions that differ in frequency between
people with the disease, people without disease, and those are regions that you want to follow
up on, because they�re quite likely to contain
genes that will contribute to the disease risk.
David Altshuler: In many of our diabetes studies, we�ve identified
common genetic variants that are associated with risk of Type 2 Diabetes. There are actually
now over 50 such regions of the human genome that have been identified as contributing
somehow to diabetes risk, but in very few of these do
we actually know the causal mutation in that gene region that is actually why or how that
gene region contributes to disease. And because
before the 1000 Genomes Project and other sequencing studies, we only had a partial
list of the genetic variance in each of those regions, it
was like trying to find the needle in a haystack without having the ability to see all the
pieces of hay and the needles.
Lisa Brooks: So what you can do is, instead of having to
sequence all these people, you can look in the
database and look at a 1000 Genomes� data and say, �Okay, in these regions, this -- these
are all the variants.� Almost all, as I say; it�s
not going to find every last one, but it�s almost all the
variation of that region, which means each study doesn�t have to sequence its own people.
It can just go to the computer and find most of the
variation that�s there.
Gilean McVean: Got a colleague here who�s interested in
multiple sclerosis. He picked up on a gene that called --
had been pulled out of genome-wide associations study. He looked in the 1000 Genomes data
and found that there was a variant that seemed to be explaining most of the signal.
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Steven Sherry: The 1000 Genomes data set for phase one is
on the order of a 180 terabytes. So that�s 180,000
gigabytes. Remember a DVD is four gig, right? So take 180,000 and divide it by four, and
that�s how many DVD�s worth of data we have. It is by far the largest public bioinformatics
dataset in the world.
David Altshuler: A number of different sequencing technology
companies have been active participants in the
project, and many different sequencing laboratories around the world have employed these
different methods. And together the project has worked to make it so that it�s much
more practical than it was to take data with one
machine, or one lab, and make it compatible and
comparable to data generated at a different time with a different machine in a different
lab. That might sound very easy, but it turns out to
be extremely difficult.
Lisa Brooks: Because once you take the raw sequence reads,
you have to map them on to the genome, then have to call variance. Point places in the
DNA where people differ called SNPs, single nucleotide polymorphisms, are more or less
easy to make calls on. Structural variants where
there�s an insertion, or a deletion, or a piece of a DNA�s slipped over, or a piece
of DNA has moved over there, or it�s been copied, the
copy number variance, those are actually much harder
to deal with, and so there�s a very active part of the analysis group talking about structural
variance.
Gilean McVean: I think the 1000 Genomes Project has been
the driving project for working out how to sequencing genome.
Lisa Brooks: So an interesting thing about this project
has been, there�s been huge numbers of analysts from a
lot of institutions. Some of them have come from the sequencing centers, but many of them
come from various other places in the U.S., in Europe and China to work on these data.
And so the group has an analysis group call every
week, and so many, many people have been working very cooperatively to produce the dataset.
Steven Sherry: Just within this last year there have been
over 5,000 institutions around the world, over 2,000 in
the United States, and 3,000 everywhere else that have downloaded parts of 1000 -- at least
some part of 1000 Genomes. There are research universities
and teaching hospitals that maintain copies of everything.
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David Altshuler: I think the legacy of these projects are numerous.
One is that we will have succeeded, I think, in
making sure that our shared genetic heritage, and our ancestry, and the DNA variants we
carry are freely available for researchers to use
to benefit patients around the world. There was a time
10 years ago -- and there�s always this tendency -- where some said maybe we should
patent all these and lock them up so we can individually
profit from that, but we tend to think that these
genetic variants belong to all of us. They�re not -- they�re not inventions, they�re
simply things we carry, and they should be available.
Gilean McVean: I think when historians look back on the 1000
Genomes Project, they�ll see it primarily as a
driver for turning the idea of whole genome sequencing for personal medical use into reality.
It was the key defining moment in making that
happen, because it�s been a catalyst for getting the
technology together, getting the resource together that can tell us what genetic variants
are out there, so that we can interpret those found
in specific individuals.
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[end of transcript]
NIH/NHGRI: 1000 Genomes Final Stereo 1 06/05/14
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