Good evening and welcome to this tutorial session on the 1000 Genomes Project data.
It's fantastic to see everyone here. I think we've sold out basically. And it gives you
a sense of how much excitement there is in the human genetics community about this project
which you've all heard a bit about. There have been a whole bunch of talks today and
there'll continue to be talks throughout the whole conference. And so you get a sense that
it is one of the big things happening in human genetics, human genomics at the moment; and
the idea of this tutorial session is to try and give you a much closer sense of what the
project data looks like, how you can get at it, how you can learn about particular regions
that you, as researchers, might be interested in, how you can actually use these data in
your own studies, hopefully to find out new interesting things about diseases or whatever
process you're interested in.
So it's worth sort of just trying to reiterate this sense of excitement. It's 10 years since
the first draft genome and was generated; and we're now at the stage of the publication
that is [unintelligible] last week is of the pilot study that's of 180 or so genomes. But
as a project, we're already up to essentially the 1,000 genome mark in terms of data generation.
And over the next few months, you'll see that coming through. So these data are really coming
through. And what we hope this project will do — and I think we're already seeing that
it is doing — is essentially being a transformative project. It's going to change the way people
can do human genetics and human genomics.
So just a couple of things about what the project is. It is a big thing. You see there
are lots of us here and in fact, the whole conference is kind of peppered with people
involved in the project. There's probably one of them sitting next to you now. So it's
a big international project and the idea is that it's trying to make the baseline reference
set study of human genomic variation that will provide the foundation for human genetics
in the next few years — maybe five, maybe 10 years, that kind of scale.
So it's a consortium with many platforms, that's many of these massively parallel sequencing
technologies — the high throughput systems, lot of different centers, lots of different
people with lots of different research groups. Both on the — everything through the informatics
through to the disease association groups, and of course lots and lots of funders all
cheerfully putting money into support this fantastic operation. And so it's going to
— what we hope is to provide a resource to support genome wide association studies,
the GWAS and the abbreviation in many different populations, not just one, but hopefully across
the world pretty much.
We set ourselves some quite specific quantitative goals. We decided to try and be hard about
this. So we set out to find at least 95 percent of all the SNPs in the accessible human genome.
I think probably more on that later. And a frequency — that are there a frequency of
one percent in the population as defined somehow. And then in genic regions, to try and push
further down into the rare variants, going towards the 0.1 percent. Importantly, it's
a project that doesn't just aim to find the SNPs, but also to find all type of genetic
variation. We believe they're all important, potentially important in disease. And so we
want to find both the shorter insertion and deletion polymorphisms, and the larger more
complicated structural variants that can have quite dramatic effects on genes.
So at one level, there's a catalogue variation, but it's also — it is trying to generate
a 1,000, or probably ultimately, about 2,500 individual genomes. So we want to provide
genotypes, the set of variants carried by these 2,000 people and we want to put those
on particular [unintelligible] backgrounds. We want to say, "This variant goes with that
variant in this particular place." And Jeff will describe later, that's going to be a
very important technique and a resource for one use of these data. Providing the datas
one thing, but very importantly to us, we decided that we wanted to make the data from
individuals which we had cell lines available. So that means that other people can go and
take the same samples and either do genetic studies or to look at molecular phenotypes,
gene expression, chromatin changes and so on. And in doing so, this notion of a central
reference data set that lots of different groups can kind of converge towards, I think
will be very powerful.
And then finally, we decided that we'd be a totally open project — that is, basically
everyone else sees the data at the same time that we do, and obviously, we're processing
it and providing some areas of it that were much more digestible. And our goal is to do
that publicly and quickly with these frequent releases.
Just to give you a little heads-up of the way in which I think people will use the data
in medical genetics. First off, they're going to use the data through a technique called
imputation to essentially do genotyping for free and existing genome wide association
studies. So I think that's one thing that people are already doing and it's actually
providing resolution and in some cases new regions of association in disease genetics.
But there are other uses as well. You might simply be interested in finding out all the
sort of polymorphic variants in this particular region of a genome. And the project's good
for that. It will give you a catalogue of the variants we found and some notion of their
allele frequencies. So you might choose some to prioritize for later work. And then a third
and very useful technique is to take the variants that we found and essentially use that as
a screen for variants in subjects that you might be interested in. Perhaps you study
a particular Mendelian disease or a particular cluster of individuals with a super-phenotype
of a particular disease and you want to find the things that are really novel and unique
to this set of individuals. And the 1,000 genomes gives you a sort of a overview of
normal variant against which to make that screen.
Just one very quick word about imputation; that's this technique that I mentioned there
— the idea that you can essentially do genotyping for free. The idea is that you
take the 1,000 genomes data, some data that you've already collected, say from genome
wide association studies, and then you use statistical techniques that Jeff will talk
about, to essentially do the filling in process. It can be very powerful and certainly, when
we get to those 1,000 genome samples, it will massively reduce the cost of genome-wide surveys
because you won't have to do the sequencing yourself necessarily.
So that's kind of the future. Where we are exactly at the moment is essentially — we
have very mature releases of data on three pilot projects, one looking at some trios,
one looking at low coverage samples, and one focusing on some exons. There are 180 of these
low coverage genomes, but they're about 700 of these exons with different coverages and
we've learned different things from each of those.
Going forward, about 2,500 samples in the full project spread across the world, but
not randomly from across the world. The idea is to collect them in regions of major medical
genetics, interest in little satellite groups of populations. So essentially the idea is
to get five lots of 100 samples from particular geographic regions such as Europe, or East
Asia, and so on.
So as I said, I think we have data on over 1,000 samples available to us as a project
at the moment. That's not in a digested form for you yet. But Gonzalo [spelled phonetically]
this morning, announced that we have a release currently on 600 samples as all of about today.
And that slide gives you a sense of where we expect to be. So another 600 samples being
sequenced by the end of — by mid next year, and then a further 800 towards the end of
that year and the beginning of the next. And in addition to the sequence data across the
whole genome, we're going to collect exon sequence for those as well. And importantly,
other types of information about genetics, SNP genotyping on dense 2.5, five million,
10 million SNO arrays; and additional ray CGH get better resolution than genotyping
for copy number variance and structural variance. So the idea is to try and integrate all these
different sources of information to provide this very high quality set of 2,000 to 2,500
genomes.
Okay, that's enough from me. Let's hear from the people who are going to give you much
more detail insight into some of these. First off, we're going to have to have Garbor [spelled
phonetically] talking in a lot more detail about what the 1,000 genome data look like.
Steven is then going to tell you about how you get your hands on the data, kind of important.
Paul's going to tell you actually about a browser that we have and that he and his team
have been developing, which allows you to look at a lat of their data in the context
of genome annotations, pull out bits of it and say really explore the data without having
to get all the data down onto your computer. Yan's [spelled phonetically] then's going
to tell you about structural variance as a particular class of variant that a lot of
people are interested in. And then Jeff is going to finish up by telling you about how
the data can actually be used in medical genetics and if there are people standing at the ends,
there'll be some questions and answers.
On that note, however, there will be some time for questions at the end of each of these
sessions. And if people have question, things that they want cleared up during the talk,
then please stand up and shout or use one of the microphones. If there's something you
don't understand, don't just think there thinking, "I don't understand this." Just shout because
there'll be other people in the room who also want to know the answer. And the idea of this
is to have a tutorial session. Okay, it's not just a series of lectures. Finally, I
must ask you to please put your phones on silent. And that includes everyone up here.
Okay, thank you very much and over to Garbor.