Michael Pazin: So I’m Mike Pazin, and I’m going to go
through the first half of these ENCODE-like projects or other projects in functional genomics.
Man, this is a really happy thing for me to be able to present, because there’s so much
exciting work going on here, so it’s an easy thing to do.
It was also fun doing this because, as you heard from Dan’s intro, we have a lot of
contacts -- both at the administrator level and at the scientist level -- with these different
projects.
And I was reminded -- this reminded me when I got in touch with some of my colleagues,
and I said, “Do I have the information on your projects correct?” -- and just how
helpful everybody is to work with, and it makes it an exciting time in the science area.
So I’m going to start off by telling you very brief stories about a few projects, projects
that are chosen because they’re representative of what’s going on, science-wise.
I’ll start off with projects that are more similar to ENCODE and then work my way towards
projects that are more different from ENCODE, and then pass this on to Dan.
And I’m going to focus on consortia-type projects because that’s the theme of what
we’re doing here, but I have to say I want to thank all of the individual investigator-initiated
efforts, which have obviously played an important role in this area as well.
And I’ll start off by showing you a little bit of timeline -- yeah, you can see with
the pointer there.
ENCODE, of course, is purple, as we all know.
And ENCODE is a relatively long-running project among this group.
But Reference Epigenome Mapping Centers, which I’ll talk about first, shown here in green,
is a project that started during the ENCODE cycle and has ended its date of production.
And one of the many important things I would say about REMC, in addition to the very useful
data they produce, is they got this idea started, the International Human Epigenome Consortium.
And they graciously refer to its starting as at the time that the second project, Blueprint,
joined on.
And I’ll talk a little bit about -- I have Blueprint and also Canadian project CEHRC.
So first I’ll tell you a little bit about REMC, Reference Epigenome Mapping Centers,
and this is funded by the NIH Common Fund.
And the goal of these -- of the REMC projects -- where did my pointer go? -- is to generate
a public community resource, a common theme of these kinds of projects.
And the way of doing this is to broadly sample tissues and organs across the human body -- all
right.
And generally a tissue or organ that’s profiled by REMC has been done with many assays, sort
of a grid-like layout.
They had a publication package; they came out just last month on nature, a number of
papers on biological findings, the resource, and how the data can be used.
I’d also like to point out: the NIH Common Fund Epigenomics Program goes beyond the REMCs.
It also includes disease-focused efforts, analysis projects, and technology development
efforts as well.
But here, I’m presenting what REMC is up to.
So REMC, like ENCODE and many of these other projects, is built on years of study of gene
regulation.
It’s built on the foundation and mechanistic studies of how gene regulation works.
The data and production effort for REMC is completed this time, and it has a high assay
diversity.
So it’s one of those kinds of projects that does lots of different assays in the cell
types it studies.
And it also has high sample diversity, so there’s a broad distribution with respect
to parts of the body which have been sampled, organs and tissues.
But also of note and perhaps unique for REMC is that -- their careful study of both fetal
and adult human tissues.
And the findings are quite different between the two, so it’s important that they’re
both there.
Next, I’d like to move to IHEC, an international consortium in some ways, like ICGC is for
CCGA -- okay -- and the goal of IHEC is to understand the role of epigenome and human
health, disease, and what are the influences of lifestyle, environment, aging, and genetics.
All right -- and this is, of course, well-aligned with thoughts about precision medicine and
genomic medicine, understanding how we can use Omics assays to learn about people’s
health and their health risks, and perhaps the effects of treatment.
So goals here are to support the prevention of disease; diagnosis and treatment of disease.
And because IHEC is focused on epigenomes, there’s the chance to read out in real time
-- well, not real time, but as a person’s health status is changing, whether they’re
responding to a treatment or how their disease is progressing, rather their one -- than their
one-time, over-life genetic risk.
Working groups in IHEC are shared across the projects that participate in IHEC, things
like bioethics, assay standards, metadata standards, and data ecosystems.
Before I started working at NHGRI, I never appreciated how important metadata is, but
now I get it.
It’s the thing that makes our data interoperable.
It’s the thing that allows me to use your data and allows you to use my data, if I had
any data.
So we put a lot of effort into coordinating this cross-project; it’s a very important
thing.
And IHEC has a standard set of data collection that’s minimally required on projects, and
it also has a data portal, where you can see data from all of the projects.
So as a first example of an IHEC project, I’d like to bring to your attention: Blueprint.
And the goal is to generate epigenome resources using a clearly-defined set of human samples,
a mixture of diseased and healthy tissues.
So all projects in IHEC moving forward from Blueprint incorporate both healthy and diseased
people, to do comparisons.
All right -- and Blueprint is funded by the EU, and I really admire their logo here, that
has the two different -- well.
And the diseases that they’re focusing on are a pair of cancers -- lymphoma and leukemia
-- and also an autoimmune disease: type one diabetes.
And, in addition to collecting data, data analysis is a very important part of their
work.
They recently had a publication package in science.
And they’re doing this work because they’re very interested in discovering markers for
disease, which could support diagnosis.
They’re also interested in supporting epigenetic targets, new avenues for therapy; how could
we treat the disease knowing about the epigenomics -- or epigenomic signatures?
I’d also like to talk about the Canadian IHEC project, CEHRC.
The platform centers are part of the CEHRC as a whole.
The overall project goal is to translate epigenomic discoveries into human health benefits.
The platform centers are one part of that, and they’re located in Vancouver, in Montreal.
And they’re primarily studying a few different cell types: blood, breast, brain, and thyroid.
And the particular disease of interest for their project is cancer.
And I especially need to give out a shout-out to the Canadian project, because they’ve
taken the lead within IHEC on getting the data portal up and running, getting all of
the data out; their work can be shared.
And this benefits us all, and I point out that, since ENCODE is an associate member
of IHEC, you can find some of the ENCODE data there as well.
So if we look at IHEC, there’s pretty high assay diversity, especially when you consider
that most projects go beyond the minimum required by IHEC.
And if you look at IHEC as a whole, there’s high sample diversity.
Across IHEC as a whole, many different body regions are sampled, though it’s not unusual
that some of the projects focus on particular biological areas.
And some of the IHEC projects at -- are looking at small numbers of individuals, so there’s
the potential to begin to find inter-individual differences.
Next, I’d like to talk about PsychENCODE, which is funded by Mental Health at IMH, and
it’s a consortium that includes 11 projects.
And the goal is to identify noncoding functional genomic elements in the human brain that are
important in brain development and in brain disease.
This is a large-scale project, so they’ll be looking at 1,000 brains and looking at
different brain regions.
And primarily this is to analyze psychiatric phenotypes -- schizophrenia, Alzheimer’s,
and bipolar disorder.
So PsychENCODE, again, has relatively high assay diversity; different types of data are
sampled, and it has a high potential to detect variation across individuals.
With 1,000 brains being compared, it’s not hundreds of thousands or millions cohort,
but it’s not one or two individuals either; there’s a lot of potential to see natural
genetic variation.
And, depending on how you think of it, there’s a lot of bio-sample diversity within one particular
organ, but not broad diversity across the body.
So next, I’d like to tell you a little bit about Genomics of Gene Regulation.
This is an NHGRI project, and this one is significantly different than the other projects
that I’ve told you about.
The goal here is to learn how to -- how to construct predictive gene regulatory network
models.
So these projects involve substantial data collection, but the point of this is not to
generate data collections as a resource; rather, to learn how to develop these models.
All right, and a number of different biological systems are being used, include the adaptive
and innate immune system, and part of skin.
And while many of the projects focus on transcriptional regulation networks, one focuses on post-transcriptional
regulation.
And the data from this project will be -- will be hosted at the ENCODE DCC -- as will the
metadata -- to try and make it user-searchable and make it more sharable for people in the
outside community.
So again, the goal is to learn these gene regulatory networks.
And, within each project, very often closely related cell fates or cell states are profiled
because, in order to get at how these transitions occur -- what goes in what network -- it’s
the comparison that are important, rather than studying the broad part of the body plan.
And while there’s high assay diversity across this [unintelligible] group of projects, we
haven’t forced them to all use the same assays.
So you don’t necessarily see the same assays across projects.
So I’d like to talk about Function of Noncoding Variants, a predictive project.
And I neglected to tell you that, if you’re in the audience, in your folder there’s
a spreadsheet or table which has this information and URLs to some of this and so forth, which
you could follow along or refer to after this meeting.
But this project, again, has a quite different goal, and the goal is to learn how to computationally
prioritize noncoding variants, to know which are most important to follow up.
And the reason for this is -- as you’ve heard, an important NHGRI goal -- is to learn
how to interpret the role of genetic variation in human disease.
And the vast majority of common variance lie outside of protein coding regions.
So if we throw out this large portion of data, it’s going to undermine our power to understand
all of the genetic components of human disease.
All right, and genetic variation in noncoding regions is known to both cause and modify
disease.
I would point out that there are Mendelian diseases that are severe -- like fragile X,
for instance -- that have long been known to be the result of noncoding mutations.
Also, more recent studies that have pointed for frontal temporal dementia and for ALS
-- again, noncoding mutations account for the largest fraction of known inheritability.
In that case, though, it’s merely 30 percent of it.
These awards are in process at NHGRI and our partner on this initiative, NCI.
And the last project I’m going to tell you about is 4D Nucleome.
This is brand new -- in fact, so new that nothing is funded.
The goals of this project are to understand the principles of nuclear organization; why
does the particular nuclear organization seed in a cell exist, and what does that organization
tell the cell in terms of its cellular function development and perhaps disease?
A lot of this work is going to focus on technology development, though some of the work will
be devoted to reference maps and also predictive modeling of how the structure and function
of nucleus is correlated.
This -- so those of you that are familiar with this area know that, historically, there’s
been a lot of work done with imaging.
More recently, there’s been work that’s done with genomics, and for the most part
there’s a vast gulf between the two.
So one of the ideas that this project would like to do is to try and reconcile and bridge
this gap.
This is, again, funded by the NIH Common Fund, and awards could start as early as the end
of this fiscal year in this project.
So I will stop there and hand it off to Dan.
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