opportunity to come talk to you guys about what we’ve been thinking about in my lab.

So, what I’m going to talk about primarily is stuff that’s going on. So, all of this

is unpublished. Feel free to think about it, share it, whatever. But it’s very much work

in progress. Some of it is hot off the press. So, do take it with a pinch of salt. So, what

we think about a lot is autoimmune diseases in my lab. And we kind of want to think about

which genes go wrong in disease, and we think about these regulatory genes. But actually

what we’re interested in are the causal genes. And my pointer doesn’t work. I can

use this pointer. It’s all coming up Chris today.

So, we’re thinking more about causality than anything else. So, when we say dysregulation,

we’re interested in pathogenesis, right? That’s ultimately what we’re after. And

so, just a 30,000 foot view of the immune system. If you remember, you start with a

stem cell. You have two major lineages in the immune system, that the lymphoid and the

myeloid lineages. So, things like macrophages are all the way down here. And your T cells

and B cells are all the way down here. If you think of you think of them as adaptive

versus innate. And what happens is every now and then, this goes wrong. So, the immune

system’s primary function is to protect the body from things that are foreign. And

so it’s got this amazing capacity to tell the difference between your cells and the

rest of world. And it’s really good at this, but occasionally it screws up. And it kind

of -- what happens is that it starts attacking certain tissues.

So, if it doesn’t like myelin, you get multiple sclerosis. The immune systems manage to go

into the brain and attack the myelin sheath [spelled phonetically] very specifically around

neurons, chew it up, and you get lesions into your brain. You can get things like skin attacks

which give you Sjogren’s syndrome, scleroderma, you can get type 1 diabetes, which we now

know is an immune disease. If it doesn’t like aspects of the GI tract, you wind up

with Crohn’s disease, ulcerative colitis, or celiac disease if it doesn’t like the

epithelia joint; specific joint dislikes, should we say, give you rheumatoid arthritis

or ankylosing spondylitis. And if it just doesn’t like DNA, if it doesn’t like nucleic

acid, it attacks everything, then you wind up something called lupus, right? What’s

really interesting is that these are very, very, specific dislikes. So, MS is not rheumatoid

arthritis. It’s a very specific attack against myelin. It’s not a specific attack against

anything else.

And what we really want to understand is what these diseases are. So, something’s going

wrong with the immune system. We don’t really understand what it is. What we do know is

that all of these diseases are common. They’re complex genetic diseases. There’s a large

portion of heritability. They track in families. But they’re not Mendelian. It’s not one

catastrophic mutation, right? And, of course, as GWAS came along, I’m going to talk about

multiple sclerosis, which is something that I work on. But you can take this as read for

any immune disease. As GWAS came along, we hadn’t really gotten a lot of traction on

the genetics of these diseases. And then, sort of we barely managed to identify two

loci in the genome in one of the first GWAS studies. Then a little while later, we managed

to get another one. A meta-analysis of these two sets of studies from international consortia

kind of gave six new hits, and we’re starting to climb this power curve of discovery.

Then a further meta-analysis with more markers and a few more samples gave us an additional

three new hits. Even more samples gave us another 25 new hits. The immunochip gave us

47. That took us up to 100. And our current studies, which are about 16,000 cases, 26,000

controls and replication in another 36,000 samples, we’ve got another 100 odd new hits.

So, we’re standing at around 200 loci right now in GWAS, right? That explains -- including

the HLA -- it explains about 55 percent of the heritability. We estimate that in the

common space there’s probably another 600 to 800 loci that we don’t know about yet.

We kind of do know about them. They’re not genome-wide significant yet. But we know they’re

there. And we know the approximate complexity of the disease is about 1,000 independent

variants.

And so, when ENCONDE came along and we did -- we were a very small part of this paper

from John Stam sort of showing that in Crohn’s disease and in multiple sclerosis, there is

strong enrichment of the risk SNPs on regulatory regions active in very specific subsets of

the immune cells. And in multiple sclerosis in particular, you can see CD3 cells, CD19s,

B lymphocytes, and CD14s, which is interesting. There’s a lot of pathogenesis coming out

of T cells as well. But these are more B cell like. And so, dysregulation in multiple sets

of immune cells seems to be an issue here. But this kind of sends us chasing down this

idea that is now extremely common. And this is one of the great right, right? So, 10 years

ago GWAS wasn’t going to work. And five years ago, everyone was asking why we haven’t

solved disease yet. Five years ago, everything was coding. And now, everything is now regulatory.

And it seems really obvious. But even two, three years ago, this was not that obvious.

And so, this chases us down -- starts us chasing down this rabbit hole of which genes are getting

dysregulated and how does that cause disease. And so, that’s what we are going to talk

about today -- further evidence that in specific immune cells, you get dysregulation that maps

into specific transcription factor binding sites as is from Kyle Farh and Brad Bernstein

showing that the MS SNPs are particularly enriched for NF-kappa B transcription factor

ChIP-seq peaks for instance. And so, there’s something that’s fairly specific dysregulation

in immune cells, which is great in bulk, hard when you actually want to identify specific

effects on specific genes in specific cells. And so, that’s the task at hand. And so,

when you look at some of the loci, you know, you put up a GWAS locus. Here’s a classic

locus in MS. Well, there’s NF-kappa B one and mannose-binding protein A. And you could

sort of make a case for mannose-binding protein A, but really everyone’s going to assume

that NF-kappa B one is one is the appropriate gene. And it turns out that that’s right

for various reasons. And so you can start working on that because you kind of are reasonably

sure that’s the gene.

When you look at another locus of course, that gets a lot more difficult. You’ve got

this big association peak. There’s a bunch of genes in here, and the problem isn’t

that they’re not good candidates. There’s a bunch of good candidates in here. ORMDL3

is here. IKZF3, which is Helios, which is a transcription factor that controls T regulatory

cell differentiation is there. A bunch of other immune cells. And so, you’re kind

of going, “What’s going on here?” So, we kind of thought, “Okay. If there is regulation,

and we have SNPS, how do we unite the genetics with the epigenomics?” And a lot of people

are thinking about this. You’re going to hear a lot more stories about this. You’re

already heard some. Here’s how we’ve been thinking about it.

So, we’re kind of amateur math geeks, and so we thinking about how we can transfer some

of this probability and do some functional fine mapping. So, you have a set of SNPs in

the genome. We’re going to talk about hypersensitive sites now. But instead of DHS, you can think

of any regulatory mark. We’ve been working a lot with hypersensitive sites because we

like them. They’re stable. They’re nice. They tell you a lot. We’re going to expand

this to the other sets. But think about DHS for now. And you’ve a gene in the locus.

So, this is my like tiered view of a locus.

So, each of these guys is associated to disease. And -- oh, this is going to chop off my -- thanks.

Oh well. So, what that says is posterior probability of association or PPA, okay? So, when you

do a GWAS for each of these SNPs, you get a P value of whether it’s associated to

disease or not. You can convert that simple P value into basically a posterior probability

which tells you, what is the likelihood that this SNPs is the one driving the signal, okay?

We’re not going to talk about the math magic that underlies that. I’ll bore you with

it in person over a coffee if you like. But basically, for each of these SNPs, you can

do a magical transformation and get the probability that that’s the SNP that’s driving signal.

If it’s very associated, and nothing else is associated, it’s going to be really probable

to drive the signal. If there’s a whole bunch of SNPs that are equally associated,

you’re going to have to spread the probability that it’s caused all over all of those guys,

right? That’s the intuition here. So, of course, some of these SNPs are actually on

DHSs. And so, you can transfer that probability. I can’t even talk anymore, sorry. That probability

to the DHS. You could also do something fancy like say this guys is about this far away

from this DHS, so I’m going to give it some proportion here. That’s -- we’re not doing

that right now. But basically, what I can do is come up with a way to score every regulatory

region for what their probability of explaining what the association in that region is, right?

And if I sum every one of those -- of course not every SNP is on those -- but if I sum

all of these posteriors, that gives me the global probability that, in this locus, association

is mediated by these regulatory regions. Doesn’t have to be all of it. But if most of the signal

is on DHSs, then you’re going to get a high percentage, right? It’s going to be close

to one. If it doesn’t look like it’s being mediated by regulatory regions, you’re going

to get a low proportion.

So much is easy. What’s cool is you can get think about how you correlate these guys

to the genes they control. So, if I had a magic way of saying, “Well, this DHS is

correlated to this gene this much, then I can wait how much of the posterior of association

gets transferred into this gene, right?” So, if this guy’s perfectly correlated -- if

this is what determines whether this gene is expressed -- then if this explains all

of the association to a trait, then presumably, it’s active on this gene. Because the DHS

isn’t just a DHS. It’s regulating something, right? So, that’s the intuition. And you

partition this all this way. And what it says here is CP times PPA, okay? So, that’s just

the correlation posterior between this DHS and this gene times how much weight you’ve

given it from the association data. And that way, you wind up building this model of this

gene posterior. So, if I sum all of these, all of the contribution of each DHS from the

SNPs going into this gene, I can get a sense of what the probability that this gene is

driving association in this region is. And I can do that for any gene.

So, I now derive a score basically for how likely this gene is to be pathogenic, if that

pathogenesis is mediated by DHS regions. And we know they’re enriched, so that’s a

reasonable hypothesis, okay? It’s not the only way to do it, but it’s one way to think

about this. And so, you have to solve a couple of technical problems to do this. One is,

you’ve got to correlate your DHSs to your genes. And so, that’s really simple. You

just observe if there’s a peak, and what the level of expression of a gene is, and

then you do a correlation, on-off versus level of expression of a gene. And you do that for

each DHS you find.

Two issues. First of all, you’ve got to decide what the same DHS is. And secondly,

you need measurements where you’ve measured both DHS and gene expression, okay? So, to

do this thing, we use an alignment approach. This is what real DHS data looks like out

of hotspot. These are peaks. This is an arbitrary part of the genome and your job is to figure

which ones of these represent the same element across samples. We’re not terribly good

at that as human beings. Fortunately, computers are a lot better at this than we are.

So, you can put it in a clustering approach and kind of decide that these look the same

that are a little jittered, but they kind of look similar. And then these guys are kind

of the same, but you’re may be a little less confident because there’s more spread.

And these guys are kind of the same as well, but there’s even more spread, okay? And

the way we do this is with mark-off clustering. It’s a way to cluster stuff. There are other

ways to do it. It work reasonably well. And the way you think about this -- oh, and that’s

gotten chopped up as well. That’s brilliant. Okay. So, one way you might want to do this

is to say, is this detectable? And so, you go into the Roadmap data, and fortunately

there are replicates.

And here’s my assertion. If I see a peak here in replica one of a tissue, then I should

expect to see that peak in replica two of a tissue as well, right? Biologically replication

just as we do in any other experiment. Really simple. And so once I decide this is my cluster,

that’s what comes out of the algorithm, you don’t just go and apply that mindlessly

to data. That’s not how you do analysis, right? You check and you see what you can

detect. And of course, the wider and the sloppier this peak is, the less likely it is to be

true. And so you can do a statistical test. And so, once you’ve decided what the cluster

is, if there’s a peak anywhere in that cluster, you mark that sample as a one. And if there’s

no peak, you mark it as a zero. If you have replicates where the labels somewhere over

here on that wall, you can then say, “Okay, do I" -- "if I see ones in both replicates

I’m going to score that tissue as a two. I’m going to score it as a one if there’s

only replicate." So, if its’ discordant. "And I must -- I’m going to score it as

a zero if there’s none there.” And then you can do a test.

So, I’ve done this without knowing about replicas. And then I add the information about

what goes with what and I ask, “Are they consistent?” So, if I get things like “Look,

in cell type one, I get a one. And in two, I get a one. I get all ones.” That suggests

this isn’t consistent. It’s not replicating. And if I get a lot of twos a lot of zeros

and very few ones, that looks consistent. So, it’s replicating. It’s either not

there or it’s there. And so I can do a statistical test. It’s not terribly important what the

test is. It’s a simple chi-square approximation. We do this over 57 tissue replicates. So,

from Roadmap. And we find that just feeding this in when we cluster, we can get about

a million out of 1.99 million. So, about 54 percent of our clusters pass are fairly stringent

threshold -- a fairly lenient threshold. And that’s because very often these things are

kind of diffuse. The clusters don’t really look good. And so, we’re probably not doing

great at the clustering, and it’s unreliable, right? There’s also a bunch of singleton

in these data that get thrown out because they don’t replicate. But most of this is

actually the clustering.

So, we can get about a million features about the genome. And we don’t worry about recovering

more stuff and improving the clustering. Right now, we’re just working with these million.

So, these other thing is, you’ve got relatively low power. And so, what’s nice about this

is this -- what you can clearly read here -- what you can do is estimate how much the