Jay Shendure: With Nancy’s introduction, I was remembering
one of my favorite movies when I was a kid, "Brewster’s Millions,” where he had to
spend $30 million, and it’s Richard Pryor. $30 million in 30 days, but I did the math
here, and it’s $125,000 a second to try and convey an idea.
Okay, so I’ll start with a slide that’s largely redundant with the conversation this
morning. The interpretation of genetic variation is a mission critical challenge for this institute
and this community. At least two examples of there it is currently rate, limiting our
variants of uncertain significance, and genome-wide associations. A related point, which I think
there’s also probably consensus on is that simply sequencing more genomes isn’t going
to effectively address the underlying challenges.
So I guess the question is, which way forward? So computational prediction of variant functionality
is certainly viable and worthwhile goal, and we and others have put a lot of work into
developing algorithms and their great various functional annotations to a single score.
Based on our experiences today, I worry a little that trying to improve computational
approaches to the place where we would like them to be may turn out to be a bit like squeezing
blood from a turnip, so to speak, where the turnip represents present-day functional annotations
and there’s only so much informational content that we’ll be able to squeeze out. And so
my point is not that we shouldn’t be pursuing improved methods for computational prediction,
like the FunVar initiative, which is great, it’s that we need more and better training
data to compliment or proceed these efforts.
So -- okay. One path forward to address the challenges and framework in the scale program
would be to pursue experimental measurement of the functional consequences of a very large
number of variants. So for a budget of $10 to $20 million in each of five years, I don’t
think it’s absurd to think of numbers like 10 million as estimates of what might be possible,
which equates to about one in every thousand of all possible SNPs. I’ll add that there
are of course a lot of different ways to slice the question how to pursue large-scale functional
studies, and what I really tried to focus on is how to maximize this number of how many
variants it was looking at, perhaps at the expense of other considerations.
So 10 million is an ambitious number, but if you suspend your disbelief for a second
and say, we could do it, what would we gain, first, absolutely massive training set for
improving predictive algorithms for coding regions. This would be about a thousand fold
larger than the data sets that are available today for training. For non-coding regions,
it would be effectively intimately larger, since we hardly have anything at all. Second,
this goes back to one of the points this morning about biology being a goal, so sequence structure
function maps for diverse classes of sequence elements. So really, informing biology that
base peer-level of resolution is not really accessed by current efforts in function on
genomics. And then lastly, the measurements themselves, which could potentially improve
interpretation of BUSS as well as fine mapping of associations.
So how would one in practice get to functional analysis of 10 million variants? The answer
of course would have to require, would like to require some large degree of multiplexing.
This is a workflow from a collaboration with Len Pinacio [spelled phonetically] and Nadav
Whetev [spelled phonetically]. Joe Ecker brought up this general classes of approach earlier
today. So in a nutshell, this involves first generating a comprehensive allelic series
of let’s say a single mammalian answer and one reaction, with each variant linked to
a transcribed bar code, then putting this population of enhancer variants through a
massively parallel functional assay, and then finally using sequencing as a readout where
you read out the transcribed bar codes and R&A, and infer or estimate the effect sizes
of each possible mutation and the enhancer. And this is all in one experiment.
So here’s an example of such an experiment. This is a 250 base pair intronic enhancer
of ALDOB with position on the x-axis and lawful change on the y-axis. And so we’ve effectively
generated, and this is remarkably reproducible and quite sensitive, measurements for the
functional effects of about 750 non-coding mutations in one experiment. So, of course
as we came up this morning as well, we want to do functional settings on code adherence
as well, so here it’s more challenging, because ideally, you want to introduce one
and only one mutation to a full length orph. So Jacob Kitzman who just started his lab
at the University of Michigan recently worked out one method to do this, termed PALS, where
one uses microarray derived oligos to program an allelic series of all possible code on
substitutions to an orph in a single reaction.
And then some of my colleagues at UW, Serita [spelled phonetically] and Stan Fields [spelled
phonetically] have applied this method to the analysis of BRCA1, in particular BRCA1-BARD1
interactions, and just generated beautiful maps of the relative effects of nearly all
possible amino acid substitutions in this region of BRCA1 that interacts with BARD1.
And then these can be then correlated with structure to form biological understanding
of the interaction, but also, evaluated for utility in predicting the pathogenicity of
clinical variants. And so there are a lot of groups that have developed methods related
to the examples that I gave, and the take-home message is that the underlying technology
that would be necessary are already there, or are rapidly maturing.
In practical terms, what would be the rate limit in factors for scaling this? So first,
producing allelic series at scale would be one challenge. And the second, and this would
probably be the harder one, would be adapting diversity of cellular and molecular assays
to multiplex readout. But I think in sharp contrast with other large-scale projects,
sequencing would be the least of the challenges, and I think it would be kind of a different
portfolio of what would be hard about this project. So, in terms of actual generating
allelic series, it’s much easier to imagine how one would get to a number like 10 million,
if we are talking about dense or saturating mutagenesis, which encompasses both of the
examples that I gave you, and you can imagine tackling 2,500 one kilobase regions would
get you to 10 million, assuming that you add three substitutions and a deletion for every
residue or nucleotide.
And this can be spread between coding sequences, so let’s say 500 clinically actionable genes
and several thousand cis-egulatory elements. What I think is, you know, I also came up
with it this morning, and Joe Ecker talked a bit, it’s a bit difficult, more difficult
to imagine with the current methods, is how to multiplex the functional analysis of sparsely
located variants, so viable candidates for GWAS associations. But you can imagine, with
CRISPR, other related methods, there are possibilities, but probably on a per variant basis, this
is going to be much more expensive than these dense approaches. Okay, so the other challenge
is functional assays and the concepts in the slide are adapted from another one of my colleagues
that you dubbed, Doug Fowler [spelled phonetically]. And the key point here is that one doesn’t
need to develop 1,000 functional assays to study 1,000 sequences of interest.
So, rather, you can funnel related types of sequences into identical or at least similar
assays. And this, of course, applies to regulatory elements where, you know, they can be read
out with transcriptional activation, at least, in answers. But you can also imagine that
all members of a particular class of printing domains could be read out using related assays.
And then the second point is that once you have these allelic series constructed for
a particular element, regulatory element or protein of interest, you can essentially put
the same allelic series through a variety of biochemical or cellular assays, including,
for example, many cell lines. And so, we’re really talking about building a matrix where,
you know, you have 10 million rows and the columns are the experimental measurements.
But once you build the first column, it becomes easier to do more.
So the limitations in this kind of data are familiar. We live with a context problem for
the whole history of molecular biology. Functional assays are not human’s, molecular functionality
is not pathogenicity, but these are related attributes, and I think these can be informative
about one another. And so, and you can also imagine that the choice of context to pursue
would presumably be informed by other projects like ENCODE and Roadmap and so on and so forth,
namely what, you know, what cell type a particular enhancer is, is relevant to studying, for
example.
So, how would the resulting data be used? So, first, this would be a massive database
of experimental measurements associated with individual SNPS or point mutations, so it
would be dramatically larger than the training datasets that are available today, and might
also -- and I imagine there are probably different opinions on this -- but could be directly
useful as effectively pre-computed interpretations for view assays in the most relevant genes,
and this contrasts with the model of first waiting to see the variant, and then trying
to go back and do the functional analysis, which I think is pretty inefficient.
Second, the focus on translational implications is great, and again, as I think Garrett brought
up this morning, let’s not lose sight of biology as a goal, and a data set like this
would be informative about both structure and function at single base pair resolution.
And then once you learn lessons about, let’s say, particular, you know, mutations, in particular,
residues in protein domains, you can then extrapolate those residues to other members
of a functional class.
So, what would success depend on? So if you worked backwards from this kind of a budget
and a goal of 10 million variants, you get to about $30,000 per kilobase of DNA subjected
to saturation mutagenesis, which, to me, seems reasonably generous. As it turns out, dochtolagote
synthesis is already using microarrays, is already very, very cheap. So that, in itself,
would probably not be the major cost barrier, but success would depend on, you know, having
better methods for producing allelic series, and then, again, probably more importantly,
developing functional assays that are compatible with multiplexing, scalable and recyclable
to different elements.
So it’s a different set of activities and sequencing, but I think that the knowledge
of NHGRI could prove critical in figuring out how to effectively coordinate and scale
a project like this. A lot of the technical know-how to get started is already there.
You can imagine this proceeding in kind of a phase fashion with lessons with respect
to assay development and so on and so forth kind of applied as you go along. So I’ll
stop there and I’m happy to take any questions.
[applause]
Male Speaker: Time for two questions.
Female Speaker: Jay --
Jay Shendure: Yeah.
Female Speaker: -- well, I wasn’t sure if your assays covered
this, but one gnawing question is that we often assume a base change is a missense change,
when, in fact, it could be a splice change. So, is there a way to take the coding region
of a gene and determine whether every single base change impacts splicing in one of these
assays?
Jay Shendure: So, yeah, one could -- I mean, you know, again,
there can be both coding and non-coding basis that affects splicing --
Jay Shendure: -- so probably the best way to do it would
be to adapt genome editing methods to similar kinds of multiplexing, where you’re effectively,
you know, doing dense mutagenesis of a region with those methods, and that’s probably
the way to go, and I think that’s certainly a possibility. Yeah, David?
Male Speaker: So, I think you’re on to exactly the right
thing. I personally think this is incredibly important. I think, two just additions, I
mean, I think it’s great. One is, or -- there’s going to be assays of convenience that report
in some molecular function. In your slide, it says, you know, molecular function doesn’t
equal pathogenicity. One way of framing this is that we need to train our understanding
of what assays are relevant based on the fact that they faithfully report the relationship
between genotype and phenotype, that’s point one.
And then point two is, that is of direct relevance to pharmaceutical development, because the
input to drug screening is an assay, and the key issue in drug development is, is that
assay predictive of downstream clinical response? Because if it’s not, you’re going to spend
10 years until you figure it out when you have a drug and you do clinical trials. So,
this is an incredibly important sort of triangle, if you will, of the assay, its genetic variation,
the human, its disease variation, and then, that goes into screening or informed screening.
So, let’s not settle for the multiplex convenient, lots of stuff we can measure. No, no, I’m
saying, and let’s use it to figure out what should we be measuring.
Jay Shendure: Absolutely. So I think there’s -- so I agree
with everything you said, and I think you made this point indirectly earlier this morning
as well. I think there’s an argument for getting started with assays of convenience.
And, you know, and I think in many cases we’ll be surprised by how little we know about which
assays are the most relevant. But, I -- your point is fully taken, and I agree 100 percent.
Male Speaker: So I totally agree with David, and a corollary
of that is that we should, you know, in tandem, link this to actual, you know, clinical genetic
data, so that you could say, okay, these p53 mutations that I’m predicting to have this,
you know, effect, do I really see them in either normal comparisons or in hereditary
cancers and so on? So I mean you’d like to be able to have that kind of synergy between
the actual record that you’re seeing and what you’re kind of measuring in the lab,
but, you know, many of us agree that this kind of high-throughput functional screening
is the way to go, particularly if you can pre-compute.
Jay Shendure: Yeah, my guess is that groups of allele -- there
will be groups of alleles that behave similarly, and you can probably, you know, use what data
is available to, you know, validate a particular subset as likely being clinically relevant
and go from there, but yeah, so --
Male Speaker: Thanks, Jay.
Jay Shendure: Yeah --
Male Speaker: Sorry to stop the questions, but it’s getting
late, and I do want to move on.
[end of transcript]