AI transcript
As you may know, we launched a new short-form news show last year, 16 Minutes,
where we cover recent news, the A6NZ podcast way.
What’s Hyped with Real? Why They Matter for Advantage Point in Tech?
And that show has continued in a separate feed for quite some time now.
You can subscribe to it, if you haven’t already,
in your podcast app by searching for 16 Minutes A6NZ.
But I’m also sharing the latest episode here in this show feed,
since we sometimes cover not just multiple news items,
but a single topic, prompted by recent headlines,
like we did on our episodes on esports and the opioid crisis.
This week, the topic is personal genomics, the promise, the perils,
where are we really today and where could we be going next?
We start with an article by Peter Aldhaus on, quote,
“Ten years ago, DNA tests were the future of medicine.
Now, they’re a social network and a data privacy mess.”
The article refers to a series of events,
everything from companies like 23andMe and the FDA,
to some of the headlines we’ve seen lately
around criminals being caught based on their relatives’ DNA.
There’s also a number of companies cited in the article who offer such tests.
To be clear, none of the following discussion
should be taken as investment advice.
Please see a6nz.com/disclosures for important information.
So that’s a context and super quick summary.
Now, let me introduce our A6NZ expert,
general partner Jorge Conde, who has a long history in this area.
Since it’s a turn of a decade and the first episode of January,
I thought it’d be great for us to do sort of a Janus-themed
look back, look forward,
starting with quick reactions on reading the piece.
Well, when I read the BuzzFeed piece, which was super interesting,
it took me back to a very specific moment in time.
And I was living in this world.
I was in the personal genomic space.
I had just started a startup that was looking
to essentially interpret full genome data at scale.
If there was something in DNA that could be found
to be relevant or actionable,
we were building technology to detect that.
But what I thought was really neat is,
I’m reading this 10-year retrospective,
if I go back to that moment in time,
I actually participated in a piece that was in some way
a 10-year prospective look
on what the future of personal genomics would look like.
And this is in the 2008 timeframe, more or less.
I get an outreach from, of all things, GQ Magazine.
They had an author, a guy by the name of Richard Powers,
who had just written a book and won all kinds of awards.
He wanted to write about the experience
of what it would mean to have his full genome sequenced
and essentially revealed to him.
And we had started this company known
with the idea that we would be among the first
to fully sequence individuals
and interpret their DNA for them.
But what’s really interesting is,
if you almost read that piece as a companion
to this backward-looking look,
you get the forward-looking look
of what the next 10 years
in personal genomics would look like.
– What was it called?
– It was called “The Book of Me.”
– Oh, fantastic.
What a great title.
So then what is your take?
What’s hype, what’s real here
when it comes to the promise of personal genomics?
The whole complaint of this article
is that we were promised one thing.
They were supposed to be the future of medicine,
but hey, instead we got this big data privacy mess.
– So looking 10 years back of what was hype,
or at least over-expectation,
was that people, in general,
would have a deep curiosity to understand their DNA.
– You’re saying that part is hype?
I would think that part is reality.
– Ah, well, what’s really interesting
is if you look at several companies named,
I think all had at some level an idea
that there would be a large number of people
that wanted to very deeply understand
any sort of secrets or actionable insights
that you could draw from your own genomic information.
And while those people definitely exist,
I don’t think that a large market materialized
around those people.
In fact, one of the eye-opening things for me
when I was starting my company back in 2008,
my ancestry.com was primarily selling subscription services
for getting into these sort of ancestry databases.
– Yeah, online family trees, yeah.
– So I remember I downloaded the S1ancestry.com’s
subscription revenue with something on the order
of $200 million that year.
So another question is,
do people fundamentally want to understand their DNA
in terms of health risks and the like?
Or do people have a fundamental curiosity
to know who they are and where they come from?
– Oh, that’s where you’re saying the difference
between what’s the actual market for this kind of,
there’s a curiosity, but not necessarily a market
for DNA around it.
– Exactly, so people want to understand
who they are and where they come from.
And if it happens to come from DNA data, great.
If it happens to come from looking at ancestry databases,
that seems to be a pretty reasonable substitute
for getting that insight.
– So okay, so you’re saying one of the things that’s hype
is that people may not necessarily want DNA data
specifically, what else is hype?
– I think one of the other things that was potentially
hyped certainly at that time in 2008, 2009, 2010 timeframe
is that there would be something deeply concrete
about DNA that would determine
what your potential health risks
and therefore what your potential outcomes might look like.
You know, sort of it’s this idea that DNA is destiny
when it comes to your health.
Now that’s certainly true in some subset of diseases.
The subset of diseases that are known to be monogenic.
– Right, so single factorial driving it.
– Exactly, when there’s a mutation in a gene
that results in a specific condition,
like a sickle cell anemia.
– Right, which we talked about in our CRISPR episode.
– But when you start to look at things
that are much more complex, much more multifactorial.
– Like cancer, many other diseases.
– Cancer, metabolic disorders, you know,
pick any number of cardiovascular risks.
There are certainly genetic contributors,
but as a lot of experts in the field say is,
you probably get that same level of information
from getting a good family history.
– Right, so basically the second hype piece you’re saying
is that it is not a direct link, a map from oh,
here’s your DNA and then oh, here’s all the diseases
you’re gonna get, not get, et cetera.
And here’s the precise risk you have
for this disease based on me analyzing your DNA.
So I think that’s probably an area
where expectations were probably higher
than where we were in reality,
in terms of how actionable is this information
for someone that is seeking to manage their health.
– So that’s maybe one of the things
where maybe the promise hasn’t quite come through yet.
– And I think another area that is really interesting
is a lot of these businesses were conceived
as subscription businesses.
Where I would give someone a DNA kit for Christmas,
they would get their genome scan
and then they would engage with that
on some regular basis.
And I would suspect that the vast majority of people
that had those DNA scans done, oh, so many Christmases ago,
probably haven’t logged in in a while.
So if you had a genome scan done in 2009
and you did another genome scan in 2019,
I can almost guarantee you that your ancestral makeup
would look different over the course of those 10 years.
– Simply because of the available data.
– We just know more, that’s right.
You, of course, haven’t changed who you are,
but who an ancestry map tells you you are has changed.
– Okay, so that’s where maybe things were hyped
or not delivered yet or promised
and didn’t quite come through.
Now let’s quickly talk about the reality.
So where are we today?
What is possible right now, truly, with personal genomics?
– Well, the first thing I would say is genomics more broadly
has delivered a lot over the course of the last 10 years.
In fact, I will say this as an expert,
not as an entrepreneur in the genomic space,
but as a parent of many children,
one of the fascinating things that I saw was
the time when my wife was pregnant with her oldest child,
you still could not get enough of a signal
from maternal blood as to whether or not
there was sufficient fetal DNA in circulation
to determine whether or not there were
genetic abnormalities.
By the time we had our last child,
that was routine standard of care.
The other example is when the child is actually born,
the mandated genetic tests when a child is born by a state,
and some of the ones you could also opt into,
that menu of tests that were available multiplied
in the relatively few years between the time
when we had our first child and our last child.
– So roughly a decade span.
– So we’ve seen a lot of advance just in the use
of genetic information and the practice of medicine,
and that’s a remarkable advance forward.
So now let’s focus on personal genomics specifically.
One of the promises of personal genomics even back in 2009
was predicated on the fact that there would be power
in numbers.
And in large part, that’s why some of the leaders
in this space, whether it’s 23andMe or ancestry.com
that eventually came into this,
there’s so much value in them amassing a large database.
Because in some ways, as you have more samples
in a database, you get better reads on who we are,
just genealogically, you have a higher resolution
map of the world.
Now, the risk of having a large aggregated data set,
it also becomes attempting target,
sometimes for legitimate uses for investigation,
sometimes perhaps for illegitimate uses.
– Right, this is where privacy concerns come in, exactly.
– Very interesting enough.
This privacy question, it sounds very futuristic,
but even in 2009, these concerns were very real.
If you read the terms and conditions
that services had to their credit,
they were very explicit that this information
could be used in unintended ways.
– Oh, in fact, the article even points out
that one of the companies had to actually expand
their definition of a violent crime
in order to cover it in their terms and services.
And secondly, that some of them are actually
moving to opting in to whether you can even be included
in that aspect of that database,
which is also fascinating that people can actually choose.
– Well, in GNOME, we made a decision early on
where we said we’re actually not gonna aggregate
all of the data, we’re not gonna centralize it.
We did something inverse.
What we decided to do was we would sequence an individual
and place that sequence, that genomic data
on an encrypted key that would live
in a decentralized network.
And the thought was you could keep the queries centralized.
So let’s say a researcher wanted to understand
how many people in a population have this mutation
associated with this disease.
You would push the queries down
to the edges of the network.
The analysis would run locally.
The result, and only the result would come back,
get centralized, then you’d have an aggregated
answer to that question.
– That’s fascinating.
Funnily, even though it’s a very different example,
it reminds me of differential privacy.
And that was also something that Apple
made a bigger deal about in the last few years,
but in fact, it was based on a paper
from Microsoft researchers like a decade ago.
It’s a fundamental insight they have
for how to separate these two things.
So it’s kind of funny, the synchrony of all that.
– Yeah, and arguably we were 10 years too early.
We came up with it.
– That was about timing.
– The other thing, when we thought
through these questions of privacy,
a lot of these were perceived risks.
We didn’t know, but we wrote a lot of risk factors
out to getting yourself sequenced.
And among them, we had things that sound fantastical,
like if someone had an entire readout of your genome,
they could essentially synthesize your genome
and then plant your DNA at a crime scene.
– Right, fascinating.
– Right, and all of a sudden you have
these genetic fingerprints of a place
where you’ve never been.
My co-founder, George Church,
who’s a professor of genetics at Harvard Medical School,
he insisted that we include,
if someone were getting sequenced,
we couldn’t ask them to get buy-in
from all of their family members.
But we could require that if any of them had a twin,
an identical twin, that that twin
would also have to sign up.
– Of course, that makes perfect sense.
So, okay, is there anything else
in what is possible right now
on the personal genomics front?
– Oh yeah, so in the present,
you could argue that on the ancestry side,
we’re getting much better at sort of
getting a high resolution view of who we are
and where we come from and all of that.
And it’s an end of one example,
but if you take the case of 23andMe,
over the course of the decade,
it has amassed a large enough genomic dataset
that it’s clearly valuable
from a research and development standpoint.
It wasn’t that long ago
when they announced a collaboration
with GlaxoSmithKline with GSK,
where GSK is essentially paying them
something on the order of $300 million
to get access to this dataset,
to be able to drive some insights from it
and potentially even follow up with people
on a very opt-in basis.
And so, that will show you that
at least on the original promise of personal genomics,
that this is one example. – The values there
and the data, yeah. – That’s been delivered, right?
And so, there is power in numbers.
And I think the question like in it,
with any other technology, with any other resources,
can we find the right balance
where we’re benefiting the commons
and not at the expense of the individual?
And I think that’s where a lot of the debate
happens in terms of are we doing the right things?
– So, that’s where we are now.
Let’s talk about the future
since we’re doing this whole Janus-themed episode.
So, given that there was this past of promise
that was and wasn’t delivered,
present of where we are,
where are we going with personal genomics next?
Or what is actually possible
based on what we already know today?
– Yeah, well, I think there are certain things
that are possible based on what we know today.
The first one is as these datasets become more rich,
the ability to derive insights from them,
that’ll be relevant for how we diagnose or treat disease.
I think that becomes increasingly more valuable over time.
So, what I mean by that is,
one of the big knocks on drug discovery and development
is that it takes a long time,
it’s very expensive and the risk of failure is high.
One of the sort of lesser known data points
is that if you have a genetic insight driving the program,
saying that I think that a particular molecular compound
is going to be effective in a particular patient population
that’s defined by some sort of genetic or genomic marker,
that molecule, that compound,
that drug has a much higher,
significantly higher chance of success.
– What does that mean practically?
Does it mean that we can actually basically,
is it natural extrapolation of that,
that there may be a future
where we do get personalized tailored medicine
based on those molecules?
– That’s right.
The extrapolation of that is that we’ll get better,
faster, cheaper drugs that are tailored
to the right population.
– Yeah, sort of like personalized cocktails
at a mass manufactured level.
– Essentially, yeah, personalized cocktails of therapies
that at least are targeted to specific populations.
– That’s actually the better way of saying that.
– Another potential future thing,
especially if we’re doing a 10 year perspective look
from today is people talk about personal genomics.
I think genomics is but one omic.
– Ah, yes, multi-omics.
– That’s right.
– Very big thing.
– If the big revolution over the course of the last 10 years
is that we were able to sequence,
read DNA at a massive scale at a low cost
at high fidelity and all those things,
that’s increasingly true across many other ways
in which biology transmits information.
And I can bore you with all of the omics.
– Go through a couple of the hit list.
I mean, proteomics is one I know from when I was at park.
– So genomics is DNA, proteomics is proteins,
transcriptomics is RNA, epigenomics is gene regulation
and how genes levels are set.
Metabolomics, the set of metabolites in your body
and of course microbiomics
and how the microbiome influences with all of that.
We’re increasingly going to read biology
across many, many frequencies.
And by the way, we can also increasingly read biology
at a higher and higher resolution,
which means you could read all of this information,
not for a single individual,
but increasingly from a single cell.
And that’s a very different thing
because now you could, for example, in a tumor,
you can understand how are the immune cells reacting
to the tumor cells?
How are the tumor cells reacting to the immune cells?
If you can read biology at that resolution,
we’re going to learn a lot more about biology.
Now, when you add to the fact
that it’s not just omics being transmitted by the cell
that we can capture at high fidelity,
but increasingly we have more sensor data
than ever before, more ability to crunch data
than ever before, I think if you look over the course
the next 10 years, it won’t be a question
of personalized genomics.
I think that will at some level be a data term.
It’ll be the question of, you know,
can you quantify individuals fully?
And we’re getting closer and closer to that.
– What would you say though?
I have to ask this
because we don’t want to be sitting here 10 years from now
and asking, so what did we get wrong 10 years ago?
Or hey, when you and I talked about this topics,
is it possible that multi-omics
is also one of these much hyped things as well?
I mean, we can’t predict the future obviously,
things play out, it’s always a matter of timing sometimes
when not, if, where are we really on the spectrum
of hype versus reality?
– Yeah, it’s a good question.
I think if we take the last 10 years as any guide,
there tends to clearly be sort of two stages,
two phases, two ages.
The first age is using technology to learn
and the second age is to use technology to act.
If we look at what happened with personal genomics
is that first age where we took to learn to gather data
ended up being I think a lot longer
than people probably originally anticipated
and we’re seeing the benefits of how we can act on that
towards the tail end of the last 10 years.
I think it’s probably reasonable to assume
that if we look over the course of the next 10 years
to your question, the dividing line between hype
and reality on something like multi-omics
for me is really the dividing line
and when do we shift from learning from information
to acting on information.
– So one thing I wondered about frankly is the parallels
and your team talked about this a lot
in terms of the parallels between engineering
and the engineering phase coming to biology
which is that when it comes to DNA in genomics,
the thing that’s been most fascinating for me to watch
for the last decade is that there is a Moore’s law
in genomics and it’s much faster than the regular Moore’s law
and yet that pacing outcome of practical application
is not necessarily on par with what happened
with the semiconductor.
So that’s where the analogy really breaks down
despite an accelerated effect.
So one question for me is what is missing in the ecosystem?
Like is it that there isn’t the ability to manufacture?
Is it a missing market as you alluded to earlier?
Are there missing components or materials?
You know, when I think of the history of innovation,
what still needs to be built out
in addition to this core fundamental technology
for this vision to come to reality?
– Ah, that’s a great question.
But first of all, I think the reason why we see a faster
than Moore’s law trend in genomics
is because the ability to sequence and interpret DNA
is really the confluence of three or four engineering marbles.
You know, if you look at the next generation sequencer,
really what it is is, you know, you’re tracking
the history and evolution of our ability
to engineer better microfluidic systems
in order to move around tiny amounts of liquids.
– All about microfluidics from Xerox.
– There you go.
So you’re seeing improvements in the ability
to engineer better chemistry.
And this is both at the nucleotide level
so we can, you know, get more efficient reactions.
And at the surface chemistry of the platform,
so you can actually run more and more reactions
in tighter and tighter real-estates.
You get more density, that’s a second wave.
The third wave is we have massive improvements in optics.
So if you’re gonna run a bunch of chemical reactions
in very, very, very small real estate,
you need to be able to detect those.
– Optical detection.
– Optical detection.
So when the actions that drive sequencing
are occurring at such density
that they fall below the pixel detection level
of the optics, you can’t see the difference between them.
So we had to see improvement in optics.
And then all of that generated data
that had to be deconvoluted with advanced computation.
– And those are the four factors.
– And those are the four factors.
– Microfluidics, optical detection,
improvements in chemistry and data, fantastic.
– So that’s that revolution.
So now why haven’t we seen sort of the output look the same?
The difference there is the output of Moore’s law
is better and smaller semiconductors.
Those could be placed within a system
that’s been designed by people,
like human beings that could be optimized.
And therefore you can get new products.
– In the case of genomics, the output of that information
has to go into a system that was not designed by human beings.
– It was designed by nature.
So Jorge, bottom line it for me.
So in this journey from looking backward
and looking forward,
where are we in the personal genomics revolution
and what should our takeaway be?
– We’re still in the early days of this revolution.
If we look over the long course of time,
we are still very much in the learning phase
and the data collection phase
and the information gathering phase.
And it will be some time before we make a mass shift
into the taking action phase
or into the productization phase
from all of this information.
But when you look where we are heading,
that day will arrive.
And that’s why we are incredibly optimistic
about what the future of genomics of multiomics
and biology more broadly will bring to our benefit.
And when it comes to the privacy, in its full iteration,
we will get the maximum power from genomic information
when virtually everyone is sequenced.
If we have perfect information,
we can theoretically draw better insights.
But that will come at important cost and considerations
for how we treat the concerns of individuals
that are contributing to that data
in a way that you’re still protecting the individual
but still benefiting the comments.
– Thank you for joining this episode.
– My pleasure.
This is a turn of the decade (and January-themed) look backward/ look forward into personal genomics, given recent and past retrospective and prospective pieces in the media on the promise, and perils, of the ability to sequence one’s DNA: What did it, and does it, mean for personalized medicine, criminal investigations, privacy, and more?
General partner Jorge Conde, who has a long history in the space, covers everything from where genealogy databases and large datasets come in to fetal testing, multi-omics, and other themes spanning the past, present, and future of personal genomics in conversation with Sonal Chokshi for episode #18 our news show 16 Minutes, where we cover recent headlines, the a16z way, from our vantage point in tech — and especially what’s hype/ what’s real. While we typically cover multiple headlines, this is one of our special deep-dive episodes on a single topic. (You catch up on other such deep dives, on the opioid crisis and other evergreen episodes, at a16z.com/16Minutes). And if you haven’t already, be sure to subscribe to the separate feed for “16 Minutes” to continue getting new episodes.
image: Petra Fritz / Flickr