0:00:02 Hi everyone! Happy New Year! I’m Zonal.
0:00:06 As you may know, we launched a new short-form news show last year, 16 Minutes,
0:00:09 where we cover recent news, the A6NZ podcast way.
0:00:12 What’s Hyped with Real? Why They Matter for Advantage Point in Tech?
0:00:15 And that show has continued in a separate feed for quite some time now.
0:00:17 You can subscribe to it, if you haven’t already,
0:00:21 in your podcast app by searching for 16 Minutes A6NZ.
0:00:25 But I’m also sharing the latest episode here in this show feed,
0:00:27 since we sometimes cover not just multiple news items,
0:00:30 but a single topic, prompted by recent headlines,
0:00:34 like we did on our episodes on esports and the opioid crisis.
0:00:38 This week, the topic is personal genomics, the promise, the perils,
0:00:41 where are we really today and where could we be going next?
0:00:44 We start with an article by Peter Aldhaus on, quote,
0:00:47 “Ten years ago, DNA tests were the future of medicine.
0:00:51 Now, they’re a social network and a data privacy mess.”
0:00:53 The article refers to a series of events,
0:00:56 everything from companies like 23andMe and the FDA,
0:00:58 to some of the headlines we’ve seen lately
0:01:01 around criminals being caught based on their relatives’ DNA.
0:01:04 There’s also a number of companies cited in the article who offer such tests.
0:01:06 To be clear, none of the following discussion
0:01:08 should be taken as investment advice.
0:01:11 Please see a6nz.com/disclosures for important information.
0:01:14 So that’s a context and super quick summary.
0:01:16 Now, let me introduce our A6NZ expert,
0:01:20 general partner Jorge Conde, who has a long history in this area.
0:01:23 Since it’s a turn of a decade and the first episode of January,
0:01:25 I thought it’d be great for us to do sort of a Janus-themed
0:01:26 look back, look forward,
0:01:29 starting with quick reactions on reading the piece.
0:01:32 Well, when I read the BuzzFeed piece, which was super interesting,
0:01:35 it took me back to a very specific moment in time.
0:01:36 And I was living in this world.
0:01:38 I was in the personal genomic space.
0:01:41 I had just started a startup that was looking
0:01:45 to essentially interpret full genome data at scale.
0:01:47 If there was something in DNA that could be found
0:01:50 to be relevant or actionable,
0:01:53 we were building technology to detect that.
0:01:54 But what I thought was really neat is,
0:01:56 I’m reading this 10-year retrospective,
0:01:57 if I go back to that moment in time,
0:02:01 I actually participated in a piece that was in some way
0:02:03 a 10-year prospective look
0:02:06 on what the future of personal genomics would look like.
0:02:10 And this is in the 2008 timeframe, more or less.
0:02:13 I get an outreach from, of all things, GQ Magazine.
0:02:15 They had an author, a guy by the name of Richard Powers,
0:02:19 who had just written a book and won all kinds of awards.
0:02:21 He wanted to write about the experience
0:02:25 of what it would mean to have his full genome sequenced
0:02:27 and essentially revealed to him.
0:02:29 And we had started this company known
0:02:30 with the idea that we would be among the first
0:02:32 to fully sequence individuals
0:02:34 and interpret their DNA for them.
0:02:35 But what’s really interesting is,
0:02:37 if you almost read that piece as a companion
0:02:38 to this backward-looking look,
0:02:39 you get the forward-looking look
0:02:40 of what the next 10 years
0:02:42 in personal genomics would look like.
0:02:43 – What was it called?
0:02:44 – It was called “The Book of Me.”
0:02:45 – Oh, fantastic.
0:02:46 What a great title.
0:02:47 So then what is your take?
0:02:48 What’s hype, what’s real here
0:02:51 when it comes to the promise of personal genomics?
0:02:53 The whole complaint of this article
0:02:54 is that we were promised one thing.
0:02:55 They were supposed to be the future of medicine,
0:02:58 but hey, instead we got this big data privacy mess.
0:03:02 – So looking 10 years back of what was hype,
0:03:04 or at least over-expectation,
0:03:06 was that people, in general,
0:03:11 would have a deep curiosity to understand their DNA.
0:03:12 – You’re saying that part is hype?
0:03:14 I would think that part is reality.
0:03:15 – Ah, well, what’s really interesting
0:03:18 is if you look at several companies named,
0:03:21 I think all had at some level an idea
0:03:24 that there would be a large number of people
0:03:27 that wanted to very deeply understand
0:03:29 any sort of secrets or actionable insights
0:03:31 that you could draw from your own genomic information.
0:03:33 And while those people definitely exist,
0:03:37 I don’t think that a large market materialized
0:03:38 around those people.
0:03:40 In fact, one of the eye-opening things for me
0:03:42 when I was starting my company back in 2008,
0:03:46 my ancestry.com was primarily selling subscription services
0:03:48 for getting into these sort of ancestry databases.
0:03:50 – Yeah, online family trees, yeah.
0:03:54 – So I remember I downloaded the S1ancestry.com’s
0:03:56 subscription revenue with something on the order
0:03:59 of $200 million that year.
0:04:00 So another question is,
0:04:03 do people fundamentally want to understand their DNA
0:04:05 in terms of health risks and the like?
0:04:07 Or do people have a fundamental curiosity
0:04:09 to know who they are and where they come from?
0:04:11 – Oh, that’s where you’re saying the difference
0:04:13 between what’s the actual market for this kind of,
0:04:15 there’s a curiosity, but not necessarily a market
0:04:16 for DNA around it.
0:04:18 – Exactly, so people want to understand
0:04:20 who they are and where they come from.
0:04:22 And if it happens to come from DNA data, great.
0:04:24 If it happens to come from looking at ancestry databases,
0:04:27 that seems to be a pretty reasonable substitute
0:04:28 for getting that insight.
0:04:30 – So okay, so you’re saying one of the things that’s hype
0:04:34 is that people may not necessarily want DNA data
0:04:36 specifically, what else is hype?
0:04:37 – I think one of the other things that was potentially
0:04:41 hyped certainly at that time in 2008, 2009, 2010 timeframe
0:04:45 is that there would be something deeply concrete
0:04:47 about DNA that would determine
0:04:50 what your potential health risks
0:04:53 and therefore what your potential outcomes might look like.
0:04:55 You know, sort of it’s this idea that DNA is destiny
0:04:57 when it comes to your health.
0:05:00 Now that’s certainly true in some subset of diseases.
0:05:02 The subset of diseases that are known to be monogenic.
0:05:04 – Right, so single factorial driving it.
0:05:06 – Exactly, when there’s a mutation in a gene
0:05:08 that results in a specific condition,
0:05:09 like a sickle cell anemia.
0:05:11 – Right, which we talked about in our CRISPR episode.
0:05:12 – But when you start to look at things
0:05:15 that are much more complex, much more multifactorial.
0:05:17 – Like cancer, many other diseases.
0:05:19 – Cancer, metabolic disorders, you know,
0:05:21 pick any number of cardiovascular risks.
0:05:23 There are certainly genetic contributors,
0:05:25 but as a lot of experts in the field say is,
0:05:28 you probably get that same level of information
0:05:31 from getting a good family history.
0:05:33 – Right, so basically the second hype piece you’re saying
0:05:37 is that it is not a direct link, a map from oh,
0:05:40 here’s your DNA and then oh, here’s all the diseases
0:05:42 you’re gonna get, not get, et cetera.
0:05:44 And here’s the precise risk you have
0:05:46 for this disease based on me analyzing your DNA.
0:05:47 So I think that’s probably an area
0:05:48 where expectations were probably higher
0:05:50 than where we were in reality,
0:05:52 in terms of how actionable is this information
0:05:54 for someone that is seeking to manage their health.
0:05:55 – So that’s maybe one of the things
0:05:57 where maybe the promise hasn’t quite come through yet.
0:05:59 – And I think another area that is really interesting
0:06:03 is a lot of these businesses were conceived
0:06:06 as subscription businesses.
0:06:09 Where I would give someone a DNA kit for Christmas,
0:06:11 they would get their genome scan
0:06:13 and then they would engage with that
0:06:14 on some regular basis.
0:06:17 And I would suspect that the vast majority of people
0:06:21 that had those DNA scans done, oh, so many Christmases ago,
0:06:23 probably haven’t logged in in a while.
0:06:28 So if you had a genome scan done in 2009
0:06:31 and you did another genome scan in 2019,
0:06:35 I can almost guarantee you that your ancestral makeup
0:06:37 would look different over the course of those 10 years.
0:06:39 – Simply because of the available data.
0:06:40 – We just know more, that’s right.
0:06:41 You, of course, haven’t changed who you are,
0:06:44 but who an ancestry map tells you you are has changed.
0:06:46 – Okay, so that’s where maybe things were hyped
0:06:48 or not delivered yet or promised
0:06:50 and didn’t quite come through.
0:06:51 Now let’s quickly talk about the reality.
0:06:53 So where are we today?
0:06:57 What is possible right now, truly, with personal genomics?
0:07:00 – Well, the first thing I would say is genomics more broadly
0:07:03 has delivered a lot over the course of the last 10 years.
0:07:05 In fact, I will say this as an expert,
0:07:08 not as an entrepreneur in the genomic space,
0:07:10 but as a parent of many children,
0:07:13 one of the fascinating things that I saw was
0:07:15 the time when my wife was pregnant with her oldest child,
0:07:17 you still could not get enough of a signal
0:07:19 from maternal blood as to whether or not
0:07:22 there was sufficient fetal DNA in circulation
0:07:23 to determine whether or not there were
0:07:25 genetic abnormalities.
0:07:27 By the time we had our last child,
0:07:30 that was routine standard of care.
0:07:32 The other example is when the child is actually born,
0:07:36 the mandated genetic tests when a child is born by a state,
0:07:39 and some of the ones you could also opt into,
0:07:42 that menu of tests that were available multiplied
0:07:44 in the relatively few years between the time
0:07:46 when we had our first child and our last child.
0:07:48 – So roughly a decade span.
0:07:50 – So we’ve seen a lot of advance just in the use
0:07:52 of genetic information and the practice of medicine,
0:07:54 and that’s a remarkable advance forward.
0:07:57 So now let’s focus on personal genomics specifically.
0:08:01 One of the promises of personal genomics even back in 2009
0:08:04 was predicated on the fact that there would be power
0:08:06 in numbers.
0:08:09 And in large part, that’s why some of the leaders
0:08:12 in this space, whether it’s 23andMe or ancestry.com
0:08:14 that eventually came into this,
0:08:17 there’s so much value in them amassing a large database.
0:08:20 Because in some ways, as you have more samples
0:08:22 in a database, you get better reads on who we are,
0:08:24 just genealogically, you have a higher resolution
0:08:26 map of the world.
0:08:30 Now, the risk of having a large aggregated data set,
0:08:33 it also becomes attempting target,
0:08:36 sometimes for legitimate uses for investigation,
0:08:38 sometimes perhaps for illegitimate uses.
0:08:40 – Right, this is where privacy concerns come in, exactly.
0:08:41 – Very interesting enough.
0:08:44 This privacy question, it sounds very futuristic,
0:08:46 but even in 2009, these concerns were very real.
0:08:48 If you read the terms and conditions
0:08:50 that services had to their credit,
0:08:53 they were very explicit that this information
0:08:55 could be used in unintended ways.
0:08:57 – Oh, in fact, the article even points out
0:08:59 that one of the companies had to actually expand
0:09:01 their definition of a violent crime
0:09:03 in order to cover it in their terms and services.
0:09:05 And secondly, that some of them are actually
0:09:08 moving to opting in to whether you can even be included
0:09:10 in that aspect of that database,
0:09:12 which is also fascinating that people can actually choose.
0:09:15 – Well, in GNOME, we made a decision early on
0:09:19 where we said we’re actually not gonna aggregate
0:09:20 all of the data, we’re not gonna centralize it.
0:09:22 We did something inverse.
0:09:27 What we decided to do was we would sequence an individual
0:09:30 and place that sequence, that genomic data
0:09:32 on an encrypted key that would live
0:09:34 in a decentralized network.
0:09:38 And the thought was you could keep the queries centralized.
0:09:40 So let’s say a researcher wanted to understand
0:09:42 how many people in a population have this mutation
0:09:43 associated with this disease.
0:09:45 You would push the queries down
0:09:47 to the edges of the network.
0:09:49 The analysis would run locally.
0:09:51 The result, and only the result would come back,
0:09:53 get centralized, then you’d have an aggregated
0:09:55 answer to that question.
0:09:56 – That’s fascinating.
0:09:58 Funnily, even though it’s a very different example,
0:10:00 it reminds me of differential privacy.
0:10:02 And that was also something that Apple
0:10:03 made a bigger deal about in the last few years,
0:10:05 but in fact, it was based on a paper
0:10:07 from Microsoft researchers like a decade ago.
0:10:08 It’s a fundamental insight they have
0:10:09 for how to separate these two things.
0:10:11 So it’s kind of funny, the synchrony of all that.
0:10:13 – Yeah, and arguably we were 10 years too early.
0:10:14 We came up with it.
0:10:15 – That was about timing.
0:10:16 – The other thing, when we thought
0:10:17 through these questions of privacy,
0:10:18 a lot of these were perceived risks.
0:10:21 We didn’t know, but we wrote a lot of risk factors
0:10:23 out to getting yourself sequenced.
0:10:26 And among them, we had things that sound fantastical,
0:10:29 like if someone had an entire readout of your genome,
0:10:31 they could essentially synthesize your genome
0:10:33 and then plant your DNA at a crime scene.
0:10:35 – Right, fascinating.
0:10:35 – Right, and all of a sudden you have
0:10:37 these genetic fingerprints of a place
0:10:38 where you’ve never been.
0:10:39 My co-founder, George Church,
0:10:42 who’s a professor of genetics at Harvard Medical School,
0:10:44 he insisted that we include,
0:10:45 if someone were getting sequenced,
0:10:47 we couldn’t ask them to get buy-in
0:10:49 from all of their family members.
0:10:52 But we could require that if any of them had a twin,
0:10:53 an identical twin, that that twin
0:10:54 would also have to sign up.
0:10:56 – Of course, that makes perfect sense.
0:10:57 So, okay, is there anything else
0:10:58 in what is possible right now
0:11:00 on the personal genomics front?
0:11:01 – Oh yeah, so in the present,
0:11:03 you could argue that on the ancestry side,
0:11:04 we’re getting much better at sort of
0:11:06 getting a high resolution view of who we are
0:11:07 and where we come from and all of that.
0:11:09 And it’s an end of one example,
0:11:12 but if you take the case of 23andMe,
0:11:13 over the course of the decade,
0:11:16 it has amassed a large enough genomic dataset
0:11:18 that it’s clearly valuable
0:11:20 from a research and development standpoint.
0:11:21 It wasn’t that long ago
0:11:22 when they announced a collaboration
0:11:24 with GlaxoSmithKline with GSK,
0:11:26 where GSK is essentially paying them
0:11:28 something on the order of $300 million
0:11:30 to get access to this dataset,
0:11:32 to be able to drive some insights from it
0:11:34 and potentially even follow up with people
0:11:36 on a very opt-in basis.
0:11:38 And so, that will show you that
0:11:40 at least on the original promise of personal genomics,
0:11:41 that this is one example. – The values there
0:11:43 and the data, yeah. – That’s been delivered, right?
0:11:45 And so, there is power in numbers.
0:11:46 And I think the question like in it,
0:11:49 with any other technology, with any other resources,
0:11:50 can we find the right balance
0:11:51 where we’re benefiting the commons
0:11:52 and not at the expense of the individual?
0:11:54 And I think that’s where a lot of the debate
0:11:56 happens in terms of are we doing the right things?
0:11:58 – So, that’s where we are now.
0:11:59 Let’s talk about the future
0:12:02 since we’re doing this whole Janus-themed episode.
0:12:04 So, given that there was this past of promise
0:12:05 that was and wasn’t delivered,
0:12:07 present of where we are,
0:12:10 where are we going with personal genomics next?
0:12:11 Or what is actually possible
0:12:12 based on what we already know today?
0:12:14 – Yeah, well, I think there are certain things
0:12:15 that are possible based on what we know today.
0:12:18 The first one is as these datasets become more rich,
0:12:20 the ability to derive insights from them,
0:12:23 that’ll be relevant for how we diagnose or treat disease.
0:12:26 I think that becomes increasingly more valuable over time.
0:12:28 So, what I mean by that is,
0:12:31 one of the big knocks on drug discovery and development
0:12:33 is that it takes a long time,
0:12:35 it’s very expensive and the risk of failure is high.
0:12:37 One of the sort of lesser known data points
0:12:42 is that if you have a genetic insight driving the program,
0:12:45 saying that I think that a particular molecular compound
0:12:48 is going to be effective in a particular patient population
0:12:52 that’s defined by some sort of genetic or genomic marker,
0:12:54 that molecule, that compound,
0:12:56 that drug has a much higher,
0:12:59 significantly higher chance of success.
0:13:00 – What does that mean practically?
0:13:02 Does it mean that we can actually basically,
0:13:04 is it natural extrapolation of that,
0:13:05 that there may be a future
0:13:07 where we do get personalized tailored medicine
0:13:08 based on those molecules?
0:13:09 – That’s right.
0:13:11 The extrapolation of that is that we’ll get better,
0:13:13 faster, cheaper drugs that are tailored
0:13:14 to the right population.
0:13:16 – Yeah, sort of like personalized cocktails
0:13:18 at a mass manufactured level.
0:13:21 – Essentially, yeah, personalized cocktails of therapies
0:13:23 that at least are targeted to specific populations.
0:13:24 – That’s actually the better way of saying that.
0:13:25 – Another potential future thing,
0:13:27 especially if we’re doing a 10 year perspective look
0:13:31 from today is people talk about personal genomics.
0:13:34 I think genomics is but one omic.
0:13:35 – Ah, yes, multi-omics.
0:13:36 – That’s right.
0:13:37 – Very big thing.
0:13:39 – If the big revolution over the course of the last 10 years
0:13:42 is that we were able to sequence,
0:13:45 read DNA at a massive scale at a low cost
0:13:47 at high fidelity and all those things,
0:13:50 that’s increasingly true across many other ways
0:13:52 in which biology transmits information.
0:13:54 And I can bore you with all of the omics.
0:13:55 – Go through a couple of the hit list.
0:13:58 I mean, proteomics is one I know from when I was at park.
0:14:00 – So genomics is DNA, proteomics is proteins,
0:14:04 transcriptomics is RNA, epigenomics is gene regulation
0:14:05 and how genes levels are set.
0:14:08 Metabolomics, the set of metabolites in your body
0:14:10 and of course microbiomics
0:14:12 and how the microbiome influences with all of that.
0:14:14 We’re increasingly going to read biology
0:14:16 across many, many frequencies.
0:14:18 And by the way, we can also increasingly read biology
0:14:20 at a higher and higher resolution,
0:14:23 which means you could read all of this information,
0:14:24 not for a single individual,
0:14:27 but increasingly from a single cell.
0:14:28 And that’s a very different thing
0:14:30 because now you could, for example, in a tumor,
0:14:33 you can understand how are the immune cells reacting
0:14:33 to the tumor cells?
0:14:35 How are the tumor cells reacting to the immune cells?
0:14:37 If you can read biology at that resolution,
0:14:40 we’re going to learn a lot more about biology.
0:14:41 Now, when you add to the fact
0:14:44 that it’s not just omics being transmitted by the cell
0:14:46 that we can capture at high fidelity,
0:14:48 but increasingly we have more sensor data
0:14:49 than ever before, more ability to crunch data
0:14:51 than ever before, I think if you look over the course
0:14:54 the next 10 years, it won’t be a question
0:14:55 of personalized genomics.
0:14:58 I think that will at some level be a data term.
0:15:00 It’ll be the question of, you know,
0:15:02 can you quantify individuals fully?
0:15:04 And we’re getting closer and closer to that.
0:15:05 – What would you say though?
0:15:05 I have to ask this
0:15:08 because we don’t want to be sitting here 10 years from now
0:15:10 and asking, so what did we get wrong 10 years ago?
0:15:12 Or hey, when you and I talked about this topics,
0:15:14 is it possible that multi-omics
0:15:16 is also one of these much hyped things as well?
0:15:18 I mean, we can’t predict the future obviously,
0:15:20 things play out, it’s always a matter of timing sometimes
0:15:23 when not, if, where are we really on the spectrum
0:15:24 of hype versus reality?
0:15:25 – Yeah, it’s a good question.
0:15:28 I think if we take the last 10 years as any guide,
0:15:31 there tends to clearly be sort of two stages,
0:15:33 two phases, two ages.
0:15:35 The first age is using technology to learn
0:15:37 and the second age is to use technology to act.
0:15:39 If we look at what happened with personal genomics
0:15:44 is that first age where we took to learn to gather data
0:15:46 ended up being I think a lot longer
0:15:48 than people probably originally anticipated
0:15:51 and we’re seeing the benefits of how we can act on that
0:15:53 towards the tail end of the last 10 years.
0:15:54 I think it’s probably reasonable to assume
0:15:57 that if we look over the course of the next 10 years
0:16:01 to your question, the dividing line between hype
0:16:03 and reality on something like multi-omics
0:16:05 for me is really the dividing line
0:16:08 and when do we shift from learning from information
0:16:10 to acting on information.
0:16:12 – So one thing I wondered about frankly is the parallels
0:16:14 and your team talked about this a lot
0:16:15 in terms of the parallels between engineering
0:16:17 and the engineering phase coming to biology
0:16:19 which is that when it comes to DNA in genomics,
0:16:21 the thing that’s been most fascinating for me to watch
0:16:24 for the last decade is that there is a Moore’s law
0:16:27 in genomics and it’s much faster than the regular Moore’s law
0:16:31 and yet that pacing outcome of practical application
0:16:33 is not necessarily on par with what happened
0:16:34 with the semiconductor.
0:16:36 So that’s where the analogy really breaks down
0:16:37 despite an accelerated effect.
0:16:41 So one question for me is what is missing in the ecosystem?
0:16:44 Like is it that there isn’t the ability to manufacture?
0:16:46 Is it a missing market as you alluded to earlier?
0:16:49 Are there missing components or materials?
0:16:50 You know, when I think of the history of innovation,
0:16:51 what still needs to be built out
0:16:54 in addition to this core fundamental technology
0:16:56 for this vision to come to reality?
0:16:57 – Ah, that’s a great question.
0:17:00 But first of all, I think the reason why we see a faster
0:17:02 than Moore’s law trend in genomics
0:17:06 is because the ability to sequence and interpret DNA
0:17:11 is really the confluence of three or four engineering marbles.
0:17:14 You know, if you look at the next generation sequencer,
0:17:17 really what it is is, you know, you’re tracking
0:17:18 the history and evolution of our ability
0:17:21 to engineer better microfluidic systems
0:17:22 in order to move around tiny amounts of liquids.
0:17:24 – All about microfluidics from Xerox.
0:17:25 – There you go.
0:17:27 So you’re seeing improvements in the ability
0:17:30 to engineer better chemistry.
0:17:32 And this is both at the nucleotide level
0:17:35 so we can, you know, get more efficient reactions.
0:17:37 And at the surface chemistry of the platform,
0:17:40 so you can actually run more and more reactions
0:17:41 in tighter and tighter real-estates.
0:17:43 You get more density, that’s a second wave.
0:17:47 The third wave is we have massive improvements in optics.
0:17:50 So if you’re gonna run a bunch of chemical reactions
0:17:52 in very, very, very small real estate,
0:17:54 you need to be able to detect those.
0:17:54 – Optical detection.
0:17:56 – Optical detection.
0:17:58 So when the actions that drive sequencing
0:17:59 are occurring at such density
0:18:01 that they fall below the pixel detection level
0:18:03 of the optics, you can’t see the difference between them.
0:18:05 So we had to see improvement in optics.
0:18:08 And then all of that generated data
0:18:12 that had to be deconvoluted with advanced computation.
0:18:13 – And those are the four factors.
0:18:14 – And those are the four factors.
0:18:16 – Microfluidics, optical detection,
0:18:18 improvements in chemistry and data, fantastic.
0:18:19 – So that’s that revolution.
0:18:22 So now why haven’t we seen sort of the output look the same?
0:18:25 The difference there is the output of Moore’s law
0:18:28 is better and smaller semiconductors.
0:18:31 Those could be placed within a system
0:18:32 that’s been designed by people,
0:18:34 like human beings that could be optimized.
0:18:37 And therefore you can get new products.
0:18:40 – In the case of genomics, the output of that information
0:18:43 has to go into a system that was not designed by human beings.
0:18:44 – It was designed by nature.
0:18:46 So Jorge, bottom line it for me.
0:18:49 So in this journey from looking backward
0:18:50 and looking forward,
0:18:52 where are we in the personal genomics revolution
0:18:54 and what should our takeaway be?
0:18:57 – We’re still in the early days of this revolution.
0:19:00 If we look over the long course of time,
0:19:03 we are still very much in the learning phase
0:19:05 and the data collection phase
0:19:07 and the information gathering phase.
0:19:10 And it will be some time before we make a mass shift
0:19:11 into the taking action phase
0:19:14 or into the productization phase
0:19:15 from all of this information.
0:19:17 But when you look where we are heading,
0:19:18 that day will arrive.
0:19:20 And that’s why we are incredibly optimistic
0:19:23 about what the future of genomics of multiomics
0:19:26 and biology more broadly will bring to our benefit.
0:19:29 And when it comes to the privacy, in its full iteration,
0:19:33 we will get the maximum power from genomic information
0:19:36 when virtually everyone is sequenced.
0:19:38 If we have perfect information,
0:19:40 we can theoretically draw better insights.
0:19:44 But that will come at important cost and considerations
0:19:47 for how we treat the concerns of individuals
0:19:50 that are contributing to that data
0:19:52 in a way that you’re still protecting the individual
0:19:54 but still benefiting the comments.
0:19:55 – Thank you for joining this episode.
0:19:56 – My pleasure.