[RADIOLAB INTRO]

 

PAT WALTERS: Hey, this is Radiolab. I'm Pat Walters. And today we have the fifth episode of our miniseries ...

 

[MUSICAL: G!]

 

PAT: G. Last episode we had a story from Lulu Miller about eugenics. It was all about scientists who were applying the Darwinian idea that species can be shaped by natural selection to humans. To us. Like, instead of waiting for nature to choose which individuals of the human species were most quote-unquote "fit," they thought they could speed things along, and in the process create, like, a perfect human race. Which as we got into last time, was a disaster. And at a certain point, Lulu argued that these eugenicists in emphasizing, like, their one narrow idea of perfection over everything else, they sort of missed the point of Darwin.

 

LULU MILLER: Darwin talks about one thing, this one ingredient that he marvels at, he doesn't understand why it’s there. The thing to which we all owe our existence on Earth. Variation.

 

PAT: Variation. That, like, what makes a species resilient is difference. But …

 

STEVE HSU: Ah, that's a very selective reading of Darwin, you know?

 

PAT: Turns out, not everyone agrees with her.

 

STEVE HSU: It is true, of course, that variation is really important to evolution, but it's variation coupled with selection that actually gives you success. So, you know, I disagree with her interpretation.

 

PAT: So not too long after we talked to Lulu, I did an interview with a scientist who takes a very different position on all this. And he's come into a bit of controversy. Some people argue that he's taking those old ideas, that certain people can decide which humans are fit enough to exist and which ones aren't.

 

STEVE HSU: I think you have to be very careful because nobody here is trying to optimize one specific trait or number. What we're doing is identifying outcomes that I think most people agree maybe are not good.

 

PAT: And arguably, he’s walking those ideas into the future.

 

PAT: Hello?

 

PAT: Okay, to back up a beat.

 

STEVE HSU: Hello.

 

PAT: Hi. Is this Steve?

 

STEVE HSU: Yeah, this is Steve. Who’s this?

 

PAT: Hi, Steve. This is Pat.

 

STEVE HSU: Hey Pat, how are you?

 

PAT: This is Steve Hsu.

 

STEVE HSU: I'm a theoretical physicist who also works in computational genomics.

 

PAT: Steve's in his early-50s, has short, black hair, little wire-rimmed glasses. And he says he's been interested in genomics, the power of DNA, really since he was a kid.

 

STEVE HSU: Yeah, actually so when I was a kid I watched too much TV.

 

PAT: [laughs] Is that right?

 

STEVE HSU: Yeah, this was the '70s. There was no parenting going on.

 

PAT: [laughs] Okay.

 

STEVE HSU: Things were so laissez-faire back then. And so when I went home in the afternoons, the very favorite thing I would watch was Star Trek, the original Star Trek.

 

PAT: Mm-hmm.

 

[CLIP, STAR TREK: Captain to bridge.]

 

STEVE HSU: With William Shatner as Kirk, and ...

 

[CLIP, STAR TREK: Spock here.]

 

STEVE HSU: Spock.

 

STEVE HSU: And in the Star Trek universe, in the late 20th Century ...

 

[CLIP, Khan: My genetically-engineered intellect allows us to survive.]

 

STEVE HSU: They had the so-called Eugenics Wars in which some genetic supermen were created ...

 

[CLIP, Captain Kirk: Chekov, who is this man?]

 

STEVE HSU: … by technology.

 

[CLIP, Chekov: A product of late 20th-century genetic engineering.]

 

[CLIP, Captain Kirk: What do you want with us?]

 

STEVE HSU: You know, they were smarter and more capable. And they almost took over the Earth. And all that was quite vivid in my mind when I was growing up.

 

PAT: And he says it also made him wonder. Steve, like lots of kids growing up in the 1970s had to take an IQ test at some point, and he says he scored really high. Like, in the top 99 percentile. And he thought to himself, why is that?

 

STEVE HSU: Am I getting better vitamins? Is it because my mom and dad, you know, make me go to bed early at night? Is it environmental causes that are making me different from my peers, or is there something different about my DNA?

 

PAT: And he says he, at one point took this question to his local library.

 

STEVE HSU: I found this whole section where they had book after book about studies done on identical twins, how they measure IQ or how they measure cognitive ability. So I just -- it was very stark in my mind that something about our DNA could influence the power or effectiveness of your brain.

 

PAT: This was still the late 1970s though, and at this point, science just wasn't quite ready to tackle those questions in a serious way.

 

STEVE HSU: At that time we had way to actually directly measure DNA. And even by the time I was in college, we had not -- we were nowhere being able to sequence a human genome.

 

PAT: So Steve went in a different direction. He went to grad school for physics.

 

STEVE HSU: Said, "Well, I'm gonna work on black holes and quantum field theory because where I ... you know, it would take huge technological breakthroughs for us to make progress on these questions in my lifetime."

 

PAT: But ...

 

[CLIP: applause]

 

PAT: It wouldn't take as long as he thought.

 

STEVE HSU: Yes, because 15 years later ...

 

PAT: This is when, roughly?

 

STEVE HSU: This was 2000 ...

 

[CLIP, President Clinton: Thank you.]

 

PAT: June 26th, 2000, to be exact.

 

[CLIP President Clinton: Good morning. The moment we are here to witness was brought about ...]

 

PAT: Standing behind a podium in the East Room of the White House, President Bill Clinton announced ...

 

[CLIP, President Clinton: We are here to celebrate the completion of the first survey of the entire human genome. More than a thousand researchers across six nations have revealed nearly all 3 billion letters of our miraculous genetic code.]

 

STEVE HSU: I saw it come to fruition.

 

[CLIP Newscaster: Full genome sequencing.]

 

[CLIP Newscaster: Genome sequencing.]

 

[CLIP Katie Couric: Mapping the female genome.]

 

[CLIP Newscaster: Research projects are underway ...]

 

STEVE HSU: The huge advances in our capability to read out somebody's DNA.

 

PAT: And along with these advances, Steve watched as the price of all this ...

 

[CLIP, Newscaster: The first human genome was sequenced at a cost of $1 billion.]

 

PAT: Dropped dramatically.

 

[CLIP, Newscaster: Today the cost is 10 grand.]

 

STEVE HSU: This kind of super exponential decrease.

 

[CLIP, Speaker: Genomic sequencing for the several thousand dollar range.]

 

[CLIP, Speaker: Around about a thousand dollars.]

 

STEVE HSU: In cost of genotyping.

 

[CLIP, Speaker: You have $100 genome.]

 

[CLIP, Speaker: $50.]

 

[CLIP, Speaker: The cost is going to come down even more.]

 

STEVE HSU: And I realized, wow.

 

[CLIP, Newscaster: It is amazing how fast ...]

 

STEVE HSU: If this technology's advancing this rapidly ...

 

[CLIP, Speaker: It'll be possible to note the genetic makeup of a baby before it’s born.]

 

[CLIP, Newscaster: Before it’s born or even conceived.]

 

STEVE HSU: Some of these crazy science fiction ideas about genomics and genetic engineering will come true. And if I get to be one of the scientists who makes real some amazing trope from science fiction, that would be the most awesome thing in the world.

 

PAT: And you might be thinking -- I was, anyway -- that maybe Steve missed the point of that Star Trek episode about the Eugenics Wars, but that was his interpretation.

 

STEVE HSU: So those thoughts were in my mind when I thought, what is it gonna take for us to figure out the genetic architecture of human intelligence?

 

PAT: So around 2011, Steve started to pivot from physics to genomics.

 

STEVE HSU: Just hoping to find a particular place in the genome that was influencing IQ score. But now ...

 

[CLIP, Diane Sawyer: I'm Diane Sawyer. Tonight on 60 Minutes ...]

 

PAT: Steve and his team ran into a problem.

 

[CLIP, interviewer: You mentioned the human genome and all of the things that it was supposed to do. Actually, a lot of people have been disappointed.]

 

[CLIP: They should be.]

 

PAT: Genetics turned out to be way more complicated than a lot of people thought at first.

 

[CLIP, interviewer: Because great things were promised, and it hasn't really happened.]

 

[CLIP: Yeah, it was definitely oversold, and ...]

 

PAT: The initial hope had been, like, let's find the one gene that causes this trait, and the one mutation that causes this disease. And in early years, scientists found a few of those sort of one-to-ones, but only a few.

 

MEGAN MOLTENI: They failed to turn up, you know, some of the hidden connections they had expected to find or they turned up stuff but then they couldn't be replicated.

 

[CLIP: I mean, there were scientists saying within a decade all human diseases would be cured. Well, here we're a decade out, not too many have been done.]

 

MEGAN MOLTENI: Um, that was kind of the -- the first phase.

 

PAT: This is Meghan Molteni.

 

MEGAN MOLTENI: I'm a staff writer at Wired.

 

PAT: And as she explained to us ...

 

MEGAN MOLTENI: What they found was that there are ...

 

PAT: It quickly became clear that most traits in a human being aren't caused by a single gene or even a handful of genes. They often arise out of the complex interaction of hundreds or thousands or even tens of thousands of genes and other bits of DNA working in concert. So the challenge sort of shifted. It wasn’t about finding a single gene or mutation for any one thing. It was about mapping these huge swathes of the genome and looking for variations.

 

STEVE HSU: And so we realized we need many, many more genotypes and much, much more data.

 

MEGAN MOLTENI: And so how they addressed that was they actually began banding together into these big international consortia.

 

STEVE HSU: To pool together data.

 

PAT: Hmm.

 

MEGAN MOLTENI: And dial up the statistical power for everyone.

 

PAT: So in 2017 ...

 

MEGAN MOLTENI: The UK BioBank dumps 500,000 genomes into the public square.

 

[CLIP, advertisement: 23andMe.com]

 

MEGAN MOLTENI: At some point, 23andMe provides contributions.

 

PAT: Other researchers throw their data into the mix.

 

MEGAN MOLTENI: And so by the summer of 2018, they're comparing the DNA of 1.1 million people.

 

PAT: And so you have this giant data set that essentially launched this next wave of genetic studies.

 

STEVE HSU: Yes, that is the data set that has been studied to produce predictors.

 

MEGAN MOLTENI: And that kind of brings us to -- so that kind of brings us to these genome-wide association studies.

 

PAT: So, explaining these can get pretty complicated, but the basic idea is this. Let's say you have a bunch of tall people and you want to find out what makes them tall genetically. So you take the genomes of all the tall people, billions and billions of letters, and you feed that data into a computer. The computer then scans all the billions of letters and it looks for patterns. Like, what do these people have in common? And the computer might say, like, "Hmm, a bunch of them have a certain mutation at spot 273,674. And a bunch of them also have a mutation in spot 923,672. And another mutation in spot 38,479. And on and on and on. I'm just making this up, but Steve actually did this analysis for height.

 

STEVE HSU: And the AI algorithm identified about 20,000 different locations in your genome that determine height.

 

PAT: Or at least influence height. So 20,000 spots in the genome that have some influence over how tall you are. In the biz, this is called training the algorithm. And once it's trained, once it's found all of these patterns ...

 

STEVE HSU: Once it has figured all of that out ...

 

PAT: What you can then do is prediction.

 

STEVE HSU: For people that it's never seen before.

 

PAT: So what you do is you take the genome of someone new.

 

STEVE HSU: What we call out of sample data. This person is not involved in the training.

 

PAT: And feed just their genome into the algorithm.

 

STEVE HSU: Does this guy have A or B at this location? How about at this location? How about at this location? You do that 20,000 times. And then the thing will predict this guy's gonna be 6'2".

 

PAT: Wait, and how accurate exactly is this thing?

 

STEVE HSU: Well, so we recently, just a year and half ago, succeeded in building a predictor which has an accuracy of about plus or minus an inch.

 

PAT: That's pretty amazing.

 

STEVE HSU: Yeah.

 

PAT: And we don't know, like, what those genetic variations are doing, we just know that the computer said there's something different happening in these places for the taller people than for the other people.

 

STEVE HSU: That's correct. The AIs are almost like black boxes. You train them and then you got to carefully test them, but once -- once you validate that, it's like, oh my God, the lord or the martians just came and gave me this black box, which does this thing. It predicts height.

 

PAT: And not just height.

 

STEVE HSU: We have a pretty good bone density predictor. It turns out that's pretty heritable.

 

PAT: Huh.

 

STEVE HSU: Things like diabetes, atrial fibrillation, breast cancer, prostate cancer.

 

PAT: Wow.

 

MEGAN MOLTENI: So you can -- you can GWAS like, literally anything.

 

PAT: Again, Megan Molteni.

 

MEGAN MOLTENI: And so where this gets interesting is that ...

 

PAT: Perhaps unsurprisingly, just in the last year or so ...

 

MEGAN MOLTENI: Steve Hsu starts wanting to use these genome-wide association studies as a way to predict intelligence. Intelligence is a complex trait. So we know there are genes involved. We know there are lots of them but environment, so where you grow up, even how much you get to eat, like, all of that matters too.

 

PAT: But based on dozen of studies, lots of them done on twins, when it comes to intelligence, or cognitive ability, basically whatever we want to call what an IQ test measures, which as we know has its limitations ...

 

MEGAN MOLTENI: The evidence that we know today suggests the genes are responsible for somewhere between 20 to 50 percent of how smart people are, or how we can -- how we can measure what we call intelligence.

 

STEVE HSU: And so that gives you hope that if you had enough data, your AI algorithm could figure out how to crudely predict cognitive performance from -- from your genotype alone.

 

PAT: Now as Steve set out to do this, the first hurdle he and his team ran into was ...

 

STEVE HSU: Only a fraction of the 1.1 million people in the study had IQ score data available.

 

PAT: And without that, it would be sort of impossible to predict someone's IQ.

 

MEGAN MOLTENI: But -- but nearly every GWAS researcher on the planet was collecting educational attainment. And that's just the number of years that people have been in school.

 

PAT: So from the million-plus people, what they did know, is, like, if the person had gotten a high school diploma or a PhD. And what Steve and actually a bunch of other researchers would eventually figure out is that the genetic pattern that predicts educational attainment ...

 

MEGAN MOLTENI: Is actually very good at predicting IQ. So it's quite comparable.

 

STEVE HSU: Flash-forward to today, we have predictors for cognitive ability that correlate about 0.3 or 0.4 with actual IQ score.

 

PAT: So just to put that in context, a correlation of 1 means the computer would be able to predict the IQ score exactly every time based on genetics alone. And a correlation of 0 means the computer would basically be guessing at random. So 0.3 to 0.4, like, that doesn't seem ah, very high to me. That actually seems pretty low.

 

STEVE HSU: Well, that analogy, let me give it to you this way. If you admit a bunch of kids to college, and you have their SAT scores ...

 

PAT: Mm-hmm.

 

STEVE HSU: You can predict their GPA in college, and the correlation between those two variables is also about 0.3 or 0.4.

 

PAT: Gotcha.

 

STEVE HSU: So, we're kind of at that kind of level of capability from pure genome.

 

PAT: And the way Steve’s using that predictive capability ....

 

STEVE HSU: At the moment we're starting to use it in multiple clinics around the world.

 

PAT: That's where I start to get kind of freaked out. We'll get into all that after a quick break.

 

[ALEX: Hi. My name is Alex Libeskin and I'm calling from Los Angeles, California. Radiolab is supported in part by the Alfred P. Sloan Foundation, enhancing public understanding of science and technology in the modern world. More information about Sloan at www.sloan.org.]

 

[ADVERTISEMENT]

 

PAT: Okay, we’re back from break. I’m Pat Walters, this is Radiolab’s G. Back to my discussion with Steve Hsu.

 

PAT: So, you -- you also have a company.

 

STEVE HSU: Yeah. Actually, it's a -- it's a tech start-up with investors, venture funds and, you know, wealthy individuals.

 

PAT: And what's your company called again?

 

STEVE HSU: It's called Genomic ...

 

[CLIP: Genomic Prediction.]

 

STEVE HSU: ... Prediction.

 

PAT: Okay, and you -- you're starting to apply these things you've discovered to -- to testing.

 

STEVE HSU: Yes, genetic testing for embryos.

 

PAT: So first of all, the embryos we're talking about here are ones that were produced through IVF.

 

STEVE HSU: Yeah, IVF kids.

 

PAT: You know, basically test-tube babies. Take a little sperm, an egg, combine them in a Petri dish for later transfer.

 

STEVE HSU: So every year, about a million babies are born worldwide using IVF.

 

PAT: A million. I didn’t realize it was that high.

 

STEVE HSU: Yeah. It’s about a million worldwide.

 

PAT: So that means there are actually many millions of embryos produced. Because when you do IVF, you often produce more embryos than you’ll end up using. Which means that often times what IVF couples face is a choice.

 

STEVE HSU: Which embryos to use, and which would be the best to use. And so it is now common to do genetic testing on those embryos.

 

PAT: But typically, really basic stuff.

 

STEVE HSU: The most common thing right now is just to test to see whether the number of chromosomes is normal, to screen against Down syndrome.

 

PAT: A relatively simple test.

 

STEVE HSU: That is by far the most common kind of genetic testing that's done. But now though, if you think of the technical breakthroughs that I described to you, you can -- you can screen against much more than just Down syndrome. And so, the company that we started takes the same standard biopsy that's already used to do the chromosome count screening ...

 

PAT: But takes it a step further and actually sequences a chunk of the genome of that embryo.

 

STEVE HSU: ... and we can then apply all of the genomic predictors that I described to you to -- to an embryo. So the doc might say, you know, embryo number four looks like it’s definitely gonna have type one diabetes. Embryo number three has a very strange outlier for heart attack, et cetera, et cetera.

 

PAT: And you're applying the information you have about intelligence to those embryos and those decisions as well?

 

STEVE HSU: So, that's the most challenging question, and that's the one that everybody wants to focus on.

 

PAT: Yeah.

 

STEVE HSU: Because we can do it. But you know, whenever a journalist contacts me, even if the person I know has an unhealthy fixation on IQ and doesn't wanna talk about all the health -- positive health things ...

 

PAT: Mm-hmm.

 

STEVE HSU: ... associated with this technology and just wants to focus on the one thing that, you know, everybody gets heated up about, I still ...

 

PAT: Yeah, not naming names here.

 

STEVE HSU: Yeah. I still want to have the conversation because society needs to understand what is actually going on.

 

PAT: Yeah.

 

STEVE HSU: So our current policy, and we arrived at this after really a lot of thought and not wanting to get out ahead of where society is on this, the only thing that we report about the intelligence of the individual embryo is if the embryo is an outlier in risk for, I think the medical term is, gosh, what is it? Is it mental disability? Something like that.

 

PAT: I think it's intellectual disability?

 

STEVE HSU: Intellectual disability, right.

 

PAT: Yeah. And that, the predictor would be saying that it's likely that this embryo would have, uh, an IQ score below a certain number?

 

STEVE HSU: Yes. I think intellectual disability is probably something like IQ of 75 or something like this. And it would mean that the IVF physician will get a report saying, "Embryo number four has a very unusually large number of the variants that depress intelligence." And I think that's a reasonable thing to want to know.

 

PAT: Are -- is there a parallel track happening in your mind? Because the -- the other people who've had -- gotten excited about making super-people, besides the science fiction people are, like, the Nazis.

 

STEVE HSU: Yeah.

 

PAT: Yeah.

 

STEVE HSU: You know, the idea that you would dehumanize some people because they're less able is extremely dangerous. But this notion that, oh, we shouldn't do any of this research because there was a guy called Adolf Hitler ...

 

PAT: Right.

 

STEVE HSU: That's kind of crazy to me.

 

PAT: Explain why. Like, how do you ...

 

STEVE HSU: I would say every technology, really powerful technology, whether it's AI or genomics or nuclear energy, they have risks. That's always the case.

 

PAT: Sure. But I guess I don’t trust the IQ part of it. Like, it’s pretty well established that the IQ score isn’t a good way to determine intellectual disability. Like, I think it’s way more complicated than that.

 

STEVE HSU: Right, but keep in mind none of this is a sure thing in the sense that we're not saying that we know embryo three will have IQ below 75. We’re not saying that. We’re just saying the chances the child will have a lot of difficulty in modern society, that probability is elevated.

 

PAT: I guess this is maybe where we disagree, is that, like, I don’t -- I’m not convinced you can know what the quality of someone’s life is gonna be like based on an IQ ...

 

STEVE HSU: We’re not talking about any of that. We’re just saying that conditional on that score, if I go out in the population and I look at people with that score, a lot of them have not had very, you know, I think, positive lives. And I’m pretty sure that most mothers, when they’re pregnant, when they go to sleep at night, they’re not dreaming about that outcome. They’re dreaming about another outcome for their children.

 

PAT: It just feels like a bad idea.

 

STEVE HSU: The only question here is imagine that your sister is going through IVF, and you happen to have the genotypes of all the embryos of your sister's potential kids, and you find out, hey, embryo four is predicted to be, you know, in less than the first percentile for cognitive ability.

 

PAT: Mm-hmm.

 

STEVE HSU: But all the other ones are in the normal range or maybe even above average. Would you tell your sister?

 

PAT: Uh ...

 

STEVE HSU: I mean, that's the basic question, right?

 

PAT: Yeah.

 

STEVE HSU: Who -- anyway, we're gonna make some kind of brutal decision. Like okay, these two we implant, those eight we donate to science.

 

PAT: Yeah.

 

STEVE HSU: Right? So it was gonna happen anyway to eight of their 10 embryos, right? So the thing is, if we can help your sister a little bit, let's help. That's my attitude.

 

PAT: Mine, too.

 

STEVE HSU: I also wanna emphasize that typically the embryologist will actually just look at the shape. Like, do the cells grow in a nice symmetrical pattern?

 

PAT: Huh.

 

PAT: They just literally look at the shape of it and say, like, "Oh, that one looks nice?"

 

STEVE HSU: Yeah. Would your sister rather go with the gut feeling based on the shape, or would she rather go with some genetic evaluation?

 

PAT: Well, I don't know because -- it's probably really hard for parents to know -- to really interpret this information. They might just hear 75 and then hear probably intellectually-disabled. I don't want that. And I think that freaks me out.

 

STEVE HSU: Yeah, I do think that we need really good genetic counseling so that people understand the world that we're entering into, because again no one's saying that embryo four is gonna have IQ below 75. That's extremely bad. That -- that's actually not the message. The message is that the risks are a lot higher than for all your other embryos. That’s all we’re saying.

 

DAN BENJAMIN: Well, um ...

 

PAT: But as for what a lot higher really means ...

 

DAN BENJAMIN: I mean, there’s different ways of quantifying the strength of the prediction. I think the way to interpret it is it’s a good predictor on average, and that’s what makes it useful in research.

 

PAT: I talked to this guy named Dan Benjamin.

 

DAN BENJAMIN: But in general, it’s not a good predictor at the individual level.

 

PAT: He is a professor of economics at USC. And one of the founders of that big consortium of scientists.

 

DAN BENJAMIN: ... of the Social Science Genetic Association Consortium.

 

PAT: That helped pull together the million or so genomes that Steve’s genetic IQ predictor is effectively built on. And he says, think about that comparison Steve made to the SAT.

 

DAN BENJAMIN: You know, it’s very hard to predict who’s gonna do well in college. And SAT scores are among the better predictors of that.

 

PAT: Like on average, the kids who score well on the SAT will do well in school.

 

DAN BENJAMIN: But I mean, if you look at a university ...

 

[MOVIE CLIP: Have you boys seen your grade point average yet?]

 

DAN BENJAMIN: There are gonna be some pretty poor performers.

 

[MOVIE CLIP: It stinks! It's the lowest on campus.]

 

DAN BENJAMIN: And you might ask yourself, "Why did the school admit those students?"

 

[MOVIE CLIP: He's right. You're right. You know, what we gotta do?]

 

DAN BENJAMIN: Shouldn't they have been able to tell ...

 

[MOVIE CLIP: Toga party.]

 

DAN BENJAMIN: Those students were gonna do poorly, and ...

 

[MOVIE CLIP: Toga! Toga! Toga!]

 

DAN BENJAMIN: ... the answer is they couldn't tell. For any individual person, SAT scores are not a good predictor. There are just too many other things that matter.

 

PAT: And just to sort of shift away from the SAT back to, like, using a polygenic score to try to say something about an individual, if you had to quantify what not a very good prediction means, how would you do that?

 

DAN BENJAMIN: Well, if you picked two people randomly in the sample, and you asked, "How likely is it that the one with the higher polygenic score is actually the one who got more years of schooling," the answer is about 60 percent.

 

PAT: 60 percent.

 

DAN BENJAMIN: Yes. There is a 40 percent chance that you’ll get it backwards. And I think more to the point, in the context of embryo selection, the prediction is less likely to be right. It’s gonna be something like reducing it from 60 percent to 55 percent.

 

PAT: So according to Dan, these predictions can be useful in large groups, but on an individual level, they're just a little bit better than a coin toss.

 

DAN BENJAMIN: So I worry that companies that are offering this service, they’re exaggerating the potential gains, and also are not being upfront about what the risks are.

 

PAT: Two things on that real quick. First, Dan says, because we don’t really know how the genetic variations that predict IQ score really work, if you select against them you might accidentally be selecting for other stuff, like mental illnesses or certain diseases.

 

DAN BENJAMIN: There are these risks and we don’t even know what all of the risks are.

 

PAT: And, number two, which might even be more disturbing, Dan says the genetic data all of this is based on came only from white people of European descent. That’s the only data they could get their hands on. And consequently, if you try to use a test like Steve Hsu’s on someone who's not a white person of European descent, it pretty much doesn’t work. That correlation drops to the floor.

 

DAN BENJAMIN: So even the possibility of doing it is pretty much limited to well-off, white people at this point.

 

PAT: And so when Steve just sort of says with this confidence that the genetic predictor is pretty much like the SAT, that’s just not the whole story. It’s more complicated than that. And what worries me is that, you know, you have guys like Dan Benjamin advocating for this more complex understanding of what the statistics really mean and, you know, saying they’re meaningful in big groups but they’re not meaningful in individuals. But you know, on the other side you have Steve’s story, which is just so straightforward and compelling. And, you know, if Steve’s story and Dan’s story have to face off, I just feel like Steve’s will win every time. You know, because his story is so simple.

 

PAT: It does feel like in the end what you're able to tell someone, like, isn't that much, because it isn't a one-to-one predictor or even really close to that.

 

STEVE HSU: Well, it will get better, I guarantee you, as we get more data. The -- the predictive power will get much better. And I think it will eventually achieve a kind of capability kind of like what we can do with height where you -- you know, the -- the error is about an inch, maybe plus or minus 10 points of IQ.

 

MEGAN MOLTENI: You know, I will say that ...

 

PAT: Once again, Megan Molteni.

 

MEGAN MOLTENI: Right now, while he may be an outlier in terms of what he’s willing to offer based on kind of where the models are now, I don’t think he will be an outlier for long. The information is going to become available and it’s only gonna get better.

 

STEVE HSU: And of course there are many ethical things that we have to sort out. And I think different societies will decide different things about how they want to deal with this. And so, you know, if a particular country said, "We do not want you to ever report anything about cognitive risks. We just don’t want you to report that." I would totally respect that. But then I think you have to respect if the nation of Singapore, if they decide this is an important thing to do, well you better respect them, too.

 

PAT: Mm-hmm.

 

STEVE HSU: Otherwise you’re some kind of racist, colonialist guy who says only my ethics count. So I realize -- you know, I realize the NPR audience may not like it, but, you know, this is the world that were entering into.

 

PAT: Have people used the tool yet? Like, has the predictor tool been used by consumers yet?

 

STEVE HSU: So there are different segments to the product. The full-blown thing, where you report genetic test scores, polygenic scores, for a variety of traits is in the process of being used.

 

PAT: What does the process mean? Like ...

 

STEVE HSU: Well, you know, I'm not ready to report the birth of any child or anything. But let's just say samples have been analyzed.

 

PAT: Okay. And it's been -- and information has been provided to couples?

 

STEVE HSU: I don't know that I can comment on exactly any specifics along those lines. I'd probably have to check with our CEO.

 

PAT: One last thing. Just the other day as we were finishing this story, Steve wrote to us and said that while the company has provided risk reports to IVF doctors to various diseases like breast cancer and diabetes, they have not, quote, "Given a report warning of high risk for intellectual disability." Yet. And at the end of this, I find myself thinking about the, like, thousands of couples out there who are doing IVF and the thousands more who will do it in the years and decades ahead, and honestly wondering, like ...

 

REBECCA PITKIN: I’m Rebecca Pitkin.

 

KEVIN: I’m Kevin.

 

REBECCA GARDNER: I’m Rebecca Gardner.

 

JILL: Jill.

 

KATE: I’m Kate.

 

KATIE JAMES: Katie James.

 

WOMAN: I would love to just not use any name at all.

 

PAT: Would they want to know ...

 

KEVIN: We just finished our second cycle of IVF.

 

JILL: We have one embryo.

 

KATIE JAMES: I’m in the early stages of IVF cycle one.

 

PAT: ... what Steve Hsu can tell them?

 

KEVIN: Wow. That’s a really hard question.

 

Rebecca: Kinda makes me feel uncomfortable.

 

REBECCA GARDNER: Hmm. I think I would have a lot of anxiety over that decision.

 

WOMAN: When you hear that it’s not that accurate.

 

KEVIN: I mean, everybody wants their children to be brilliant and healthy.

 

REBECCA PITKIN: Um-- [baby crying] sorry, one second. I don’t see what harm it could do to test for that.

 

WOMAN: There are way too many questions that we would need to ask before we would ever consider that.

 

KEVIN: I think I would -- I would probably want to know.

 

WOMAN: You do want to know, but then you’re looking at a world where people who have a lot of money can select for the ones who are more intelligent.

 

REBECCA PITKIN: If it were offered to me and it wasn't cost-prohibitive, yeah, I think I would move forward with the testing.

 

JILL: It is something I think we will test for if we can afford it.

 

WOMAN: Before we do our next round, we probably will do genetic testing.

 

REBECCA GARDNER: I have to think about it long and hard.

 

WOMAN: Yeah. It’s a really tough question.

 

PAT: This episode was produced by Simon Adler, who also wrote all the music you heard in it. With help from Rachael Cusick and me. Our fact-checker is Michelle Harris. We had engineering help from Jeremy Bloom. And special thanks to Catherine Bliss. Radiolab's G is supported in part by Science Sandbox, a Simons Foundation initiative dedicated to engaging everyone with the process of science. We'll be back early next week with the final episode of G.

 

[ANSWERING MACHINE: To play the message press two. Start of message.]

 

[MEGAN MOLTENI: This is Megan Molteni calling from the Wired offices in San Francisco. Radiolab was created by Jad Abumrad and is produced by Soren Wheeler. Dylan Keefe is our Director of Sound Design. Suzie Lechtenberg is our Executive Producer. Our staff includes Simon Adler, Becca Bressler, Rachael Cusick, David Gebel, Bethel Habte, Tracie Hunte, Nora Keller, Matt Kielty, Robert Krulwich, Annie McEwen, Latif Nasser, Sarah Qari, Arianne Wack, Pat Walters and Molly Webster. With the help of Shima Oliaee, W. Harry Fortuna, Sarah Sandbach, Malissa O'Donnell, Neel Dhanesha, Ruth Samuel and Imani Leonard. And our fact checker is Michelle Harris. Thanks, bye!]

 

[ANSWERING MACHINE: End of message.]

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