Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas - 165 | Kathryn Paige Harden on Genetics, Luck, and Fairness

Episode Date: September 20, 2021

It's pretty clear that our genes affect, though they don't completely determine, who we grow up to be; children's physical and mental characteristics are not completely unrelated to those of their parents. But this relationship has been widely abused throughout history to underwrite racist and sexist ideas. So there has been a counter-reaction in the direction of removing any consideration of genetic heritage from how we understand people. Kathryn Paige Harden argues in favor of a more nuanced view: DNA does matter, we can clearly measure some of its effects, and understanding those effects is a crucial tool in fighting discrimination and making the world a more equitable place. Support Mindscape on Patreon. Kathryn Paige Harden received her Ph.D. in psychology from the University of Virginia. She is currently a professor in the Department of Psychology at the University of Texas at Austin. She is the leader of the Developmental Behavior Genetics Lab and co-director of the Texas Twin Project. She was the recipient of the Award for Distinguished Scientific Early Career Contributions to Psychology from the American Psychological Association. Her new book is The Genetic Lottery: Why DNA Matters for Social Equality. Web site University of Texas web page Google Scholar publications Amazon author profile Wikipedia Twitter

Aging is real. And so are the benefits of adding vital proteins collagen peptides to your daily routine. New vital proteins collagen sparkling water. Your daily glow-up now in three fresh flavors. Strawberry blossom, lemon, lime, and blood orange. Improved skin health in as little as 30 days thanks to collagen peptides? Cheers to that. Or go with our classic collagen peptides. So you can stay vital, stay you. Visit vital proteins.com to learn more and where to buy. These statements have not been evaluated by the Food and Drug Administration. This product is not intended to diagnose, treat, cure, or prevent any disease. Hello, everyone. Welcome to the Mindscape podcast. I'm your host, Sean Carroll. In recent years, it's been a lot of discussion and controversy about the idea of scientific racism, the use of

scientific data or techniques to purportedly justify racist policies or attitudes or thoughts about other kinds of human beings. Now, as soon as I say the phrase, scientific racism and tell you what it is, hackles get raised, right? There's going to be a lot of things. There's going to be. some people listening who say, it's not racism, it's just science. We're just doing science. We're classifying people. We're purely understanding the world. Why are you trying to ruin it by calling it racism? Other people are going to say, it's not scientific, right? This is not, this is pseudoscience. This is just a perversion of science. And on either side, people become very emotional very, very quickly. I mean, even if you're in the middle, you tend to sort of get very wary about this kind of discourse,

just because other people are so passionate, and let's be frank, it lowers your cognitive abilities when your emotional valence goes way up like that. So whatever feeling you have, when I start talking about this issue, I'm doing that intentionally. Savor that feeling. Get an idea of what that feeling is because our guest today, Paige Harden,

who is a psychologist at the University of Texas, wants us to move beyond that feeling. She wants to be able to talk about issues like genes, and DNA and how they influence, whether strongly or weekly, life outcomes, your educational attainment, what kind of job you're going to get, whether you're going to get a mental illness, become homeless, things like that. And the whole project of relating genetic information to later life outcomes is very fraught with the danger that it can be misused in racist or other discriminatory ways. And what Page wants to do is say, that's no reason not to use it, right?

a sort of counter reaction that says, therefore, we shouldn't mention DNA or genes at all. When we talk about human beings, we should treat every human being equally and not worry about their genetic heritage. Page's argument is that if we really want to make life better for people, if we really want to fight for social justice in effective ways, we need to use all the information, all the knowledge, all the scientific insight we can get. And there's no doubt from the data that there is a relationship between genes and outcomes. But what we can try to do

is use information we get about that relationship to bring that kind of equality to human life that Elizabeth Anderson talked about in a podcast a while back, not a quality of opportunity, not a quality of outcome, but a quality of dignity, treating people in ways

that lets them lead their lives in society with sort of equal amounts of dignity for every person. And that's something where, understanding how people are different, even genetically, is going to be important. She has a new book out called The Genetic Lottery, Why DNA Matters for Social Equality. The idea of the lottery being that we don't choose our DNA. Our DNA affects who we grow up to be, but we don't get credit or blame for it, right?

We shouldn't anyway. So how do you live in a world where people are given unequal amounts of talents from the start? So I was really happy with how this podcast came out. I think that Paige does a very good job, both at explaining the science, and in making the case that we have to take that science seriously. Even if it means that we have emotional reactions, let's look beyond them. Let's really think hard about it. Let's try to get it the truth.

That's something we can all get behind here at Mindscape. So let's go. Page Harden, welcome to the Mindscape podcast. Thank you for having me. You have written a book about, an interesting combination of topics, right? I mean, you have genetics and biology in there, but also psychology, sociology, politics, and even philosophy, you know, political philosophy is in there. So there's a lot of details and I want to go through them, but just so we don't

miss the point, I thought it would be best to start with a summary of the point. And so I have, a quote here from your publisher's website, I guess, Princeton University Press, and then I'll just state it out loud and you can comment on what that means. It says here, Hardin shows why a refusal to recognize the power of DNA perpetuates the myth of meritocracy and argues that we must acknowledge the role of genetic luck if we are ever to create a fair society. All right, that's it. That's big stuff. Is that accurate? That is big stuff. You can leave it to publishers to put it in in very dramatic ways. but it's not wrong.

I do think that is the point of the book. So I'm trying to describe both the science of behavioral genetics, but also trying to think about what does it mean? How can we make sense of it? Why are people uncomfortable with it? And why are discomfort with it can, in fact, actually get in our way if we're worried about more equal policies and interventions. Right.

So I guess the background to this is that on the one hand, there's just science. I mean, we're figuring things out with biology and sociology. But on the other hand, there's a history of misuse of these kinds of ideas. So there's a tendency to either continue to misuse it or to say we shouldn't use it at all because it would be misused. And you're trying to go against both of those tendencies. Yes. Well, I mean, I guess I would slightly disagree with you, which is that I never feel like there is in reality just science. There was always science in context.

Scientists are always people who are coming into the scientific enterprise with their own sets of preconceptions and ambitions and motivations and our interests of scientists don't come out of nowhere. And I think that's more or less important for different fields. But I think particularly when we're talking about research that's connecting DNA, genetic differences between people to so. socially valued behaviors and psychological characteristics. So things like intelligence or things like how far someone goes in school. There is this history, which I think most people are somewhat familiar with, of how that research has been appropriated and misappropriated historically, but I think even continuing today.

And so in many camps in psychology and in the social sciences, there's been the pendulum swinging in the opposite direction. of saying this has been so historically intertwined with people who are trying to kind of allege the inferiority or superiority of some people that we really need to kind of avoid this work entirely. That there's a scholar that I quote in the book that says there's no way to study the genetics of something like intelligence without being racist or classist. And I disagree with that perspective too.

And so in many ways, the book is trying to unravel or disengage the science from the politics in which it's been entwined for such a long period of time. Yeah, no, I'm very much on board here. I mean, I think that you're completely right in your amendment to the way that I said that there's just science. I mean, there's never just science. Completely 100% agreed. And that's a very important point, especially in this context. But, you know, it's also there's this human tendency to not want to balance two things, right? To say that, you know, to just go to extremes and say one thing or the other.

So if you're talking about genetics at all, you're a racist. And that's a caricature. But there are people who are pretty close to that attitude. And we should be reaching out to them and saying, no, look, you know, here we can learn new things about the world, right? And use them to make the world a better place because refusing to learn them is not going to change the world. Is that fair? I think that's fair. I mean, I think for people, you know, my personal strategy is when people really object to the idea of doing this type of work at all. And by this work, I mean connecting DNA differences between people, DNA sequence variation, to what are ultimately social behaviors and social phenotype, social characteristics. is to remember that often those objections are coming from a very real place of their own lived

experiences with racism and classism, with being familiar with the history of it. And so I think, particularly for people from marginalized communities, they're coming into this work with a very different kind of set of priors about the cost-benefit analysis. A major goal of the book then for me is to try to take those concerns seriously, but also articulate why I think the cost-benefit analysis is different than many people who fear this work might imagine it to be from my perspective as a psychologist. So let's actually start, I think, the substantive discussion with the sociology, politics side of things. I mean, what are the kinds of outcomes? What are the situations where equity and equality

arise or inequities and inequalities arise that you care about? What are the sort of ultimate things we're trying to connect to the underlying genetic component? Yeah. That's such a good question because I think inequality is one of those words that can be used in so many different ways. and the differences in their implicit meaning can cause people to kind of talk past each other. At the simplest level, I'm just talking about how do people's lives end up differently in goods that we care about? So these can be material goods, things like how much money you make, how much wealth you accrue over your lifetime. These can be what I would consider more psychological goods, like your opportunity to get an education,

and how much have you learned. They can be things like your subjective well-being. So if you rate, you know, how satisfied are you with your life? Or my life is the best possible life I can imagine for me. It's a common way that people get at just subjective, you know, people's one item rating of how other lives are going. We can think about actual lifespan, like how long do you live? we can think about more psychiatric things.

So depression, suicide, freedom from physical pain, freedom from psychiatric disorders. And I think what we're seeing right now pretty uniquely is how tied up those different inequalities and outcomes are with one another. So if you look back in history, you know, the wealthiest people, They maybe had better food, but they weren't necessarily buffered from infectious diseases. They didn't actually live that much longer than commoners did. But if you look now, even before the coronavirus pandemic, you're seeing that the wealthiest Americans were also the most educated, also lived the longest, but also reported the greatest subjective well-being, the greatest freedom from pain.

They even report that they like their weekends more. So you're having all of these different differences in how people's lives end up that are really entangled with one another. And a major axis of which they all tend to separate is by education level. So the gap between people who have a college education versus don't in all of these different forms of inequality is large and mostly getting larger in the latter half of the 20th century and into the 21st. That's a really interesting point because, I mean, people talk about wealth inequality. It's very easily measurable, right? And it's growing along many measures. But the fact that wealth inequality also correlates with other kinds of inequality very strongly.

So the effect is even bigger than you might guess is one that I hadn't really thought about before. Yeah. So it's not just that people are, you know, having more money to spend on consumption. But they have longer to spend it. they're living longer. And it seems to be translating into utility, into happiness in a different way. So you mentioned that the book touches on a lot of different subjects. And I think part of the reason why it does, you know, why do I end up going into political philosophy, for instance, is that as a psychologist, I think about, okay, well, I can see these patterns of correlations

in our data. But who are the people debating which of these we should take most seriously? what should be our currency of justice, what terms of inequality do we care about? And that really seems to be the province of philosophers. Well, I think that you mentioned somewhere either in the book or in a talk, the work of Elizabeth Anderson, one of our favorite philosophers. And she was a previous mind-scape guest, probably the leading person talking about equality these days. And, you know, most people enjoy the podcast, et cetera. She was great. But there is just a slice of people who see the word equality in the title of a podcast episode and assume that you're insisting on equality of outcomes for everybody, that everyone has the same wealth and the same education and everything.

And even though no one is asserting that or looking for it, certainly Elizabeth Anderson wasn't. So there is some subtlety, like you already said, in what we mean by our goals when it comes to justice and equality and fairness. Yeah. And I loved that episode with Elizabeth Anderson and Thank you. I find her writing on this to be so lucid and persuasive, and she's been really influential in my own thinking about this. Precisely because I think she is offering people a way out of a kind of tired old equality of opportunity, by which people often mean treating everyone exactly the same and, you know, whatever results we don't have to care about.

Or a quality of outcome, which also people use as kind of, this bug bear to be scared of. Do we mean that we're leveling people down to the same, you know, equality meaning identity of outcome and sameness? And, you know, something that I found really interesting about Anderson's work, as she's, you know, she's really thinking more about what do we mean by, like, relational equality? How do we relate to each other as equals? she has this really great paper that is, I might be slightly misrepresenting the title,

but it's called Human Dignity as a concept for the economy. And she's really talking about, like, well, in what ways do we treat people as equals in the sort of like human respect sense, which is different from making sure that everyone has the exact same amount of money in their bank account, but it's also different from, and we're going to treat all people exactly the same and we don't care about differences in their life outcomes that result from there. So an example that I use in the book as thinking about the American with Disabilities Act as an equal opportunity legislation. But what the Americans with Disabilities Act is doing is it's not saying, well, everyone has the same staircase, If there's differences in your enjoyment of the building, like, we don't have to care about that.

It's saying we recognize that there are human differences. And what we're trying to equalize is people's ability to participate in our common public space. And I think that is something that's really been lost in a lot of the conversations about educational inequality. You know, when I think about equality vis-a-vis education, me personally, I'm less interested in equalizing everyone's chances of getting a PhD in physics, I'm interested in how do we equalize people's ability to participate politically and economically in a way that makes them feel respected as equals, regardless of their levels of higher education. And I think that's something that America has really fallen behind on. What do Best Buy, Wayfair, Marco Polo, and Among Us have in common? They trust Linode as an alternative

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slash mindscape and start a free account today. And something that you emphasize and Anderson also emphasizes is the role of luck in all of these considerations. You know, The word meritocracy appeared in that quote that I started with. There is this sort of American myth, or maybe it's a broader myth than that, but the idea that, you know, we work hard and we deserve what we get, et cetera, et cetera. But the reality is that in genetics or in other areas of life, luck has a lot to do with it. And I mean, maybe say a little bit about, you know, how you think about that aspect, because morally it's a tricky thing. You know, do we take away from people who just get lucky, or do we just live with it? that. Yeah. Yeah. It's such an interesting, it's such an interesting rabbit hole to fall down,

even just considering the multiple meanings of the word merit, right? So I think one of the ways that we use it is, you know, in this very moral sense, right, what we deserve, like, the content of someone's character, like a merit badge. Like, you've done something to earn it. And it's an accolade that we give out, you know, as a function of dessert. And then there's a completely instrumental definition of merit, I think, which is, you know, this is something I've talked about with my father a lot because he used to be involved in hiring for FedEx. He's a pilot. And there's a number of things that pilots are hired on that are obviously unearned, right?

Like you have to be not too short to fit into a cockpit, but not too tall to fit into a cockpit. And you need to have correctable vision to 2020. These are morally arbitrary human functionings. And yet we do think that we should hire pilots on their merits, right? Which is their ability to fly a plane. I think what gets so lost in our discourse about this is, you know, people kind of shuffling back and forth between these two definitions. There's a really wonderful essay on merit and meritocracy by the philosopher Amarches Zen that I really appreciate that kind of describes this distinction between merit as earned versus merit as instrumental. But I think it affects so much of the discourse about genetics.

So this is why in the book I try to spend a lot of time talking about the difference between valuable, right, something inherently valuable about a person versus genetics as. associated with traits that are valued right now, given how we've constructed society, which I think matches on to these kind of two working definitions that people have of merit in their heads and in these conversations. I would say a similar kind of conceptual confusion surrounds the phrase, a quality of opportunity.

What exactly makes opportunity, what is opportunity and what makes equal? It's also something that kind of gets fuzzy in these conversations. Well, with that groundwork laid, let's talk about the DNA. Let's talk about our genomes here. So we've been having fun with the philosophy, but let's crunch some numbers and collect some data. So what is it for the people out there who are not experts, and I include myself there, what do we measure when we measure what is going on in a person's genome? I mean, are we looking?

I know that the state of the art is rapidly advancing, more rapidly. that I can keep up with. So are we looking at like literally the strands of DNA and counting base pairs or are we dividing up into genes and looking at frequencies? What's going on? Yeah. So I mean, I think what's so interesting is that for, you know, most of the history of behavioral genetics, we weren't measuring anything about DNA at all. You know, the idea of connecting genetic differences between people to differences and how their lives turned out predates anything about our knowledge of, you know, it predates Watson and Cricks. It predates the word gene. It certainly predates our ability to measure something specifically about people's genes.

And the opacity of that approach that we were, you know, in the era of doing twin studies or adoption studies, making inferences about the fact that people's DNA made a difference for their life outcomes without actually measuring anything about people's DNA. may, you know, contributed to that work being really controversial. There's been a huge C change with the ability to sequence, and by sequence, I mean read people's DNA letters directly, right? So everyone's genome is made up of a molecule with four, what are we called, base pairs, GCTNA, and amongst other things, people can differ in.

their sequence of DNA letter. So you might have a T in one spot, whereas I have a G in that spot. So most commonly now what people are measuring are exactly that, these one DNA letter differences between people in their DNA sequence. And those are called single nucleotide polymorphisms, and they're commonly abbreviated snips. Because of the structure of the genome, chunks of our genome are sort of co-inherited with one another.

which means that we measure a snip, but that is, quote, unquote, tagging a number of different variants that are likely to be co-inherited with that one variant that you've measured. So just because you've measured that a single base pair differs from one person to another. So number one, let's just pause and reflect on the fact that it's really amazing you can measure that one base pair. It's amazing. It's so cool. It's like, you know, I think about that all the time. I was actually just giving a lecture to a number of graduate students and PhD students and mostly economics and sociology that are trying to learn about genetics. And, you know, I just wanted to pause right there and be like, it is wild, right?

Like, it is completely wild and that we can do it, that we can do it cheaply, right? You know, we genotype participants in our lab and it costs us about $55 a person. And we can do it non-invasively, so you don't need blood. saliva or you know cheek swabs and um i worked in animal labs when i was in college sort of like laboriously doing PCR with with uh rat blood and it's wild to me how much genetic information we can get so cheaply and so easily now compared to to what it what it used to be i'm glad that you pause there yeah i want people to be impressed by this because it's it's been changing a lot And so, but what you just said is provocative because you're saying that we see that there is a difference, presumably of one base pair between this person's DNA and this person's DNA.

But it is associated presumably with extremely high probability with changes elsewhere. So we're not we're not lining up the two DNA strands and measuring every single base pair on them, right? Even at this advanced level. We're doing something a bit more coarse-grained than that. Yes. Yeah. So there are studies that are moving towards, you know, whole genome sequencing, which is a more fine green rate of the entire genome. But most commonly, currently, people are using what are called snippet rays, which are, you know, these single DNA letter differences that we know are correlated with other variants that are mostly nearby on the genome. And that are reasonably common in the population. And by common, it's usually like more than 1% of the population or more than 5% of the population. And within that population, we're talking about people who share recent genetic ancestors. So if we're thinking about like the global pool of human genetic diversity, we're missing a lot because we're missing rare variation.

And most of our studies are also missing variation that is maybe uncommon or absent in European ancestry populations, but more common elsewhere. So we're zooming in on like a pretty narrow slice of that genetic diversity. And then we're trying to measure it directly and then see if it's, you know, essentially correlated. And we'll get back to how do we get from correlation to cause maybe later in this podcast correlated with things we've measured about people. And what we've measured can be their height or it could be there how far they went in school or it could be their income or it could be their,

glaucoma, you know, pick your phenotype, depending on your discipline, probably. We actually had Joe Henrik on the podcast who makes a big deal about the psychology of weird populations. And so you're saying that there's a similar thing going on in genetics where almost all of our information is about a tiny subset of human diversity. Oh, definitely. The genetics of weird population. There's a sociologist turned geneticist, Melinda Mills at Oxford, whose work is really excellent, and she's published a couple sort of meta-science papers on this. And it's, I'm forgetting the statistic, but it's really a shocking amount of what we know about the genetics of behavior comes from white people in the UK,

Iceland, and white people in the U.S. So it's three countries that contribute the predominance of our information here. So, you know, there's a very. real drawbacks to that because in terms of, you know, all of science works on variation and by limiting ourselves in terms of variation, we're limiting ourselves to what we can find. And many of the problems that we see in social science of weird populations is also found in the genetics of weird populations. Well, that's good because one of the goals of Minescape is to let young people who will eventually be graduate students know that there's a lot of work remaining to be done.

So it sounds like... Oh, definitely. Yes. Much of the world is remaining to be genetically understood in this way. Okay, so we can, as you said, get a correlation between these features of someone's DNA and what? What are we trying to correlate them with? You gave some lists, but in your work in particularly. Yeah. So in my work, we've worked primarily with things related to education. So how far you go in school? And then things related to, to what psychologists call externalizing, what economists call risk tolerance, what epidemiologists might call health risk behavior, which are things like ADHD, conduct disorder, risk for alcohol problems,

opiate use, that sort of thing. So those are the two domains of G. G.WAS work, genome-wide association study work that my group has worked on. And there's, you know, there's groups, it's really actually an amazing field in terms of being dominated by this kind of international team science model. So there's teams all over the world who are, you know, attacking various medical phenotypes,

likeiatric phenotypes and behavioral phenotypes. So it's right, we're already treading into murky waters here, right? It's not just height or obesity. It's behaviors. And that's just a harder thing. So you already mentioned the sticky issue of there's a. a correlation that's easy enough to plot, but causation is what we care about. What are the kinds of techniques you use to ask whether or not there is really a causal relationship between what

you're measuring in the DNA and someone's educational attainment, for example? Yeah. So I think there's kind of two major classes of problems in terms of, okay, so you've done a study, you've measured these snips in people, you've done a study of a million people, you've correlated these snips with something you've measured about them. You've observed these correlations. What do they mean? So the first class of problems has to do with what I've already talked about, which is that you're not actually measuring every single aspect of the genome.

And the part of the genome that you've measured might be, its association might be driven by another genetic variant that's just been co-inherited with it. So that is a problem where people, you might basically, in the, the book I say, you know, it's like a badly drawn treasure map, right? Like, you know that like, you know, the X is in this jungle, but like your aerial view of the jungle when you're going over it versus like now you're actually walking through the jungle trying to find the exact treasure spot are kind of two different things. So you kind of localized it maybe to a region of

the genome, but you don't know the variant, the causal variant. So a lot of times people talk about these studies in terms of fine mapping studies, which are, okay, I think it's in this area, now I'm going to measure that part of the genome more closely, more reliably, with more specificity to try to figure out where in this area is driving the effect. So that's the kind of first class of problems. The second class of problems is that people's genetics are correlated with their culture, right? Because people have sex with people who are close to them.

not everyone gets to have sex with everyone else, repeat over generations, and you get a genome that is structured by a multi-generational history of our social rules about who has sex with whom. And so you could see a correlation between a gene and an outcome. That's not because the gene is causing something in my biology that's causing the outcome, but just because that gene happens to be more common

in people from this culture, this particular part of the world, and they also differ in whatever I'm studying for entirely environmental or cultural reasons. Historically, people have tried to get at that problem by essentially trying to use information from across the entire genome to estimate what are called principal components of ancestry, which are basically statistical measures of how similar people are by virtue of sharing recent genetic ancestors and controlling for those in genetic studies. So, you know, instead of comparing people who are very diverse in terms of their genetic background, I'm trying to find people who are fairly homogenous in terms of who their recent genetic ancestors are.

I'm trying to quantify their similarity based on when I'm measuring about their genome, and then I'm trying to statistically control for that. That's partially successful, but not fully successful. The best strategy is to not try to compare unrelated people, but actually try to compare family members. So the title of the book is The Genetic Lottery, which is a metaphor I like for lots of reasons. But one of the reasons is when we're thinking about the genetic differences between two siblings whose parents have the same genes, the genetic differences between them are kind of unbraided from that larger package of culture and environment and geography. Because which genes you happen to inherit from your parents is random.

And so to really try to get at, is it a genetic cause versus just an aspect of the genome that's correlated with. your environment, you need this kind of natural experiment of the fact that your parents could have given you either one of two copies of their genes and you happen to get one. And it's that randomness that gives you some callable purchase. Maybe someday in the future, we'll live in a post-scarcity society and then your company can waste money on inefficient hiring if it wants to. Until then, save your money and only pay for quality candidates on Indeed. job site that makes hiring incredibly simple. On Indeed, you can attract, interview, and hire all in one place.

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That's indeed.com slash mindscape, offer valid through September 30th, terms and conditions apply. And I know that in social sciences and computer science, there's been greatly improved sophistication in how we think about causation, people like Judeoical, etc., working with Bayesian networks and massive probability distributions. Like, do you need that level of sophistication for what you're doing here? Or is it just we look at the controlled experiments we are given access to and work with what we have? You know, I would say like there's kind of no more controversial word maybe in social science genetics than cause, right? The C word cause. Cause and predicts are words that really get us into trouble, which is I spend time in the book, a whole chapter, in fact, as you know, really defining what I mean by cause. So I think to make sense of this, it's not so much about the sophisticated,

of the analytic techniques so much as being very clear about, you know, what is a model of what a cause is that's kind of going into this type of research. And also what does that not entail? What is a cause not in this? So in this case, you know, the best thing that we have access to in humans is this kind of natural experiment of children being randomized. to genotypes conditional and their parents' genotypes. And so it fits really naturally into a framework of causation that's that's arisen around kind of like a randomized control trial, right, which is really trying to peek at the counterfactual.

What would have happened if, you know, a cause is a difference maker, essentially. What's important about that is that it doesn't necessarily mean that the mechanisms are biological. It doesn't necessarily mean that the cause is deterministic. In social science, we think about chancy causes. Chancy causes is chancy difference makers all the time, right? Like, you know, does use of iPhones amongst 12-year-olds increase their risk for depression? It does not mean that if you got your 12-year-old an iPhone, they would necessarily become depressed. I think the problem is that it's difficult for us to take that kind of chancy and determinations. average difference maker type of framework for causes when we're talking about genes.

We tend to pour in a bunch of other assumptions about what genetic causes are relative to social science causes. Well, my wife, Jennifer Willett, actually, she's a science writer, and she wrote a book on the science of self. And so she looked a little bit into these questions. And I remember very vividly how, at least at the time, because the state of the art is changing a lot, but there were very, very few individual genes that mapped cleanly onto an actual trait of a person, right?

Yeah. Ear wax is one of them. Yeah, okay, good. So it's not like you're looking for a base pair or a gene that makes you tall or makes you live long or makes you grumpy, right? It's a much more subtle kind of nuanced thing. There is no genes for everything that we are looking at here. And I think this is another thing that's hard about thinking about genetic causes,

is everything we're talking about is polygenic, influenced by many. many, many, many, many, many genes, each of which have a small effect. So when we think about genes influencing something, I think many people think of like, you know, Mendel's pea plants. Like if you got this version, then you were a wrinkly pea versus a smooth pee. Or kind of like the early 2000s pop science where people talked about the gay gene, which that didn't turn out to be scientifically accurate at all. Instead, what we're talking about is, you know, thousands or even hundreds of thousands of variants,

each of which have minuscule probabilistic effects, but an aggregate add up to something that starts to make a difference at a population level. And I think that that kind of thinking about a vast multiplicity of small-chancey things is different, I think, than how many people are originally taught about genetics. And the other complication, I'm not sure how relevant it is here, but I know from previous podcasts and talking to friends that there's not just the genome. There's how it gets expressed, right? Yes, yes. That's something that could be environmental as well as genetic, although your parents, your mom in particular do influence. Is that something you can keep track of or control for or is that just a noise in your data? So, I mean, people definitely do.

And our lab also does this kind of work. You know, DNA is a relatively inner molecule. Like I describe it as like you can have a cookbook that's sitting on your shelf, but that does not mean you have dinner on the table, right? Like something has to happen in order for there to be, you know, a product to be created. and that is very dynamic. And so there's many different processes that people talk about in terms of getting from this genetic recipe, which is, you know, we can talk about whether that metaphor is useful or not to a protein dish,

something that's made. In our lab, we look at, for instance, DNA methylation, which is one kind of molecular biomarker that's giving you some information about which parts of the genome are being expressed at a certain time in a certain tissue. What I think is amazing is that essentially despite that, despite the fact that having a gene or genetic variant doesn't mean that it's being expressed in your body or in your brain, we nonetheless are seeing these associations between just a gene sequence variation and the things that we're measuring. So part of what makes that relationship between sequence variation

and outcome probabilistic is this kind of more like epigenetic and environmental interaction that's happening. And yet on average, we still see, you know, like that kind of genetic signal coming through despite all the complexity that's layered on top of it by the other levels of our of our biological and social systems. Okay. So will we we get into the details of making these, these, I almost want to say predictions. But anyway, you know, identifying tendencies or chances or prospects. I was saying when I was doing copy edits for my book, I went through and did Control F to look for every instance of the word predict and scrutinized it about whether or not

there was a better word there. So you can call them predictions, but we should talk about what we made by that. Well, yeah, good. And also we should talk about how exactly they're made because I know that at some point we talk about the idea of a polygenetic score, right, which is somehow taking this enormous amount of data in a DNA and making into one number and predicting things on the basis of that number. So what is that? Why is there one number? Why would we ever think that one number was good enough? And is it just like the first step toward a future where we're much more multivariable? Yeah, so Apologetic score is, you're right, it's one number that aggregates kind of our best guess of your likelihood of showing a phenotype, of showing a particular outcome based entirely on information about your DNA sequence. And the way that it's constructed is that researchers, and they might have been a different group of people, or it might have been me, have done a large, what are called discovery studies,

where you have maybe 50,000 or 100,000 or a million people, and they have estimated the correlation between all of these measured SNIP, genetic variants and the outcome of interest. And now you have a huge data set that has, you know, every row is the genetic variant measured, and the column is the, you know, the estimated correlation between that genetic variant and, let's say, height in this example. And so I take those, and I measure DNA in a new group of people, and I use the results of the previous study as a way to add up genetic information on this new group of people. So if they have inherited two copies of this particular snip from their parents, then it would be two times whatever that estimated correlation is.

If they've gotten zero, it would be zero. And then I just literally sum that up over their whole genome. So it's incredibly coarse, right? I mean, it's a huge biologically nonsensical grab bag if you think about it. Right. So in the case of education, it could be genes that are correlated because of this uncontrolled population stratification. It could be genes that make you better doing math. It could be genes that make you more of a morning person. It could be genes that, you know, changed your risk of going through puberty earlier. And we know that girls who go through puberty are discouraged from more difficult math classes because they feel weird and they get more attention to. from boys. You know, it could be any number of processes that are all collapsed together there into this one number. So I think the question is like, well, why would you do that? Like, why would you make this kind of biological grab bag? One of the, and I do think that

people, well, you know, one way that the science will be moving will be trying to like have less crude, gross kind of crunchy measures compared to apologies. And one reason that you do that is that even though each of the individual snips that go into a positive score have these like infantasmally small correlations, their aggregation turns out to be as strongly correlated with some of the outcomes that we care about as are other really gross, crunchy variables, right? So like if I measure a family's socioeconomic status, you know, it's their education, their income, their occupational status, that's also aggregated.

a huge number of processes that are differing between affluent children and poor children in America, a cacophony of different mechanisms. But it's telling me something meaningful about differences in the population. And so I think of really polygemic scores as being another sort of clunky aggregate measures like our measures of SES. It's telling us that that people differ. It's allowing us to quantify those differences, but it's in buying some predictive power, you're sacrificing this mechanistic specificity. And so I think the challenge then is to kind of go back and trace out some more specific mechanisms. And in many ways, following the arc of social science in terms of we observed that, you know, poor children did worse in school long before we had really clear mechanistic. stories about why. I think I called it a polygenetic score, but that was wrong. It was polygenic

score. Polygemic, which is, you know, it's a very common error. And I, you know, I don't know why, like when we were, when we, when the field was coming up with this, we just dropped a syllable. It would kind of make more sense for it to be a polygenic score versus a polygenic score. Thank you. You're being very kind to me. Thank you for that. Thank you for assuaging my guilt. But just so I'm super clear is the idea that for every outcome we're interested, we develop a different genic score? Yes. Yes.

So, you know, a polygemic score is estimated based on a set of GWAS results that have been conducted for different phenotypes. So, you know, if I calculated your polygemic score, quote unquote, four height, right? What is my best guess of the genetic variance you have that are statistically correlated with being taller? That would be different than your polygemic score for educational attainment. Got it. Good.

At the same time, what we see is that, you know, you might have done a study of educational attainment. How many years have you gone through school, which is something that is determined in your teenage years or your 20s or if you've gotten a PhDs sometimes not until your 30s when you're done with school. And that polygenic score, quote-unquote, for educational attainment is also associated with, you know, kind of all. the intermediate spots in that trajectory of education, right? So the educational attainment polygium score isn't just correlated with how far people go in school, but also their grades in high school and whether or not their teacher thought they had attention problems in elementary school. And so you see, you know, it's different for different domains, but I think it's a mistake to think of a polygenic score as being too narrowly about the one thing that research was

study and the original study. But it's, I just is making sure it's not like. like you have N-IQ. You have any polygenic score. You have different polygenic scores for different time. Yeah. Yeah. Too bad. We could rank people on their polygenic score. That would be the end of it, right?

Then we just know how worthwhile people were as human beings. So at the same time, you do see, you know, you see what are called genetic correlations. And they can sometimes be surprising and they give us clues, right? So if you do a genetic study of height and then you do a genetic study, well, actually, I'll give you a real example. Like, if you do a genetic study of educational team, and then you do a genetic study of schizophrenia, you end up with some of the same genes. And some of them work in the same direction and some opposite.

And that has actually turned out to be like kind of a puzzle for researchers to figure out. So I think in academia we like our neat silos, right, that the medical geneticist study medical phenotypes like lung cancer and the psychologist study, you know, maybe nicotine addiction and the economist study labor markets. But in real life, those things go together. You know, your success in the labor market and your likelihood of smoking and your likelihood of developing lung cancer. And so as a result of life being messy, the genetics are messy too.

You get some of the same genetic associations for things that people think of as being in very kind of different camps, medical versus behavioral. There are some things you just can't do at home, from seeing live music to hiking your favorite trail. What you can do is exercise. With Peloton, you'll have a workout experience like no other without ever leaving home. What I like best about Peloton is a convenience not just of the time of day when you're working out, but the type of workout that you're getting. Maybe you're in the mood for just cardio, or maybe you want something that gets you strength training as well. Peloton can give you yoga, Pilates, outdoor runs, meditation, and more. And with epic artist collaborations and instructor-created playlists,

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That's O-N-E-P-E-L-O-T-O-N.com. Well, also, my impression is that some of the things that you're correlating with are things like the probability of becoming homeless. And if we say that there's a genetic relationship, sorry, let's back up, the idea of a home wasn't invented when the gene was invented. So if you say that there's a genetic relationship between or a relationship between your genes and let's say the proclivity for mental illness or the susceptibility to become a drug addict, you can sort of see the causal path in your mind, whereas the causal path to homelessness is a bit removed. So, I mean, how sneaky do you have to be when you even contemplate correlating DNA information with outcomes like homelessness or getting a PhD or something? something like that. I mean, there must be a million confounding variables in the way.

Yes. Well, I mean, I think, you know, I use the example of homelessness in the book, really because I'm trying to give a vivid example of something that is obviously a social problem that is responsive to local social policies. Like here in Austin, we've had this huge debate about criminalizing camping for unhoused populations. And so there was a very low, large homeless encampments under freeways for about two years. And then the repeal and the camping ban was unrepealed. And so now it's criminalized again. So it's clearly a social problem that we deal with with social policy.

And within a society, not everyone is equally likely to become homeless. and the sorts of vulnerabilities that set you up for risk, things like mental illness, things like doing poorly in school, ultimately, you know, as I say in the book, not being homeless is being unable to afford housing, right? It is about like an intersection of, you know, how you've exchanged your skills for money in the labor market and like the affordable housing in your society. So I use that example because, you know, I'm really trying to. to make a point about how we our biology is affecting kind of our embodied traits right and that

might be like a temperamental thing or that might be like our risk for serious mental illness and then those embodied traits are are refracted through this political economic social context um in ways that matter right like in ways that like we see we see when we're driving around and um both as a scientist, but also as someone who's, you know, trying to kind of make sense of my moral responsibilities in a complex world, I really want to think about that whole picture, both, you know, what can we understand about why some people are born with a higher risk of becoming schizophrenic than other people, which I actually don't think is a very controversial statement. And then how does society act on those embodied differences in ways. that create these forms of social inequality.

So from a scientific perspective, you know, it seems like such a strange thing to, at first, maybe a counterintuitive thing to connect DNA to income, right? Like income is clearly social, right? But if we observe genetic patterns that are correlated with income, what is that telling us about which embodied characteristics and which skills are being rewarded and which aren't in the way that we've currently constructed the social system.

So I think when G-WOS first started, we, you know, maybe people thought we would have like really nice, pretty biological proximate phenotypes to study. But it turns out that the things that we collect data on, on like a million people is how far they've gone to school and whether or not they own their home. And so we're actually kind of working backwards. not it was like we've jumped like eight levels of analysis and now how can we use those associations to kind of kind of you know

as a kind of trail of breadcrumbs to follow back to figure out like what are the intervening process easier can we give the listeners some intuition for this quantitative size of these effects like if you if you are able to map out someone's genome very effectively, to what extent does that predict something like income or educational attainment? Is it a 1% effect?

Is it almost all of it? I don't even know what variables you use to quantify it. It's such a good question. And I think part of the reason why it's such a good question is because the tools that scientists use, at least social scientists use, to quantify effect size, you know, most commonly something like an R squared, which is a percent variation accounted for. you know, we often don't have a good intuition for how the relationships that we see play out around us would be quantified on that kind of R squared metric. So just to kind of put some more concrete numbers on it, you know, if you look at Americans and you want to say, okay, there's all this variation in whether or not someone completes college, we know that after.

children are more likely to complete college than children raised in less affluent families, what is the percent variation accounted for in college completion rates by family income, right? And zero would be everyone has an equal chance of graduating from college regardless of their family income. And one would be, I can predict with absolute certainty your risk of, your rate of college completion, your likelihood of college completion by knowing how much money your parents made. So the best estimates for that, that in the U.S. today are around 11 to 15%. Okay.

So, you know, for what? For a family income and rate of college completion. Okay. So, right. So like when we look around and we say the relationship we observe

between being richer and being more likely to complete college is about, let's round out. Let's say around 15% of the variation. So that's 85% that's not related to that, right? Which we can also, you know, We know rich kids who slacked off in school and we know kids who are raised in poor families who did really, really well. Any college professor can look at the vast, you know, I teach at UT and my students come from a vast, vastly different economic circumstances. And I can see that students who are coming from poor families have more challenges.

But I also know that it's not destiny. It's not deterministic. So that is about the same effects. that we see for a polygenic score in relation to completing college. So, you know, I know about as much about a person's chance of graduating from college if I know their polygium score, if they're of European genetic ancestry. I'm so likely to identify as white, and that's a very important caveat, as I would from knowing how much money their parents made in the years before they went off to school.

So is that a big effect size? a small effect side. I think that depends on your, you know, what are you trying to do? Am I trying to make forecasts about this person's fate? Then it's not very good. Yeah. Am I trying to explain the broad dynamics of how people in American society differ in a really key outcome in their life? Then I do think that matters. And so it's that kind of middle, middle ground of it's neither, I don't think it's a, you know, I don't think it's genetic astrology. I don't think it's worthless, but I don't think it's deterministic either. It's in between. Well, and the next obvious question then is if you know both their polygenic score and their family's income, does that make you

predict things better or is it kind of redundant information? Yeah. So it's, you know, it's not redundant information, which, um, you know, a really common question is like, why would we do genetics when we can do the environment? And, you know, I think that's kind of, kind of a false either or this is not the layer of information about someone this is not the most important layer of information about someone but it's an additional layer of information about someone that's telling us something that would be otherwise hard to quantify and see and that's giving us kind of a new ability to capture variation in um in people's lives and we see that non-redundancy even if we're looking at other levels of analysis.

So for instance, you know, I can say, what do I know about you if I know your apologetic score above your socioeconomic status? But I can also say, well, what's the average SES in this school? What's the concentration of affluent or poor children in this high school? What's the average polygemic score in the school? And those are correlated, but only at around, you know, 0.4.5, right? So even our sense of how students are clustered in different educational contexts, we get this kind of different piece of information about that, too.

Well, let me ask basically the same question, except instead of considering completely external factors, consider things we can measure about people other than their genetics, right? Yeah, yeah. So the example I have in mind, I think this is in your book again. I think you call it the leaky genetic pipeline where if you know certain things about people's polygenic scores, then you can predict whether or not they will keep taking math classes, right? You know, later in school. And that's completely plausible to me. But also, I remember, you know, when I was in junior high school,

I would have been able to predict pretty well which of my classmates would have gone on to take the higher math classes and which ones wouldn't have. So in that case, I mean, just from talking to people, getting an impression of what they're like, thinking about their test scores and their grades and things like that, is the genetic information still new or is it redundant with that kind of thing? So I think it's still new. And I would say in three different respects. So the specific study that you're talking about is when we are looking at a sample of American high school students and we're looking at which math class were they tracked

to in the ninth grade. and then how did they move through the math curriculum over the course of high school? This was in the 1990s when math was only compulsory for about two years in most U.S. states. So you could take algebra and then geometry and then drop out or you could take geometry and algebra two and pre-calculus and calculus and that's actually the modal route

taken by most of science PhDs, probably some people, took calculus in high school. And so what we were, we saw is that the educational attainment polygium score predicted both and by predict I mean it captured non-negligible variation in which math class people were assigned to in the ninth grade and their likelihood of dropping out from year to year. What's interesting about that analysis is that the polychemic score predicted math dropout even controlling for people's grades in their previous math class. So if you're thinking about like an observable characteristic that a school district would have or a local

high school would have. You're looking at kids who both have made B pluses in their geometry class and who both have the same level of family SCS. And the polygenic score is still predicting which one drops out of math versus not. So I think that speaks to the power, the potential power of some genetic measures in some genetic context, in some research contacts, having some, you know, extra bang for our box, it's giving us information that would be hard to see just from, say, someone's transcript. The other thing that's interesting about the DNA measures is that they have two special characteristics that most psychological characteristics don't have, like test scores or self-reported

interest in taking math. And the first is that your DNA sequence doesn't change. It's association with things might change. but it doesn't change itself. It's not reciprocally affected by the experiences that you're going through in your life. So, you know, one metaphor my colleagues and I often use is, you know, if you are going to like a radiologist and they're doing an imaging study, they give you a molecular tracer that is not metabolized the way that your body usually metabolize

something if you drink drink it so that you can see the structure right its inertness is what allows you to see the structure as it moves through because it's not being changed by the structure and that is something that's very hard to come by with any of our normal psychological variables um my interest in math is affected by whether or not i have a shitty math teacher last year but my DNA sequence is not um and the second characteristic that it has is that and again this is a very important caveat, conditional on your parents' genetics, your genetics is randomly assigned, right? So conditional on your, you know, knowing your parent's DNA, which DNA you have is random. And we have almost no variables like that in observational social science, right? So there's

nothing, there's nothing about someone's test scores or self-reported interest or motivation that I can say if I measure something about your parents, I can treat variation in this as, you know, reasonably exogenous to other things, like as randomly assigned. And that allows for a different set of analyses and inferences about what's causing what. And so I think it's just to repeat that, you know, you're getting observing predictive power over things you ordinarily measure. It's inert temporally, and it's randomly assigned contingent on parents. Okay, yeah, that is extremely helpful. So what are we going to do with it other than just, you know, judge people?

You know, I mean, part of the reason why it's a tricky question, right, is because people do sort of count things redundantly. Like, if they think that, you know, a certain group is unlikely to be good at math, but one person within that group turns out to be really good at math, there are those who will still judge them negatively. Like, you can't be good at math. You're in that group, right? So how can we flip that script a little bit and use this new information to help people, like you say, achieve what they're capable of achieving and live the lives that we would like them to be able to live? Yeah. You know, you started off this interview by reading the very, very eloquent, pithy publisher copy that

my that my, that Princeton University Press wrote for my book. And I think it's a good thing that they, you know, they wrote it because I think if I wrote it, it would probably be something like, you know, Paige Hardin wants to make genetics boring. And what I mean by that is, you know, my goal would be for genetics to be, you know, using polygogenic scores or, um, incorporating siblings or twin designs as part of your research. to become a really, really routine part of kind of the everyday workings of developmental, clinical and educational psychologists. You know, like I wanted to be like propensity score matching or instrumental variables analysis.

Like, you know, a routine technique that we, that we teach to refine the rigor of someone's inferences. And I think if we did that, we could do. do a lot better more quickly at identifying bright spots, identifying the features of people's environments, particularly of children's environments that are most efficacious at actually producing the outcomes that a psychologist, we say we want to produce, produces outcomes in children. So currently, you know, all social scientists are really faced with this problem of like messy free-range humans are hard to do experiments with and everything's correlated with everything else. And we can look to our data and we can say, you know, parents who eat dinner as a family by 5.30 p.m. have kids that do better in school. but that doesn't tell us that actually intervening on family dinner time would actually be the best, most cost-effective way of improving children's academic performance.

And that's kind of a silly example with family dinner, but that is actually the case with almost all the variables we study. And genetics gives us another way of seeing how are people who are similar in this one-measured capacity. but who have happened to find themselves in different environments, how do they differ so that we can identify what are the most promising environmental levers for change? That's what I want people to do with genetics. You know, a lot of times people are like, well, I'm not interested in genes. And I'm like, yeah, but you're interested in kids. Yeah.

In kids get their genes from the same people who give them their environments. So you're interested in kids and you're interested in figuring out. which environments help kids succeed, you kind of have to be a little bit interested in genes, if only to get it out of the way from messing up what you're trying to do. The obvious analogy, which maybe I got from your book and I'm now forgetting, but if there were a gene that said that you were much more susceptible to sudden heart attacks, but it's preventable if you do the right thing, then of course you'd want to know whether or not you were susceptible to that,

so you could prevent it. And presumably there are similar stories to be told about, oh, this student would benefit from this kind of educational environment or plan or something like that that we can learn, ideally the goal would be that we could learn from their genetic information. And I think even, you know, people often go, you know, when they're thinking about kind of like that heterogeneity in people's outcomes and kind of matching people to interventions,

it's easy to go straight to where you went, which is like kind of the more personalized medicine route or the more personalized education route. Can we use this to identify people who are very at risk for outcomes for four outcomes? I think there's a more basic level even before we get there, which is that we know in almost all randomized control trials of psychological or educational interventions,

that there are vast differences between people, and how they respond to that intervention, right? So if I, you know, I used to be a practicing therapist, I'm not anymore, but if someone comes to me for therapy and I even say, okay, we're going to do CBT for depression, you know, some people respond to that and some people don't. If you do a tutoring program that has a small average treatment effect for kids' math skills, that small average treatment effect can mask enormous range and some kids benefiting hugely and some not at all. Often what you see in educational interventions and psychological interventions is what we call a Matthew effect in which not only is there variation in how people respond, but it's the people

who are least at risk that get the most help, right? It's the students who are already doing well, who benefit the fastest. It's the fact that rich kids learn more vocabulary from Sesame Street than poor kids do. So I think even if we don't start matching kids to interventions, just knowing whether our current slate of interventions, who is being served by those? You know, are we helping people who are most at risk for poor outcomes or are we helping people who are, you know, is it a rich get richer effect? Those types of heterogeneity studies can be really difficult to do.

And genetics doesn't solve all the problems. but by adding this another layer of information, particularly a layer of information that's, again, invariant, like your DNA sequence can't be changed by the intervention. I think we can have a better tool for seeing who is being served by which of our policies and interventions, just knowing that I think would be useful information. Like if we could know, like, this, you know, statin drug,

it works on average, but like it really doesn't work for people who are most at genetic risk for heart attacks. We would consider that a problem. Like we would want to know that. I think it's the same thing for, okay, well, this educational intervention, it works on average, but for the people who are most, you know, from what we can see from their genetics are most at risk for bad outcomes. It's not serving them. Like, why wouldn't we want to know that information? And can we bring this back to the notion of the lottery and luck?

I mean, yes, our genes are in some sense random. So what does this teach us about the question of what do we do about that? Or how do we conceptualize that? You know, I mean, do we, does it change the way in which we assign merit or achievement to people? I mean, I think that's a difficult question because it is a title of your book. I don't think the science commits, commits anyone to a certain set of moral. or political belief. Sure.

You know, I think people, people can take that information, the observation about the world, that people are born different in ways that matter for their lives. And run with that in lots of different directions. You know, for me personally, it's helped me clarify

some of my intuitions about what makes a social structure or society good. Like when I think about a good society or a just society, I'm thinking about one that is, you know, more like a meadow and less like a monoculture. One in which people who have, you know, genetic diversity can all have a place to thrive and participate and not a place in which, you know, one genetically influenced set of skills and traits. is favored to the expense of everyone else. So it's just thinking about the arbitrariness of, you know, what I've passed onto my own kids, what kind of society do I want to leave for them in their difference and their genetic difference and how I think that's really intuitive for parents.

You know, they might look at their own kids and they can see how they're different from one another and they want structures like their schools and their neighborhoods that accommodate those differences and allow both you know all of their children to succeed um you know thinking about social justice i know that's a freighted word social justice from that perspective that a just society is one that kind of scales up that vision of accommodation of difference that i would want for my own family um that's a big part of what i'm trying to articulate in this book. Yeah, no, I think it's a very good vision.

But we're past the hour mark at the podcast now, so we're allowed to let our hair down a little bit and ask the crazier questions. We'll go back to that very articulate thing you just said. But, you know, okay, if I think about other podcasts I've done, like with Fyodor Ornov, who's an expert on CRISPR and so forth, when are we going to reach the point where we just identified the bad genes and fix them before they get propagated down to the next generation, right? And then everyone will be smart and beautiful. Well, there's so much in that question there.

I think, you know, there's a couple things I want to respond to. And first is it's back to this small effect size, right? So, you know, even leaving aside something as controversial as educational payment or income, like if you just want to CRISPR your baby to be taller. Yeah. That would be, there's not a gene. that's going to do that for you, right? Like the stuff that's being identified is, again,

thousands or hundreds of thousands of genetic variants. And so no one is really talking about CRISPR in that context. They might be thinking of talking about, you know, egg selection for egg donation or embryo selection, where you're selecting a polygienic score, but that's a different context than CRISPR, which is, I think, really more about these more monogenic, you know, single locust variations of large effect.

at least to my understanding of it. I don't think anyone's really proposing crispering 100,000 variants in your genome at this point in time. The second thing is this idea of good gene and bad gene. We have socially valued traits and socially disvalued traits. And the genetics don't really conform all the time

to our intuitions about that. So going back to that example of education, and schizophrenia, if you have a genetic variant that makes it more likely for you to get a STEM PhD, but also more likely to become schizophrenic, is that a good gene or a bad gene? You know, I don't think we have a sense of that. You know, the classic example is, you know, if you have one copy of this gene, you're more resistant to malaria if you have two of sickle cell disease, right? Like, is that a good gene or a bad gene?

So I think that, you know, part of, there's a lot of different ways that we can define eugenics, but eugenics literally means, you know, good genes. It's projecting our social values, you know, down into the biology, which I think our biology tends to confound those kind of neat distinctions that we have. So those are the major things that I would respond to about that. And then I, you know, going back to what I said earlier about, you know, I want a society that's, you know, a meadow, not a monoculture, you know, when I think about this idea of like everyone being smart and beautiful, like, smart in the way that businesses' PhDs are smart. Like, I actually think that would be a really, really boring culture. Everyone was smart in that particular way.

I would vote against that. Don't worry. Beautiful in the sense that runway models are beautiful. You know, if we think about like our revealed preferences, like where do people often want to live and where we're vibrant communities, it's not communities in which everyone is like kind of narrowly, phenotypically the same. Sure. I guess, I mean, the reason why I ask, mostly ingest, I don't think that, I'm not advocating for making sure everyone is smart and beautiful. But I kind of think, because it is very complicated, whenever you talk to the biologists about

this, they will instantly say, look, it's much more complicated. And you think you're not going to be able to go in there and tinker with DNA and get the babies you want. It just doesn't work that way. But I also think people are going to try. You know, sure, people don't want to live in a monoculture. But if you ask them, do they want their babies to be smart and beautiful? They're going to say yes. And I don't know what to do about that.

I mean, I don't know what the social policy should be. And I think that, you know, the scientific. impulse to say, look, it's much more complicated than you think is going to run up into the untrained impulse to say, I'm going to do it anyway. We already see that happening. Yeah. I mean, I agree with you about that. I think we've already seen evidence of that and direct to consumer genetic testing companies where it's, you know, we're going to match you to your, you know, the wine that you're going to like the most or your Spotify playlist based on your DNA. And that's obviously a scientific, but that doesn't mean that there aren't consumers for

it. There's a couple of different conflicting intuitions here. You know, one thing that I want to make salient that, as I think is often lost in these conversations, is that for me personally, I value women having autonomy over their reproductive choices, even reproductive choices that I don't agree with for personal reasons. And I think that that value needs to be salient in all over conversations. about the uses of reproductive technology, you know, not losing sight of the fact that, like, there are women who are making choices about their bodies

and the babies that they bring into the world, and that needs to be, I just kind of want to center that as a consideration. It's also interesting for me because, you know, I live in a fairly conservative part of the world, and my own experience as being pregnant have been that even roots, teen genetic testing that's not controversial amongst the scientific community is approach with great, great delicacy because, you know, many women where I live in Texas don't agree with the idea of any sort of prenatal genetic testing. Like when I was pregnant with my first child, I remember

my OB was suggesting that I do like the standard 20-week scan to see if there's any sort of fetal abnormalities. And then she was like, and then if there is, we could get, you know, a genetic test based on this. And she said it so delicately as if I was going to be offended, like, by the idea of, you know, the genetic testing. And so I think oftentimes our conversations around, you know, people adopting embryo selection seem to me a little bit divorced from the other aspects of how, like, sort of reproductive politics play out. this country in which there are a lot of women who are really, really skeptical of anything that, that smacks of that. So that's not so much an answer.

It's just like a response of like two factors that I think are often lost in conversations around this topic. No, I think that I'm very glad you said those things because even though I am trying to be provocative about the Gattaca future that we, that we're walking into, I mean, like you say, there are much more direct than immediate issues that we have to worry about. and we can get distracted by some of these other, you know, shiny things to worry about. I think you're important. I want to worry on all life scales.

And that's, I mean, I think it's a good question. You know, the, I don't think it's being distracted about like out of the future. You know, my ultimate goal would be to try to empower as many women as possible with as much accurate science about what the genetics is and isn't aren't, is and isn't saying about what they can do with these reproductive. choices, but also honor their autonomy in making those choices. That would be my personal sort of broad brush-structs approach to thinking about this problem. And I can't think of any other better place to end than that. That's an extremely admirable goal. So Paige Harden, thanks so much for being on the Mindscape podcast. Thank you so much for having me. This is a great conversation. School's first federal credit union serving school employees and their families. Spring cleaning isn't

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