Melinda Mills on Sociogenomics

Transcript

Key DE: DAVID EDMONDS

MM: MELINDA MILLS

DE:This is Social Science Bites with me, David Edmonds. Social Science Bites is a series of interviews with leading social scientists and is made in association with SAGE Publishing. Over the past century, the average family size in the West has declined sharply and parents are having their first child when they’re much older. Typically, sociologists trying to understand this revolution would investigate, say, levels of female education or the availability of contraception. But have they been missing a trick? Might there be another type of explanation? Oxford University sociologist Melinda Mills has started to collaborate with geneticists in a new field, sociogenomics. Melinda Mills, welcome to Social Science Bites.

MM:Thank you very much. I’m pleased to be here.

DE:The topic we’re talking about today is sociogenomics. Heck of a mouthful. What is that?

MM:Well, sociogenomics is what it sounds like. So it’s a combination of sociology and social science, which studies things related to your family background or the context that you live in or the country or the time you were born, with genetics. And specifically, it combines it with molecular genetics, so whole genome data where we actually look at the individual’s genetic data and loci. So social science and genetics-- sociogenomics. SAGE SAGE Research Methods Podcasts 2018 SAGE Publications, Ltd. All Rights Reserved.

DE:And it’s a relatively new discipline. Is it part of the big data revolution?

MM:It’s very much connected because for the first time in history, we’re actually able to look at large samples of whole genome data. So you might have heard of the UK Biobank. That’s a half a million cases. There’s also very large studies throughout the world. And we’ve also had the growth of what we call direct-to- consumer genetic testing, so large companies such as Ancestry.com or 23andMe. So individuals are interested in their genetic data. But we also have an availability of a lot more kinds of big data. And for this kind of enterprise, you need a lot of data and you combine it together. So it’s really part of this big data revolution, most certainly.

DE:Your background is all in sociology. How did you get into the science?

MM:So my background is in sociology and demography. And I was looking originally at topics related to family and family formation, so reproduction and reproductive choice, so in simple terms, when do you choose to have a child, so age at first birth, and the number of children you have, for example. And I was looking at them in a very socially deterministic way. I was looking at things such as child care institutions or gender equality or the kind of jobs that women and men had and was childcare available and affordable and all of these things I was looking at. And I was using those as explanations and predictors for when people had children and the number of children they had. And then I met some biologists and geneticists. We went out for a drink. And they made jokes about me the entire evening. So they said, oh, this is Melinda. She studies fertility, but she doesn’t think it has any biological basis. And it was at that point that I realized it was quite absurd how sociologists and social scientists keep themselves into these very small ways of thinking and we unduly limit ourselves. For that reason, I started talking more with these biologists and geneticists to see how they were looking at the exact same topics but with very different data and approaches.

DE:So you’ve started collaborating with them. Describe what you do. What is your research approach?

MM:I’ll give you an example of some of the studies we’re working on. So we look a lot at the things that we would normally look at as social scientists, so behavior. Some of the studies we participated in are related to well-being and depression or educational attainment. And the one that we have led has looked at reproductive choice, so the timing of when you have children and the number of children. So we’re very good at measurement and these kind of factors, how you measure that. And then we work with biologists and geneticists to isolate genetic loci that are predictive of the timing of, for example, when you have your first child or the number of children. And we isolate those and we turn the results over to biologists who then look at, OK, what is the biological function of those genes? And then as social scientists, we create a score, sort of your reproductive score. And we add those into our statistical models together with the social science variables that are the usual suspects, so your family background, your partner, or your educational level. And we add those together also with the genetic data. And we look at the interaction between those and we see OK, how can we explain this behavior? But you can also translate it to looking at diseases or other outcomes, as well. So it’s really using real genetic data and social science data.

DE:Give me an example of a finding.

MM:OK. So one of our more recent findings that we published in Nature Genetics, we isolated 12 genetic loci that are predictive of when you have your first child and how many children you have. But what was even more interesting about this finding was that the biologists then took the data and looked in more detail and they found that there was actually some biological functions. Something we did that was quite different is we didn’t only look at women, which is often the case when we look at fertility. We also looked at men because we suspected that they

Page 3 of 9 Melinda Mills on Sociogenomics SAGE SAGE Research Methods Podcasts 2018 SAGE Publications, Ltd. All Rights Reserved. also played a role. So we looked at women and men, which is more usual within the social sciences, but it hadn’t been looked at in fertility as often. And they found really interesting results. So these genetic loci that we isolated were related to sperm motility and mobility and they were also related to follicle-stimulating hormone and hormones for women and infertility diseases for women such as endometriosis and polycystic ovarian syndrome. So the findings that we found-- we actually found genes that were very much implicated with infertility.

DE:That’s fascinating. A sociological approach has helped, as it were, the scientists, the medics, identify what is genetically abnormal with a group of people having difficulty with fertility or disease.

MM:Yeah. So it was really interesting because when we first started approaching people to work together, they said, well, what kind of variables are you looking at? That’s behavior. Those are soft variables. How can you measure those? And I thought to myself, well, age at first birth and number of children, actually, that’s a hard variable. You know when your children are born-- well, many people do-- and how many children you have. So actually, these are really hard outcomes. You actually know what’s happening. Whereas we found that the studies on infertility were often a very small, selective sample, so people that were worried about their fertility. So they were getting results that weren’t from a broader representative population. And we really measured it differently. We took a broader population approach, too, and looked at men and women.

DE:How does this play into the great nature-nurture debate?

MM:Well, it’s very interesting because you come into this as a social scientist and we often explain things in terms of nurture, that it was socially-determined. And the geneticists on the other side, many of them were looking at things because of expertise and related to, OK, we found these genetic loci. What do they do biologically? So to give an example, a study that we published recently in

Nature Human Behavior, we looked at genetic loci for certain outcomes such as education, reproduction, so the timing and number of children, BMI, and height. And we wanted to look at whether these differed across which country you were born in or which time period. So think of an example of when my mother was deciding when to have children compared to when I was choosing when to have children. There were very different pressures. So we speculated, OK, wait a minute. Across these different time periods and these different contexts, we probably have different genetic loci maybe at play. But also, they may react in a different way, as well, too. So this is something that we’ve looked at. And that just gives you an example of how important the context is to social scientists and sociologists. It’s often assumed that these genetic loci or genes are universal across birth cohorts or historical periods and the country. And just the lay person on the street might think, hey, wait a minute, is that the case? And of course, we know ancestrally-- and we know that there’s different sorts of groups. But it’s really important to think about the neighborhood you grow up in, the family you grow up in, the country, the period, the historical period. We argue that these all play a role. But not only do we argue it; we actually empirically look at it and see which genes are important in different contexts.

DE:But when you put it all together, you put your nature and your nurture, you’ve got your genetic patterns and you’ve got your context-- what country you’re from, your income, your educational attainment level and so on-- you can use that to predict with a degree of accuracy when people will have their first child, how many children they will have?

MM:Yeah. So what’s really interesting is that you would have-- on the one hand, the genetic loci are maybe predicting 1% to 10% of the variance. So think of a variance as explaining from 0% to 100%. So they explain maybe 10%. But the puzzle isn’t complete. And meanwhile, the social scientists are using social science variables like education, income, family background. They’re explaining

Page 5 of 9 Melinda Mills on Sociogenomics SAGE SAGE Research Methods Podcasts 2018 SAGE Publications, Ltd. All Rights Reserved. maybe 50% of why you’re having your children when you are and things like that. But when we combine the medical, the genetic, the social science, it’s when it becomes interesting and we actually get to a better predictive explanation. We’re not up to 100% yet. But we actually, by combining all of these parallel studies and disciplines in genetics, medical science, and social sciences, we’re getting quite close to say to people-- answer basic questions. How late can you wait? How many children do I expect I might be able to have? And when will I start having fertility problems? And that’s when it becomes very interesting, very usable.

DE:I can see some ethical challenges ahead. What might the policy implications be of this work?

MM:Well, policy and genetics are not two things that have a history that we’d like to think about. And policy and genetics have a difficult past. And let’s just address the elephant in the room, eugenics. This is definitely not what we’re doing. So what we’re doing is looking at-- take, for example, tobacco addiction. And there’s been some interesting studies done on this. What they found is that policies were introduced to reduce smoking in public places, but also to increase the costs of tobacco. Now, for the people that were the casual smokers on Friday night with a beer or just not that addicted to smoking, those policies worked on them. But then what they found is there were people that were quite genetically hardwired, that had the genes related to tobacco addiction, and no matter what policy you would introduce, those people wouldn’t really be affected by it. They would just go more into poverty because they really couldn’t stop smoking. So then you can think about, OK, maybe we should have some pharmaceutical treatment for these individuals because banning them smoking out in public places or increasing the price won’t help. So you can think in terms of genetic predispositions in certain topics. This could be interesting, informative, for more personalized policy.

DE:And in terms of reproduction itself, have you been thinking about how

Page 6 of 9 Melinda Mills on Sociogenomics SAGE SAGE Research Methods Podcasts 2018 SAGE Publications, Ltd. All Rights Reserved. government policy might be shaped by this information about how many children parents may expect to have?

MM:Well, there’s two things here. I think one is really important. And fertility is about choice. So there are a lot of people that choose to be childfree and not to have children. It becomes very difficult if the state takes over a pro-natalist policy, which some have done. That wouldn’t be something that we’re advocating. But what we have seen and what we’ve come across a lot is there’s a lot of people that want to have children, but realize after waiting or postponing to have children that they can’t have them. So that’s more where our interests are in relation to policy. So there’s a few things. You can look more in relation to fertility awareness. And that’s definitely something that all countries could improve in. It’s not in the sexual education system here or elsewhere. So people, they’re quite surprised, if you talk with fertility doctors, that they come-- OK, I’ve got my career. I’ve got my house. I’ve got it all arranged. I’m late 30s. I want to have a child now. And they’re surprised that they can’t have them. So that basic awareness is important. But also, we’re hoping to really work towards understanding and helping people understand, might I be someone who would be more likely to have fertility problems early? And we have hundreds of different markers. And almost 1,000-- now in one of our studies, we’re looking at if we combine these different genetic predispositions and social predispositions, what can I expect? And I think people want that. We certainly won’t come to 100% predictive power, but we’re inching towards helping people understand how late they can wait, for example.

DE:That’s one of the public policy implications. But I can see there’s enormous potential private industry interest in this. The commercialization possibilities must be huge. The insurance industry, for a start, would love to have this kind of information.

MM:Yeah. I think there’s definitely been a lot of ethical questions about this

Page 7 of 9 Melinda Mills on Sociogenomics SAGE SAGE Research Methods Podcasts 2018 SAGE Publications, Ltd. All Rights Reserved. information. So and I think what some people do or do not realize-- in different countries, there’s a variance in this-- but insurance companies actually, to a large part, already do have genetic data. So some of the large insurance companies do blood tests and people sign out a consent form and many of them already have that kind of data. But the consent forms are quite clear in that it’s for health research or health consent. But if you think about it, and there have been some ethical questions and some people have researched and looked into this, what if your employer gets it? Who gets access to this type of data? There are quite a few studies saying that the APO gene, the Alzheimer’s gene or whatever you want to call it, can be predictive of early onset Alzheimer’s, for example. Or you have markers for schizophrenia or depression or something related to something that could influence your insurance policy or you as an employee. So these kind of questions, I think, are important to address and discuss openly in the public forum. To the same token, this also discourages people from participating by donating their data. And I think it’s really important that we get this type of data so we can do these kind of studies and research. But with these concerns, unless they’re not addressed openly and consent is very clear, we could have potential problems or people might think that potential problems might arise from abuse of their data.

DE:So you’ve been collaborating with these strange beasts, the scientists, for getting on six years or more now. What’s that been like with your background in, as you say, demography and social science? Has it been difficult to communicate with them?

MM:Well, when we got together, it was definitely very difficult. We were publishing in different journals. We spoke almost different languages. We established within the first few meetings that there were three languages that we spoke that were similar. One was English. One was statistics. And the other one was computer programming. So those were the three things that we were able to communicate on seamlessly and very easily. So we started from those sort of things. And we

Page 8 of 9 Melinda Mills on Sociogenomics SAGE SAGE Research Methods Podcasts 2018 SAGE Publications, Ltd. All Rights Reserved. could share different computer code and we could think about different models. And then we inched towards saying, OK, well, this is how we would look at it. And now our collaborations are great. And there’s a very well-known geneticist who we’re now working with. And he was laughing the other day. He goes, I never would have thought that I needed sociology, but I do. I asked him if I could record it. So he was realizing that this contextual factors and all of these nurture component was actually really, really important. So for us, it’s not just us gaining from the natural sciences. It’s reciprocal, which is a wonderful thing.

DE:Melinda Mills, thank you very much indeed.

MM:Thank you.

DE:Social Science Bites is made in association with SAGE. For more interviews, go to socialsciencespace.com.

Page 9 of 9 Melinda Mills on Sociogenomics