By Sarah Laskey, PhD, Health R&amp;D Scientist at 23andMe
We’ve seen the data, and it&#8217;s no surprise — a lot of people just don&#8217;t get enough sleep. So last September, 23andMe launched a Sleep Study to learn how lifestyle impacts sleep quality.

It sounds simple, but it was a big milestone for 23andMe Research. It was our first “interventional study.” This is the first time that 23andMe research participants changed something about their lifestyle and measured the impact.  Participants who had reported trouble sleeping in the past made some lifestyle changes — like avoiding caffeine after noon, or exercising daily — and they recorded the impact of those changes on their sleep quality.

This was the first time we had asked 23andMe research participants to make lifestyle changes. We had no idea how closely they would follow the study plan. In the end, we were blown away by how many people made the Sleep Study a priority in their daily lives. Over 6,600 people enrolled in all! This blog post is dedicated to those participants. Without them, none of this would be possible.
&nbsp;
<img loading="lazy" decoding="async" src="https://blog-api.23andme.com/wp-content/uploads/2013/04/Empty-Bed.jpg" alt="" class="aligncenter wp-image-17852 size-full" width="675" height="711" srcset="https://blogcms.23andme.com/wp-content/uploads/2013/04/Empty-Bed.jpg 675w, https://blogcms.23andme.com/wp-content/uploads/2013/04/Empty-Bed-285x300.jpg 285w" sizes="auto, (max-width: 675px) 100vw, 675px" />
<h6>Now, on to some Sleep Study results!</h6>
At the beginning of the study, participants chose up to three lifestyle changes they wanted to make. They had six options:

<ul>
<li>Avoid caffeine after noon</li>
<li>Exercise for at least ten minutes each day</li>
<li>Avoid naps, especially naps longer than 15 minutes</li>
<li>Set a consistent bedtime</li>
<li>Avoid screen time (phone, computer, or TV) within 30 minutes of going to bed</li>
<li>Spend ten minutes engaged in meditation or mindfulness practices within two hours of going to bed</li>
</ul>

Study participants spent two weeks recording their normal behavior in regular sleep diaries. This was followed by four weeks of lifestyle changes. We asked participants about their sleep quality (measured using the Pittsburgh Sleep Quality Index, a.k.a. PSQI) before the study and after it finished.
<h6>And… it worked! </h6>
Participants reported better sleep quality at the end of the study than they did at the beginning across the board. No matter which lifestyle change(s) people chose to make, their sleep quality improved. <a id="a1" href="#f1">[1]</a>

<div id="attachment_25987" style="width: 1034px" class="wp-caption aligncenter"><img loading="lazy" decoding="async" aria-describedby="caption-attachment-25987" src="https://blog-api.23andme.com/wp-content/uploads/2018/01/PSQI_by_intervention_recolor@3x-1024x442.png" alt="" class="wp-image-25987 size-large" width="1024" height="442" srcset="https://blogcms.23andme.com/wp-content/uploads/2018/01/PSQI_by_intervention_recolor@3x-1024x442.png 1024w, https://blogcms.23andme.com/wp-content/uploads/2018/01/PSQI_by_intervention_recolor@3x-300x130.png 300w, https://blogcms.23andme.com/wp-content/uploads/2018/01/PSQI_by_intervention_recolor@3x-768x332.png 768w, https://blogcms.23andme.com/wp-content/uploads/2018/01/PSQI_by_intervention_recolor@3x-880x380.png 880w, https://blogcms.23andme.com/wp-content/uploads/2018/01/PSQI_by_intervention_recolor@3x-1180x510.png 1180w, https://blogcms.23andme.com/wp-content/uploads/2018/01/PSQI_by_intervention_recolor@3x.png 1600w" sizes="auto, (max-width: 1024px) 100vw, 1024px" />This chart shows the average sleep quality of participants who chose each lifestyle intervention. We used the Pittsburgh Sleep Quality Index (PSQI). Scores range from 0 to 21, and lower scores represent better sleep quality. Participants took the PSQI survey twice. Once before the Sleep Study started, and once after it finished.</div>
That is great news. But of course, like most scientific studies, this one raised as many questions as it answered. While it looks like all of the lifestyle changes successfully improved sleep quality, we still can&#8217;t say for sure why sleep quality improved in every group.
In fact, maybe we should be surprised that all of the interventions worked so similarly. 
<h6>What Worked</h6>
Remember, study participants recorded their behavior in sleep diaries, so we can tell which lifestyle behaviors people changed the most during the month-long study:


<div id="attachment_25988" style="width: 1034px" class="wp-caption aligncenter"><img loading="lazy" decoding="async" aria-describedby="caption-attachment-25988" src="https://blog-api.23andme.com/wp-content/uploads/2018/01/adherence_simple_shading_1@3x-1024x483.png" alt="" class="wp-image-25988 size-large" width="1024" height="483" srcset="https://blogcms.23andme.com/wp-content/uploads/2018/01/adherence_simple_shading_1@3x-1024x483.png 1024w, https://blogcms.23andme.com/wp-content/uploads/2018/01/adherence_simple_shading_1@3x-300x141.png 300w, https://blogcms.23andme.com/wp-content/uploads/2018/01/adherence_simple_shading_1@3x-768x362.png 768w, https://blogcms.23andme.com/wp-content/uploads/2018/01/adherence_simple_shading_1@3x-880x415.png 880w, https://blogcms.23andme.com/wp-content/uploads/2018/01/adherence_simple_shading_1@3x-1180x556.png 1180w, https://blogcms.23andme.com/wp-content/uploads/2018/01/adherence_simple_shading_1@3x.png 1600w" sizes="auto, (max-width: 1024px) 100vw, 1024px" />This chart shows the behavior recorded by study participants in their sleep diaries. For each plot, the green line shows the average behavior reported by participants who chose to make that lifestyle change, and the gray line shows average behavior of people who didn&#8217;t choose that lifestyle change. The first two weeks are shaded in light gray, and during that time there&#8217;s not much difference between the green and gray lines — those are the two &#8220;baseline&#8221; weeks when participants were asked to record their normal behavior, before making any lifestyle changes. <a id="a2" href="#f2">[2]</a></div>
From this chart, you can see that participants made the biggest change to their screen time before bed, while they made much smaller changes to their napping habits. This is probably because of differences in participants&#8217; baseline lifestyle. This is because only about 10 percent of participants took naps on a normal day before the Sleep Study started, there wasn&#8217;t much room for improvement. On the other hand, over 80 percent of participants looked at screens within a half hour of bedtime at baseline. So people who chose to limit screen time during the Sleep Study ended up making a more dramatic lifestyle change, on average.
<h6>More Questions</h6>
But if limiting screen time was a more dramatic lifestyle change for most participants, and avoiding naps was relatively minor, how did both changes lead to similar improvements in overall sleep quality? Well, maybe small changes to napping habits can be as effective as big changes to screen exposure. Or maybe the lifestyle changes themselves weren&#8217;t the whole story. Maybe just paying close attention to your sleep hygiene is enough to improve your overall sleep quality over time.
We&#8217;ll have to do more research to find out for sure. So keep an eye out for more 23andMe interventional studies in the future — maybe even including Sleep Study 2.0. We&#8217;re just getting started.
<footer class="footnotes">Notes:</footer>
<footer class="footnotes">
<a id="f1" href="#a1">[1]</a> The analysis shown here represents an intention-to-treat analysis. This means that for participants who only answered the pre-study PSQI survey and didn&#8217;t answer the post-study PSQI survey, we assumed their sleep quality didn&#8217;t change over the course of the study. If we had only considered participants who answered the PSQI survey both before and after the Sleep Study, we would have seen a more dramatic (but potentially biased) improvement in sleep quality. Even with the more conservative intention-to-treat analysis, the p-value for the difference between average sleep quality at the beginning of the study and average sleep quality at the end is less than 10-16. (In other words, we can be very confident that the improvement in self-reported sleep quality is real, and not just due to random noise.)
</footer>
<footer class="footnotes">
<a id="f2" href="#a2">[2] </a>You may notice that the green line regresses back toward the gray line in later weeks of the study. This is also driven by the intention-to-treat analysis (see footnote 1). When participants didn&#8217;t fill out a sleep diary, we assumed they went back to their baseline behavior. Since participants were more likely to miss a diary near the end of the study, that pushed the average behavior near the end of the study closer to baseline levels.
 
23andMe, Inc. Copyright 2018. Information contained herein and developed by 23andMe is proprietary and may not be reproduced without written permission.
</footer>
<footer class="footnotes"></footer>

Health R&D Scientist, 23andMe

Sarah Laskey

23andMe Secrets to a Good Night’s Sleep

By Jey McCreight, Ph.D., Research Communications Scientist
It takes a village to do research. Scientific discoveries from the 23andMe Research Team are made possible both by our three million research participants and by our collaborators. We sat down to talk with one of those collaborators, Louis Muglia, M.D., Ph.D., Professor of Pediatrics at the University of Cincinnati, Vice Chair for Research, Director of Human Genetics, and co-director of the Perinatal Institute at Cincinnati Children’s Hospital Medical Center. Dr. Muglia lead the recent collaborative study on preterm birth that was <a href="http://www.nejm.org/doi/pdf/10.1056/NEJMoa1612665">published in the New England Journal of Medicine</a>.
<img loading="lazy" decoding="async" class="alignright wp-image-25978 size-medium" src="http://35.88.189.111/wp-content/uploads/2018/01/Muglia_Photo-300x200.jpg" alt="" width="300" height="200" srcset="https://blogcms.23andme.com/wp-content/uploads/2018/01/Muglia_Photo-300x200.jpg 300w, https://blogcms.23andme.com/wp-content/uploads/2018/01/Muglia_Photo-768x512.jpg 768w, https://blogcms.23andme.com/wp-content/uploads/2018/01/Muglia_Photo-880x587.jpg 880w, https://blogcms.23andme.com/wp-content/uploads/2018/01/Muglia_Photo.jpg 1024w" sizes="auto, (max-width: 300px) 100vw, 300px" />
Jey McCreight: What motivated you first to start studying gestational time and preterm births?
Louis Muglia: Well, I’m trained as a pediatrician initially. When I was a resident in Boston, we spent a lot of time in neonatal intensive care units, and that really made a lasting impression on me. I mean, you see these rooms full of tiny babies that are born at 23, 24, 25, or 30 weeks gestation that are suffering huge medical problems, who are many times either dying or devastated for life. The only reason they have those problems is that they were born at the wrong time. And as you probably know, preterm birth has been the leading cause of infant mortality for many, many years. But it’s recently become the leading cause of all child under five mortality because we’ve made more progress in other areas.
And we don’t understand why birth timing occurs when it does in women. We don’t know the mechanisms that get us there. To me, as an endocrinologist as well – I did pediatrics and pediatric endocrinology – pregnancy is a great endocrine physiology system. Hormones are changing like at no other time in a person’s life except when they go through puberty. It’s just a fascinating way to think about getting your handle on what’s causing those regulatory changes, because in my mind, doing genetics and genomics is really the only way you’re going to get our foot in the door in a non-biased fashion to understand molecular pathways. Because you’re not going to do anything that disrupts a pregnancy. In my opinion, it’s the single most important problem we have left to solve in maternal and child health.
JM: So what made you first interested in collaborating with 23andMe?
LM: I say it was the potential to utilize existing data that could drive discovery ahead in a way that would take extraordinary resources and an inordinate amount of time to do without them. We didn’t know what data was available from 23andMe related to pregnancy &#8230; but I figured why not? I mean, they’ve got millions of research participants, and even if there was some small fraction of them who had answered a question relating to pregnancy outcomes, it would be a shame not to be mining that information and using it to shape our thinking.
JM: Did you have any initial concerns about working with a consumer genetics company or using self reported phenotypes when you were getting started?
LM: Absolutely. The concern I had was that we would just get noise — you know, the self-reported data, especially around the length of pregnancy &#8230; And the other thing that we were specifically interested in was understanding why women went into labor spontaneously. We weren’t as interested in women who delivered via C-section because they had pre-clampsia, or the baby wasn’t growing well, or there were other medical complications in the pregnancy. And I thought those two factors with self-reported gestation length were just going to make the information a little bit too heterogeneous for us to be able to get robust associations.
I wasn’t so worried about false positives, though. Because, something that emerged from it, we were going to validate in our carefully phenotyped replication cohorts anyway. So we knew we had 8,000 moms who we could look at who were well phenotyped to determine whether the hits we were getting with the 23andMe data and self-reported data, when we weren’t exactly sure on the mechanism of pregnancy outcome, was actually giving us the information we thought it was.
People had told me that large numbers of samples kind of trump the concerns with noisy data. I didn’t believe it. I absolutely believe it now. I’m a firm believer that big numbers of samples make all the difference.
JM: We’ve converted you?
LM: Heh, well, truly.
JM: What’s your opinion now after collaborating with 23andMe? Did anything change as you actually went through the process with us?
LM: Yes, this has shaped the way I think about how we should be collecting data that’s available to us every day. Anything that we’re doing on a large-scale population level with self-reported data, we should be capturing the information from these really critically important medical issues and not worrying so much about whether the reporting quality is perfect, but just getting the big numbers of subjects. So things like the <a href="https://allofus.nih.gov/">All of Us Initiative</a> that’s coming out from the NIH, the <a href="https://www.research.va.gov/mvp/">Million Veteran Program</a>, all of these things — we need to make them bigger and ask the right questions associated with them, and hope we get enough people answering the questions, and not worry so much about whether every answer is going to be perfect.
And you know, it makes me want to explore more opportunities with 23andMe. Our first pass at this was looking at the mom’s genome because we had the most evidence that there were findings there. I’d love to go back and develop a plan — we’re just working on this right now — to look at the fetal genome. There’s not a question on the survey right now that lets us do that. They don’t ask the individual who’s getting their DNA tested at 23andMe “How many weeks gestation were you born at?” They ask the mom “How many weeks was the gestation of your first pregnancy?” That gave us the mom’s genome information related to pregnancy. So I’d love to go back and build in a question into a survey that actually asks that information.
JM: How has the rapidly increasing availability of large datasets like ours changed your research over the years compared to what you were doing earlier?
LM: Well, it’s really made humans our animal of choice to do investigation on. The benefit we’ve had in the past – and this is my background – is I was a mouse molecular geneticist. I did gene targeting in mice, taking specific pathways, asking how they affected physiology, and then trying to take that and extrapolating it to human biology. And the rate limiting factor was, when you try to extrapolate it, it would never line up right. You just couldn’t. The physiology had changed too much between typical laboratory animal species like mice or sheep or rats, and women.
So we eventually had to have the ability to study human pregnancy. It’s not until really big datasets like this became available that we’re really able to get into pathways that we can ask how common variants affect these outcomes. And for pregnancy outcomes, you might guess that they’re going to undergo the most evolutionary selective pressure of virtually any trait, right?  I mean, that’s reproductive fitness that’s being selected for. Any common variant that exerts a big detrimental effect during pregnancy is not going to be around for very long. So you’re looking at things that are going to have relatively small effects, and probably have beneficial effects in some other way that protect you from some other human malady. So I think you need big data sets to reveal those small effect sizes, and I think with the 23andMe collaboration that was proven wonderfully for preterm birth.
JM: You were bringing up natural selection, which totally makes sense in regards to this. I noticed that you had previous studies where you used comparative genomics to identify human genes involved in birth timing that were rapidly evolving with respect to other primates.
LM: Right.
JM: But those genes didn’t seem to be the same ones that you had identified in your recent study, and I was wondering why you thought that might be?
LM: I think we really didn’t achieve genome-wide level significance in those initial studies. So we did a dimensionality reduction by just selecting out the most rapidly evolving genes. And I think those findings are incredibly interesting and still quite useful in terms of thinking in context, but we did that study in a relatively limited population from Finland. I think that initially heavily targeted our data. The larger population that we did affected a much more diverse European ancestry population than we originally would get in Finland. I think when you start doing these big data sets, the most robust sequences emerge. And it turns out the ones that are emerging are probably not the ones that are under the most rapid selective change in humans. I think some of them are, but many of them are part of a toolbox that is conserved and functioning in many ways. The genes we got like the WNT family that came up in the paper, they’re involved in many aspects of development that are very hard to have rapidly evolve because you’re going to affect too many fundamental developmental pathways.
JM: You mentioned that some of your previous studies were done in Finnish populations, and I know this particular study was still limited to women of European ancestry. Are you planning on following up with any non-European populations? Because I know from some of <a href="https://www.medpagetoday.com/obgyn/pregnancy/5031">your previous research</a> that women of African American ancestry are four times more likely to have recurrent preterm births.
LM: Yes. What we wanted to do initially for gene discovery was evaluate cohorts that had the least environmental risk for preterm birth — so, you know, had good maternal care, were not under socially deprived circumstances. We know that that’s not the most at-risk population, but we think that it probably allowed us to more sensitively detect the genes associated with the outcomes. And now what we’d like to do is go back (and look at) other ethnicities and begin to ask “Are those same genes associated?” We’re starting to do that in existing cohorts of women that have been collected, but we’re also embarking on a big study that’s being supported by the <a href="https://www.gatesfoundation.org/">Gates Foundation</a> to do genome wide association in about 10,000 African ancestry women and South East Asian ancestry women.
JM: Fantastic. Well that’s exciting future research to look out for.
LM: Yeah, you know, super important. My guess is we’ll get many of the things that replicate in African ancestry populations. I don’t think that I have any doubt that they won’t, but I think we’ll get new things too that reflect different environmental pressures in places with higher susceptibility and higher rates.
JM: One thing that came out of this study was the gene EEFSEC, which involves using selenium. You were proposing that this potentially could be a test for identifying women at risk for preterm births. Do you have any research plans to follow up with that in the future?
LM: Yes, and this is part of the same study that the Gates Foundation is supporting. So we identified the gene you mentioned, EEFSEC, which is involved in generating selenoproteins. And again, this is why you do genome-wide associations:  This was not anywhere on my radar screen, thinking about selenoproteins involved in pre-term birth, but they make a lot of sense. They’re antioxidant, they’re anti-inflammatory, they probably protect from premature aging of gestational tissues. We don’t know exactly how they’re working, but in this low environmental risk population, we identified this gene involved with selenoprotein metabolism as being associated with preterm birth risk. That sort of raised the notion of whether dietary deficiency in other populations that are potentially at risk for inadequate intake of selenium, which is an essential micronutrient, could that be driving their risk for preterm birth in addition to the genetic predisposition. So right now we’re doing a big epidemiological study in low- and middle-income countries where we’re looking at the relationship of maternal selenium levels halfway through pregnancy with the risk for preterm birth at the end of the pregnancy.
JM: You were saying earlier that your previous research used mouse models. Do you plan on returning to mouse models to further study some of the genes implicated in this study?
LM: Yes, for sure. The thing that we did in the mouse was we started out picking genes we thought might be important and then seeing if they were important for the mouse — and sometimes the answer was yes, sometimes the answer was no — and then going to human. What we’re doing now is we’re taking the SNPs we’ve identified in humans, and we’re going to do two things:  We’re going to in vitro models to look to see if they alter regulation of the associated genes, and what that does to network expression of RNAs in cells, but we’re also taking those and engineering the variants into mice using CRISPR Cas9. Because many of these, even though they’re in non-coding regions, are highly conserved between humans and mice, and it will give us an inroad into what it’s doing in pregnancy duration.
We just did this for our first, the WNT4 allele that we identified that we worked up in the New England Journal paper. We think we identified that estrogen receptor binding site as being the causative mechanism for the change that we were seeing. That region is 98 percent conserved in mice over a 50 base pair region. We engineered the one point variant in, and we just have our founder lines to be able to do that analysis now. So we’re hoping that that will be an informative approach moving forward. For some other experiments that we’re doing, we’re doing genome editing in non-human primates, so we may go to that as well.
JM: Exciting. The CRISPR revolution has even touched your research, I see.
LM: Absolutely, we do a lot of CRISPR Cas9 stuff.
JM: Are there any other research projects that are on the horizon that we haven’t touched on yet that you’d like to share with us?
LM: I think one of the things that is really interesting to think about is classically we’ve always thought of the mom’s genome as sort of being one unit for analysis and the baby’s genome being another unit for analysis. But I’m really hoping to get to this notion of looking at what I would call a combined genome of pregnancy — a metagenome of pregnancy — where you have both maternal and fetal genomes interacting in a way that mutually shapes pregnancy outcomes. So mom’s genes program maternal environment which then have effects on fetal health, but my guess is that there are fetal genes that shape mom’s pregnancy environment that affect mom’s health. I think looking at these bidirectional signaling pathways between the mom and the baby are something we’d really like to do to really fully understand how you optimize health in human pregnancy.
JM: That would be really exciting to look at — I hope that study works out, and all the future ones.
LM: Yes, we’re really excited about doing that. What you need for those studies are maternal-infant dyads, largely. And probably the way we’re collecting information with 23andMe would make it a challenge to be able to do that — maybe not impossible, but still a challenge.
JM: I know we have trios (parents and a biological child) in our database, so hopefully there’s some data in there for you.
LM: Yeah, that’d be great! I think the other thing is integrating; now we have a solid foundation for a handful of genes that are involved in human pregnancy. We can begin to now make sense of many of the gene expression and other omics datasets that have been collected around pregnancy to really nucleate on the core set of genes we’ve identified. Because that’s actually the most solid data that we have related to essential elements of pregnancy timing for humans.
JM: That’s great. Is there anything else you’d like to share that we haven’t covered yet?
LM: No, you know, I just thank 23andMe for all that they’re doing to collect this kind of information and make it available to the research community. It’s just such a fantastic resource, it really is. I think the more we can use this as a model to gather information that can really change health, by what we’re doing every day anyway, I think we’re going to accelerate our progress enormously.
JM: Well thank you, that’s our goal too, and we love collaborating with researchers and advancing science.
LM: You guys have been terrific to work with.

23andMe Blog

23andMe Blog Team

A conversation with Louis Muglia on his collaboration with 23andMe studying preterm births

By Jey McCreight, Scientist, Research Communications
Most New Year’s weight loss resolutions fail even with the best of intentions – but what if people had a helping hand?
According to 23andMe research, many people reported making healthy changes to their lifestyle after receiving personalized genetic reports with information about diet and weight.
We know this from the data we’ve gathered from our customers who viewed our Saturated Fat and Weight Report. This report, launched in the summer of 2016, looks specifically at how the interaction between saturated fat intake and the rs5082 genotype impacts body mass index, BMI. Those with the GG genotype are likely to weigh more on diets high in saturated fat, and are told that their genetics may make limiting saturated fat intake especially important for maintaining a healthy weight. Because limiting saturated fat intake may also be important for reducing the risk of heart disease1, the report encourages all customers to do so, regardless of their genetic results. <img loading="lazy" decoding="async" class="alignleft wp-image-25967 size-large" src="http://35.88.189.111/wp-content/uploads/2018/01/rs5082_geno_distrib-1024x281.png" alt="" width="1024" height="281" srcset="https://blogcms.23andme.com/wp-content/uploads/2018/01/rs5082_geno_distrib-1024x281.png 1024w, https://blogcms.23andme.com/wp-content/uploads/2018/01/rs5082_geno_distrib-300x82.png 300w, https://blogcms.23andme.com/wp-content/uploads/2018/01/rs5082_geno_distrib-768x211.png 768w, https://blogcms.23andme.com/wp-content/uploads/2018/01/rs5082_geno_distrib-880x241.png 880w, https://blogcms.23andme.com/wp-content/uploads/2018/01/rs5082_geno_distrib.png 1116w" sizes="auto, (max-width: 1024px) 100vw, 1024px" />
What we learned is that one month after viewing the Saturated Fat and Weight Report, many participants reported making changes to their diets – and the news was even better six months later.
One month after viewing the report, we asked research participants about their plans to make dietary changes. While about half of participants said they hadn’t considered changing what they eat, some said they had already started to make dietary changes (20%), planned to make changes (10%), or at least considered making changes (17%).
Six months later, the same participants answered another survey about dietary changes in this time period. Of those who had initially reported that they had already started to change what they eat, 82% were still maintaining those dietary changes. 80% of participants who had planned to make changes followed through with those plans, and 60% of participants who considered making changes ended up doing so. Even among participants who had not given it much thought originally, 31% ultimately made healthy dietary changes.
<img loading="lazy" decoding="async" class="alignleft wp-image-25971 size-full" src="http://35.88.189.111/wp-content/uploads/2018/01/fig5_laskey_v2.png" alt="" width="916" height="596" srcset="https://blogcms.23andme.com/wp-content/uploads/2018/01/fig5_laskey_v2.png 916w, https://blogcms.23andme.com/wp-content/uploads/2018/01/fig5_laskey_v2-300x195.png 300w, https://blogcms.23andme.com/wp-content/uploads/2018/01/fig5_laskey_v2-768x500.png 768w, https://blogcms.23andme.com/wp-content/uploads/2018/01/fig5_laskey_v2-880x573.png 880w" sizes="auto, (max-width: 916px) 100vw, 916px" />Those dietary changes were in line with what the Saturated Fat and Weight Report suggested: decrease saturated fat intake, and correspondingly increase unsaturated fat intake.
<img loading="lazy" decoding="async" class="alignleft wp-image-25963 size-large" src="http://35.88.189.111/wp-content/uploads/2018/01/fig3_laskey-1024x540.png" alt="" width="1024" height="540" srcset="https://blogcms.23andme.com/wp-content/uploads/2018/01/fig3_laskey-1024x540.png 1024w, https://blogcms.23andme.com/wp-content/uploads/2018/01/fig3_laskey-300x158.png 300w, https://blogcms.23andme.com/wp-content/uploads/2018/01/fig3_laskey-768x405.png 768w, https://blogcms.23andme.com/wp-content/uploads/2018/01/fig3_laskey-880x464.png 880w, https://blogcms.23andme.com/wp-content/uploads/2018/01/fig3_laskey-1180x622.png 1180w, https://blogcms.23andme.com/wp-content/uploads/2018/01/fig3_laskey.png 1320w" sizes="auto, (max-width: 1024px) 100vw, 1024px" />Additionally, reduced saturated fat intake was significantly associated with BMI category (p = 3 x 10-44) and genotype (p = 5 x 10-12) as evaluated by a chi-squared test. This suggests that providing personalized results &#8211; in this case, the gene-by-environment interaction between rs5082 genotype and dietary saturated fat intake &#8211; can encourage people to make healthy lifestyle changes.
<img loading="lazy" decoding="async" class="alignleft wp-image-25965 size-full" src="http://35.88.189.111/wp-content/uploads/2018/01/fig5_laskey_v2_p1.png" alt="" width="916" height="308" srcset="https://blogcms.23andme.com/wp-content/uploads/2018/01/fig5_laskey_v2_p1.png 916w, https://blogcms.23andme.com/wp-content/uploads/2018/01/fig5_laskey_v2_p1-300x101.png 300w, https://blogcms.23andme.com/wp-content/uploads/2018/01/fig5_laskey_v2_p1-768x258.png 768w, https://blogcms.23andme.com/wp-content/uploads/2018/01/fig5_laskey_v2_p1-880x296.png 880w" sizes="auto, (max-width: 916px) 100vw, 916px" />Like any study, these findings may not be generalizable to other populations. Survey questions nonrandomly targeted only people who said they had viewed the report. Respondents were also disproportionately female, and female respondents were slightly more likely to report making changes in response to the Saturated Fat and Weight Report. Finally, given the sensitivities that surround the topic of diet and weight loss, this study may be subject to <a href="https://en.wikipedia.org/wiki/Social_desirability_bias">social desirability bias</a> &#8211; the tendency of respondents to answer inaccurately in order to present themselves favorably &#8211; as there were no direct measurements of dietary intake.
We believe that the results presented here support the need for ongoing research looking at the effectiveness of personalized genetic results as a motivator for positive lifestyle change. Although there are limitations, it appears that simply receiving the report may have motivated healthy changes for some people of all genotypes, with the effect being even stronger for those with the GG genotype.“It&#8217;s nice to know that there&#8217;s a big population out there of engaged, excited customers who want to make healthy changes,” says study lead Sarah Laskey, PhD, Health R&amp;D Scientist at 23andMe. “This is what&#8217;s so exciting about our new Weight Loss Intervention study &#8212; hopefully that enthusiasm will enable us to make big strides in the science of genetics and weight loss!”
For more details, <a href="https://blog-api.23andme.com/wp-content/uploads/2017/10/23andMe_ASHG_2017_Sarah.pdf">check out Sarah’s poster</a> from the 2017 American Society of Human Genetics annual meeting.
[1] <a href="https://www.ncbi.nlm.nih.gov/pubmed/26068959">Hooper L et al. (2015). “Reduction in saturated fat intake for cardiovascular disease.” Cochrane Database Syst Rev. 6:CD011737</a>

Tipping the scales: Participants make healthy dietary changes in response to direct-to-consumer genetic test results

Editor&#8217;s Note: This post was first published in 2017. It&#8217;s been slightly edited.
Just in time for New Year’s resolutions, 23andMe is embarking on a first-of-its-kind study that will engage with customers to learn more about genetic variants associated with weight loss and the effectiveness of using different diets or exercise to lose weight.
<h2>New Weight Loss Study</h2>
23andMe’s new “Weight Loss Intervention Study” will attempt to uncover more about why each of us responds differently to exercise and diet.  While scientists know that a person’s weight is determined by a complex combination of lifestyle, environment, and genetics, the interplay of those influences is still poorly understood.
“We’d like to better understand the genetic, demographic, psychosocial, and behavioral characteristics that predict weight loss success overall and on different lifestyle interventions,” said Liana Del Gobbo, Ph.D., 23andMe’s lead scientist on the study.
“This will help us begin to pave the way towards more personalized lifestyle recommendations.”
<h2><img loading="lazy" decoding="async" class="size-medium wp-image-25181 alignright" src="https://blog-api.23andme.com/wp-content/uploads/2017/03/weight-300x300.png" alt="" width="300" height="300" />Intervention Study</h2>
The size of this intervention—involving 100,000 eligible customers and examining three different weight loss strategies—is unprecedented, and no one has attempted a genome-wide association study of behavioral weight loss.
Although genetic association studies have focused on weight, specifically body mass index (BMI), no trials have looked at behavioral weight loss. This is important because the genetic variants that influence BMI may differ from those that affect weight loss. Also, for this kind of genome-wide association study to be effective, many people must participate in uncovering genetic variants. That is what makes 23andMe uniquely able to conduct this research now.
And the time to do this kind of work is right. Obesity is reaching epidemic levels with dire public health consequences. That, combined with the difficulty of losing weight—most people who try to lose weight fail, and even those who succeed in losing a few pounds often gain them back—makes gaining new insights into the genetics and behaviors leading to weight loss that much more important.
<h2>Genetic Weight Report</h2>
The study also comes less than a year after 23andMe launched its Genetic Weight Report, which combines large-scale genetic and non-genetic data with machine learning to provide personalized portraits of how genetics and lifestyle impact weight.
This study builds on the work that went into that report. For this research, participants will be recruited from a group of over a million 23andMe customers who consented to research and are potentially eligible to participate. To be eligible, participants must be 23andMe customers, overweight, but in good health, between 18 and 70. There are also other additional eligibility requirements. Recruitment for the study closes in early January, and then over 12 weeks, participants will follow one of three different interventions.
Participants will hear from researchers every other week via emails to help them through the study, offering weight loss advice, meal suggestions, and live researcher forums for support and updates. Within a few weeks of the study completion, researchers will report back to customers some of what we’ve learned. There will be another follow up with participants six months and then again a year later.
“Ultimately, we’d like to learn how to make traditional lifestyle interventions more scalable and cost-effective. This intervention will be an exciting first step,” said Liana.

23andMe Launches Landmark Weight Loss Study

By Thao Do, PhD., 23andMe’s Education and Academia Program Manager
This year, 23andMe has organized and participated in dozens of educational events to connect with thousands of students and educators across the United States. We want to meet the students and professors in person, visit their schools, explore available resources, and experience first-hand the challenges that they face. These experiences help us develop the strategy for 23andMe’s Education Program.
Our most recent community outreach effort was an education, speaking, and listening tour through Virginia, Maryland, and Washington D.C. with an international athlete, Jonathon “JP” Prince. 
To raise awareness of 23andMe’s Education Program we sponsored JP as over the last four months as he ran from California to New York City  “Forest Gump” style. Throughout his route, he visited local universities, sharing his own personal DNA story, and handing out free DNA kits. 
<div id="attachment_25940" style="width: 1034px" class="wp-caption aligncenter"><img loading="lazy" decoding="async" aria-describedby="caption-attachment-25940" src="https://blog-api.23andme.com/wp-content/uploads/2017/12/JaDAl1Z8hhBGEXAKhs-eAvSoTHujRRsCZ67QA9w6mm67U_WJwXvOOKaTnSY8yzLupBWS38nuUdpBqBxN9ggwU5Y-lXm6K7vqLPuRombshz6zC_w0uUruliItAAw27OoNKHRnxlIr-1024x768.png" alt="" class="wp-image-25940 size-large" width="1024" height="768" srcset="https://blogcms.23andme.com/wp-content/uploads/2017/12/JaDAl1Z8hhBGEXAKhs-eAvSoTHujRRsCZ67QA9w6mm67U_WJwXvOOKaTnSY8yzLupBWS38nuUdpBqBxN9ggwU5Y-lXm6K7vqLPuRombshz6zC_w0uUruliItAAw27OoNKHRnxlIr-1024x768.png 1024w, https://blogcms.23andme.com/wp-content/uploads/2017/12/JaDAl1Z8hhBGEXAKhs-eAvSoTHujRRsCZ67QA9w6mm67U_WJwXvOOKaTnSY8yzLupBWS38nuUdpBqBxN9ggwU5Y-lXm6K7vqLPuRombshz6zC_w0uUruliItAAw27OoNKHRnxlIr-300x225.png 300w, https://blogcms.23andme.com/wp-content/uploads/2017/12/JaDAl1Z8hhBGEXAKhs-eAvSoTHujRRsCZ67QA9w6mm67U_WJwXvOOKaTnSY8yzLupBWS38nuUdpBqBxN9ggwU5Y-lXm6K7vqLPuRombshz6zC_w0uUruliItAAw27OoNKHRnxlIr-768x576.png 768w, https://blogcms.23andme.com/wp-content/uploads/2017/12/JaDAl1Z8hhBGEXAKhs-eAvSoTHujRRsCZ67QA9w6mm67U_WJwXvOOKaTnSY8yzLupBWS38nuUdpBqBxN9ggwU5Y-lXm6K7vqLPuRombshz6zC_w0uUruliItAAw27OoNKHRnxlIr-880x660.png 880w, https://blogcms.23andme.com/wp-content/uploads/2017/12/JaDAl1Z8hhBGEXAKhs-eAvSoTHujRRsCZ67QA9w6mm67U_WJwXvOOKaTnSY8yzLupBWS38nuUdpBqBxN9ggwU5Y-lXm6K7vqLPuRombshz6zC_w0uUruliItAAw27OoNKHRnxlIr-1180x885.png 1180w, https://blogcms.23andme.com/wp-content/uploads/2017/12/JaDAl1Z8hhBGEXAKhs-eAvSoTHujRRsCZ67QA9w6mm67U_WJwXvOOKaTnSY8yzLupBWS38nuUdpBqBxN9ggwU5Y-lXm6K7vqLPuRombshz6zC_w0uUruliItAAw27OoNKHRnxlIr.png 1600w" sizes="auto, (max-width: 1024px) 100vw, 1024px" />JP and Thao on a pit stop during their campus tour.</div>
&nbsp;
JP’s love for his mother inspired him to make this North America DNA Vol. RUN #TheHumanRace./For most of her life, JP’s mom, who was adopted, didn’t feel as if she belonged anywhere. After using the 23andMe she discovered DNA relatives, and experienced a connection she never had before, JP said. So touched by her emotional reaction, JP dedicates his cross-country run to her with a mission to “champion unity, hope, respect and love for Americans and its global neighbors. “
I joined JP on one segment of this journey. We visited ten campuses: Hampton University, Virginia Commonwealth University, George Washington University, Northern Virginia Community College, Howard University, Georgetown University, University of Maryland, George Mason University, Virginia Tech, Radford University, in three states and seven cities in seven days.
We traveled in a colorful 23-chromosomes/DNA-wrapped sprinter van and delivered DNA talks in classes and auditoriums in front of hundreds of students, administrators, professors, department heads, and deans. We facilitated impromptu group discussions about genetics and diversity and inclusion. We shared lots of hugs, fist bumps, selfies, and connected with students, heart-to-heart.
Our stories complement each other marking two sides of any DNA journey — my story was about how genetics could influence the future of science and research, while JP shared his mother’s emotional human story. I wasn’t the only one from 23andMe who got to journey alongside JP, a  few months earlier, Jacquie Haggarty, 23andMe’s associate counsel also joined in on the fun. She flew to Alabama, ran several miles along I-85 with JP, and gave DNA kits and talked to students at Auburn University.
JP’s journey along with many of our other community outreach events has inspired a new <a href="https://education.23andme.org/genetics-club/">Genetics Club campaign</a>. Students can apply to the Genetics Club to let us know that they are starting a club at their school. To make it all a little funner, 23andMe will send each club a jumbo box of T-shirts and stickers. We are also sharing a magazine, written by students, for students and all about genetics called the DNA Decoder. 
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Thao Do

23andMe Education Speaking and Listening Tour

Our customers often share their stories with us, which are both moving and motivating as we get to see the real impact 23andMe results can have in people’s lives.
One such story came in recently from Bev Glaspey of Lake Placid, Florida. Bev called it her ‘Family Christmas Story,’ because it was about the best kind of present, a gift of family.
This is their story.
<div id="attachment_25935" style="width: 1034px" class="wp-caption aligncenter"><img loading="lazy" decoding="async" aria-describedby="caption-attachment-25935" src="https://blog-api.23andme.com/wp-content/uploads/2017/12/IMG_1636-e1513622666512-1024x609.jpg" alt="" class="wp-image-25935 size-large" width="1024" height="609" srcset="https://blogcms.23andme.com/wp-content/uploads/2017/12/IMG_1636-e1513622666512-1024x609.jpg 1024w, https://blogcms.23andme.com/wp-content/uploads/2017/12/IMG_1636-e1513622666512-300x179.jpg 300w, https://blogcms.23andme.com/wp-content/uploads/2017/12/IMG_1636-e1513622666512-768x457.jpg 768w, https://blogcms.23andme.com/wp-content/uploads/2017/12/IMG_1636-e1513622666512-880x524.jpg 880w, https://blogcms.23andme.com/wp-content/uploads/2017/12/IMG_1636-e1513622666512-1180x702.jpg 1180w" sizes="auto, (max-width: 1024px) 100vw, 1024px" />At their first meeting in early December, from left to right, Florence&#8217;s half-brother Robin, her daughter Bev, and Florence.</div>
Bev’s mom, Florence Axelson was born in Chicago in 1922, and adopted when she was 1 year-old. For all of her 95 years she had almost no information about her birth family. The one exception was that back in the mid-1970s Florence applied for a passport and in the process she also had to get a copy of her birth certificate.
Getting that document also got Florence the first bit of information about her origins, her biological mother’s name. But when she tried to use that bit of information to get more details about her adoption she hit a brick wall. The records, she was told, had been destroyed in a fire.
There was little more that she had to go on, and over time life got in the way of searching. That didn’t mean it wasn’t something she thought about. In fact, all of her daughters, Bev and Bev’s two sisters, all wondered about their mother’s family. Even simple things, like what their family’s nationality was, remained unknown.
That is until Bev purchased a 23andMe kit for herself and her mom. Bev started exploring their DNA Relatives, finding cousins she never knew she had. The closest was a cousin named Cathy, who was the key to unlocking Florence’s story.
Cathy&#8217;s mom, who is still alive, is Florence’s half-sister.
“Mom found out she was the firstborn child to a young unwed mother,” Bev said.
She learned that she didn’t just have one half sibling, but her biological mother had 12 children in all, three of her half-sisters and one half-brother are still living.
Florence was able to talk to two of her half sisters, and then at 95 she was able to meet her half brother Robin and his wife Kim for the first time when he came in early December to visit her at her assisted living facility.
“I was able to find mom’s birth mother’s family,” Bev said. “This has been a wonderful gift to our family. “

One Family’s 23andMe Christmas Story

Every day for 55 years, Ida Pollack thought about her baby, wondering if she’d made a mistake in letting him go.
As she’d told the Omaha Herald recently, she always asked herself the same thing: “Did I make the right decision?’”
Now she finally has the answer thanks to 23andMe.
A few weeks ago Ida, and the son she raised Travis, met her first born, Barrie Bode, who Ida gave up for adoption as a frightened 19 year-old.
The meeting in New York City gave Ida the peace of mind that even though giving him up was hard it was the right thing to do.
“I was glad I did after I met him and found out what kind of life he had. He had wonderful parents,” she said.
<div id="attachment_25930" style="width: 1034px" class="wp-caption aligncenter"><img loading="lazy" decoding="async" aria-describedby="caption-attachment-25930" src="https://blog-api.23andme.com/wp-content/uploads/2017/12/LMW0670-e1513285850598-1024x560.jpg" alt="" class="wp-image-25930 size-large" width="1024" height="560" srcset="https://blogcms.23andme.com/wp-content/uploads/2017/12/LMW0670-e1513285850598-1024x560.jpg 1024w, https://blogcms.23andme.com/wp-content/uploads/2017/12/LMW0670-e1513285850598-300x164.jpg 300w, https://blogcms.23andme.com/wp-content/uploads/2017/12/LMW0670-e1513285850598-768x420.jpg 768w, https://blogcms.23andme.com/wp-content/uploads/2017/12/LMW0670-e1513285850598-880x481.jpg 880w, https://blogcms.23andme.com/wp-content/uploads/2017/12/LMW0670-e1513285850598-1180x645.jpg 1180w, https://blogcms.23andme.com/wp-content/uploads/2017/12/LMW0670-e1513285850598-271x149.jpg 271w, https://blogcms.23andme.com/wp-content/uploads/2017/12/LMW0670-e1513285850598-372x204.jpg 372w, https://blogcms.23andme.com/wp-content/uploads/2017/12/LMW0670-e1513285850598-574x315.jpg 574w" sizes="auto, (max-width: 1024px) 100vw, 1024px" />Family gathering for the first time in New York City. From Left to right, Barrie Bodie, his son Alex, Travis and Ida. </div>
&nbsp;
Their story isn’t just about Ida, but about Barrie and his brother Travis. Coming together in New York just before Thanksgiving was the culmination of months of talking and emails between Travis and Barrie.
They’d been able to find each because Barrie’s son had used 23andMe and so had Travis. Alex and Travis matched on the DNA Relatives tool as predicted first cousins — Travis and Barrie are biological half brothers.
When he first saw the match, Travis was a bit confused.
“I know all my first cousins,” he said, but he didn’t know who Alex was.
But Travis also knew that first cousins can share a similar amount of DNA as a nephew or half nephew. And he knew that his mother had given up a baby for adoption when she was a teenager. So Travis called her and asked her about the baby’s date of birth. So when Travis finally connected with Alex, he knew what to ask. “Was your dad adopted?” And “what is his date of birth?”
The dates matched his mother’s first born. Travis figured out that Alex’s father, Barrie, was his brother, and his mother’s first born.
The two connected talking on the phone for hours. For Ida the news took a little time to settle in, but soon they were making plans to meet in person.
For Barrie, who over the decades had made efforts to find his biological family, meeting his mother was a bit overwhelming. Like a lot of adoptees, he wasn’t even sure his biological parents would want to meet him.
“You always have that fear that the person might not want you to find them,” Barrie told the Omaha World Herald.
In this case, Ida and Travis welcomed him and his son Alex with open arms. For Ida meeting her son has opened a new chapter in her life.
“It’s something you’ve been thinking about your whole life, and suddenly it’s the beginning of another story,” she said.
[vc_video link=&#8221;https://www.youtube.com/watch?v=S2KutYiOTC8&#8243; align=&#8221;right&#8221;]

A Family Reunited Using 23andMe

It takes a village to do research. Scientific discoveries from the 23andMe Research Team are made possible by our three million research participants and academic collaborators. Jey McCreight, Ph.D., a research communications scientist for 23andMe, sat down with Abraham Palmer, Ph.D., professor of psychiatry and vice chair for basic research at the University of California, San Diego School of Medicine, to talk more about our recent collaboration on delay discounting that was <a href="https://www.nature.com/articles/s41593-017-0032-x">published in Nature Neuroscience</a>. 

What is Delay Discounting?
Jey McCreight: What is delay discounting, and what inspired you to study this particular trait?
Abraham Palmer: I have wanted to study delay discounting, which tends to favor small immediate rewards over larger delayed rewards since I was an undergraduate. The concept resonates with me because being able to work for a larger delayed reward is so critical to the survival of many animals, including humans. The idea that pigeons, for example, are calculating, in real-time, the value of a delayed reward is amazing. We do it too. Some of it clearly is at the conscious level, but I’m convinced that people also have innate differences that are due to genetic factors. So this is a trait that impacts health, but it is also an important concept in microeconomics.
<div id="attachment_25919" style="width: 209px" class="wp-caption alignright"><img loading="lazy" decoding="async" aria-describedby="caption-attachment-25919" class="wp-image-25919 size-medium" src="/wp-content/uploads/2017/12/Abraham-Palmer-photo-199x300.jpg" alt="" width="199" height="300" srcset="https://blogcms.23andme.com/wp-content/uploads/2017/12/Abraham-Palmer-photo-199x300.jpg 199w, https://blogcms.23andme.com/wp-content/uploads/2017/12/Abraham-Palmer-photo-768x1156.jpg 768w, https://blogcms.23andme.com/wp-content/uploads/2017/12/Abraham-Palmer-photo-680x1024.jpg 680w, https://blogcms.23andme.com/wp-content/uploads/2017/12/Abraham-Palmer-photo-585x880.jpg 585w, https://blogcms.23andme.com/wp-content/uploads/2017/12/Abraham-Palmer-photo-784x1180.jpg 784w, https://blogcms.23andme.com/wp-content/uploads/2017/12/Abraham-Palmer-photo.jpg 1063w" sizes="auto, (max-width: 199px) 100vw, 199px" />Prof. Abraham Palmer</div>
From a practical perspective, I’m excited to use these and future results to identify specific genes. Part of my lab works on human genetics, and part of it works on mice and rats. If we can identify genes using human genome-wide association studies (GWAS), we can study them in rodents, where we can also measure delay discounting. That allows us to understand how those genes influence delay discounting, which is hard or impossible to do in humans.  
JM: But mice can’t exactly fill out a survey like a 23andMe research participant, can they? 
AP: Ha, yes, the whole field of behavioral neuroscience is based on elaborate methods that circumvent the fact that we can’t just ask a mouse how it is feeling or whether it wants 0.8 ml of water now or 1.6 ml of water in 72 seconds. If we gave them a paper survey, they’d sniff it for a few minutes, shred it, and make a nest out of it!
<h2>Studying Humans</h2>
JM: What are the benefits of using a cohort of humans versus a mouse model? Disadvantages?
AP: The problem with people is that they are too damn smart. So they might see these questions as about current interest rates, their expectations about the return on their investments, or similar financial issues. Or they might have a cultural bias that waiting is always better. Those factors obscure the “innate” genetic differences that we are trying to study.
But a really nice thing about humans is that they are so damn smart. We can ask them these questions in a minute or two. With rodents, we have to spend a couple of weeks “teaching” them how to answer these questions. Also, rodent studies do use a real reward, typically water or food, and demand for those rewards might change from subject to subject, or over the course of a session, or between sessions. But rodents don’t have bank accounts and don’t know what the prime interest rate is. They’ve also been raised in very uniform environments, which minimizes variability due to environmental factors. In <a href="https://www.ncbi.nlm.nih.gov/pubmed/23710681">a paper written by Jerry Richards</a>, we estimated that the heritability of delay discounting was about 60 percent. (A bit of trivia: Jerry is the one who first got me interested in delay discounting when I was an undergrad and he was a postdoc in Lew Seiden’s lab at The University of Chicago).
One of my concerns is that delay discounting in humans may or may not be similar to delay discounting in rodents. There are many differences, including the abstract “reward” offered to humans versus the food or water “reward” offered to rodents. Another issue is the timescale: we’d never be able to train rodents to wait days weeks or months for a reward, and I’ll bet that they wouldn’t wait that long even if they understood the question. I think that their time horizon is much shorter than ours.
So that raises a question: are decisions about abstract rewards months away really equivalent to a behavioral paradigm in which a rat chooses between water now or more water in 72 seconds? Quite frankly, no one knows; there is pharmacological and neuroanatomical evidence to support the model, but I think questions remain. I hope to tackle those questions in the coming years, and these results will benefit that effort.
<h2>Genetics of Behavior Traits</h2>
JM: What do we generally know about the genetics of behavior and personality traits? How heritable are they compared to other traits or diseases?
AP: My postdoc Sandra Sanchez-Roige and I just finished <a href="https://www.ncbi.nlm.nih.gov/pubmed/29152902">a review of the genetics of personality</a>, so this information is fresh in my mind. Twin studies using various personality traits suggest heritability in the ~40 percent range. SNP heritability is estimated more like 5-18 percent (SNP heritability is expected to be lower).
JM: What are the benefits of using GWAS to investigate behavioral traits? Are there any limitations?
AP: I think of behavioral genetics as a broad field that includes diseases and also normal variation, which may contribute to disease in extreme cases. Personality provides a continuous (rather than case:control) measure, and can be studied in “normal” populations, yet may provide insights into diseases. The limitations that come to mind include power (sample size) and the obvious role of environmental (including but not limited to cultural) influences.
<h2>Genetic Studies of ADHD</h2>
JM: Why is it important to look at an individual process (such as delay discounting) rather than a disease (such as ADHD)?
AP: I wouldn’t say it is an either/or proposition, but I think delay discounting can add a lot to studies of disease phenotypes. For example, ADHD can be dissected into various components; that is the goal of the <a href="https://www.nimh.nih.gov/research-priorities/rdoc/index.shtml">Research Domain Criteria initiative</a>, which is a big deal at the National Institute of Mental Health. Delay discounting is one component of ADHD, but I expect that some ADHD genes will have a big impact on delay discounting while others will not. If we know which is which, we know how to study the mechanism of those ADHD genes. That means that having data about both ADHD and delay discounting will allow us to better dissect the diseases into processes, neural pathways, and molecular mechanisms. For me, that is the goal of doing GWAS in the first place.
<h2>Big Data</h2>
JM: What advantages does the size and variety of 23andMe’s dataset offer a researcher? Did it help speed up the work you were doing or allow you to research something you couldn’t otherwise?
AP: Well, size matters a lot. 23andMe was able to collect phenotypes from 23,217 subjects in about four months. That’s fantastic. My lab has none of the infrastructure needed for that scale of data collection. Having genotypes already available was also key, and the research participants were willing to do this work on a volunteer basis, which meant a lot to me.
JM: One can imagine that financial security plays a large part in an individual’s ability to delay receiving a reward &#8211; do you control for this?
AP: We had hoped to include socioeconomic status (SES) as a covariate, but because 23andMe only had that data for a fraction of the subjects we decided not to. If we had to exclude research participant for which we had no SES data, it would have reduced our sample size by about 25 percent. However, one advantage of doing this project with 23andMe was that we knew that research participants were reasonably affluent; for example, they could afford to pay to have themselves genotyped, which is a sign that they are not living “hand to mouth.”
In other cohorts/populations, this is a major concern. For example, someone who is a drug addict might urgently need the smaller amount of money that is available now because they need to buy more drugs. Moreover, Carl Hart from Columbia once pointed out that delay discounting may also measure how much the subject trusts the researcher to give them money later. If you have learned that you can’t trust other people to make good on their commitments, or if you don’t find the scientists trustworthy, you might assume there was a degree of risk in waiting for a delayed reward. So, delay discounting may measure different things, depending on the population used. I saw 23andMe as an advantage because these factors were less likely to be in play, but also possibly a limitation because highly impulsive people might not be well represented because they might be less likely to join 23andMe in the first place.
<h2>Impulsivity</h2>
JM: How accurate are self-reported measures of delay discounting compared to direct measures of the behavior? Do people overestimate their ability to wait for greater rewards because of how “impulsivity” is perceived?
AP: That is a great question. When designing this project, I wanted to include some tasks intended to measure impulsivity. These can be implemented as Java applets, allowing us to measure behavior directly. I still hope to do that study one day. We considered trying to test behavior by telling the research participants that one of their decisions would have real consequences; that is, they would actually get $10 now or $20 in three months. Other papers have suggested that this has little effect on people&#8217;s answers, which made us think we could forgo giving any real rewards. In hindsight, I’m glad we did because giving ten dollars (or more) to 23,217 people would have been expensive!
<h2>A Sex Bias?</h2>
JM: Your most significant SNP was on the X chromosome. Do you think this is why women tend to show more delay in discounting than men?
AP: I wouldn’t read too much into that. Although it was the most significant finding, that locus has a very small overall effect. I was surprised when we saw that women in this cohort put less value on future rewards. I panicked when I first saw that because I had assumed (and thought I knew) that men would discount more. So Sandra and I had this panicked evening where we thought we’d made some critical error in the data processing, but then we realized that our results were consistent with prior studies. Men are indeed more impulsive when other measures/definitions are used, but delay discounting is different for whatever reason. So all that panic was for nothing!
<h2>What&#8217;s Next?</h2>
JM: What are the next steps for your research?
AP: This is a down payment. I’m hoping that by this time next year I’ll have at least 100K genotyped subjects who have answered these questions. We are working with at least half a dozen groups already to collect that data, and we are very open to more collaborations. To be honest, I probably won’t be satisfied until I have a million. Another next step is to translate these results into rodents studies, as we just discussed. A third direction is to get more measures of impulsivity, hopefully including behavioral measures. And of course all of this requires endless grant writing, which I mostly enjoy, but I’ve been financing this project out of discretionary funds so far. Now I need to reload, and that means grants of possibly philanthropy.
JM: Where do you see the field of psychological genomics going in the future?
AP: I ask people that question all the time – what will we be talking about in five years. I don’t even ask about 10 years in the future because it is laughable; no one can predict that far in the future, the field is developing so quickly. I think digital mental health is a big deal. One aspect of digital mental health is collecting “big data” from people to monitor, predict, and possibly improve aspects of mental health. That big data, especially when it is from genotyped research participants, will be a huge resource. I suspect we could reframe questions about impulsivity by measuring aspects of behavior using smart phones or watches or credit card activity or whatever.
Another huge area is the use of electronic medical records in genotyped subjects. I’ll bet that polygenic prediction of delay discounting will predict all sorts of good and bad health outcomes; we are already looking at that with Lea Davis and Nancy Cox and others at Vanderbilt University. Of course every year more and more people will be whole genome sequenced, which will allow us to examine rare variants and their impact on all phenotypes. Model systems, both using whole animals and cellular systems, will be extremely important in realizing the promise of molecular insights from GWAS. In terms of how this will impact patients, I hope to see new and better drugs developed, and we all hope that drug treatments will be personalized based on an individual’s DNA.
JM: Who is the mastermind behind the science limericks on your lab webpage?
AP: Aren’t those cute? I gave a talk years ago at University of Illinois Urbana-Champaign — I think Gene Robinson was the one who invited me — and they had a tradition that the audience would write limericks about the seminar. When I finished taking questions, one of the graduate students got up and read them. I was so impressed I got copies.

Abraham Palmer Discusses Collaboration with 23andMe

Some people want what they want when they want it, while others can patiently wait for a bigger and better reward later.
Researchers at the University of California, San Diego, want to find out if there is a genetic basis for that and are studying what is known as ‘delay discounting’ — a tendency to overvalue immediate rewards more than future ones which can lead to impulsive behavior.
“Studying the genetic basis of delay discounting is something I’ve wanted to do for the entirety of my 20 years of research,” said senior author <a href="http://profiles.ucsd.edu/abraham.palmer">Abraham Palmer, Ph.D</a>., professor of psychiatry and vice chair for basic research at the University of California, San Diego School of Medicine. “But it takes a huge number of people for a genetics study to be meaningful.”
That’s where 23andMe came in, helping Palmer and his team to gather enough data from enough people to make this research possible.
Palmer’s and other’s interest in delay discounting is in part because many studies have shown that more extreme impulsivity is also commonly found in people with a host of neuropsychiatric conditions, such as Attention Deficit and Hyperactivity Disorder (ADHD), major depression, smoking dependency and being overweight.
<img loading="lazy" decoding="async" class="aligncenter size-large wp-image-25873" src="https://blog-api.23andme.com/wp-content/uploads/2017/11/marshmallow-image-1024x717.jpg" alt="" width="1024" height="717" srcset="https://blogcms.23andme.com/wp-content/uploads/2017/11/marshmallow-image-1024x717.jpg 1024w, https://blogcms.23andme.com/wp-content/uploads/2017/11/marshmallow-image-300x210.jpg 300w, https://blogcms.23andme.com/wp-content/uploads/2017/11/marshmallow-image-768x538.jpg 768w, https://blogcms.23andme.com/wp-content/uploads/2017/11/marshmallow-image-880x616.jpg 880w, https://blogcms.23andme.com/wp-content/uploads/2017/11/marshmallow-image-1180x826.jpg 1180w, https://blogcms.23andme.com/wp-content/uploads/2017/11/marshmallow-image.jpg 1224w" sizes="auto, (max-width: 1024px) 100vw, 1024px" /> 
 
The most famous example of research around delay discounting is a 50-year-old study known as the<a href="https://www.theatlantic.com/health/archive/2014/09/what-the-marshmallow-test-really-teaches-about-self-control/380673/"> Stanford Marshmallow Experiment</a>. As part of the study, researchers offered children a choice of either an immediate reward — one marshmallow — or if they could hold out for 15 minutes alone in a room with the marshmallow in front of them, there’d be a bigger, better reward — two marshmallows. The researchers later followed up with these children to look at different measures of success and found that those kids who were able to hold out faired better on things like SAT scores and were less likely to be overweight.
Scientists have since learned through studies of identical and non-identical twins that “delay discounting” is strongly heritable. But little is known about its genetics, or how it might relate to these other conditions. So Prof. Palmer and his team at UC San Diego worked with 23andMe scientists on the studying and gathering data from 23andMe customers who consented to participate in research and complete a survey to measure their impulsivity. (Check out an interview we did with Prof. Palmer <a href="https://blog.23andme.com/articles/abraham-palmer-ucsd">here</a>.)
“In less than four months, we had responses from more than 23,000 research participants,” said Pierre Fontanillas, Ph.D., a senior statistical geneticist at 23andMe. “This shows the power of our research model to quickly gather large amounts of phenotypic and genotypic data for scientific discovery.”
The first findings from this work — and the first-ever genome-wide association study of delay discounting — was published this week in the journal <a href="https://www.nature.com/articles/s41593-017-0032-x">Nature Neuroscience</a>.
The study identified several genetic variants associated with delay discounting and found significant genetic correlations between delay discounting and ADHD, smoking addiction, depression, and BMI. While researchers have long know that these conditions were correlated, this is the first time that scientists have also been able to see that these are also genetically correlated, according to the paper.
The researchers said that this also highlights the advantage of this research model for the study of behavioral traits and neuropsychiatric conditions. In fact, the same team that worked on this study also recently completed a study of similar size looking at the genetic factors that contribute to alcoholism that appeared in the journal Addiction Biology.
Palmer believes a larger study with a more diverse population would yield more results.
You can find the full paper in <a href="https://www.nature.com/articles/s41593-017-0032-x">Nature Neruoscience</a>.

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