[MUSIC PLAYING] We are the Association for Child And Adolescent Mental Health, or ACAMH, for short. And this is ACAMH Learn. Hi, everyone. My name is Elisavet Palaiologou. And I'm a postdoctoral research fellow at the Division of Psychiatry at University College London. So in this presentation, we will explore the role of nature and nurture or genes and environment in anxiety and depression. So this talk, we will discuss five main points. So first, how common are anxiety and depression? And how do they develop over time? Second, what do family twin studies tell us about the role of genetic and environmental influences in anxiety and depression? Third, what have genome-wide association studies and polygenic scores added to our understanding? Fourth, how genes and environments work together to shape risk for anxiety and depression. And finally, we will conclude with discussing whether genetic and early life factors can help us identify those most at risk early on. So starting with the first point, what are anxiety and depression? So anxiety is characterised by extreme worry and fear about something in the future. Symptoms can be cognitive and physiological and may include feeling restless or on edge, experiencing racing thoughts, or a fast heart rate, sweating, and trembling. Depression is characterised by low mood and loss of interest. And symptoms may include persistent sadness, hopelessness, low energy, changes in sleep or appetite, or feeling worthless and withdrawing from others. Feeling anxious or experiencing low mood from time to time is a normal part of life. However, when symptoms persist for a prolonged period of time and begin to interfere with daily life functioning, then they may consider it indicative of an anxiety or depressive disorder. And both conditions are a leading cause of disability worldwide, affecting 1 in 4. And it was estimated that two account for over 60% of the global cases of mental health. So they're incredibly common. The prevalence has also increased over time. And studies show that they disproportionately affect females and young people between ages 16 to 24. Both conditions start early, with anxiety, here in yellow, often beginning in early childhood. And depression, which falls under the mood disorders category, in blue, tends to begin around adolescence. Notably, evidence suggests that most people who experience symptoms of anxiety or depression at some point in their life will experience first symptoms before the age of 25. The development of both conditions is also highly heterogeneous. This means the symptoms can differ in when they begin or how severe they become and whether they persist over time. And evidence suggests that within a given sample population, different groups of people exist that follow unique patterns of symptom development. And these are what we call trajectory groups. And this is something that we wanted to understand better, especially because until recently, there were no studies of anxiety exploring symptom development from early childhood to young adulthood. So we used data from the Twins Early Development Study, or TEDS, which is a UK-based longitudinal cohort that has been following twins that were born in England and Wales between 1994 and 1996. And the study has now been running for over 30 years, which made it ideal for us to study the development of anxiety across three developmental periods, from childhood to young adulthood. So what we did was to model how anxiety symptoms develop from age 4 to 26, using a modelling technique called growth mixture modelling, which allows us to identify the different trajectory groups in our sample. So the model of that fit the data best suggested four distinct pathways. And here they are. So we identified a low stable class in pink, here, which included the majority of our sample. And this included people who had no or very low symptoms of anxiety across development. We also identified an adolescent onset group in orange, which included people who had low symptoms in childhood but then whose symptoms increased over adolescence and into young adulthood. Then we discovered a childhood-limited group, which captured individuals experiencing high symptoms in childhood that decreased over development, and finally, a persistent class in blue, which included individuals with consistently high symptoms across development. As you can see, this class include a very small proportion of our sample. But this captures people who may often present to clinics for treatment. And these analyses illustrated how heterogeneous anxiety is and its development. And despite the fact that many individuals may seem to be in the low stable group in our sample, it also highlights that about a third of our sample experienced symptoms of anxiety at some point in their lives. And here, we have findings for from a depression trajectory study that was conducted using the Avon Longitudinal Study of Parents and Children, otherwise known as ALSPAC, that includes individuals born in the Avon area between 1991 and 1992. And just like TEDS, ALSPAC has been following these individuals for over 30 years and has collected data on lots of developmental measures. So just like our trajectories, colleagues found that similar heterogeneity exists in depression, too, with again, a third of the population experiencing symptoms of depression at some point in their lives. And I should note that these trajectories run from late childhood to young adulthood. So they also captured wide periods of development. And what's really important to note here is that evidence suggests that individuals with high symptoms in adolescence and adulthood or who symptoms do not decrease across development are at risk and are at risk for experiencing really poor life and clinical outcomes. Some of these outcomes may include subsequent mental and physical health diagnosis, experiencing financial difficulties and unemployment, experiencing self-harm or having suicidal thoughts, and substance use problems. So this evidence together really highlights that it's important to identify those at risk for following poor symptom developments early on. And this has been a priority for government, policy, funding bodies, and researchers. But before we were able to identify who is those at risk early on, it is necessary to better understand the factors and processes that contribute to the development of depression and anxiety. So anxiety and depression are what we call complex mental health conditions because they do not arise from a single cause or follow a single pathway. Instead, they're influenced by a range of social, environmental, and psychological factors, including traumatic life events, unemployment, being raised in a harsh family environment, experiencing bullying, and also genetic factors. All of these factors operate together through different mechanisms, some of which we'll discuss later on. But let's now take a step back and explore how we have come to understand and learn what shapes risk in depression and anxiety and the role of genetic factors play. So this brings us to the second point of the talk, which will be exploring what family and twin studies have taught us about the role of genetic and environmental influences in anxiety and depression. So family studies take advantage of the natural structure of families, looking for similarities between parents and biological children and between siblings raised together. So one of the earliest findings from family research was that anxiety and depression tend to run in families. So for example, individuals who have a first-degree relative, such as a parent, with a condition are roughly twice as likely to develop themselves compared with people who do not have a close family member with a disorder. However, because family members share both their genes and their environments, family studies cannot tell us whether this increased risk is due to genetics, shared environment, or both. So to go beyond family studies and explore the relative contribution of genes and environments, researchers turned to twins. And twin studies are often described as a natural experiment because they compare individuals who differ in genetic relatedness. So identical twins share all of their genes, whereas nonidentical twins share around half on average. So if identical twins are more similar than nonidentical twins when it comes to a trait like anxiety and depression, then that may suggest that genetic factors contribute to this increased similarity. And in turn, the genetic factors contribute to the trait and the variation we see between people in the population. However, this interpretation depends on the equal environments assumption. And namely, that identical and nonidentical twins do not differ systematically in their environmental experiences they are exposed to. Or in other words, they share their environments to the same extent. So to illustrate this concept, I'll use height as an example. So if we compare twins on height, identical twins tend to be more similar than nonidentical twins. So we can see that there is a difference in the similarity between the two pairs of twins. So what if height was entirely explained by environmental influences? So given the assumption we make that identical and nonidentical twins share in their environments to the same extent, if height was explained entirely by the environment, the two bars and, hence, the similarity scores would be the same between identical and nonidentical twins, because they shared environments to the same extents. But this is not what we actually observe. Instead, identical twins are more similar to each other. So we deduct that the greatest similarity we see in identical twins is due to shared genetic influences. And this exact same pattern and logic applies to anxiety and depression, too, as well as to many other complex traits. So overall, comparisons like this tell us if genes are likely to play a role in the variation we see in a trait, like anxiety, depression, or height, as we saw in our example. But how much do genes matter? And to actually answer this question, we need to look at the difference in the similarity between identical and nonidentical twins. So we take this difference from similarity. We double it. And then we get an estimate called heritability. So heritability is an estimate of how much of the differences in a trait between people in a population are explained by genetic differences. Put simply, heritability tells us how much genes explain why people differed in a trait like anxiety or depression. And for those interested in the formal equation of to calculate this, it is here, where rMZ indicates the similarity in identical twins, rDZ is a similarity in nonidentical twins. And h squared is heritability. But let's unpack this concept a bit more. So as we said, heritability is an estimate of how much of the differences in a trait between people in a population is explained by genetic differences. Heritability is estimated at the population level. So we cannot use it to explain what causes a trait in an individual. So for example, if a trait is 40% heritable, this does not mean that 40% of the person's trait is due to their genes. It rather means that 40% of what makes people in the population different to one another with regards to their experiences of that trait is due to genetic factors. So if for example, everyone in the population had the trait, then there would be no differences between people. And thus, heritability would be zero. It is also important to emphasise that heritability is population, time, and measure specific. So it is not fixed or universal. And the estimate can change from population to population and time to time. So what is the evidence on the heritability of depression and anxiety? Heritability estimates can range from 0 to 100. And findings from large twin meta-analyses suggest that anxiety and depression are moderately heritable. So that heritability for depression is around 37%, for generalised anxiety, around 32%, and for phobias, between 20% and 37%. And this tells us that genetic factors explain around 30% of the differences we see between people in anxiety and depression within a population. And when comparing the heritability estimates for anxiety and depression with each other and with other conditions, we see that heritability for anxiety and depression are quite similar. But in the grand scheme of things, they're quite lower than other psychiatric conditions like autism or schizophrenia. And here, I should mention that by estimating heritability, we also infer the contribution of environmental factors to the variation of a trait in the population, because both of these are on opposite sides of the same coin. And this brings me to the first set of take-home messages. So the first take-home message is that heritability is an estimate of how much of the differences in a trait between people in a population are due to genetic factors, and that genetic factors account for differences in the experiences of anxiety and depression to a moderate extent. However, what is important to note here is that twin studies estimate the relative contribution of genetic factors. But they cannot tell us about the genetic architecture of a disorder. So what this really means is that they cannot tell us where the DNA differences are in the genome, or as we call them, which genetic variants are involved. And they also don't tell us how many genetic variants are involved or how large their effect sizes might be. And this is why the field had to move beyond twin studies and look directly at differences in our DNA through molecular approaches, which brings me to the third question of this talk, which is what have genome-wide association studies and polygenic scores added to our understanding. So one of the methods, one of the types of studies that fall under the molecular approaches category is Genome-Wide Association Studies, or GWAS. So a GWAS is a way of exploring areas of the genome to identify differences in our DNA that are associated with a particular trait or condition, such as depression or anxiety. So in our field, we refer to common DNA differences as genetic variants. So I'll use this term from here onwards. So the first step of running a GWAS requires selecting the population. And GWAS requires very large samples, often thousands and increasingly hundreds of thousands of participants. And some participants will have the condition of interest, and others will not. And then DNA is collected, usually using saliva or blood samples. And rather than examining all the DNA bases in detail, these studies focus on just exploring a very large number of common genetic variants. And the next step is to test whether any of these variants are more common in people with a disorder or in people with higher symptom levels than people without it. So in other words, the question becomes about whether there are particular genetic variants that are statistically associated with the condition. So in our case, that would be depression or anxiety. So what are the key findings from anxiety and depression genome-wide association studies? So the first key finding is that many regions of the genome are associated with these conditions. So the most recent GWAS of depression identified more than 600 regions, or loci, as we call them, across the genome that are associated with depression. For anxiety, the progress has been slower, with one of the most recent studies identifying 58 associated loci. However, the number of identified loci has increased as studies become larger. And this is because larger sample sizes give us more statistical power and greater precision to detect effect sizes and associations that smaller studies may miss. The second key learning is that risk is not driven by a single gene, as it was assumed 15 to 20 years ago. But rather, risk is shaped by many genes of small effect size each. This is why anxiety and depression are described as polygenic, which directly translates to many genes. And risk reflects the combined effect of many variations of the genome. And these variants are not directly for anxiety or depression, but rather, they are associated with small differences in vulnerability, so for example, in how brain cells communicate, how emotional circuits develop, and how the brain responds to stress. And this brings me to the second take-home message, which is that anxiety and depression are underpinned by many genes of small effect size each. So once discovering the genetic variants associated with a trait under study, the next step was to find a way to use this information in research. And this led to the development of polygenic scores, which, as they essentially summarise, the effect of the genetic variants, each weighted by how strongly they're associated with the trait under study to estimate an individual's genetic vulnerability for that trait. They give an indication of whether someone carries a relatively higher or lower genetic vulnerability for a trait. But what is important to mention here is that polygenic scores are not diagnostic or deterministic. And they're not sufficient on their own to predict whether someone will develop a disorder. Instead, polygenic scores reflect an individual's vulnerability for a trait like anxiety and depression. And so far, there have been common practise in genetics research and have been valuable in helping us study how genes and environments work together, differences in risk profiles, and whether prediction can be improved when genetic information is combined with other clinical and environmental factors. So for this second part of the talk, I'll provide two brief examples on how polygenic scores have been applied in research in depression and anxiety to better understand gene-environment interplay and specifically gene-environment interaction and prediction. So in other words, predicting who might be at risk for developing a condition later on. And this brings me to the fourth question of this talk, which is, how do genes and environments work together to shape risk? So here, we will discuss gene-environment interaction. So gene-environment interaction is one of the mechanisms underlying anxiety and depression. And gene-environment interaction is when the effect of an exposure or environment can differ depending on someone's genetic vulnerability, and vice versa, when the effect of genetic vulnerability can differ depending on the environment. And I'll use a flower analogy to illustrate this. So here, in the top row, we have seeds, which represent genetic vulnerability or a person's underlying genetic predisposition. And this middle row here represents the environment or exposure, so for example, exposure to stress or adversity. And this final row represents the outcome, so whether to what extent someone will experience symptoms of a condition or how severe their symptoms will be. So what I would like to highlight here is that the same exposure does not always produce the same outcome because the underlying genetic liability differs. And similarly, the same genetic liability does not always lead to the same outcome because the exposure or the environment difference. So if we apply this thinking to anxiety and depression, this means that the same stressful experience may have very different effects in different people because that will depend on their genetic vulnerabilities. And similarly, the same genetic vulnerability does not always produce the same outcome because of the environment difference. So in mental health, answering these kinds of questions and these study designs may be useful in helping us understand why not everyone experiencing the same adversity is affected in the same way. So here, I'll present evidence from a study that explored gene-environment interaction using data from the Lifelines cohort that is based in the Netherlands. And in this study, they explored the interaction between polygenic scores for depression and anxiety and different environmental factors, such as long-term difficulties and stress. And they looked at whether the interaction changes in their individuals' predicted depression and anxiety levels. And the authors have included a large figure presenting the results. But I'll just focus on one panel to illustrate some of the key findings. So this figure shows an example of how genetic vulnerability and environmental stress can work together to influence symptoms of depression and shape risk. And I know this is a complicated figure, so bear with me as I explain it step by step. So on the x-axis, we have polygenic score, which, as we said, reflects an individual's genetic liability for depression. People on the left side have lower genetic vulnerability. And people on the right side have higher genetic vulnerability. On the y-axis, we have predicted level of depressive symptoms. So higher values indicate higher levels of depression. And the three lines here represent different levels of long-term difficulties. So the orange line represents individuals experiencing relatively low levels of long-term difficulties. The green line represents average levels, and the blue line represents higher levels of long-term difficulties. So if we focus on the people with lower genetic vulnerability, over on the left side of the graph, we see that levels of depression are relatively low across all three groups. So what this means is that one genetic liability is lower, differences in depression symptoms across stress levels are small. But as genetic liability increases and we move towards to the right of the x-axis, the pattern changes. And we see that people with higher polygenic score, who are also experiencing high levels of adversity, in blue, have much higher predicted levels of depression than those with fewer long-term difficulties. So what this suggests is that long-term stress can amplify the effect of genetic vulnerability on depression. And the reverse is also important to highlight. Even in high stressful situations, not everyone will go on to experience depression because people differ in their underlying genetic vulnerability. So high stress can amplify vulnerability, but it does not determine the outcome by itself. And as mentioned, the paper tested the interactions between genes and several other environmental exposures and across both depression and anxiety. And for both depression and anxiety, the paper reported the presence of interactions with stressful life events, social support, and loneliness, as well as long-term difficulties. So taken together, these findings suggest that genetic liability and environmental stress do not operate in isolation. But they work together to show vulnerability for depression and anxiety. And both genetic liability and environmental stress matter, which also highlights that neither factor is deterministic on its own. Genetic liability is not destiny, and environmental adversity does not affect everyone in the same way. And in this particular paper, the environmental factors that were examined were mostly negative experiences. And these appear to act as amplifiers of underlying genetic vulnerability. However, it is worth noting that, by contrast, low exposure to adversity or positive experiences such as social connection could act as buffers of genetic liability. But this would have to be studied directly in future studies. And this is important because if environmental exposures can amplify or buffer genetic vulnerability, then those environments may represent meaningful targets for intervention. However, at the same time, it is important to be cautious in interpreting these findings. So the interaction effects were small. So they need replication and further validation in other studies. And importantly, evidence of the interaction does not by itself show that one factor causes the other. So further research is needed exploring these factors as targets for intervention. And this brings me to the third take-home message, which is that genetic liability is not destiny. So genetic factors act in context. And psychosocial factors may amplify or buffer genetic liability. And this brings us to the second example, which has to do with building prediction models to identify those at risk. And for this example, we will also aim to answer the fifth question, which is, can genetic and early-life factors help us identify those most at risk early on? So I'll present some preliminary findings from a paper we're currently preparing for publication that builds on our trajectory work that I presented earlier. So let's look at the trajectories for one last time. And let's just focus on what's visible at age four this time. So that age, we can broadly distinguish between children with relatively higher and lower symptom levels. But within each of those groups, it would be very difficult for a clinician to know which developmental pattern a child is likely to follow. So for example, among children with a high symptoms at age four, some will go on to show persistent symptoms, as we saw, whereas others will improve over time, which would be that grade group. And amongst children with relatively low symptoms at age 4, some will remain low, but others will develop symptoms across development. And being able to make this distinction matters clinically, because a watchful waiting approach, which is often recommended as a first line of treatment for anxiety and depression, may be appropriate for young people, but perhaps not for all. So for example, children whose symptoms are likely to persist, they could benefit from early support or intervention, while those at risk for developing symptoms in adolescents who may seem like they're doing fine in early childhood would perhaps benefit from closer monitoring and support across development. So our aim was to develop models and assess the utility of genetic and early life factors in predicting individuals' trajectory. So in other words, seeing whether we could use these factors from different domains to predict which children are at risk for falling more severe symptom trajectories across development. And this approach is similar to other models that have been developed for other areas of health, like cardiovascular risk calculators, such as the QRISK, which combine multiple factors such as age, smoking, and blood pressure to estimate an individual's risk of future cardiovascular disease. So the main difference is that, in our case, we wanted to use early life experiences and measures collected before the age four and genetic information to predict who might follow more severe symptom trajectories from early childhood to young adulthood. So this work was, again, done in the Twins Early Development Study. And we included a range of factors, including polygenic scores, factors of a family environment, and child characteristics. There were over 250 in total. And we built models trying to see whether we could predict what kind of patterns children would follow across development. So for those interested in the modelling part of the project, we used modelling approaches that were designed to handle large numbers of variables in order to reduce the risk of overfitting. And you will be able to find more details about the approach once the paper is ready in the coming weeks. So we found that the model performed reasonably well overall, correctly distinguishing between the different trajectories about 70% of the time. In other words, the model was able to correctly identify what symptom pattern a child was likely to follow 70% of the time. And what we thought, was particularly excited about it is that the prediction was based on genetic and early-life factors collected before the age of four, while the outcome reflected anxiety trajectories across 22 years of development. So this work highlights the potential utility of genetic and early-life factors in the prediction of trajectories or developmental factors, which could ultimately help the identification of individuals at risk for following poor trajectories early on. On their own, polygenic scores predicted only slightly better than chance. So there would not be of clinical benefit on their own. However, their contribution may improve as genome-wide association studies continue to grow, and polygenic scores become more precise. And although this model is promising, further research is needed to validate findings in other samples and before we consider whether its performance would be adequate for use in clinical practise. And this brings me to my fourth and final message, which is that combining genetic factors and early-life factors has the potential to aid the identification of individuals at risk for more severe symptoms. But before I summarise, it is important to mention some challenges in the field, and especially challenges related to polygenic scores. So we've talked about how polygenic scores are calculated and their use in different research contexts. However, what is important to mention here is that they're not equally informative for everyone at the moment. So most have been developed using studies that were conducted mainly on European ancestry samples. And given that genetic variation differs across populations, polygenic scores built in one ancestry population often do not predict as well in others. And this means that the predictive performance is often lower in populations that have been underrepresented in genetic research. And hence, they may not be equally beneficial for everyone. And I've got a graph here, which I think it depicts very well the problem. So this bar shows the proportions of different ancestry groups in the world population. So if we then look at the left, the other three bars show the extent to which these groups are reflected in psychiatric genetic research for conditions such as bipolar disorder, major depression, and schizophrenia. And what becomes clear is that the ancestral diversity we see in the world is not reflected in the research samples to the same extent. So European ancestry groups are heavily overrepresented, while many other ancestry groups are underrepresented. And while this is slowly changing, it is important to recognise that current polygenic scores still have this limitation. Especially as in the context of prediction models, polygenic scores will be a useful addition if they can work well for everyone. So this brings me to the end of the talk. So here, we'll just summarise the key messages that we talked about along the way. So in this presentation, we explored how genes and environments may shape the risk for anxiety and depression. Specifically, we explored the role of nature and nurture in helping us understand the variation that exists in the presentation of the two conditions as well as the role in prevention. So from the family and the molecular approaches, we learned that genetic factors account for differences in the experiences of anxiety and depression to a moderate extent. We also learned that anxiety and depression are underpinned by many genes of small effect size each. From exploring gene-environment interaction, we learned that genetic vulnerability is not destiny, and that genes act in context. And the psychosocial factors may amplify or buffer the impact of genetic risk. And when it came to answering the question about whether we can identify those at risk early on, we saw that from the prediction modelling, that it is becoming possible to identify individuals at risk for developing anxiety and depression using genetic and early life factors. And with this, I'd like to thank you for your attention. And here are my contact details, in case anyone would like to get in touch for any questions or anything related to this. Thank you. [MUSIC PLAYING]