Metadata errors: an under-appreciated source of bias in Mendelian randomization studies

Posted on August 20, 2021 by lindsey.pike

In two-sample Mendelian randomization (MR), a type of epidemiological method, we combine the results from different genetic studies to study the causal relationship between human characteristics and disease. For example, we might take results from a genetic study of smoking and a different genetic study of cancer. We can combine their results to understand whether smoking might be a cause of cancer. If the same position in the genome is associated with smoking in the first study and with cancer in the other study, this can provide evidence that smoking is a causal factor in cancer. However, it’s also possible that this position in the genome could be related to smoking and cancer via separate pathways. This phenomenon is known as “horizontal pleiotropy” and is a common source of bias in Mendelian randomization research.

Another, often under-appreciated, source of bias are errors in metadata. To understand this we need to understand what genetic results look like in practice. Below is an example of a genetic results file with 5 rows and 6 columns (a typical file might actually have several million rows).

Each row refers to a single position in the human genome that varies between people. These positions are referred to as “genetic variants” (also known as polymorphisms). The particular type of variant that an individual carries is known as their allele.

Below is an example of metadata. The metadata helps us understand the contents of the results file. It tells us what the columns represent.

Some columns in the results file will describe the relationship (“effect” column) between the genetic variant and some human characteristic (e.g. smoking) and there will be additional columns that help researchers interpret this relationship. These additional columns include things like the identity of the allele that is used to model the relationship (e.g. if people have allele “A” they may be more likely to smoke compared to people without this allele) or information on how common the allele is in the population. These columns are also known as the “effect allele” and “effect allele frequency” columns. Metadata errors refer to mistakes in how these columns are reported. For example, maybe allele1 is reported as the effect allele column when in fact it should have been allele 2 that is described in this way. Sometimes the information provided in metadata is ambiguous. For example, the metadata tells us that the “freq” column represents allele frequency but there are two alleles. Is this the frequency of allele1 or allele2? We can’t be sure. Another type of error refers to mistakes in the reported results, for example reporting that a genetic variant increases the probability that a person smokes when in reality it has no effect (in other words the effect is zero). This is known as a summary data error. Failure to identify these errors can lead to mistakes in Mendelian randomization analyses, such as finding that smoking protects against cancer (when we know the opposite is true).

As research complexity increases, so does the potential for errors

These types of errors were fairly easy to avoid during the early years of Mendelian randomization research, when studies tended to be hypothesis-driven and focused on small numbers of relationships (although errors still occurred). Mendelian randomization study designs are, however, increasingly complex and hypothesis-free, sometimes assessing relationships amongst 100s or even 1000s of characteristics and diseases. New online platforms and databases that collate genetic results from many different sources, and provide tools that can automate analyses, make these studies easier to undertake than ever before. The downside is that they probably make meta and summary data errors more likely.

Maximising metadata quality to reduce errors

We address this issue in a new pre-print: “Design and quality control of large scale Mendelian randomization studies”. We present an R package and set of quality control tools that identify meta and summary data errors, which we developed for the Fatty Acids in Cancer Mendelian Randomization Collaboration (FAMRC). The FAMRC is a pan-cancer MR study that seeks to evaluate the causal relevance of fatty acids for risk of major cancers. We wanted to maximise the quality of the genetic study results we collected from the cancer studies, to ensure the integrity of our Mendelian randomization analyses. After implementing our tools, we found major meta and summary data errors in 7 (13%) of 55 genetic studies in the FAMRC.

What types of metadata errors did we find?

The basic principle of our quality control approach is to identify errors through

comparison of the results of individual studies in the FAMRC to external studies
comparison of reported to expected results.

For example, we identified genetic variants that are known to cause cancer and checked that the same variants had the expected relationship in the FAMRC. In the figure below, every data point represents a single genetic variant that is known to increase cancer risk. The horizontal or X axis shows the known relationship in the GWAS catalog (this is a database of known genetic associations with 1000s of human characteristics in 1000s of genetic studies) and the vertical or Y axis shows the relationship in one of the studies in the FAMRC. Each axis shows the Z score, which is basically a standardised measure of how each genetic variant affects cancer risk (positive values mean that the variant increases risk of cancer and negative values indicate they decrease risk). As you can see, in the FAMRC study on the vertical Y axis, almost all the variants have negative Z values (indicating they reduce cancer risk), when in fact they are known to increase risk (the true relationship is represented by Z scores in the GWAS catalog). This discrepancy was caused by a metadata error, where the effect allele column was incorrectly labelled. We also found that the “frequency of the effect allele” was wrong. How common the allele is in the population was opposite to what we’d expect, based on comparison with other studies, confirming the presence of metadata errors.

Various other types of errors were identified, including one study reporting that 100s of genetic variants had very strong effects on fatty acid levels when in fact they had no effect at all. For example, in the figure below, the many red data points refer to genetic variants in the FAMRC that had a very large effect on fatty acids but were not reported in the GWAS catalog, suggesting a potential problem with the genetic results.

We also compared the reported results (how the genetic variants affected fatty acids in the FAMRC) to predicted results (how we would expect the genetic variants to affect fatty acids). In the figure below we see a “fanning-out” pattern, when what we should see is a strong linear relationship (i.e. the data points lying on a single straight line). This relationship can be summarised with the “slope” metric. We should see a slope of 1 (this means if the reported result increases by 1 the predicted result will also increase by 1), which is not the case. We confirmed with the data provider that low quality genetic variants had not been excluded from their study. Once the low quality variants had been excluded, the discrepancies disappeared.

Avoiding metadata errors: recommendations for researchers

When conducting Mendelian randomization analyses using results from genetic studies, researchers can avoid metadata and other errors by:

Requesting results for genetic variants that are known to affect their disease of interest. Researchers should check that these variants have the expected effect in their dataset.
Comparing the frequency of genetic variants to expected frequencies in a reference dataset. We created a special reference dataset that can be used for this purpose (accessible via the CheckSumStats R package).
Not assuming that results have had low quality variants excluded, but instead seeking confirmation of this with data providers. Our quality control tools also provide a way to check this.

Further attention is needed to address the growing diversity of GWAS

One issue we only partly addressed was the “two-sample assumption”: that the studies being compared come from the same population. In our own analyses, we found that the frequency of genetic variants was very similar across European-origin studies, indicating satisfaction of the assumption. On the other hand, our tools were not really optimised for this purpose. The need to assess the “same population” assumption is becoming more urgent with the growing diversity of genetic studies.

In conclusion, meta and summary data errors are an under-appreciated source of bias in MR research, especially in complex study designs. We developed an R package and set of tools that can be used to flag meta and summary data errors in the results of genetic studies, which in turn can be used to enhance the integrity of Mendelian randomization analyses. Our tools and methods are available to other researchers via the CheckSumStats R package.

Contact the author

philip.haycock@bristol.ac.uk

Using genetics to understand the relationship between young people’s health and educational outcomes

Posted on January 22, 2021January 21, 2021 by lindsey.pike

Amanda Hughes, Kaitlin H. Wade, Matt Dickson, Frances Rice, Alisha Davies, Neil M. Davies & Laura D. Howe

Follow Amanda, Kaitlin, Matt, Alisha, Neil and Laura on twitter

Young people with health problems tend to do less well in school than other students, but it has never been clear why. One explanation is that health problems directly damage educational outcomes. In that case, policymakers aiming to raise educational standards might want to focus first on health as a means of improving attainment.

But are there other explanations? What if falling behind in school can affect health, for instance causing depression? Also, many health problems are more common among children from less advantaged backgrounds – for example, from families with fewer financial resources, or whose parents are themselves unwell. These children also tend to do less well in school, for reasons that may have nothing to do with their own health. How do we know if their health, or their circumstances, are affecting attainment?

It is also unclear if health matters equally for education at all points in development, or particularly in certain school years. Establishing how much health does impact learning, when, and through which mechanisms, would better equip policymakers to improve educational outcomes.

Using genetic data helps us understand causality

Genetic data can help us answer these questions. Crucially, experiences like family financial difficulties, which might influence both a young person’s health and their learning, cannot change their genes. So, if young people genetically inclined to have asthma are more absent from school, or do less well in their GCSEs, that would strongly suggest an impact of asthma itself. Similarly, while falling behind in school might well trigger depression, it cannot change a person’s genetic propensity for depression. So, a connection between genetic propensity for depression and worse educational outcomes supports an impact of depression itself. This approach, of harnessing genetic information to better understand causal processes, is known as Mendelian randomization.

To find out more, we investigated links between

health conditions in childhood and adolescence
school absence in years 10 & 11
and GCSE results.

We used data from 6113 children born in the Bristol area in 1991-1992. All were participants of the Avon Longitudinal Study of Parents and Children (ALSPAC), also known as Children of the 90s. We focused on six different aspects of health: asthma, migraines, body mass index (BMI), and symptoms of depression, of attention-deficit hyperactivity disorder (ADHD), and of autism spectrum disorder (ASD). These conditions, though diverse, have two important things in common: they affect substantial numbers of young people, and they are at least in part influenced by genetics.

Alongside questionnaire data and education records, we also analysed genetic information from participants’ blood samples. From this information, we were able to calculate for each young person a summary score of genetic propensity for experiencing migraines, ADHD, depression, ASD, and for having a higher BMI.

We used these scores to predict the health conditions, rather than relying just on reports from questionnaire data. In this way, we avoided bias due to the impact of the young people’s circumstances, or of their education on their health rather than vice versa.

Even a small increase in school absence predicted worse GCSEs.

We found that, for each extra day per year of school missed in year 10 or 11, a child’s total GCSE points from their best 8 subjects was a bit less than half (0.43) of a grade lower. Higher BMI was related to increased school absence & lower GCSE grades.

Using the genetic approach, we found that young people genetically predisposed towards a higher BMI were more often absent from school, and they did less well in their GCSEs. A standard-deviation increase* in BMI corresponded to 9% more school absence, and GCSEs around 1/3 grade lower in every subject. Together, these results indicate that increased school absence may be one mechanism by which being heavier could negatively impact learning. However, in other analyses, we found a substantial part of the BMI-GCSEs link was not explained by school absence. It’s unclear which other mechanisms are at play here, but work by other researchers has suggested that weight-related bullying, and negative effects of being heavier on young people’s self-esteem, could interfere with learning.

*equivalent to the difference between the median (50^th percentile) in population and the 84^th percentile of the population

Diagram showing the pathways through which higher BMI could lead to lower GCSEs; either through more schools absence aged 14-16, or other processes such as weight-related bullying. — Our results suggest increased school absence may partly explain impact of higher BMI on educational attainment, but that other processes are also involved.

ADHD was related to lower GCSE grades, but not increased school absence.

In line with previous research, young people genetically predisposed to ADHD did less well in their GCSEs. Interestingly, they did not have increased school absence, suggesting that ADHD’s impact on learning works mostly through other pathways. This is consistent with previous research highlighting the importance of other factors on the academic attainment of children with ADHD, including expectations of the school environment, teacher views and attitudes, and bullying by peers.

We found little evidence for an impact of asthma, migraines, depression or ASD on school absence or GCSE results

Our genetic analyses found little support for a negative impact of asthma, migraines, depression or ASD on educational attainment. However, we know relatively little about the genetic influences on depression and ASD, especially compared to the genetics of BMI, which we understand much better. This makes genetic associations with depression or ASD difficult to detect. So, our results should not be taken as conclusive evidence that these conditions do not affect learning.

What does this mean for students and teachers?

Our findings provide evidence of a detrimental impact of high BMI and of ADHD symptoms on GCSE attainment, which for BMI was partially mediated by school absence. When students sent home during the pandemic eventually return to school, the impact on their learning will have been enormous. And while all students will have been affected, our results highlight that young people who are heavier, who have ADHD, or are experiencing other health problems, will likely need extra support.

Contact the researchers

Amanda Hughes, Senior Research Associate in Epidemiology: amanda.hughes@bristol.ac.uk

Epigenetics regulate our genes: but how do they change as we grow up?

Posted on August 11, 2020August 12, 2020 by lindsey.pike

Rosa Mulder^1,2 Esther Walton^3,4& Charlotte Cecil^1,5,6

Follow Esther and Charlotte on Twitter.

Epigenetics can help explain how our genes and environment interact to shape our development. Interest in epigenetics has grown increasingly within the research community, but until now little was known about how epigenetics change over time. We therefore studied changes in our epigenome from birth to late adolescence and created an interactive website inviting other researchers to explore our findings.

What is epigenetics?

The term ‘epigenetics’ refers to the molecular structures around the DNA in our cells, that affect if, when, and how our genes work. Even though nearly every cell in our body contains the exact same copy of DNA, cells can look and function entirely differently. Epigenetics can explain this. For example, every cell in our body has the potential to store fat, but in adipose tissues the cells’ epigenetic structures cause the cells to actually store fat.

Before birth, epigenetics plays a role in the specialization of cells from conception onwards by turning genes ‘on’ and ‘off’. After birth, epigenetics help our body develop even further, and maintain the specialization of our cells. However, the way epigenetics influence how our cells function is not only programmed by our genes, but may also be affected by the environment. Hence, our development and health is shaped by both our genes and our environment. Researchers are therefore trying to measure epigenetic processes to understand the role that epigenetics plays in this process of ‘nurture affecting nature’.

Both nurture and nature influence our health; understanding epigenetics helps us to find out how they might interact.

How can we measure epigenetics?

One of the types of molecular structures that can affect gene functioning is ‘DNA methylation’. Here, a small molecule (a methyl group of one carbon atom bonded to three hydrogen atoms; Figure 1) is attached to the DNA sequence. DNA methylation affects the three-dimensional structure of the DNA and can thereby turn it ‘on’ or ‘off’. DNA methylation can now easily be measured in the lab with the help of micro-chips; very small chips that can detect hundreds of thousands of methylation sites in the genome at a time, from just a small droplet of blood. Such chips are now used in large epidemiological cohorts such as ALSPAC to measure the level of DNA methylation for each of these sites. In epigenome-wide associations studies (EWASs), researchers study the associations between each of these methylation sites and a trait, such as prenatal smoking, BMI, or stress.

Figure 1: DNA sequence with DNA methylation

How does DNA methylation change throughout development?

Until recently, EWASs have mainly been cross-sectional, studying DNA methylation only at one time-point. So, even though research indicates that epigenetics is important in postnatal development, we do not know how true this is for DNA methylation sites measured with these epigenome-wide arrays. Studying a mechanism that supposedly changes over time without knowing how it changes can be problematic: say that we find an association between smoking during pregnancy and DNA methylation at birth, can we still expect this association to be there at a later age? To fully interpret EWAS findings, and to compare research findings between different studies, we need a full understanding of how DNA methylation changes throughout development.

We therefore set out to study DNA methylation from birth to late adolescence, using DNA methylation measured in blood from the participants of ALSPAC in the UK, as well as from participants from another large cohort, the Generation R Study in the Netherlands.

We studied the change in levels of DNA methylation over time as well as variation in this change between individuals. If DNA methylation is indeed mainly linked to the basic developmental stages we go through as we grow up, we would expect methylation changes to be largely consistent between individuals. However, if DNA methylation is affected more by the different environments we live in, and individual health profiles, we would expect a proportion of sites to change differently for different individuals.

Between ALSPAC and Generation R, we created a unique dataset containing over 5,000 samples from about 2,500 participants with DNA methylation measurements at almost half a million methylation sites measured repeatedly at birth, 6 years, 10 years, and at 17 years. With various statistical models we studied different trajectories of change in DNA methylation.

We found change in DNA methylation at just over half of the sites (see for an example Figure 2a). At about a quarter of sites, DNA methylation changed at a different rate for different individuals (Figure 2b). We further saw that sometimes change only happened in a specific time period; for example, only in between birth and the age of 6 years after which DNA methylation remained stable (Figure 2c), and that sometimes differences in the rate of change only started from the age of 9 years (Figure 2d). Last, for less than 1% of the sites on the chromosomes tested (we did exclude the sex chromosomes), we saw that DNA methylation changed differently for boys and girls (Figure 2e).

Figure 2. Different examples of methylation sites, with every graph representing one methylation site with age on the x-axis and level of DNA methylation on the y-axis. Every line represents change in DNA methylation over time for one individual, showing (a) change in DNA methylation, (b) different rates of change for different individuals, (c) change during the first six years of life, (d) different rates of change starting from 9 years of age, (e) different change for boys and girls, and (f) change, but no differences in rate of change in a site associated to prenatal smoking.

How can we use these findings in future research?

These results show that there are sites in the genome for that show change in DNA methylation that is consistent between individuals, as well as sites that change at a different rate for different individuals. We have published the trajectories of change for each methylation site on a publicly available website. This makes it easier for other researchers to find sites that are developmentally important and may be of relevance for health and disease. For example, a methylation site previously associated with prenatal smoking, remained stable over time (Figure 1f), indicating that prenatal influences of smoking may be long-lasting, at least up to adolescence. In the future, we hope to associate traits, such as stress and BMI, to these longitudinal changes, to further our understanding of the developmental nature of DNA methylation and the associated biological pathways leading to health and disease.

¹Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands

²Department of Child and Adolescent Psychiatry/Psychology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands

³MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK

⁴Department of Psychology, University of Bath, Bath, UK

⁵Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands

⁶Department of Psychology, Institute of Psychology, Psychiatry & Neuroscience, King’s College London, London, UK

Further reading

Mulder, R. H., Neumann, A. H., Cecil, C. A., Walton, E., Houtepen, L. C., Simpkin, A. J., … & Jaddoe, V. W. (2020). Epigenome-wide change and variation in DNA methylation from birth to late adolescence. bioRxiv. (preprint)

Epidelta project website: http://epidelta.mrcieu.ac.uk/

How might fathers influence the health of their offspring?

Posted on September 30, 2019 by lindsey.pike

Dr Gemma Sharp, Senior Lecturer in Molecular Epidemiology

Follow Gemma on Twitter

Follow EPOCH study on Twitter

A novel thing about the Exploring Prenatal influences On Childhood Health (EPoCH) study is that we’re not just focusing on maternal influences on offspring health, we’re looking at paternal influences as well.

One of the reasons that most other studies have focused on maternal factors is that it’s perhaps easier to see how mothers might have an effect on their child’s health. After all, the fetus develops inside the mother’s body for nine months and often continues to be supported by her breastmilk throughout infancy. However, in a new paper from me and Debbie Lawlor published in the journal Diabetologia, we explain that there are lots of ways that fathers might affect their child’s health as well, and appreciating this could have really important implications. The paper focuses on obesity and type two diabetes, but the points we make are relevant to other health traits and diseases as well.

The EPOCH study will look at how much paternal factors actually causally affect children’s health. Image by StockSnap from Pixabay

How could fathers influence the health of their children?

These are the main mechanisms we discuss in the paper:

Through paternal DNA. A father contributes around half of their child’s DNA, so it’s easy to see how a father’s genetic risk of disease can be transmitted across generations. Furthermore, a father’s environment and behaviour (e.g. smoking) could damage sperm and cause genetic mutations in sperm DNA, which could be passed on to his child.
Through “epigenetic” effects in sperm. The term “epigenetics” refers to molecular changes that affect how the body interprets DNA, without any changes occurring to the DNA sequence itself. Some evidence suggests that a father’s environment and lifestyle can cause epigenetic changes in his sperm, that could then be passed on to his child. These epigenetic changes might influence the child’s health and risk of disease.
Through a paternal influence on the child after birth. There are lots of ways a father can influence their child’s environment, which can in turn affect the child’s health. This includes things like how often the father looks after the child, his parenting style, his activity levels, what he feeds the child, etc.
Through a father’s influence on the child’s mother. During pregnancy, a father can influence a mother’s environment and physiology through things like causing her stress or giving her emotional support. This might have an effect on the fetus developing in her womb. After the birth of the child, a father might influence the type and level of child care a mother is able to provide, which could have a knock-on effect on child health.

There are lots of ways in which fathers might influence the health of their offspring. This figure was originally published in our paper in Diabetologia (rdcu.be/bPCBa).

What does this mean for public health, clinical practice and society?

Appreciating the role of fathers means that fathers could be given advice and support to help improve offspring health, and their own. Currently hardly any advice is offered to fathers-to-be, so this would be an important step forward. Understanding the role of fathers would also help challenge assumptions that mothers are the most important causal factor shaping their children’s health. This could help lessen the blame sometimes placed on mothers for the ill health of the next generation.

What’s the current evidence like?

In the paper, we reviewed all the current literature we could find on paternal effects on offspring risk of obesity and type 2 diabetes. We found that, although there have been about 116 studies, this is far less than the number of studies looking at maternal effects. Also, a lot of these studies just show correlations between paternal factors and offspring health (and correlation does not equal causation!).

What is needed now is a concerted effort to find out how much paternal factors actually causally affect offspring health. This is exactly what EPoCH is trying to do, so watch this space!

This content was reposted with permission from the EPOCH blog.

Depression: where we’re at and where we’re going

Posted on May 13, 2019May 20, 2019 by lindsey.pike

To mark Mental Health Awareness Week, IEU PhD researcher Alex Kwong takes us on a tour of the research on depression in young people.

Follow Alex on twitter

What is depression and why should we care?

Depression is one of the biggest public health challenges we’re currently facing and is expected to be the highest global burden of disease by 2030. The world health organisation (WHO) estimates that around 300 million people worldwide currently experience depression and that at least one in five people will experience depression at some stage of their life. Treatment is not always successful with only around 40-60% of individuals responding positively to antidepressant medication, and other forms of treatment such as cognitive behavioural therapy (CBT) or other talking based therapies requiring long waiting times of up to two years. It’s no surprise to see that depression and other mental health treatments are considered to be in a ‘crisis’ as we continually look for new and effective ways to combat this disease.

Research suggests that depression may first begin to manifest early in adolescence and young adulthood. This may have serious downstream consequences as depression during adolescence is related to both concurrent and later self-harm and suicide, corresponding mental health problems (like anxiety, addiction and psychosis) and impaired social functioning (reduced cognitive functioning and reclusiveness), to name a few. It also appears that depression during adolescence and young adulthood may actually be getting worse. Now whether or not this is because young people are talking more about their mental health than before remains to be seen, but that has not stopped researchers identifying potential causes for depression in adolescence in the hope of developing new and effective treatments and interventions. The message seems to be clear: by stopping/reducing depression in young people, we can potentially improve the quality of life later on.

What is responsible for depression in young people?

The lived experience of depression between young people differs from one person to the next, meaning there is no ‘one-size-fits-all’ approach. But with the help of research, we have begun to identify things that individuals experiencing depression have in common, that could be useful for treating and even preventing depression in young people. What follows is a whistle stop tour of some of the findings of potential causes of depression in young people.

Bullying

It may seem obvious, but childhood and adolescent bullying is one of the strongest predictors of current and later depression. One recent study found that individuals who had been bullied during adolescence were almost 3 times more likely to be depressed at age of 18. Bullying is particular prevalent during school years but can also occur well into the workplace or later education, which can have lasting effects on an individual’s mental health. Stopping bullying from occurring will be difficult, but that does not mean we cannot support individuals who have been bullied in order to help prevent depression from occurring or getting more severe.

Parental Depression

A lot of research has focused on the role of parental mood and later depression in young people. The role of parenting cannot be understated as numerous studies have shown that children of depressed parents are more likely to go on to have depression themselves, see research by Pearson et al, Stein et al and Gutierrez-Galve et al. However, it’s not clear if this is passed on genetically from the parent to child, or if there is something in the “environment” that transmits depression from parent to child. Whilst we don’t know for sure, the answer looks like it could be a bit of both. Parents may pass on depression genetically to their children, but depressed parents may also create an environment that makes the child more liable to depression. It is even possible that the parent passes on their genetics and the child then creates an environment for themselves that makes them more liable to depression. This is a form of gene-environment correlation that I won’t discuss in detail, but research is beginning to tease this apart with regard to parent and childhood depression.

Genetics

Interest in the genetics of depression has been heightened in the last few years. We always knew from twin studies that depression was likely to be heritable (i.e., that depression can be passed on from generation to generation), but convincing some that depression could have a strong genetic basis was tough (for a really good debate on this involving Professor Marcus Munafò, you can listen to this episode of BBC Start the Week). Most recently it has been shown that common genetic variants associated with depression in adulthood seem to predict greater levels of depression in children and adolescents, as well as varying patterns of depressive mood across adolescence. Importantly, it’s clear that there is no ‘one gene’ for depression. Instead, there are multiple genes which can be referred to as ‘polygenicity’ or ‘polygenic risk scores’; “poly” meaning multiple and “risk” indicating that individuals carrying multiple risk genes are more liable or ‘at risk’ to depression. By using polygenic risk scores we can begin to identify individuals experiencing depression early by using knowledge of their genetic make-up. However, it is really important to state here that genetic liability to depression does not equal genetic determinism. Just because someone is more genetically liable to depression, does not mean they will get depressed. There are multiple other factors at play, and we do not know how genetic liability to depression impacts on other pathways (i.e., does having genetic liability make you more likely to seek out an environment that could leave you more depressed?); but many researchers are beginning to ask these questions.

Taken together, these findings highlight how diverse depression is and how many factors could underlie depression in adolescence. There are a ton of other factors that have been related to adolescent depression that I have not had time/space to talk about. That is not to say they are not important, because most likely some are. As research develops and we are able to utilise different methods, we will get a better picture of what underpins depression in adolescence and what can be done to prevent and treat it.

What can we do?

Well for one, we have to keep up the research. We don’t know nearly enough about the underlying mechanisms and pathways that truly underlie adolescent depression. Researchers are beginning to examine this further with novel and promising techniques, but we also have to streamline the time it takes for research to be put into practise. The prolific mental health blog “The Mental Elf” states that it takes 17 years for research to reach clinical practise. That’s a long time and means a lot of people could miss out on the treatment they deserve.

Secondly, we have to be more forthright in how we talk about depression. You may have heard the expression ‘it is ok to be not be ok”. Avoiding telling people to “man-up” when they’re feeling depressed, speaking out and campaigns will only drive this forward. We have to normalise the fact that depression is a disease and like any other disease, it is good to talk about it. Only by talking about depression can we really move forward to end the stigma that being depressed is some kind of weakness. In fact one of my favourite instances of this recently was well explained by the England international Danny Rose.

Where do we go from here?

We appear to be reaching a turning point where more and more people are discussing mental health issues. This may be celebrities, royals or just your average Jo from down the street. But what is important is that we recognise the problem. That depression is a global burden that may be getting worse and requires our utmost attention and action. We are beginning to understand the causes of depression and how we might tackle it through research and reducing the social stigma that surrounds depression. However, the question is whether or not we can take advantage of these changes to really make a difference. Can we build on the progress we have made to finally one day beat depression? Yes. I really believe we can.

Resources for if you’re feeling down

If you’re ever feeling low, then I cannot speak highly enough for these guys: https://www.samaritans.org/

There are a lot of charities who specialise in mental health and depression who provide some excellent resources and information:
https://www.mqmentalhealth.org/
https://www.mind.org.uk/

There are some awesome twitter feeds out there who I have always found to be really helpful and supportive of mental health issues. These people really get depression and are leading the charge in one way or another so do please give them a follow:

MENtalHealth
Paul McGregor
Gareth Griffith
Miguel Cordero Vega
Louise Arseneault
Dr Erin C Dunn