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Autism Spectrum Disorder (ASD) is considered to be a lifelong neurodevelopmental condition in which individuals commonly experience difficulties with social communication and interaction (American Psychiatric Association, 2013). Autistic individuals are also likely to show differences in their behaviours and interests which may be more repetitive and restricted compared to others. Current estimates suggest a prevalence rate of around 1% for ASD in UK adults (Brugha et al., 2016). However, autistic traits have also been shown to exist in a continuum across the population with similar genetic influences as diagnosed ASD (Colvert et al., 2016; Robinson et al., 2011; Robinson et al., 2016). Despite its characterization as a lifelong condition, autism research is dominated by studies using child and adolescent samples (Mukaetova‐Ladinska et al.,2012). As such, less is known about the experiences of Autistic individuals across the lifespan. Emerging/Young adulthood is increasingly seen as an important transitional period where individuals are faced with changes to routines, increased independence, challenges and also many opportunities. In relation to autism, understanding the challenges or difficulties faced at this age may be vital to guide future interventions and support to help individuals achieve ‘good’ outcomes throughout their adult lives. One particular concern is that autistic adults commonly experience additional mental health difficulties (Hollocks et al., 2019) and have lower Quality of Life (QoL) compared to non-autistic individuals (Ayres et al., 2018). This is also echoed in studies examining continuous autism traits across the population (Pisula et al., 2015). It is increasingly recognised that autism is commonly accompanied by other neurodevelopmental and/or mental health conditions. Attention Deficit Hyperactivity Disorder (ADHD) is characterized by difficulties in regulating attention and high levels of activity and impulsivity (American Psychiatric Association, 2013). ADHD has been demonstrated to show high levels of co-occurrence with ASD (e.g. Tick et at al., 2016). Since the release of the DSM-5 in 2013, dual diagnosis of ASD/ADHD has been permitted by the diagnostic system. Although previously recognised for many years, this may have held up progress on research. Thus, despite their co-occurrence, there is limited research that examines how the combination of autismand ADHD together are associated with mental health and QoL in young adults. This study aims to expand on existing research in several key ways: i) examining how autism and continuous autistic traits are associated with mental health difficulties and different domains of QoL in young adulthood; and ii) considering the role of co-occurring ADHD and traits iii) using the genetically sensitive nature of the sample we will explore the aetiological underpinnings of the main associations between autism, ADHD, mental health and QoL. The study will use pre-collected data from participants of the Social Relationships Study (SR Study) and the Individual Difference in EEG in Young Adults Study (IDEAS). These two studies have used subsamples of participants from the Twins Early Development Study (TEDS), a longitudinal twin study of over 16,000 twin pairs born in England and Wales 1994-1996 (Rimfeld et al., 2019). This study will utilise information collected from phases of the SR study, IDEAS as well as information collected as a part of TEDS. References American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders. (5th ed). Washington, DC. Ayres, M., Parr, J. R., Rodgers, J., Mason, D., Avery, L., & Flynn, D. (2018). A systematic review of quality of life of adults on the autism spectrum. Autism, 22(7), 774-783. Brugha, T. S., Spiers, N., Bankart, J., Cooper, S. A., McManus, S., Scott, F. J., ... & Tyrer, F. (2016). Epidemiology of autism in adults across age groups and ability levels. The British Journal of Psychiatry, 209(6), 498-503. Colvert, E., Tick, B., McEwen, F., Stewart, C., Curran, S. R., Woodhouse, E., ... & Ronald, A. (2015). Heritability of autism spectrum disorder in a UK population-based twin sample. JAMA psychiatry, 72(5), 415-423. Hollocks, M. J., Lerh, J. W., Magiati, I., Meiser-Stedman, R., & Brugha, T. S. (2019). Anxiety and depression in adults with autism spectrum disorder: a systematic review and meta-analysis. Psychological medicine, 49(4), 559-572. Mukaetova‐Ladinska, E. B., Perry, E., Baron, M., Povey, C., & Autism Ageing Writing Group. (2012). Ageing in people with autistic spectrum disorder. International journal of geriatric psychiatry, 27(2), 109-118. Pisula E, Danielewicz D, Kawa R, Pisula W.(2015). Autism spectrum quotient, coping with stress and quality of life in a non-clinical sample – an exploratory report. Health and Quality of Life Outcomes, 13(1), 173. Rimfeld, K., Malanchini, M., Spargo, T., Spickernell, G., Selzam, S., McMillan, A., … Plomin, R. (2019). Twins Early Development Study: A Genetically Sensitive Investigation into Behavioral and Cognitive Development from Infancy to Emerging Adulthood. Twin Research and Human Genetics, 22(6), 508–513. doi:10.1017/thg.2019.56 Robinson, E. B., Koenen K.C. , McCormick M.C., Munir K., Hallett V., Happé F., Plomin R., & Ronald A. (2011). Evidence That Autistic Traits Show the Same Etiology in the General Population and at the Quantitative Extremes (5%, 2.5%, and 1%). Archives of General Psychiatry, 68(11), 1113. doi:10.1001/archgenpsychiatry.2011.119 Robinson, E. B., St Pourcain, B., Anttila, V., Kosmicki, J. A., Bulik-Sullivan, B., … Daly, M. J. (2016). Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population. Nature Genetics, 48(5), 552–555. doi:10.1038/ng.3529 Tick, B., Colvert, E., McEwen, F., Stewart, C., Woodhouse, E., Gillan, N., ... & Ronald, A. (2016). Autism Spectrum Disorders and other mental health problems: Exploring etiological overlaps and phenotypic causal associations. Journal of the American Academy of Child & Adolescent Psychiatry, 55(2), 106-113.

Non-heterosexual individuals (i.e. those who identify as lesbian, gay or bisexual) report higher rates of mental health problems such as depression, suicidality, anxiety and substance use problems as well as social problems such as childhood adversity and intimate partner violence. These psychosocial difficulties have in turn been shown to be associated with increased risky sexual behaviour and risk for HIV infection in the general population and among non-heterosexual men. There is evidence that shared genetic and environmental factors respectively influence the association between mental health problems and sexual orientation, and risky sexual behaviour. The aetiology of the relationship between sexual orientation and disparities in psychosocial risk and risky sexual behaviour is not yet well understood. We recently demonstrated that increased mental health problems partly explained increased risky sexual behaviour among non-heterosexual individuals and that this mediation relationship was not confounded by shared aetiological influences. However, measures of social disparity were not included and the assessment of risky sexual behaviour was mostly based on the number of sexual partners. It will be informative to test this genetic model in a different sample incorporating better psychosocial variables and a broader index of risky sexual behaviour.

Self-harm can be defined as any act of self-injury and self-poisoning carried out by an individual, regardless of intention or motivation (Hawton et al., 2003). It is not only distressing and impairing but is also predictive of suicide, which is the second leading cause of mortality among young people (World Health Organization, 2014). Self-harm can also be further delineated into suicidal self-harm (SSH) and non-suicidal self-harm (NSSH), i.e. with and without suicidal intent. There is an ongoing debate whether NSSH and SSH should be considered as two separate entities or on the same continuum (Kapur, Cooper, O’Connor, & Hawton, 2013). Previous genetically informed study has suggested no aetiological difference between NSSH and SSH (Lim et al., 2020). Studying the risk factors that lead to self-harm may be helpful in informing interventions and treatments, especially if causal pathways are identified. Genetically informed design can be exploited to strengthen the causal inference (Pingault et al., 2018). Using Mendelian randomisation (MR) method, previous genetically informed study suggested that out of 24 risk factors for self-harm, major depressive disorder (MDD), schizophrenia and attention-deficit/hyperactivity disorder (ADHD) are the most plausible causal risk factors for self-harm, with effect sizes ranging from β = 0.003 to β = 0.008 (Lim et al., 2020). On the other hand, in a twin modelling study, among 17 mental health problems, insomnia symptoms have the second highest genetic correlation with NSSH (rg = 0.53) and SSH (rg = 0.62) after depressive symptoms (Lim et al., in prep). This was also supported by a recent molecular genetic study, where insomnia has an rg of 0.37 with self-harm behaviours (Campos et al., 2020). A causal relationship from insomnia to self-harm cannot be inferred from their genetic correlations. This causal relationship can be tested by using genetically informed design such as MR, which has identified MDD, ADHD and schizophrenia as the most plausible causal risk factors for self-harm. However, the MR design relies on some assumptions that can be easily violated. It assumes that the genetic instruments used are not pleiotropic (Davey Smith & Hemani, 2014). Although there are MR sensitivity analyses which can be used to account for the violation of this assumption, the risk of the presence of pleiotropy remains. To address this issue, a newly developed method, Mendelian Randomisation – Direction of Causation (MR-DoC), can be implemented when data from MZ and DZ twin pairs are available (Minică, Dolan, Boomsma, de Geus, & Neale, 2018). MR-DoC integrates both Mendelian randomisation and direction of causation twin model to further strengthen the causal inference. It allows some relaxation of the MR assumptions regarding pleiotropy and can potentially increase the statistical power relative to standard MR approaches (Minică et al., 2018). Using the MR-DoC design, the present study will have 2 main aims: 1) To investigate the causal relationships between self-harm and the aforementioned mental health risk factors using the MR-DoC design. 2) To investigate if there is any aetiological difference between NSSH and SSH in terms of their causal relationships with the mental health risk factors.

While many children with ADHD continue their inattentive or hyperactive behaviors into adulthood, the symptoms as well as comorbidities change during developmental course. Comorbidities of ADHD, for example with conduct disorders and neurodevelopment disorders, are more frequent during childhood, while emotional disorders become more common during adulthood (Franke et al., 2018; Sadek, 2018). Therefore, the nature of the comorbidity between emotional problems and ADHD at different developmental stages is worth exploring. Differential genetic effects between the two domains of ADHD symptoms have been found. As a result, comorbidities of other disorders with inattention and impulsivity may be due to different mechanism (Ghirardi et al., 2019; Plourde et al., 2017; Michelini et al., 2015). Splitting the environmental and genetic effects of inattention and impulsivity when exploring the association between ADHD and emotional problems is important. Previous studies have investigated the influence of common genetic influences in the association between ADHD and emotional problems (Brooker et al., 2020; Chen et al., 2016). However, the mechanism of the increasing association between emotional problems and ADHD with age is not clear. We will use data from the Twins Early Development Study (TEDS). Using these data, we will be able to apply structural equation models and investigate the association between ADHD and emotional problems over time. This study will explore the co-occurrence of ADHD and emotional symptoms during childhood, adolescent and adulthood as well as the causal effect between the two traits.

The Covid-19 pandemic is a persistent global stressor that has increased short-term risk for worsening mental health in certain groups, including younger people, those with prior mental health diagnoses, women, people who are unemployed, and people who belong to minoritised ethnic communities. It is important to consider longer term trajectories of symptoms of common mental health disorders, and determine whether risk factors for poor trajectories over time are the same as those that increase short-term risk. We will examine trajectories of symptoms of depression and anxiety using data from 18 assessment time-points collected over the course of a year during the Covid-19 pandemic in the UK (April 2020 – April 2021) as part of the Repeated Assessment of Mental health in Pandemics (RAMP) and Covid-19 Psychiatry Neurological Genetics (COPING) studies. Baseline assessments were administered to 34,465 individuals, with sample size for follow-up assessments ranging from 1,612 – 25,501 individuals. Growth mixture modelling will be used to identify sub-classes of trajectories over time for: i) symptoms of depression (Patient Health Questionnaire 9-items; PHQ-9) and anxiety (Generalised Anxiety Disorder - items; GAD-7); and ii) a tripartite model of symptom dimensions (positive affect, negative affect and anxious arousal) as established via confirmatory factor analysis of all items of the PHQ-9, GAD-7, and abbreviated Mood and Anxiety Symptom Questionnaire anhedonic depression subscale (MASQ-anhedonia). If confirmatory factor analyses do not support this tripartite model of symptom dimensions, trajectories will be modelled for total scores of anhedonia (MASQ-anhedonia) over time. Finally, we will examine predictors of trajectory class membership using multinomial logistic regression, to establish whether factors that predict worsening trajectories over time are the same or similar to those known to increase risk for worse outcomes in the early months of the pandemic.

Existing evidence indicates higher psychological distress and risky sexual behaviour among sexual minorities (King et al., 2008; Plöderl & Tremblay, 2015, Newcombe & Mustanski, 2011). However, the evidence is mostly cross-sectional (Bailey, 2020). The Mendelian Randomisation-Direction of Causation (MRDoC) model can determine the direction of causal effects in these relationships and estimate the magnitude of these effects while adjusting for residual genetic confounding (Miniça et al., 2018). Considering that childhood factors such as childhood gender nonconformity and early life adversities are higher among sexual minorities (Xu et al., 2019) and each associated with adverse health outcomes (Alanko et al., 2009; Hughes et al., 2017); it is possible that both factors moderate these causal relationships. Only one study has previously demonstrated significant causal influences of same-sex attraction on both adverse health indices, and reverse causal influences flowing towards same-sex attraction (Oginni et al., under review). These findings were respectively consistent with the minority stress theory and feedback effects of adverse health outcomes. However, this study did not test the possibility of sex differences in these relationships or moderation of these relationships by childhood factors. References Alanko, K., Santtila, P., Witting, K., Varjonen, M., Jern, P., Johansson, A., von Der Pahlen, B., & Kenneth Sandnabba, N. (2009). Psychiatric symptoms and same-sex sexual attraction and behavior in light of childhood gender atypical behavior and parental relationships. Journal of Sex Research, 46(5), 494-504. https://doi.org/10.1080/00224490902846487 Bailey, J. M. (2020). The minority stress model deserves reconsideration, not just extension. Archives of Sexual Behavior, 49, 2265-2268. https://doi.org/10.1007/s10508-019-01606-9 Hughes, K., Bellis, M. A., Hardcastle, K. A., Sethi, D., Butchart, A., Mikton, C., Jones, L., & Dunne, M. P. (2017). The effect of multiple adverse childhood experiences on health: A systematic review and meta-analysis. Lancet Public Health, 2(8), e356-e366. https://doi.org/10.1016/S2468-2667(17)30118-4 King, M., Semlyen, J., Tai, S. S., Killaspy, H., Osborn, D., Popelyuk, D., & Nazareth, I. (2008). A systematic review of mental disorder, suicide, and deliberate self harm in lesbian, gay and bisexual people. BMC Psychiatry, 8(1), 70. https://doi.org/10.1186/1471-244X-8-70 Minică, C. C., Dolan, C. V., Boomsma, D. I., de Geus, E., & Neale, M. C. (2018). Extending causality tests with genetic instruments: An integration of Mendelian randomization with the classical twin design. Behavior Genetics, 48(4), 337-349. https://doi.org/10.1007/s10519-018-9904-4 Newcomb, M. E., & Mustanski, B. (2011). Moderators of the relationship between internalized homophobia and risky sexual behavior in men who have sex with men: A meta-analysis. Archives of Sexual Behavior, 40(1), 189-199. https://doi.org/10.1007/s10508-009-9573-8 Oginni, O. A., Lim, K. X., Purves, K. L., Lu, Y., Johansson, A., Jern, P., & Rijsdijk. (under review). Causal influences of same-sex attraction on psychological distress and risky sexual behaviors: Evidence for bidirectional effects. Submitted to the Archives of Sexual Behavior. Plöderl, M., & Tremblay, P. (2015). Mental health of sexual minorities. A systematic review. International Review of Psychiatry, 27(5), 367-385. https://doi.org/10.3109/09540261.2015.1083949 Xu, Y., Norton, S., & Rahman, Q. (2019). Early life conditions and adolescent sexual orientation: A prospective birth cohort study. Developmental Psychology, 55(6), 1226–1243. https://doi.org/10.1037/dev0000704

Depression and anxiety, defined as psychological distress when manifesting as a comorbidity, are common mental health conditions (Arvidsdotter et al., 2016; Choi et al., 2020). Psychological distress can burden the affected individual, in addition to their family and wider society (Othman et al., 2019). This is demonstrated by the inverse relationship which exists between psychological distress and educational attainment, full-time employment, and quality of life (Brenes, 2007). The global increase in psychological distress prevalence disproportionately affects low- and middle-income countries (LMICs; Javed et al., 2021). This burden is largely attributed to the existing treatment gap (of up to 93%) in LMICs, which is caused by inadequate treatment availability and access (Mascayano et al., 2015). For example, Sri Lanka is a LMIC that has experienced natural disasters, civil conflict and political unrest in recent decades (Kathriarachchi et al., 2019). In the context of these, a high proportion (almost 20%) of children aged 13 to 18 years in Sri Lanka are reported to have experienced a psychological disorder (Perera, 2009). Similarly, adults who were exposed to the civil war reported increasing levels of psychological distress for up to 5 years post-conflict (Jayasuriya et al., 2016). Despite the introduction of a decentralised public health system, a significant mental health treatment gap persists (van den Broek et al., 2021). Unique barriers to accessing mental healthcare in Sri Lanka include: a failure to recognise symptoms or available services, social stigma and/or a negative perception to treatment efficacy (Fernando et al., 2017). The existing mental health treatment gap necessitates a review of alternative risk and protective factors for psychological distress that can be targeted for preventive intervention. Socioeconomic status (SES) is strongly associated with psychological distress (Maselko, 2017). Two underlying mechanisms facilitate this relationship. Social causation suggests that mental disorders arise due to the adverse social and financial stress of lower SES (Lund et al., 2010; Wickham et al., 2017). Conversely, social selection finds individuals with mental disorders to have a greater likelihood of drifting into a low SES (Chiu et al., 2017; Saraceno et al., 2005). A recent longitudinal study of SES and depression in China (a LMIC) found these mechanisms to work concurrently (Jin et al., 2020). Modifiable protective factors that mediate the link between low SES and psychological distress can be targeted for preventive interventions to help break the debilitating cycle of low SES and psychological distress (Jin et al., 2020). One possible proactive intervention is dietary diversity. A growing body of research in LMICs links dietary diversity with SES and mental health outcomes, whereby low SES is associated with reduced dietary diversity (Mozaffari et al., 2021; Onyango, 2003) while transitioning from a homogeneous diet to one with a variety of food groups is protective against psychological distress (Poorrezaeian et al., 2015, 2017; Yin et al., 2019). Thus, low dietary diversity may mediate the association between low SES and psychological distress, however, this has not been previously investigated. Although we suggest dietary diversity as a mediator of the relationship between low SES and psychological distress, we recognise the possibility of alternative relationships. For example, psychological distress may be associated with compensatory hyperphagia (carbohydrate craving which is associated with decreased fruit and vegetable consumption; Razolli et al., 2017; Matsungo & Chopera, 2020), and thus mediate the association between low SES and reduced dietary diversity. An alternative mechanism is that SES, low dietary diversity and psychological distress are jointly influenced by a common factor. For example, the civil conflict and consequent economic hardship in Sri Lanka may increase poverty, serve as a source of stress with consequent psychological distress, disrupt food supply which would make a varied diet expensive and inaccessible (Conklin et al., 2016; Siche, 2020). The evidence base for these possibilities is relatively slim, hence the decision to investigate dietary diversity as a mediator of low SES and psychological distress. However, the direction of causal effects may be better tested in future studies. Furthermore, considering that genetic and environmental factors influence individual differences in depressive and anxiety symptoms (Nas et al., 2021), and SES (e.g., through active gene-environment correlations, Scarr & McCartney, 1983; Trzaskowski et al., 2014); it is possible that their covariance and the proposed mediation by dietary diversity are confounded by correlated genetic and environmental influences. In which case, interventions targeting dietary diversity to reduce the risk for psychological distress among those from low socioeconomic backgrounds will not be sufficient. In contrast, the absence of confounding by correlated aetiological (genetic and environmental) influences will further reinforce the utility of targeting dietary diversity for mental health preventive efforts. The present research, thus, aims to deepen the understanding of the interplay between SES, dietary diversity and psychological distress and disentangle overlapping genetic and environmental influences on these relationships. Considering that most mental health studies using genetically sensitive designs are carried out in higher-income countries which may not generalise to LMICs, our findings can serve as a first step in identifying targets for preventive mental health interventions in LMICs to reduce the existing treatment gap. Furthermore, the recent history of civil conflict in Sri Lanka may make our findings generalisable to LMICs with high levels of environmental stress. The present study will test 1) whether SES and dietary diversity are associated with psychological distress, 2) whether the association of SES with psychological distress symptoms is mediated by dietary diversity, 3) whether the direct phenotypic mediation effects are a better explanation for the observed covariances of the latent factors.