This proposal aims initially to undertake meta-analyses of GWAS data for cross-sectional BP. Nicholas Timpson is representing ALSPAC with regards to the EAGLE Blood Pressure Working Group and has already agreed to complete these analyses. We are planning to run meta-analyses of cross-sectional GWAS across the participating cohorts of EAGLE which include Raine, GenR and Lisa Plus in children aged between 5 and 15 years of age.

From here we hope to identify specific SNPs and genes associated with the elevated measures SBP and DBP and additionally take these significant SNPs and investigate their association in adult blood pressure consortiums to provide further insight in to the underlying genetic determinants of adult hypertension.

We also intend to analyse both the intercept and slope of longitudinally measured BP in childhood. We will utilise a range of statistical methods, including Bayesian techniques, to investigate gene:gene interactions and longitudinal blood pressure trajectories during the postnatal period. This research will produce best linear unbiased predictors (BLUPs) for blood pressure trajectories for all eligible studies within the EAGLE Consortium for future genetic and epidemiological investigations.

The aim of this study is to investigate the influence of maternal risk factors before and/or during pregnancy on the development of social communication deficits and hyperactivity in children during later life. At the extreme end of each phenotypic spectrum, characterised by Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) respectively, considerable comorbidity has been reported 1. 20-50% of children with ADHD meet diagnostic criteria for ASD, and 30-80% of ASD children meet diagnostic criteria for ADHD1. Both conditions, ASD and ADHD, share variance at a behavioural level (e.g.2,3), and the identification of shared structural brain abnormalities (e.g. 4) and genetic risk factors (e.g. 5) implies the presence of some underlying shared disease aetiology (e.g. 6).

The overlap in characteristics between ADHD and ASD, both at a behavioural and at a neural level, raises the question of whether environmental or familial exposures that have previously been associated with ASD(e.g. 7,8,9) also confer risk of ADHD symptoms (specifically, hyperactivity in this context). Are those risks found in children with ASD who lack ADHD symptoms? Establishing differences in environmental risk factor profiles is an important step towards the identification of the role of specific environmental risk within GxE interactions (as proposed in B954) and the detection of critical time windows during child development where environmental or familial exposures may exacerbate genetically mediated risk.

Using the phenotypic richness and power of the ALSPAC cohort, we will focus on the investigation of broad ASD and ADHD phenotypes during the course of child and adolescent development. Specifically,we will model individual and joint trajectories for the development of social communication deficits (mother-reported SCDC measurements: 91, 128, 166, 198 mns) and hyperactive behaviour (mother-reported SDQ scores: 47, 81, 97, 115, 140, 157, 198 mns). We will examine risk of the child following one or other trajectory in relation to reports of maternalsubstance use (smoking, marijuana use and alcohol use) during and before pregnancy, maternal infections (influenza) during and before pregnancy and a maternal history of psychiatric disorders.

We will also investigate whether these trajectories, and any associated risk, are moderated by parenting influences, such as parental warmth 10 (aggregated parenting measure at 8 months and 33 months derived by Andrea Waylen and Sarah Stewart-Brown)

Using structural equation modelling, weseek to identify risk factors that are unique with respect to the expression of the individualbroad ASD and ADHD phenotypesand, if possible, to distinguish them from risk factors that are shared, and likely to indicate a common aetiology.

BACKGROUND

Missing data is a major concern in ALSPAC and other longitudinal studies. Many analyses assume that data are missing at random (MAR), but this assumption is hard to verify from data and is often not very plausible.

ALSPAC has sources of data that offer the opportunity to learn more about the missing data mechanism, and in particular about whether MAR is true:

1. Repeated attempts to contact participants at each wave.

2. External sources of data from schools and SATs.

Repeated attempts have previously been used to learn about missing data mechanisms , , but not in longitudinal data sets.

OBJECTIVES

1. To explore the missing data mechanism for key variables on drugs, alcohol and conduct disorder.

2. To develop appropriate statistical methods, in particular for handling repeated attempts data at multiple waves.

DATA

Main outcomes: drug use, alcohol use and conduct disorder derived from clinic visits at ages 11, 13, 15 and 17, and questionnaires at intermediate ages.

Related outcomes: schools data at years 6 and 11; SATs data at ages 11 and 14.

Auxiliary variables: the number of letters sent before each clinic visit happened (invitation, reminder, last chance) and before each questionnaire was returned (initial, reminder). Ideally we would like to use further information on whether "defaulters" were phoned and dates of letters, but we understand this is not currently available.

Covariates including sex, socio-economic status, ethnicity.

PLAN OF ANALYSIS

1. Descriptive statistics, including associations of the main outcomes with the related outcomes and the auxiliary variables.

2. Using related outcomes: assuming the main outcomes are MAR when the related outcomes are included in the model, explore the missing data mechanism when the related outcomes are not in the model. This could be done using multiple imputation.

3. Using repeated attempts: develop and fit models for repeated attempts at multiple waves. Hence explore developmental trajectories (perhaps using latent class models).

Paul Clarke and Michael Spratt are doing a related project focussing on repeated attempts at a single time point, and we will work with them to ensure that the two projects complement each other. We will work with Matt Hickman (drugs and alcohol strand) and Glyn Lewis ("risky behaviours" strand) to ensure that this project fits well with their strands.

PROPOSED OUTPUTS

1. A statistical paper describing the models for repeated attempts at multiple waves.

2. An epidemiological paper describing what has been learned about missing data in ALSPAC

This project examines the role that hyperbolic discounting plays as a determinant of unhealthy

birth outcomes. If pregnant women discount hyperbolically, a reduction in personal delivery costs (via public insurance) will exacerbate the disparity between each woman's short- and long-run rates of time preference, leading to greater engagement in unhealthy behaviors with long-run impacts. This disparity in rates of time preference will also make it more costly for women to maintain traditional commitment mechanisms designed to keep them on their optimal path to achieving a healthy birth outcome.

THEORETICAL FRAMEWORK

Assume there exists a time t* (less than T) during pregnancy at which a woman is indifferent between two

choices with different rewards: (a) engaging frequently in risky behavior(s) and experiencing a premature and/or low birth weight (LBW) baby at time T (denoted by C) and (b) engaging minimally in risky behavior(s) and not experiencing a premature and/or LBW baby at time T0 (denoted by C0), where T less than T0. We assume that C0 has the greater payoff and that p(t) (p0(t)) is the probability density of a birth before T (T0). Furthermore, we assume that C0 has both a non-zero probability of a premature and a late birth, and that T and T0 are the anticipated birth dates.

When there is greater time to birth, I hypothesize the woman exhibits patience to obtain the reward of a healthy birth outcome (C0). However, as the time to birth grows shorter and shorter, she becomes increasingly impatient and seeks instant gratification via any number of risky behaviors. My research question is the following: To what extent, if at all, does hyperbolic discounting occur in pregnancy?

I assume that p(v; t) is the probability density that C is realized at time v greater than t such that p(t; t) greater than p0(t; t) for t less than T with the assumption that if the density of premature birth at time v drops off monotonically as we move away from the anticipated realization time (T or T0), then the inequality seems reasonable as the density should have dropped off less at t for C than for C0. Since progression of pregnancy up to 39 weeks reduces the expected payoff from C (due to the reduced probability of having a premature birth), it follows that C p(t; t) less than C0 p0(t; t) for t less than T. Given these previous assumptions, we can then show that up to a certain point t* (say, the beginning of the third trimester of pregnancy), a woman might have engaged in protective behaviors such as quitting smoking and drinking alcohol upon learning she was pregnant only to return to those behaviors later in the pregnancy (sometime after t* ) given their addictive nature

(see Dasgupta and Maskin [1] for the proof of this).

EMPIRICAL FRAMEWORK

The dependent variables will include number of times per day smoked, number of glasses of wine / beer

drank per day, illicit drug use, folic acid use, coffee consumption, amount of exercising, desire to lose weight, and mental status (i.e., depression, anxiety, etc.). Independent variables will include Knowledge from medical professional of the negatives and positives of certain health behaviors, External Environmental variables (previous abortion, spouse smokes, work exposure to smoke, dieting/desire to lose weight, etc.), Previous Health issues (mother was low-birth weight herself and/or mother smoked during her pregnancy, family illnesses), and Demographics (race, age, income, education, etc.). I use the trimester time (i.e., given by 8 weeks, 18 weeks, and 32 weeks) and mother's pre-pregnancy BMI as stratification variables. Thus one empirical specification would be the following:

smoke_ij = B_1 + B_2 * K + B_3 * EE + B_4 * PH + B_5 * D + e_i,

where i = 1, 2, or 3 is the trimester indicator and j = 1, 2, 3, or 4 is the BMI category. The hypothesis of interest is that knowledge of the effects of smoking will have less of an impact on a woman's actual behavior as she grows impatient by the third trimester, her external environment will inuence her to smoke more, and previous health issues will not be as significant in avoiding smoking behavior by the third trimester. This specification would extend analogously to the other behaviors mentioned.

The mammalian brain has undergone significant expansion in its evolution to man, and is over 3 times bigger than our closest primate relatives (1). Head circumference is highly correlated with brain size, and is used clinically as a proxy measurement for brain volume. Major work has been done in the identification and functional analysis of genes causing marked reduction in head circumference/brain size (Primary Microcephaly/Seckel syndrome,ref. 2-6). Several genes have been identified, all of which have centrosomal functions (6-8). Two of these, ASPM and MCPH1 have been found to undergo significant adaptive evolution in primates (9,10). However, to date, variants in these genes have not been found to be associated with normal population variation in head circumference/brain size (11).

Following on from the identification of Mendelian genes for these disorders of extreme reduction in head circumference (-4 to -10sd), it is now of interest to identify the factors responsible for variation in head circumference and brain size in the general population, especially during early development. Given the wide variation in growth of head cicumference during early childhood, all measures will be standardised using the GrowthAnalyzer (http://www.growthanalyser.org) to obtain the best possible reference standards within each sample. Measures at birth will be adjusted for gestational age.

A meta-analysis on variation in early head circumference at birth and early childhood (0 to 4 years) is proposed as part of the EGG/EAGLE consortium with a follow-up of genome-wide/ near genome-wide significant signals within the remainder of the ALSPAC sample.

Once identified and validated, such genes will contribute to the understanding of developmental pathways that influence cognitive and brain development and address whether common variants are acting in developmental pathways already implicated by mendelian genetics in brain size determination.

Acne vulgaris is a common inflammatory disease of the pilosebaceous glands characterized by inflamed (papules, pustules, nodules and cysts) and noninflamed lesions (comedomes) on the face, neck, chest and back. The prevalence of acne in adolescents has been reported as being between 35% and 90%, depending on the method of classification, with peak incidence occurring at between 14 and 17 years in females, and 16 and 19 years in males (1,2). As well as the physical pain and discomfort that acne produces, it is well documented that acne sufferers experience higher levels of psychological distress, are more self-conscious and have a poorer self-image than age-matched controls. A possible role

for genetic factors in the development of acne is based on the observation that relatives of affected individuals are at increased risk of developing acne compared with unrelated individuals. Indeed several recent twin studies suggest that acne and its severity are strongly influenced by genetic factors (3-6).

The purpose of this study is to use the genome-wide association approach to identify loci that contribute to acne severity. We intend to pool genome-wide association data in a meta-analysis using data from other centres including twins from Queensland Institute of Medical Research (Nick Martin), twins from Free University Amsterdam (Dorret Boomsma) and twins from King's College London (Tim Spector).

1 Stathakis V, Kilkenny M, Marks R. Descriptive epidemiology of acne

vulgaris in the community. Australas J Dermatol 1997; 38:115-23.

2 Kilkenny M, Merlin K, Plunkett A, Marks R. The prevalence of common

skin conditions in Australian school students: 3. Acne vulgaris.

Br J Dermatol 1998; 39:840-5.

3 Friedman GD. Twin studies of disease heritability based on medical

records: application to acne vulgaris. Acta Genet Med Gemellol 1984;

33:487-95.

4 Walton S, Wyatt EH, Cunliffe WJ. Genetic control of sebum excretion

and acne-a twin study. Br J Dermatol 1988; 118:393-6.

5 Bataille V, Sneider H, MacGregor AJ et al. The influence of genetics

and environmental factors in the pathogenesis of acne: a twin study

of acne in women. J Invest Dermatol 2002; 119:1317-22.

6 Evans, DM, Kirk KM, Nyholt DR et al. Teenage acne is influenced by genetic factors. British Journal of Dermatology, 152(3), 579-81.

AIMS

Overall aim

To investigate the association of suicide and suicide attempt in parents with suicidal behaviour, mental health, social and academic functioning in offspring.

Specific objectives

1. To investigate whether:

a) Exposure to parental self harm (fatal or non-fatal) confers an independent risk for self harm, suicidal thoughts, suicidal plans, suicidal attempts, psychiatric symptoms, diminished social and academic functioning beyond that observed in offspring exposed to parental mental or physically disabling illness and offspring of parents who have no history of self harm or mental and/or physically disabling illness.

b) Exposure to parental death by suicide confers an independent risk for self harm, suicidal thoughts, suicidal plans, suicidal attempts, psychiatric symptoms, diminished social and academic functioning beyond that of exposure to parental death by a cause other than suicide.

2. To examine whether outcomes in offspring i.e. self harm, suicidal thoughts, suicidal plans, suicidal attempts, psychiatric symptoms, social and academic indices differ depending on the gender of the self harming parent and/or gender of offspring.

3. To examine whether age of exposure to self harm of a parent modifies the relationship between exposure to self harm and self harm, suicidal thoughts, suicidal plans, suicidal attempts, psychiatric symptoms, social and academic functioning in offspring.

4. To assess whether all these exposure factors (gender of proband and offspring, age of exposure, type of self harm) are related to the age of first onset of self harm, suicidal thoughts, suicidal plans, suicidal attempts, behaviour or mental health problem in the offspring.

5. To examine whether exposure to different forms of self-harm in parents (i.e. suicide versus attempted suicide) is associated with specific self-harming behaviours (i.e. suicidal thoughts, suicidal plans, suicidal attempts, self harm with no intention to die), psychiatric symptoms, social and academic parameters in offspring.

There has been speculation that an atopic predisposition might increase susceptibility to cancer, although convincing evidence has been lacking. An exception is prostate cancer, for which a positive association with atopy1, and especially dust mite sensitisation2, has been reported, but not explained. A strong predictor of prostate cancer risk is raised levels of IGF-13. The observation that Th2 cytokines promote the expression of IGF-14 is in keeping with the hypothesis that atopy may increase risk of prostate cancer. However, it is also possible that IGF-1 may drive atopy. Laboratory studies have suggested that IGF-1 promotes Th2 responses and raised IgE5;6. Furthermore, IGF-1 is a primary mediator of the effects of growth hormone, and cord blood IGF-1 levels are positively associated with size at birth7. This may have relevance to the, albeit inconsistent, evidence linking enhanced fetal growth with atopy8;9. Interestingly, IGF-1 levels fall with increasing parity10, raising the possibility that IGF-1 might partly explain the relation between parity (birth order) and atopy in the offspring. However, epidemiological studies on the relation between IGF-1 and atopy are lacking.

Childhood Obesity and Severe Antenatal Maternal Stress

Obesity is a complex public health problem that is a growing threat to children's health, as well as a current and future drain on National Health Service (NHS) resources. Obesity already costs the NHS directly around £1 billion a year and the UK economy a further £2.3 to £2.6 billion in indirect costs. If this present trend continues, by 2010 the annual cost to the economy could be £3.6 billion a year.

By the IOTF's international standards nearly 27.5% of girls and 22% of boys in UK aged 2-15 were found to be overweight, including 5.5% of boys and 7.2% of girls who were obese in 2002. The IOTF analysis indicates a marked acceleration in the trend from the mid-1980s onwards. Using the national Body Mass Index percentiles approach (adopted by the Department of Health assuming 15% overweight including 5% obesity in 1990) 30.7% of girls and 30.3 % of boys were overweight, including 16% who were obese, by 2002.(See DH weblink)Furthermore, childhood obesity is one of the key predictors of adult obesity. Evidence of the long term effectiveness of obesity interventions is mixed (see Mend website; ) once obesity is established (see Oude Luttikhuis et al 2009). Identifying early risk factors for child obesity may have important preventive implications for future public health strategy.

In humans, adverse early life experiences such as childhood trauma, neglect and abuse are associated with the development of psychosocial disorders (Heim & Nemeroff, 1999; Kaufman et al., 2000; Heim et al., 2002; MacDonald et al., 2008). In animal models, an adverse early life experience causes abnormalities in neurodevelopment (Pickering et al., 2006), increased stress response as measured by plasma corticosterone (Meaney et al., 1993; Pihoker et al., 1993), and behavioural deficits (Pryce and Feldon, 2003; Pryce et al., 2005; Slotten et al., 2006) in adulthood.

Numerous studies report relationships between palatable food (rich in sugar and saturated fat), and emotional states and stress. In humans and in animal models, emotions influence food intake and choice of food and palatable foods high in fat and carbohydrate improve mood and modify stress responsivity. For example, subjects report a preference for palatable food rich in saturated fat and sugar during negative emotions (Macht, 2008) and adult individuals subjected to psychological stress in a laboratory setting are reported to eat greater amounts of palatable food compared to control groups (Oliver et al., 2000). In adults, depression and anxiety can also be linked to compulsive behaviours and craving for palatable food which induce feelings of pleasure (Kelley et al., 2005; Paterson and Markou, 2007). A study in UK school children reported that higher self-reported stress scores were associated with increased intake of fatty food compared to fruits and vegetables (Cartwright et al., 2003). These data suggest that the direction of the association is that humans subjected to stressful circumstances (chronically and acutely) are more likely to crave palatable, high saturated fat and high sugar containing foods or 'comfort foods'.

Dallman (2005) described the phenomenon of 'comfort food' or 'comfort eating' as self-medication with food.Adam and Epel, 2007 propose that stress-stimulated consumption of palatable food is a form of reward-based eating because palatable high-fat diet or 'comfort food' modulates hypothalamic-pituitary-adrenal (HPA) axis activity and indirectly blunts the stress response. In other words, the craving for such foods derives from their effect on the HPA axis which the presence of the stress has activated acutely or chronically. Animal data support the notion that consuming such a diet can have a beneficial effect by modulating the stress response: rats consuming molar sucrose solution and sweetened vegetable shortening (lard), in addition to regular chow, showed attenuated ACTH responses to restraint stress and lower plasma corticosteronecompared to those consuming chow alone (Pecoraro et al., 2004;la Fleur 2005). Similarly, rats exposed to lard for 2 h per day for 7 weeks also showed a decreased stress response compared to rats consuming chow only (Kinzig et al., 2008). Alterations in hippocampal GR mRNA and hypothalamic CRH mRNA, key mediators of HPA axis activity, are thought to be the mechanisms by which these diets alter the stress response (e.g. Morris 2010). Thus, data suggest that palatable high fat diets or comfort food have a role in ameliorating stress and possibly stress-related behaviour whether given chronically or as an option.

These studies focus on postnatal events in humans and animal models. It is well recognised that prenatal maternal stress alters HPA axis sensitivity in rodent models (e.g. Kapoor et al 2008; Liu et al 1997). Our work and others has provided evidence that in utero exposure to severe maternal stress can increase the risk of neuropsychiatric disorder i.e. schizophrenia and affective disorder (Khashan et al 2008; Khashan et al submitted) both of which are considered in part to result from altered HPA axis sensitivity.

Research Question

Does pregnancy-related and early postnatal exposure to domestic violence predict increased BMI in offspring of exposed mothers in the ALSPAC cohort?

Hypotheses

Primary hypothesis: a child exposed to severe early life stressors is more likely to become overweight or obese than children not exposed in this way manifest as a rightwards shift in the BMI (and or DXA derived fat mass) distribution by age 15 of exposed versus unexposed children.

Secondary hypotheses: i) as a result of exposure to maternal stress in utero, HPA axis sensitivity in exposed children is greater than in unexposed children (tested using salivary cortisol measures) ii) overweight or obesity occurs through increased intake of palatable or comfort foods in exposed versus unexposed children iii) intake of a high saturated fat and high carbohydrate diet reduces or modulate HPA axis sensitivity iv) overweight or childhood obesity is associated with reduced HPA axis sensitivity (mean salivary cortisol levels) in exposed children compared to exposed non-obese or normal weight children

Future hypotheses i) We will examine whether we may use birth weight (BW) of children exposed and unexposed as a proxy for early life stress. We would then examine BW and its association with later life BMI through the use of genotypes associated with it via the presence of key genetic loci associated with birth weight(Freathy et al 2010).

Method

We wish to use a model of early life stress indexed by the maternal experience of adverse life events and of domestic violence before conception, during pregnancy or in the first year of life, in order to assess effects on childhood growth and rates of overweight and obesity. We shall examine the distribution of the BMI and body composition (DXA) of children exposed in utero and postnatally to maternal domestic abuse and compare it to that of unexposed children and assess HPA axis sensitivity using salivary cortisol levels in obese/overweight and normal weight exposed children. Variables assigning abuse status to individual participants have already been derived (see below) and we seek permission to examine these in light of anthropometric, serological and genetic data.

All analysis will be undertaken collaboratively by Nic Timpson at the MRC CAiTE centre and as such we have not requested data transfer.

Exposure variables: Antenatal domestic violence has been assessed in the ALSPAC cohort at 18 weeks gestation. Participants were asked two questions on whether their partner had been emotionally cruel, and whether their partner had physically hurt them since the start of the pregnancy and to state how much it affected them on a scale from 'no effect' to 'affected a lot'. A woman will be considered to have experienced antenatal domestic violence if she responded positively (any level of effect) to either physical or emotional cruelty since the start of the pregnancy. Questions asking if their partner had been emotional or physically cruel have also been used to define postnatal domestic violence and this was reported at 2, 8, 21 and 33 months after the child was born, in each case referring to the period since the last questionnaire. Violence from the partner to the mother, partner to the child and mother to the child was reported by the mother in the same way at these times. The repeated responses will be summarised into a variable indicating the number of time points that violence was experienced since the birth, a separate variable for each of: violence occurring to the mother, to the child from partner or child from the mother. Other stressful life events will be assessed using the life events scale covering 13 life events including serious illness/death in the family, marital difficulties, financial problems etc. These were recorded by the mother at 8, 21 and 33 months after the birth.

Outcome variables:

Child Biometric Data: Information collected on birthweight and gestational age will be used along with the height, weight, ethnicity of the mother and parity of the child, to determine the birthweight centile of the child using the Gestation Network calculator; children in the lowest 10% will be counted as small for gestational age (SGA).BMI recorded atages21 months, 4, 7, 9, 11 and13 years of age usingheight and weight measurements in GP reports; as well asDXA fat mass at the same ages.

HPA axis sensitivity: we shall use salivary cortisol measures collected at ages xxx and compare mean and SD between exposed and unexposed children. (need to check availability).

Dietary records at age 10 yrs will be used to account for dietary/calorie intake.

Potential confounders: The following maternal characteristics will be measured as potential confounders of any association between stressors and child biometric measures: maternal age at the birth of the child, maternal socioeconomic status as assessed by the following variables: maternal education, (recorded at 32 weeks gestation simplified from a 5 item response into 3 categories: low (CSE, vocational), medium (O-level), high (A-level, degree)) and homeownership status, grouped as: own/mortgage versus rented (Cleland et al 2010; Crawford et al 2010). Smoking and alcohol use during pregnancy were determined at 18 weeks gestation. Women are considered to have smoked during pregnancy if they had smoked any form of tobacco in the 2 weeks prior to completion of the 18 week questionnaire. Women were also questioned on their alcohol use during pregnancy at 18 weeks gestation, responses were categorised into less than 1 glass (unit) per day and 1 or more glasses (units) per day.

Analysis

Simple analyses will be used to assess the association between severe early life stress exposure, measured at 18 weeks gestation and up to 33 weeks postnatally, and the biometric outcome variables.Multivariable analysis will be conducted using logistic regression to adjust for the following possible confounding factors: SES (maternal education, homeownership status), maternal marital status, maternal age, parity, ethnicity and paternal depression. Biometric variables will be handled as continuous variables initially to look for shifts in the distribution of weight in the exposed and unexposed groups by age. Bivariable associations between overweight, antenatal domestic violence and baseline characteristics of the mother and child will be assessed using chi-squared or t-tests. The association between antenatal domestic violence and child overweight will be further investigated using logistic regression. Generally, the data will be analysed in three stages: (i) calculation of unadjusted odds ratio of severe early life stressor (domestic violence and life events score) in relation to overweight; (ii) adjustment for possible confounding factors; (iii) addition of potential mediators to the model. To investigate the possible mediating effect of maternal depression on the association, mother with recorded depression (EPDS score greater than 13) will be excluded in a sensitivity analysis. A variable for domestic violence exposure i.e. no violence, antenatal violence only, antenatal and postnatal violence, postnatal violence only, will also be used to give a direct comparison of the different levels of exposure. If numbers are sufficient, gender interactions will also be explored.

A second set of analyses using a Chi square tests will assess the secondary hypotheses: the relationship between exposure and mean salivary cortisol levels. The divide the cases into exposed overweight/obese and exposed normal weight and

Multivariable analysis will be conducted using logistic regression to adjust for the following possible confounding factors: SES (maternal education, homeownership status), maternal marital status, maternal age, parity, smoking, ethnicity and paternal depression. Bivariable associations between salivary cortisol levels, antenatal domestic violence and baseline characteristics of the mother and child will be assessed using chi-squared or t-tests. The association between antenatal domestic violence and childhood salivary cortisol levels will be further investigated using logistic regression. Finally, dietary intake will be assessed as high or low in palatable food and/or calorie and a chi square test used to examine the association between maternal stress/domestic violence and dietary intake. Multivariable analysis will be conducted using logistic regression to adjust for the following possible confounding factors: SES (maternal education, homeownership status), maternal marital status, maternal age, smoking, parity, ethnicity and paternal depression.

Missing Data: We shall use the (MICE) with the ice command in Stata to impute missing values using chained equations. This will be carried out on the full dataset including all variables used in this analysis as well as additional variables known to be related to non-response.