Socioeconomic stability plays an important role in the normal development of a child. So, it is not surprising that adverse socioeconomic conditions arising from difficult financial circumstances have been associated with a number of mental health and cognitive problems. The motivation for the current line of thinking is a recent study published in JAMA: Psychiatry wherein we noted that macroeconomic shocks such as recessions can have long-term effects on the mental and emotional health of children between the ages of 0-18 months. Over the last two years, we have been expanding our work on the concept of socioeconomic depreciation (SED) or drop in family income. In this proposal, we will examine the mechanism behind the influence of SED on future behavior problems. Specifically, we are proposing that SED will affect attachment, which in turn will affect social development. Since attachment variables are not available in the ALSPAC data, ALSPAC will not be used to develop this particular hypothesis. However, ALSPAC will be used to explore the role of social development in future behavior problems. (Figure 1 available on request).

Secondly, we propose to explore the effects of prenatal exposure to SED. Borrowing from literature on fetal programming, we propose that prenatal exposure may lead to changes in temperament and increase vulnerability to behavior problems. Temperament has been described as a stable system of traits that moderates susceptibility to behavioral and emotional problems. In line with this argument, this study will examine if temperament moderates the link between SED and behavioral and emotional problems (Figure 2 available on request).

This proposal also extends our work into SED by testing for gene by environment interactions using the large public data sets from the Psychiatric Genomics Consortium (PGC). Because Dr. Faraone is on the PGC's Coordinating Committee, this aspect of the work will be straightforward. For each disorder, the PGC computed a SNP heritability, which indexes the amount of heritability due to common SNP variants. These SNP heritabilities were all much greater than zero and were statistically significant. These analyses also showed substantial and significant correlations between the heritabilities of some of these disorders. For example the correlation between schizophrenia and each of the mood disorders was greater than 0.4. This cross disorder polygenic overlap, along with prior evidence of overlap for single SNP variants and for CNVs, has confirmed prior work from genetic epidemiology indicating shared genetic risk factors among multiple psychiatric disorders. This work is relevant to genetic studies of SED because it allows us to create molecular polygenic scores for use in testing gene by environment interaction. These scores may explain, for example, whey two children raised in identical SED circumstances have different outcomes. When added to knowledge about SED, these genetic data could help allocate scarce resources where they are most urgently needed.

For this project, we propose to use ALSPAC. Details about our proposed work with ALSPAC are discussed below. ALSPAC data will be used to test the following hypotheses. The scientific outline (Section 8) provides a list of specific variables requested, and how those variables will be used in hypothesis testing. In that table, DV refers to Dependent (or Outcome) Variable, IV to Independent (or Exposure) Variable, MV to Moderating Variable, and CV to Confounding Variable.

Aims:

To describe the prevalence of pain within those with diagnosed autistic spectrum disorder.

To describe the prevalence of pain within those with autistic like symptoms including social and communication difficulties, prematurity and delays reaching developmental milestones.

To explore if depression and anxiety are higher amongst those with autism and pain.

To explore if people with autism and pain suffer from sleep deprivation.

To explore how prevalent sleep deprivation is in people who present with persistant pain compared to those who present with autism and persistent pain.

To explore if sleep is dependant on anxiety scores in those with pain more than those without pain.

To explorwe if children with a diagnosed autistic spectrum disorder have other chronic sleep, pain or anxiety conditions.

AIM - to investigate the contribution of rare coding variants in selected candidate genes to speech and language disorders

BACKGROUND

Our lab investigates genetic contributions to speech and language disorders, primarily within a cohort of families in which at least one child is diagnosed with specific language impairment (The SLI Consortium (SLIC) cohort). Investigations in this cohort have highlighted specific genetic pathways and candidate genes that we believe might be linked to speech and language development. We would like to follow these genes up in more detail in a larger cohort. The effects we have identified are rare coding mutations. In order to follow these up, we therefore need access to a large cohort in which speech and language data and genetic sequencing are readily available.

Aims:

Associations are consistently seen between cigarette use and psychosis, although direction of causation is not known. There is also a large body of literature showing cognitive impairment in patients with schizophrenia. There has been some suggestion that high rates of smoking in schizophrenia could be to alleviate cognitive impairment from both the disorder and anti-psychotic medication side-effects, as nicotine has been posited as a cognitive enhancer. We aim to investigate whether associations between cigarette use, cognition and psychotic experiences are causal, using Mendelian Randomisation.

Hypotheses:

As these are exploratory analyses, we do not have hypotheses as to what we will find in terms of causality.

Exposure variables:

Genetic instruments as proxy variables for cigarette use and for risk of schizophrenia.

Outcome variables:

Cognitive tasks at age 18, smoking at age 18, psychotic experiences at age 18 (all measured at clinic).

Confounding variables:

As this is a Mendelian randomisation design, the analyses should not be subject to the usual confounders that impact upon observational studies. However, gender and ethnicity will be accounted for in our analyses. Data on confounders has been requested to check for pleiotropy.

Aims

Genome Wide Complex Trait Analysis (GCTA) has been applied to Genome Wide Association Study (GWAS) data sets of cognitive ability to show that around half of the phenotypic variation is tagged by common variants (Davies et al., 2011). However, GCTA cannot provide information regarding which variants are more important. This study aims to find regions in the genome that make a significant contribution to the GCTA heritability estimate. Three groups of single nucleotide polymorphisms (SNPs) will be formed; the first group will consist of all SNPs available for use, the second set will consist of SNPs found in genes, whereas the third will be formed from SNPs that are found between genes. GCTA will be perfomed on each group allowing for both the genic and intergenic SNP-sets to be tested against 0 in order to determine if they each make a significant contribution toward intelligence differences. In addition the two groups will be compared against each other to show which regions matter most to human intelligence differences. Using GCTA in this way will help to show that variants within genes account for a significant proportion of the heritability of human intelligence. This will justify the use of gene and gene-set based analyses well as indicate which areas of the genome are of particular importance to cognitive ability.

Hypothesis

It is expected that for general cognitive ability both the geneic and the intergenic SNPs will make significant contributions to the total heritiability estimate. It is also expected that SNPs found within genes will also make a greater contribution toward the heritability estimates than SNPs found between genes as has been demonstrated for height (Yang et al., 2011) and indicated for schizophrenia (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014).

Exposure variables

GCTA has been performed previously on general cognitive ability in The Avon Longitudinal Study of Parents and Children (ALSPAC) group (Benyamin et al., 2013) as it has a suitably large sample size (5517 at age 9) and an execellent test to derive an Intelligence Quotent (IQ) estimate, namely the Wecshler Intelligence Scale for Children-III (WISC-III). For the proposed analyses to take place, the genotype data along with Verbal (Information, similarities, arithmetic, vocabulary, and comprehenssion) and Performance (picture completion, coding, picture arrangement. Block design, and object assembly) subscales of the WISC-III will be used to form an IQ score. GCTA analysis will be performed on this IQ score using all SNPs available for analysis. Next, the total set of SNPs will be divided into genic and intergenic SNP-sets before GCTA is performed on both SNP-sets.

Outcome variables

The proportion of phenotypic varainace attributable to the genetic variation in each of the three groups will form the outcome variable.

Confounding variables

One potential source of confounding would be that the two SNP-sets could tag the same variants due to the existence of linkage disequilibrium.

There is substantial evidence that early environmental insults increase risk for psychiatric disorders,

especially those with strong neurodevelopmental origins. Thus, there is a need to reliably identify these

environmental factors, as well as whether higher exposure level during critical periods for

neurodevelopment are related to greater risk for psychiatric disorders. Moreover, while environmental

toxins have been reliably associated with early neurodevelopment, similar investigations into longer term

and psychiatric outcomes are scarce.

Current approaches use indirect measures of fetal exposure, such as chemical concentrations in maternal

blood and urine, which may not accurately reflect fetal uptake for many toxins due to variable partitioning

across the placenta (Smith et al., 2007). Thus, a direct marker of early exposure is needed to aid our

understanding of the etiology of psychiatric disorders, as well as to aid prevention.

The current study proposes to use a unique and well-characterized birth cohort: the Avon Longitudinal

Study of Parents and Children (ALSPAC) and applying an extensively developed and piloted method to

determine multi-toxin early exposure (Arora et al., 2006, 2011; 2012; 2013; Hare et al., 2011; Austin et

al., 2013). Human deciduous teeth undergo systematic mineralization commencing prenatally and are

composed of a collagen-based, lipid-containing, calcium-rich apatite matrix, which accumulates bone

seeking elements and organic compounds. The methodology combines detailed histological analysis and

associated analytical tools to establish a comprehensive record of weekly to monthly exposure history of

multiple chemicals and metals and history of infections from the second trimester through early

childhood.

Aims

The overall goal of this project is to determine the association of early life environmental metal and

chemical toxin and infection exposures with multiple psychiatric and neurological outcomes: psychotic

like experiences, depression, mild cognitive impairment and autstic-like traits. A detailed history of

exposure during prenatal and early childhood development will be established to distinguish between two

important aspects of the exposure profile:

1) Exposure timing: We will construct weekly to monthly exposure history over the prenatal and early

childhood periods in order to identify the developmental period(s) most strongly associated with risk for

poor psychiatric and neurological outcomes.

2) Exposure intensity: Cumulative exposure may be more important than exposure during any single

developmental window and our biomarker allows measurement of cumulative exposure from the prenatal

period to the time of tooth shedding, which occurs between ages 6 to 12 years.

This project is IEU research, linked with Laura Howe's MRC fellowship and the IEU stats theme. Analysis will be carried out by Sam Brilleman with supervision from Laura Howe and Kate Tilling.

Aim: to evaluate the use of Bayesian individual knot point models for modelling growth across childhood and adolescence and estimating age of puberty onset, and to compare these models with alternative approaches such as SITAR

Exposure: age

Outcome: height and weight

Confounding variables: gender

Project justification: Self-reported measures of pubertal status can be inaccurate and there is often lots of missing data due to participant embarrassment. Height-based indices of pubertal development offer an attractive alternative that may be both more accurate and result in less missing data. In this project, we will use all available height and weight data to estimate trajectories of growth across childhood and adolescence, using models that allow person-specific ages at which the rate of growth changes. The idea is that the timing of these knowpoints may be informative about pubertal growth spurts. The models will be compared with other approaches to identifying pubertal growth spurts from growth trajectories, e.g. superimposition by translation and rotation (SITAR), a method developed by Tim Cole.

The UK10K project represents one of the first large scale applications of next generation sequencing to population based epidemiological samples and the examination of complex phenotypes. The objectives of this work are to record whole genome sequence variation at and below 1% minor allele frequency, to provide an imputation reference and to use this, not only to provide a resource for the scientific community (both genotypes and phenotypes), but also to examine genetic associations across a spectrum of genetic variation.

The study is composed of two samples drawn from European population based epidemiological studies (The Avon Longitudinal Study of Parents and children and Twins UK) and forms a collection of nearly 4000 participants now with whole genome sequence data and phenotypes (along with complementary imputed internal replication data sets). A total of 1,990 individuals from TwinsUK and 2,040 individuals from ALSPAC were consented for sequencing. Variant sites and genotype likelihoods were called using samtools and genotypes were refined and phased using BEAGLE, with similar procedures to the 1000 Genomes Project.

In addition, both ALSPAC and TwinsUK consented to release phenotype data related to cardiovascular disease as a public resource for the association analyses. For TwinsUK, liver phenotypes including bilirubin, alkaline phosphatase, GGT and albumin were released. The association analyses for the liver phenotypes are currently reaching a critical phase for the singlepoint associations, and also for those more rare variants with analyses being undertaken using variant aggregation techniques such as SKAT. Consequently, we are following up possible avenues for increasing our sample sizes, in particular data sets with existing whole genome sequence data such as ALSPAC. To this end, we are writing to seek a collaborative arrangement efforts to continue our analysis of both single point and rare variant associations. These phenotypes would not be part of the UK10K public release.

The analyses to be undertaken for this project at simple and require only a number of basic procedures. These will all be run by local ALSPAC analysts (Timpson or Stergiakouli) and only results provided for the work of the consortium. Analyses follow:

(i) GWAS for BMI (see attached protocol (based on the 1000g analysis, though this is the same for the HAPMAP imputation based analyses) for this procedure)

(ii) Sensitivity analyses (slight alterations to models run for GWAS, but no substantive changes)

(iii) For top SNP variants, expression data available on ALSPAC LCLs will be assessed in efforts to chart cis eQTLs relevant to implicated genetic signals.