Recently, a genomewide association study was undertaken within the Northern Finnish Birth Cohort ('66). This study was designed around the analysis of ~4-5000 individuals for both early phenotypes and those at a later recorded age (31 yrs). Within the cohort, there are extensive phenotypes available and as such there have been a sereis of analyses undertaken in light of the availability of genomewide data a feature only available in the last few weeks. One area of considerable interest has been with respect to the early phenotypes available for this cohort and as such, analyses on birth weight, birth length, ponderal index and gestational age have been undertaken. Of these, that concerning gestational age has yielded preliminary results of great interest (see appendix) and such that warrant immediate replication before undertaking very large-scale follow-up analyses.

The forthcoming availability of genomewide data for the a selection of the ALSPAC cohort (data which may be interrgated on an in silico basis for the initial stage of this project) majes it possible to seek initial and immediate replication of this signal. We propose initally to take the top signals for gestational age and to recover genotypes for these from the genomewide data (currently being finalised at the Sanger Institute, Cambridge). As suggested by one of the key organisers of this project (Panos Deloukas), this is a plausible exercise and the expediated delivery os select genotypes may allow for the checking of initial findings in this ALSPAC sub set.

Following this, we propose (on the basis of inference made from both the Norther Finnish data and that of the ALSPAC sub set) to take promising signals forward to replication in a sample comprised of (within ALSPAC as one of the sample bases) in both all the children and mothers. We feel that this will allow us then to formulate a roubust argument as to the possible contribution of these data to the literature surrounding the genetic predisposition to pre-term delivery (an field ALSPAC already recognises as important and contributes to directly through alternative collaborations).

This second stage of analysis would be dependent on the initial, rapid, exploration of the Finnish signal and would be derived from funding form the Oxford group.

Overall we wish to (i) seek an initial insight into the top Finnish associated signals in an expediated set of ALSPAC genomewide data (such that can be analyse immediately by Dr N Timpson) and (ii) seek permission to genotye promising signals from this stage (of which there appear naively to be 4-5) withiin the whole ALSPAC cohort.

The goal of our project is to assess the relationship between maternal nutrition in pregnancy and hypospadias, including associations with key food sources of folic acid and phytoestrogens using data from a 2004 case control study among pregnant women in the UK. Other dietary factors, including the use of different vitamin/mineral supplements, and, as in the earlier ALSPAC study, the role of a vegetarian diet, will also be examined using these data. Since the study was designed for other purposes, the abbreviated food frequency questionnaire which was used consists mainly of food groups instead of specific food items (e.g. questions ask about intakes of fresh green vegetables, dairy products, soy foods). In order to estimate intakes of key nutrients such as dietary folate and selected phytoestogens from the items included in this brief questionnaire, we propose to use external information from the ALSPAC study, which was conducted in a similar population, with more detailed information on intakes of individual food items within these food groups. These data will allow us to identify and rank the food group items on the abbreviated questionnaire as sources of the nutrients of interest, at the population level. We will also use the more detailed external ALSPAC data to examine whether there appear to be meaningful differences in the contribution of food groups to nutrient intakes across different age, ethnic and/or education strata as a result of varying patterns of food item intakes within these food groups. Based on these data, we will determine whether it may be appropriate to develop estimates of nutrient intakes/rankings for particular population subgroups, rather than for the study sample as a whole. Finally, we will use these external ALSPAC data to estimate the proportion of dietary folate and phytoestrogens not covered by the food group items in our abbreviated questionnaire, which was not comprehensive, and to examine the variability in intakes of these omitted items.

Concept: Maternal dietary intakes (food items) during pregnancy

Specific measures: intakes of food items including items from the following food groups:

- Dairy products and eggs

- Soy food

- Beans and peas

- Fruit and vegetables

- Poultry and meat

- Fish and seafood

- Chocolate

- Bread and cereals

- Oil

- Nuts and seeds

Person: Mother

Source: Questionnaire

Time Point: Pregnancy

Other variables:

- Maternal age

- Maternal ethnicity (white, nonwhite)

- Household annual income

- Highest level of maternal education

- Maternal vegetarianism during pregnancy (no, never / yes, in the past / yes, I am now)

- Maternal consumption of organic foods (frequency)

- Maternal Body Mass Index

ABSTRACT

Obesity is major health problem in many developing countries. In the United States, approximately 17% of children and adolescents are overweight and one-third of adults are estimated to be obese (1). Body mass index (BMI) is thought to have a substantial genetic component with heritability estimates of 40 to 70% (2). However, genetic variants related to obesity have proved difficult to find and replicate. With the advent of genome-wide scans, new efforts to comprehensively search the genome have begun to identify common variants associated with susceptibility to obesity, such as the recently discovered common SNP in FTO (3). As part of the Genomic Investigation of Anthropometric Traits (GIANT) consortium, we have pooled the results from genome-wide scans in three cohorts to identify genetic variants associated with BMI in early adulthood (N~5,500). We have identified over 80 promising loci associated with BMI at age 20 with a p-value of 10-5 or smaller and are currently replicating 45 of the top SNPs in an additional 10,000 subjects from other cohorts. We propose to follow-up the loci that are replicated in the ALSPAC cohort to determine if the SNPs are also associated with BMI in childhood.

BACKGROUND

Although inherited factors are thought to contribute to obesity, results from candidate gene and genetic linkage studies of obesity have been largely inconsistent [reviewed in (4,5)]. However, recently, as the result of large genome-wide association studies, common variants in the FTO gene were found to be associated with susceptibility to obesity (3) and obesity-related traits (6). The finding of FTO has spurred renewed interested in discovering additional loci associated with obesity and obesity-related traits and spawned large collaborative efforts, such as the Genomic Investigation of Anthropometric Traits (GIANT) consortium, which is an effort to pool genome-wide scan results from multiple cohorts and identify loci associated with height and obesity.

PRELIMINARY DATA

As part of the GIANT consortium, we have pooled the results from three genome-wide scans (encompassing 2.5 million single nucleotide polymorphisms that were either directly genotyped or imputed) with information on BMI at age 18 or 20 to identify genetic variants associated with BMI in early adulthood (N~5,500). We have identified approximately 87 independent loci with a p-value of 10-5 or smaller for BMI in early adulthood. Some of these loci are located near or in potentially promising genes, and we are currently genotyping 45 of the top loci in an additional 10,000 subjects from other cohorts with information on BMI in early adulthood.

PROPOSED PROJECT

Assuming that some of the loci identified in our initial meta-analysis are replicated in the follow-up cohorts, we propose to genotype those same SNPs in the ALSPAC cohort to determine whether the loci are also associated with BMI in childhood. We estimate that only a few loci will be convincingly replicated in our follow-up cohorts; however, it is conceivable that up to 10 SNPs may warrant further study. We plan to genotype these SNPs in the same children (N~7,500) that were genotyped in the recently submitted manuscript by Loos et al. (7). Similar to the study by Loos et al., we plan to evaluate the association with BMI at ages 7-11 as well as fat and lean body mass as measured by whole-body dual energy X-ray absorptiometry (DEXA) scan at age 9.

DATA REQUESTED

Biological Samples: DNA (~10 ng of DNA per genotype) for children. We plan to genotype up to 10 SNPs depending on the results of our replication study.

Clinical measurements: Height, weight, and BMI at ages 7-11 years for children. Fat, lean body, and bone mass as measured by a whole-body dual energy X-ray absorptiometry (DEXA) scan in children at age 9.

Several studies published recently have suggested that intake of n-3 fatty acids may influence certain cognitive outcomes in certain populations. This was demonstrated in a group of children with develoment coordination disorder by Richardson & Montgomery (2005). However, whether or not these effects can be found in typically developing children remains an interesting hypothesis.

We propose undertaking a statistical analysis of Food Frequency Questionnaire (FFQ) data gathered from children at 7yrs of age and cognitive measures gathered at around the same age (or slightly older). The aim of this analysis is to investigate potential relationships between reported intake of fish (and therefore n-3 fatty acids) and performance on specific cognitive tasks. The specific cognitive measures we are interested in are listed in the table below.

We will also need data from previous parent questionnaires in order to control for 28 confounding variables identified in a previous study examining mother's intake of fish during pregnancy and childhood neurodevelopmental outcomes (Hibbeln, Davis, Steer, Emmett, Rogers, Williams & Golding, 2007).

We further propose to investigate the relationship between reported dietary intake of n-3 fatty acids and the fatty acid profile of the children's blood samples and whether or not the fatty acid profile of the blood samples correlates with performance on the cognitive measures. This will be subject to availability of the data within the time scale. We understand that the sample analysis is being undertaken in the labs of Dr Joseph Hibbeln at NIH in Washington DC and that the work has commenced and will be available during 2008. Therefore this work will be subject to availablity of the results within the timescale of this project.

In this study we propose to test the hypothesis that a SNP influencing smoking addiction will reduce the likelihood of giving up smoking just before or in pregnancy, and in turn affect fetal growth and gestational age. We would also like to test whether maternal genotype has longer term effects on childhood metabolic and growth outcomes, through "programming" to an adverse intra-uterine environment. Finally if, available , the effects of maternal genotype on paternal smoking behaviour and paternal genotype on paternal smoking will also be interesting to test.

Decode Genetics reported at a Keystone Meeting (Santa Fe, Genetics of complex traits) in March 2008 that a SNP, rs1051730, in the CHRNA3 (cholinergic receptor, nicotinic, alpha) gene is associated with the number of cigarettes smoked per day within smokers. The nimor allele frequency varies from 0.30 in light smokers (1-10 cigarettes per day) to 0.40 in people who smoke greater than 31 cigarettes per day. The odds ratio for being a heavy smoker compared to a light smoker (1.41) is greater than that for being a smoker at all (1.17) indicating the association is about addiction to nicotine rather than taking up smoking in the first place. The SNP is associated with lung cancer and peripheral artery disease in the direction expected, although with stronger effects than predicted from the simple SNP vs smoking quantity and smoking quantity vs disease associations.

Specific hypotheses:

1. Genotype predicts smoking behavior before during and after pregnancy.

2. Maternal genotype influences offspring fetal growth and gestational age.

3. Maternal genotype infleunces offspring growth and metabolism in childhood.

4. Offspring genotype influences fetal growth and gestational age (independently of maternal genotype - fetal genotype may alter in utero sensitivity to the toxic effects of nicotine).

5. If paternal DNA becomes available during the course of the project we would be keen to test fathers as well to test the hypothesis that the SNP alters expectant fathers' ability to give up smoking when their partners are pregnant. (Indeed without paternal DNA we could still test whether maternal genotype influenced paternal behaviour - although without paternal genotype this test will be less powerful)

* Specific ALSPAC phenotypes being considered:

To do this we would like to genotype (at Kbioscience) all ~20,000 ALSPAC samples. We will need the following phenotypes to test our hypotheses:

1. Full details of maternal smoking details before, during and after pregnancy,

2. Birth weight, length and head circumference. Gestational age as offspring primary outcomes

3. Growth measures in childhood (height, weight and BMI aged 7-11)

4. Covariates of birth weight to check if genotype is acting through them: maternal age, maternal BMI, smoking , parity, twin status to exclude non-singletons, ethnicity as genotype frequency may alter with ethnic origin and confound analyses.

Genotyping of one SNP will only use 5-10ng of DNA per sample based on the current Kbioscience techniques.

Background & Study Rationale

Although a physically active lifestyle has established health benefits, a large proportion of children and adolescents fail to meet the health related guidelines for physical activity recommendations (Corbin & Pangrazi, 2004; Riddoch, Mattocks, Deere et al, 2007). This public health guideline advises children and adolescents to achieve one hour of moderate intensity physical activity per day, continuous or intermittent throughout the day (Riddoch, Mattocks, Deere et al, 2007). Longitudinal studies of physical activity levels and health amongst children have shown that although physical activity levels steadily increase during childhood (5-11 yrs), there is a steep decline during adolescence, 12 yrs and onwards (Janz et al, 2000; Kimm et al 2000; Sallis, 2000), shown to track into adulthood (Deflandre et al, 2001). Childhood obesity is now recognized as a global epidemic (Tremblay & Willms, 2000), and the development of interventions to promote physical activity amongst children and adolescents has as a result, become critical (Rowe et al, 2007; van der Horst et al, 2006).

There are important environmental and situational factors that relate to children's physical activity (Harwood, 2002). There is however less evidence detailing the strength of association between these factors. Gaining a better understanding of what combined impact the type of physical activity, time of day it is performed (before, during and after school), and context (school or non school day) have upon physical activity levels of children, is critical.

Aim and research questions

The main aim of this study is to assess how active and inactive children (identified using accelerometer data) differ in the types of physical activities they participate in, and the time of day they do this, on both school days and non school days (as reported in the PDPAR questionnaire).

RQ1a. Do the physical activity patterns of children who are active differ to those who are inactive, on school and non school days?

RQ1b.How is this related to the type of activity, and time of day it is performed?

RQ2. Are there differences in the times of day that active and inactive children are physically active, and how do the patterns of physical activity compare?

RQ3. Are the gender, SES and weight of active and inactive children associated with the differing level of physical activity, and does this relate to the types of activities they do, and when they do them?

To address these questions, a secondary analysis will be conducted using data from the ALSPAC study, of children aged 13 years.

Methods

The data for physical activity levels will be taken from the ActiGraph accelerometer, worn over a 7-day period. Data for the time of physical activity participation and type if activity will be taken from the Previous Day Physical Activity Recall Questionnaire (PDPAR).

Questionnaire Data

Data will represent both school and non school days. Six separate times of day will be considered for a school day and four separate times for a non-school day (see below). There will be seven different categories of physical activity behaviour for both a school and non-school day (see below).

The specific times of the day that the type of physical activity was recorded, include;

School Day-

1. Getting up - Start of School

2. Starting School - Lunch

3. Lunch Break

4. Lunch - End of School

5. End of School - Teatime/Dinner

6. Teatime/Dinner - Going to Bed

Non-School Day-

1. Getting up - Breakfast

2. Breakfast - Lunch

3. Lunch - Tea/Dinner

4. Tea/Dinner - Going to Bed

The types of activity measured are;

1. Watching TV/playing computer game/reading/homework.

2. Work around the house

3. Play a game outside

4. Do any sport/games/PE

5. Do a job/activity

6. Travel to and from school

7. Walk/cycle anywhere

Two main physical activity variables will be used to define active/inactive children;

1. Total physical activity - total average accelerometer counts/mins over the full period of valid recording. The recommended total time spent in MVPA is greater than 60 mins per day.

2. Time spent in MVPA - the average minutes of moderate and vigorous physical activity per valid day. The lower threshold of moderate intensity activity considered as 3600 counts/min.

Pediatric obesity has increased at an alarming rate in the last 30 years. As a result pediatric cardiovascular, endocrine and pulmonary obesity related morbidities have also risen. However, other pediatric complications of obesity important to public health and quality of life, such as bone, joint and muscle pains, are understudied and their prevalence, incidence and etiology remain unclear. In adults there is abundant evidence that obesity is a risk factor in the occurrence and progression of knee pain and knee osteoarthritis (KOA). In children, despite anecdotal and clinical impression there is sparse evidence on the link between obesity and musculoskeletal pain. Once this is understood the association between childhood obesity and its consequences for osteoarthritis can be investigated.

We will characterize the relationship of obesity to lower extremity (LE) pain in adolescents with the following aims:

A. Specific Aims

Specific Aim 1: To estimate the prevalence of LE pain cross-sectionally in all children aged 11-13 and specifically, compare the prevalence of obese adolescents to non -obese adolescents aged 11-13 years.

Hypothesis: Obese adolescents will have LE pain that is double that of their non-obese counterparts in an adjusted analysis.

Specific Aim 2: To estimate, using a cohort study design (in a subgroup with no pain at baseline), the incidence of LE pain among obese compared to non-obese adolescents over a period of 2 years (11 to 13 years).

Hypothesis: In looking at the subgroup with no pain at baseline, the incidence of LE pain will be 2 fold greater in the obese adolescents compared to the non-obese adolescents over these two years.

Secondary Aims:

Our secondary aims will examine other important relationships within the cohort.

To estimate the correlation and trend in the incidence of knee pain among adolescents at increasing levels of obesity.

To explore the effect of lean mass and fat mass in obese adolescents who develop knee pain at year 2.

To understand the effect of activity levels on the association between BMI and LE pain in all subjects at year 2.

To assess the impact of knee pain and obesity on functionality and quality of life.

B. Background and Significance

Childhood Obesity and the Musculoskeletal System

The prevalence of childhood obesity, defined as body mass index (BMI: weight/height2) at or above the 95th percentile of a reference population, has more than tripled in the United States since the 1970's to over 15%. Obesity leads to morbidities in children and is a risk factor for adult morbidity and mortality. Medical problems range from compromised cardiovascular, endocrine and pulmonary health. However, other pediatric complications of obesity important to public health or quality of life, such as bone, joint and muscle pains, are understudied and their prevalence, nature, and consequences unclear.

Certain musculoskeletal disorders such as Slipped Capital Femoral Epiphyses and Blount's disease are clearly linked to excessive weight but the association of obesity on non-specific lower extremity pain is unknown. Pediatric musculoskeletal (MSK) pain constitutes the third leading category for office visits among adolescents and prevalence estimates range from 6-33%, with the leading cause being trauma, followed by mechanical / overuse syndromes. In healthy Spanish children 10% of adolescents presented to doctors offices with MSK pain.In all children knee pain, soft tissue pain and other joint pains represented 65% of all complaints with adolescents localizing pain to the lower limb and lower back. [deInnocencio 2004]

The literature in obese children is sparse however two recent studies found the lower extremities to be the most common site of MSK pain in overweight adolescents compared to controls. A small Brazilian non-population based study demonstrated that obese adolescents had a two fold increase in pain compared to the normal weight counterparts. (sa Pinto 2006)

As the key structural elements for joint health rapidly evolve in healthy adolescents it may be adversely affected by obesity, through unsustainable levels of mechanical loading, which in the short term, may lead to pain and functional disability and in the long term, may irreversibly alter the lower extremity joint milieu.

C. RESEARCH DESIGN AND METHODS

C.1. Choice of Study Design

Until now, existing prevalence data on musculoskeletal pain has a wide range, has not been examined in a large population and has not looked at the impact of obesity. Thus our cross-sectional study design will estimate the prevalence of lower extremity pain in adolescents. Additionally, a longitudinal design using prospective data to estimate incidence has the advantage of assessing directionality and establish causality in the association of obesity on lower extremity pain at baseline (11 yrs) and at follow up (13 years).

C.2. Data source and study subjects

We propose to study subjects from the Avon Longitudinal Study of Parents and Children (ALSPAC) which is a large prospective birth cohort study investigating the health and development of children.

This cohort study is ongoing and starting at the age of 7 had annual assessments performed. Currently 6000 still attend clinic visits and data is being collected on the following variables of interest: age, gender, race, socioeconomic status, parental reports of pain, function and quality of life, height/weight, DXA, medical history and activity.

Inclusion criteria:

The entire ALSPAC cohort of ages 11-13 years will be included for study.

Exclusion criteria:

Subjects with underlying orthopedic conditions, surgery related to the lower extremities, juvenile idiopathic arthritis, other inflammatory lower extremity disorders and those with congenital abnormalities of the lower extremities will be excluded.

C.3. Data to be used:

We propose to use the existing data from the ALSPAC study in all children with visits at age 11 and 13 that include the following variables of interest:

Lower extremity pain is assessed by 2 questions on a validated questionnaire answered by the child and caregiver, as "yes" or "no" format with a followup one speculating on the cause.

Functionality and quality of life(QOL) will be assessed by questionnaire and QOL measures.

Anthropometric data from annual clinic visits include height and weight data. From that the BMI (body mass index) will be calculated using the formula weight/height2 as well as age- and gender-specific standard deviation scores (z scores) for weight, height, and body mass index , using the International Obesity Task Force (IOTF) definitions.

With DXA data we will assess lean and fat mass. Actigraph data will quantify activity levels. Socio-demographic data on socioeconomic status, age, gender and race will be analyzed. Finally, underlying medical history data from the clinic chart will be used to identify subjects for exclusion

D. HUMAN SUBJECTS RESEARCH

D.1. Protection of Human Subjects

a. Risks to the subjects: This protocol incurs minimal risk to the subjects as it uses an existing database with completed data. There will be no interaction with the subjects and no additional data is to be collected on behalf of this protocol.

b. Sources of materials: The data have already been obtained and are being requested to be shared with this investigator under the ALSPAC collaboration agreement. All the variables of interest will be shared as de-identified data.

c. Potential risks and benefits: There are no risks or benefits to the subjects under study. The results of the study will not be shared with the subjects and will not benefit them individually, although results will benefit public health policy initiatives.

D.2. Importance of the knowledge to be gained: The results of this study will provide prevalence and incidence of pain of lower extremties in obese children and reveal the short and long term impact of obesity on the musculoskeletal system with ramifications for further studies.

BACKGROUND AND SIGNIFICANCE

Behavioural laterality (which encompasses hand, foot and eye preference and relative skill) is one of the earliest developing and oldest behavioural traits. Hand preference is first demonstrated at between 9-10 weeks gestational age as embryos begin to exhibit single arm movements [1]. From a neuropsychological perspective lateralization in the form of hand or foot preference remains the best behavioural predictor of language and emotional lateralization [2,3]. Heritability estimates from studies of twins and their non-twin siblings have found evidence of moderate genetic effects suggesting 26% of the variance in hand preference at the population level is due to additive genetic gene effects (95% Confidence Intervals: 14.8-29.9%) with the remaining 74% of variance (95%CI: 70.1-78.4%) due to unshared environmental effects [4,5]. Significant associations have been found with the Androgen receptor (Xq11-12) [6] and LRRTM1 (2p12) [7].

While laterality appears to develop prenatally exposure to adverse environments or pathogenic insults has been hypothesised to increase the prevalence of left handedness creating phenocopies [8]. Hemiparesis and gross skeletal injuries are obvious pathological causes of changes in laterality. However, more subtle neurological insults may also result in lasting changes in lateralization without deficits in other neuropsychological domains [9]. A wide range of pathogenic risk factors have been proposed, including but not limited to, maternal illness, anoxia, birth stress, low birth weight, small focused neurological injuries and ultra-sound exposure [8-16]. Unless analysis of laterality data controls for these confounds through the collection and incorporation of high quality peri-natal information it is likely that genetic effects may obscured.