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.

A. SPECIFIC AIMS

INTRODUCTION

The principal focus of this application is to obtain resources to collect detailed audiometric data on the

adolescents enrolled in the Avon Longitudinal Study of Parents and Children (ALSPAC). ALSPAC was

specifically designed to determine ways in which the individual's genotype combines with environmental

exposures and experiences to influence health and development (Golding 2001). Its strengths include use of a

total population sample unselected by disease status, and comprehensive data on children's physical, mental

and behavioral health, family and social circumstances and environmental features. In addition, a DNA bank

has been established on over 10,000 mothers and children with consent for undisclosed genetic analysis

(Pembrey 2004). ALSPAC is, therefore, uniquely positioned to explore genetic and environmental determinants

of common diseases and impairments.

Hearing loss is the commonest sensory deficit in developed countries(Davis 1989, Smith, Bale & White 2005.)

The cumulative prevalence of hearing loss in any population rises through the lifespan (Russ 2001). Hearing

loss and hearing function have been most closely studied at the extremes of life- in the neonatal period and in

old age. However, there have been very few population studies of hearing function in adolescence and even

fewer opportunities to examine the relationship between environmental factors such as otitis media, early life

risks and noise exposure and later hearing function. Similarly, very few studies have attempted genotypephenotype

correlations with respect to hearing function on a large population-based cohort, especially in

adolescence. To our knowledge, the detailed data we propose to collect on hearing in the ALSPAC 17-18 year

cohort would be the first opportunity to link detailed hearing phenotype data with genotype data on a

representative population-based sample of adolescents.

There is growing interest in the hearing function and abilities of adolescents. Widespread use of personal

listening devices such as iPods and MP3 players has heightened awareness of the potential vulnerability of

this population to the extremes of noise exposure from modern devices using current compression algorithms

(Fligor and Cox 2004). (reference - Output levels of commercially available portable compact disc players and

the potential risk to hearing.Fligor BJ, Cox LC Ear Hear. 2004 Dec;25(6):513-27)... Earler survey-based U.S.

data suggest that up to 3.4% 18-34 years olds self-report hearing problems ( NHIS 1990), while the US

National Health and Nutritional Examination Survey (NHANES) III documented 14.9% 6-19 year olds with

unilateral or bilateral losses greater than 16dBHL at low or high frequencies. (Niskar et al.1998). Consequently, we are

moving from a conceptualization of adolescence as a time when hearing is essentially intact, to one in which

considerable variations in hearing ability begin to emerge in the population. Hearing ability at any age will be

sensitive to the effects of multiple risk and protective factors, both genetic and environmental.

Aims and Objectives

*1. In the proposed study funded by the Waterloo Foundation we will (a) assay the maternal and infant DNA for the two SNPs that Caspi considered in regard to breast feeding and IQ (rs174575 and rs 1535). (b) take the statistical models used for the ALSPAC study which showed associations between maternal seafood intake and childhood cognitive and behavioural outcomes (see Lancet paper of 2007) and determine whether such relationships are conditional upon the maternal or infant genotypes; and (c) test the finding of Caspi et al in regard to a breast feeding effect on IQ only apparent if the child has a particular genotype.

Introduction and rationale

The British 1958 birth cohort1 is based on all persons born in Britain during one week in March in 1958. Participants have been followed throughout their lives and biomedical information has been collected at various time points. Biological samples were collected from the cohort during medical examinations between Sept 2002 and March 2004. Funding for creation of a blood derived DNA bank was provided by the MRC (strategic project grant G0000934). Funding for creation of lymphoblastoid cell lines, cell line derived DNA extraction, banking and distribution was funded by the Wellcome Trust (grant no 068545).

DNA and cell line banks were created in the ALSPAC Laboratory. Blood derived DNA is available from 8018 individuals and has been used successfully in several genotyping studies.

Cell lines from 2288 individuals were created at ECCAC, Porton and a further 5239 in the ALSPAC laboratory. DNA has been extracted from all of these samples and has been organised into geographically representative subgroups of samples specifically for use as control samples in case control studies. Samples have been distributed to over 20 collaborators and were used as control samples by the Wellcome Trust Case Control Consortium2.

Further funding was obtained in 2006 and extended in 2007 for management of the cell line and DNA banks and distribution of samples (grant number GR079996). This funding will end on 31st May 2008 and we are therefore applying for additional funds to continue these functions.

The ALSPAC laboratory

The ALSPAC Genetic Epidemiology Laboratory is part of the Department of Social Medicine, University of Bristol. The department has a strong track record in genetic epidemiology. The department currently has custodianship of samples for the Avon Longitudinal Study of Parents and Children (ALSPAC), Caerphilly, Speedwell, Boyd Orr, Christs Hospital, Barry-Caerphilly studies.

The ALSPAC laboratory is equipped to create and manage biological sample, DNA and cell line banks and currently actively manages sample collections for ALSPAC and the 1958 birth cohort. The ALSPAC laboratory team works closely with the Bristol Genetics Epidemiology Laboratory (BGEL www.bgel.genes.org.uk) which was established by Professor Ian Day in 2004. The group also has a bioinformatics core led by Dr Tom Gaunt.

Prof George Davey-Smith is director of ALSPAC and has recently been awarded an MRC centre CAiTE - Centre for Causal Analyses in Translational Epidemiology, which opened in September 2007. The centre has recruited 5 academics working in the genetic epidemiology field hence increasing the expertise in this area within the department.

The ALSPAC, BGEL and MRC Centre laboratories and sample stores will move to a new purposed designed facility in summer 2008.

The laboratory is equipped for high throughput cell line production and DNA processing. Two custom designed robotic cell maintenance systems are used for lymphoblastod cell line growth. Two DNA processing robots (Tecan Genesis Freedom 2000, and Beckman Biomek 2000) are used to quantitate DNA samples with picogreen, normalise the concentration, and prepare plates for distribution to genotyping centres. A Quadra 96SV is also available for production of 384 well plates.

The laboratory is licensed by the Human Tissue Authority for storage of human tissue for research purposes and has developed custom designed Laboratory Information Management System to log processes and track samples. Samples are stored in secure cryostores and freezer stores. These are alarmed and a member of staff is contactable automatically 24hrs a day in the event of a freezer failure to prevent loss of samples.

The laboratory is a member of the Public Population Project in Genomics (P3G) DNA quantification project that was set up in 2006 to establish internationally recognised standards in DNA quantification. DNA samples from the laboratory have been successfully genotyped in a number of different laboratories using a variety of methods including the Affymetrix 500K human mapping array and Illumina Human Hap550 genotyping Bead Chip high throughput systems.

Genotyping

The laboratory distributes DNA to collaborators for genotyping although for simple SNP genotyping for the ALSPAC study we have established links with K Biosciences (http://www.kbioscience.co.uk/). The company hold stocks of DNA from the ALSPAC cohort and collaborators are encouraged to use the company. This is cost effective in terms of DNA use and genotyping costs and has simplified quality control processes. We propose to set up a similar arrangement for investigators using 1958BC DNA.

Receipt and storage of genotyping data and quality control The Laboratory has experience of handling genetics data.All genotyping data generated from ALSPAC study samples is returned to the laboratory and incorporated into an Oracle database. Before inclusion various quality control checks are run on the data including ensuring data is in Hardy-Weinberg Equilibrium and comparisons of genotypes from duplicated samples specifically included in sample sets for quality control purposes. Where data do not appear to be of suitable quality the problems are discussed with the genotyping laboratory and appropriate steps taken to improve the results. Documentation regarding the genotyping method and outcome of quality control checks is stored with the data.

Proposal for 1958 Sample ManagementAdministrative duties for 1958BC Interim Oversight Committee

All proposals to use 1958BC DNA need to be approved by the 1958BC Interim Oversight Committee. Dr Ring represents the Bristol laboratory on the committee and Dr Wendy McArdle will continue to advise regarding the feasibility of providing samples to users and technical advice regarding proposals.

Completion of a Material Transfer Agreement (MTA) is necessary before samples can be released to users. We understand that Professor Paul Burton's group in Leicester will undertake the administration associated with MTAs but they will continue to be signed off by the University of Bristol.

Ethical Approval

An application to extend the current ethical approval to include generic approval for genetic analysis, similar to that in place for the ALSPAC study will be coordinated by the Bristol team in order to allow the transfer of genetics data to Bristol and distribution to collaborators as approved by the Interim Oversight Committee.

DNA Bank Management

DNA has been extracted from all blood samples and cell lines although the yield from some samples was lower than others. Our current funding has covered extraction of further cell line pellets from low yield samples but only from those cell lines originally prepared in Bristol. By June 2008 there will be at least 400mg of DNA available from all such cell line samples but stocks of some ECCAC prepared cell lines are significantly lower. The maximum amount of DNA distributed from any sample in the last 12 months is 20mg. Therefore no further extractions will be necessary from the samples established in Bristol in the next 2 years assuming requests continue at a similar rate. However further regrowth and extraction of some of the 2288 samples produced at ECCAC will be required. Stocks of these cell lines were transferred to Bristol recently to enable us to restock the DNA bank when necessary.

DNA Distribution

We will continue to distribute DNA to users who have been approved by the 1958 Interim Oversight Committee. Dr Wendy McArdle will continue to liaise with users regarding supply of samples and any technical issues.

DNA is supplied to collaborators either as standard issue plates (1micro-g per well at 50ng/ micro-l) or at bespoke concentrations and volumes for large genotyping projects. In the last 12 months we have supplied 21 standard issue plates and prepared 5 bespoke requests. We anticipate that the level of requests for DNA will increase in the next 24 month period as results from genome wide association studies become available. We will continue to maintain a stock of "standard issue" plates for immediate issue from stock to collaborators and where necessary prepare samples at bespoke concentrations and volumes for large genotyping projects. Receiving laboratories will continue to be asked to cover the cost of transport. Requests for "cherry picked" samples, ie provision of a subset of samples which need to be individually picked from stock plates rather than an aliquot of all samples in a given plate can be accommodated. However, we would need to ask the requestor to cover the costs of the additional staff time involved in sample preparation as we have done for such requests in the past.

In addition we will set up an arrangement for providing a single SNP genotyping service withK Biosciences (http://www.kbioscience.co.uk/) if required. A stock of 1958BC DNA will be sent to the company and orders for genotyping placed via the Bristol laboratory in order to ensure that all genotyping has been approved by the 1958BC Interim Oversight Committee. In order to test this set up and associated quality control monitoring we have asked for funds to run 5 SNP genotypes on all samples. Details of the SNPs selected will be submitted to the oversight committee for approval before genotyping.

Please note that for some requests we require information about previous genotyping or phenotypes in order to select specific samples or plates of samples. We do not have access to this information in Bristol and are currently advised which samples are required by Professor David Strachan. In order to continue to provide samples selected on the basis of phenotype or previous genotyping results we will need to be advised by someone who has in depth knowledge of all 1958 data. We understand that the Centre for Longitudinal Studies will provide this support since the Bristol team currently have no access to phenotypic data.

Receipt of Genetics Data

The Bristol group has considerable experience of handling genetics data. If required we would set up systems similar to those used for the ALSPAC study to handle genetics data for the 1958 cohort. Quality control checks will be carried out including comparison of results from control samples and verifying that results are in Hardy Weinberg Equilibrium. Any problems highlighted by the quality control checks will be discussed with the genotyping lab and genotyping repeated or excluded if necessary. Results will be stored on a genetics results database designed specifically for the 1958 cohort study but based on the design of systems currently used in the laboratory. Data would be released in agreed formats when necessary. If required summary data can be provided via a website. Several Bristol staff currently have the expertise to oversee the development of such quality control, database and web site management but such processes are time consuming therefore we would require funding for a full time researcher to develop and manage the system and a fulltime data preparation assistant to assist with data cleaning and distribution.

Cell Line Bank Management

The cell line bank is established and aliquots of all cell lines are stored in Bristol with backups stored at ECCAC, Porton Down. Samples will continue to be kept in a viable condition in secure cryovessals. Sample will be regrown to replenish DNA stocks as described above. No costs for regrowth and provision of cell lines for other purposes, eg expression studies, have been included in this proposal but such studies could be facilitated if extra funds were made available to cover the staff and consumable costs.

The costs of back up storage at ECCAC are covered until December 2010 from previous 1958 funding therefore no further contribution to cover ECCAC costs is required for the duration of this proposal.

Storage of Biosamples

If required the Bristol laboratories would be able to store and distribute other biological samples held by the 1958BC. Costs have been included to cover storage of samples; distribution costs would need to be assessed on a case by case basis.