BACKGROUND

The ABCC11 gene encodes the multidrug resistance protein 8 (MRP8) (Kruh et al., 2007). This protein is involved in transport of many small molecules in normal physiology, in a variety of cellular and biological contexts. There is strong evidence suggesting that genetic variation at ABCC11 has pleiotropic effects. In particular, a functional non-synonymous SNP (rs17822931), also known as 538G-A or G180R, determines human earwax type (Yoshiura et al., 2006) and axillary osmidrosis (Nakano et al., 2009;Martin et al., 2010) and is associated with apocrine colostrum secretion from the mammary gland (Miura et al., 2007). It has also been related to breast cancer risk, although this is more controversial [(Toyoda and Ishikawa, 2010) and references therein].

rs17822931 genotype AA determines dry earwax type, while the presence of at least one G allele (GA or GG) determines wet earwax type. There are marked differences in rs17822931 allele frequencies across ethnic groups (Yoshiura et al., 2006;Toyoda et al., 2009;Toyoda and Ishikawa, 2010). There is a higher frequency of the A allele in East Asians and therefore higher prevalence of dry earwax type. In contrast, the wet earwax type is more prevalent in European and African populations due to higher frequencies of the G allele.

There is a close histological and functional relationship between ceruminous and apocrine sweat glands. This relationship is believed to explain the connection between earwax and axillary odor (Martin et al., 2010). AA homozygous individuals for rs17822931 display little of the characteristic axillary odorants (Preti and Leyden, 2010;Martin et al., 2010). In contrast, a study of Japanese individuals showed that essentially all individuals with axillary osmidrosis were GG or AG for rs17822931 (Nakano et al., 2009). This indicates that the G allele is necessary and sufficient to cause axillary odor, whereas AA genotype effectively marks non-odorous individuals.

In a recent study (Rodriguez et al., 2013) we have shown for the first time that there is a strongly significant (P=3.7x10^-20) differential usage of deodorant according to rs17822931 genotype. This represents the first evidence of a behavioural effect associated with rs17822931, that is directly relevant to the pharmacogenetics of body odour (Brown, 2013).

Two remarkable findings in relation to the behaviour of axillary deodorant use were that nearly 80% of European genetically non-odorous still use deodorant, whereas one in 20 individuals genetically odorous did not use it. This opens the possibility of a more complex scenario to explain the genetic basis of this human behaviour. To explore this possibility, an analysis of association at the whole genome level would therefore be required. However, a genome-wide analysis of genetic factors associated with deodorant usage has not been performed to date.

In addition, the potential role of ABCC11 genetic variation on other traits has not been studied yet. An interesting field is audition. One of the causes of hearing loss, pain and dizziness is cerumen impactation (McCarter et al., 2007). Ear wax impacts on audition and therefore, the involvement of rs17822931 on ear wax determination makes ABCC11 a good candidate for audition variation among individuals. A more controversial role of earwax is its potential involvement on earache. A common idea among GPs is that earwax does not cause earache and that one of effects of ear infection is the melting of earwax. However, in a pilot study in ALSPAC (approved by the executive, B586) we have found a significant association between earache at age 6-30 months and rs17822931. Muc more detailed analyses are required to understand this novel association and with the great depth of phenotypes involved, this needs an RA for at least a few months, it is not "an afternoon's work.". Similarly, the known role of rs17822931 in apocrine glands opens the possibility that ABCC11 could exert another pleiotropic effect, also acting on breast size. An interplay between breast size, breastfeeding and body odour should also be analysed.

HYPOTHESIS

That the functional SNP rs17822931 in ABCC11 has pleiotropic effects on earache and hearing, additional to its known effects on earwax, deodorant use and colostrum secretion.

AIMS

1.- To perform a phenome scan of all earache and hearing related phenotypes in ALSPAC in relation to rs17822931

2.- To conduct Genome-wide Association Studies on all reported and candidate pleiotropic effects associated with rs17822931, with special reference to deodorant use, earache and hearing.

3.- To access to record linkage data in order to gain more information from GP records to be added to the existing variables already available in ALSPAC

REFERENCES

Brown S. The Pharmacogenetics of Body Odor: As Easy as ABCC? J Invest Dermatol 2013; 133: 1709-1711.

Kruh GD, Guo Y, Hopper-Borge E, Belinsky MG, Chen ZS. ABCC10, ABCC11, and ABCC12. Pflugers Arch 2007; 453: 675-684.

McCarter DF, Courtney AU, Pollart SM. Cerumen impaction. Am Fam Physician 2007; 75: 1523-1528.

Martin A, Saathoff M, Kuhn F, Max H, Terstegen L, Natsch A. A functional ABCC11 allele is essential in the biochemical formation of human axillary odor. J Invest Dermatol 2010; 130: 529-540.

Miura K, Yoshiura K, Miura S et al. A strong association between human earwax-type and apocrine colostrum secretion from the mammary gland. Hum Genet 2007; 121: 631-633.

Nakano M, Miwa N, Hirano A, Yoshiura K, Niikawa N. A strong association of axillary osmidrosis with the wet earwax type determined by genotyping of the ABCC11 gene. BMC Genet 2009; 10: 42.

Preti G, Leyden JJ. Genetic influences on human body odor: from genes to the axillae. J Invest Dermatol 2010; 130: 344-346.

Rodriguez S, Steer CD, Farrow A, Golding J, Day IN. Dependence of Deodorant Usage on ABCC11 Genotype: Scope for Personalized Genetics in Personal Hygiene. J Invest Dermatol 2013; 133: 1760-1767.

Toyoda Y, Ishikawa T. Pharmacogenomics of human ABC transporter ABCC11 (MRP8): potential risk of breast cancer and chemotherapy failure. Anticancer Agents Med Chem 2010; 10: 617-624.

Toyoda Y, Sakurai A, Mitani Y et al. Earwax, osmidrosis, and breast cancer: why does one SNP (538Ggreater than A) in the human ABC transporter ABCC11 gene determine earwax type?FASEB J 2009; 23: 2001-2013.

Yoshiura K, Kinoshita A, Ishida T et al.A SNP in the ABCC11 gene is the determinant of human earwax type. Nat Genet 2006; 38: 324-330.

Aim: Determine whether variations in the paternally expressed GNAS splice variant XL-alpha S affect normal fetal growth?

Background: The term pseudohypoparathyroidism (PHP) refers to several rare, yet related human disorders. PHP type Ia (PHP-Ia) is characterized by an abnormal regulation of calcium and phosphate homeostasis because of resistance toward parathyroid hormone (PTH) in the proximal renal tubules; affected patients furthermore have developmental abnormalities referred to as Albright Hereditary Osteodystrophy (AHO) and often resistance towards other hormones such as TSH and GHRH that mediate their actions through Gs-alpha-coupled receptors. PHP type Ib (PHP-Ib) is characterized by hypocalcemia and hyperphosphatemia due PTH-resistance, typically without evidence for AHO. PHP-Ia and PHP-Ib are both imprinting disorder that are caused by maternal mutations within or up-stream of the GNAS locus on chromosome 20q13.3. This complex locus encodes the stimulatory G protein (Gs-alpha; GNAS exons 1-13) as well as several splice variants thereof, including XL-alpha S (GNAS exons 2-13, plus an XL-specific first exon), the extra-large form of Gs-alpha that is expressed only from the paternal, non-methylated GNAS allele.

Hypothesis: Are variations in XL-alpha S associated with normal fetal growth?

Very recently, we discovered through a collaboration with colleagues in France that paternal GNAS mutations are associated with intrauterine growth retardation (IUGR) (Richard et al., JCEM, 2013). Interestingly, mutations in GNAS exon 1, i.e. the exon specific for Gs-alpha, do not seem to lead to severe IUGR. This suggests that XL-alpha S, which uses the XL-specific first exon, contributes primarily to growth retardation. Conversely, biallelic XL-alpha S expression, as observed in patients with patUPD20q, is associated with enhanced fetal and probably also post-natal growth.

Our observations, which are surprising, led us to conclude that normal XL-alpha S expression from the paternal GNAS allele is essential for the prevention of IUGR and that XL-alpha S expression from both parental alleles results in much enhanced growth rates.

To investigate these findings in rare human disorders further and to determine whether the paternal GNAS haplotype is associated with fetal growth, we now propose to re-analyze some of the recently published GWAS data (Horikoshi et al 2013), namely the data from ALSPAC mother-child pairs.

The investigative plan would be as follows:

1) Define the region of interest at the GNAS locus on chromosome 20q13.3 and select SNPs from the maternal and fetal GWAS data within this region.

2) Deduce the paternally inherited allele, where possible, across the GNAS locus for each of these SNPs.

2) Use these deduced paternal allele data to determine whether inheritance of paternal (but not maternal) alleles is associated with impaired growth.

If this provides further evidence for a role of XL-alpha S in fetal development, we would then seek replication of the associations in further studies, e.g. in the EGG Consortium. If the association holds up, we could search in IUGR patients for paternal mutations in exon XL or its regulatory regions, i.e. in the exon specific for XL-alpha S; the identification of such XL-specific mutations would obviously be very exciting, as not much is known about the role of XL in normal human biology. In fact, our recent birth weight data for patients with either paternally or maternally inherited GNAS mutations have provided the first hints regarding its role in humans.

Specific Aims:

The aims of this study are two-fold: 1. Identification of genes involved in nicotine dependence as measured by the FTND questionnaire; and 2 identification of genes involved in nicotine withdrawal as measured by the time to first cigarette (TFC), ascertained by the question "how soon do you smoke your first cigarette after you wake up in the morning".

Hypotheses:

If a trait is heritable, such as nicotine dependence, there must be gene(s) that influence the expression of the trait. Our hypothesis is, therefore, that since tobacco smoking and nicotine dependence have been demonstrated to be heritable, these traits must be influnced by some genes. Under this condition, if we collect a sufficient sample size, we should be able to detect some of those genes contributing to the traits by association analyses. To avoid selection bias, we plan to test all genes across the human genome.

Study Design:

We plan to use linear regression to analyze the association between genotypes and phenotypes. In the analyses, genotypes are treated as independent variables and pheotypes as dependent variables. Confounding variables, such as sex, age and population substructure will be used to exclude their effects. To maximize the coverage of the genome, genotype imputation will be performed to include all variants observed in the 1000 genomes project. The results from the ALSPAC sample will be combined with the results from other samples by meta-analysis.

Aim:

To detect genetic variants reliably associated with red blood cell membrane fatty acids (FAs).

Hypothesis:

We hypothesise that variants across the genome will be associated with FA levels during pregnancy.

Variables:

The exposure variable will be imputed maternal genotype, while antenatal FA levels will be the outcome.

Data on 40 different FAs were extracted from antenatal blood samples taken from ALSPAC mothers, these included saturated, omega-3, omega-6 and omega-9 FAs. Genome-wide association analyses will be performed on each of the FAs available in ALSPAC, using a univariable linear regression model, regressing FA level on maternal genotype.

Previous studies have found associations with omega-3 and omega-6 Fas and both the FADS gene cluster on Chromosome 11 and the ELOVL2 gene on Chromosome 6 (1, 2). We propose to investigate whether these findings are consistent among pregnant women, and whether any further associations are found with other FA families.

1. Lemaitre RN, Tanaka T, Tang W, Manichaikul A, Foy M, Kabagambe EK, et al. Genetic loci associated with plasma phospholipid n-3 fatty acids: a meta-analysis of genome-wide association studies from the CHARGE Consortium. PLoS genetics. 2011;7(7):e1002193. Epub 2011/08/11.

2. Tanaka T, Shen J, Abecasis GR, Kisialiou A, Ordovas JM, Guralnik JM, et al. Genome-wide association study of plasma polyunsaturated fatty acids in the InCHIANTI Study. PLoS genetics. 2009;5(1):e1000338. Epub 2009/01/17.

Dysmorphic facial features play a prominent role in the diagnosis of a variety of classical neurodevelopmental disorders. For example, Down syndrome1, Prader-Willi syndrome2 and Foetal Alcohol Syndrome3 are all associated with characteristic facial features that are used as part of the diagnostic criteria. In recent years, the development of 3D geometric morphometrics (the statistical analysis of face shape) allowed researchers to identify more subtle facial dysmorphologies4. Hennessy et al5 found subtle differences in the 3D facial shape of schizophrenia patients compared to controls, such as a lengthened lower mid-facial height in schizophrenia patients. Hennessy et al6 also found that bipolar patients exhibited several facial dysmorphologies relative to controls and schizophrenia patients. The development of the face and the brain is closely connected7. Facial dysmorphologies therefore provide insight into the developmental origins of neurodevelopmental disorders, since the developmental biology of facial features is better understood than that of the brain5.

Craniofacial morphology is highly heritable8-11. Yet, fairly little is known about the genetic variants that influence normal craniofacial development in the general population. 3D geometric morphometric techniques can be used to identify these genetic variants, which could provide a valuable resource for future studies on craniofacial development and a variety of genetically determined neurodevelopmental disorders. For example, Paternoster et al12 found a significant association between a variant of the PAX3 gene and nasion position (the point in the skull where the nasal and frontal bones unite) in the Avon Longitudinal Study of Parents and their Children (ALSPAC) data set. Mutations in the PAX3 gene has previously been associated with Waardenburg syndrome13.

Evolutionary approaches to the study of facial traits provide three general facial traits that could be useful in the identification of genetic variants associated with craniofacial development: symmetry, averageness and sexual dimorphism. Symmetry indicates an individual's ability to resist environmental and genetic stresses during development14-16. It follows that fluctuating asymmetry (a deviation from bilateral symmetry in normally bilaterally symmetrical traits) is associated with a variety of genetic disorders and some indicators of poor health during development16-20. For example, Hammond et al19 found significantly higher facial asymmetry in young boys with autism spectrum disorders, compared to age/sex/ethnicity matched controls.

Facial averageness is defined as the proximity of a face to the sex-specific average face for that population21. In other words, how closely the face resembles the majority of same sex faces in the population. Symons22 hypothesised that average features could be functionally optimal as a result of the stabilising effect of natural selection. Theoretically, averageness might also denote genetic heterozygosity23. Few studies have investigated the relationship between facial averageness, genetic disorders and more general health. Nevertheless, genetic diseases are often associated with multiple facial dysmorphologies1,2,5,6, which do make these faces fairly distinctive compared to the population mean. Rhodes et al24 also found a weak positive association between facial averageness and general health scores.

Sexual dimorphism is defined as the phenotypic difference between males and females of the same species. In other words, how masculine or feminine a person is. Male and female faces diverge at puberty due to the action of sex hormones16,25. The age of onset, rate and extent to which faces become sexually dimorphic differ between individuals and are partly determined by genes26. Masculinity in male faces is generally assumed to serve as an indicator of immunocompetence21,27,28, although recent work has called the relationship into question28-30. Femininity in female faces does not seem to be associated with immunocompetence31,32, but is significantly associated with late follicular oestrogen levels33. Abnormal sexual dimorphism is also a characteristic of certain genetic diseases, such as Klinefelter syndrome34.

The primary aim of this study is to identify common genetic variants, and ultimately putative genes, that are associated with facial symmetry, averageness and sexual dimorphism using Genome-Wide Association (GWA) methodologies. We specifically chose the ALSPAC dataset because it is, to our knowledge, the largest dataset with both facial images and GWA data. To accomplish this aim, the 3D facial images will be manually delineated by defining 38 feature points in the software program MorphAnalyser (developed by Dr Tiddeman, Aberystwyth University, UK); this method is identical to the method used in Coetzee et al35. The delineated 3D images will then be used to calculate indexes for symmetry, averageness and sexual dimorphism in MorphAnalyser. The ALSPAC team have cleaned and imputed a GWA study dataset consisting of 8365 individuals with genotype calls for ~2.5 million common variants spread across the genome. We will use this resource to conduct a 2 stage (discovery and replication) genome-wide association study. Initially the discovery phase will include analysing ~ 5000 individuals that have both genotypic data and facial images. Power analysis (PowerGwas/QT version 1.0)36 indicate a sample size of 5000 is adequate to provide 80% statistical power to detect single nucleotide polymorphisms (SNPs) that explain as little as 0.8% of the variance in facial traits. The association between each of the ~2.5 million SNPs (exposure variables) and facial symmetry, averageness and sexual dimorphism (outcome variables) will be independently tested in the ALSPAC cohort using linear additive regression. SNP associations that exceed the standard significance threshold for genome-wide significance (p less than 5 x 10 -8)37, will be identified and replicated independently in additional cohorts, making up the second replication phase for the GWA study.

To determine which genes (and pathways) are most likely associated with these facial traits we will conduct a range of post-hoc analyses including (a) assigning SNPs to genes, (b) epistasis modelling and (c) pathway analyses. Briefly, multiple genes are ascribed to each SNP and these genes are then prioritised using epistasis modelling and pathway analysis38, allowing us to further identify which biological processes regulate these facial traits. In addition, we will calculate heritability estimates for each of the three facial traits; do a GCTA analysis to estimate how much of the phenotypic variance in the facial traits are explained by common SNPs; test the relationship between admixture and the facial traits (especially averageness); test the relationship between genome-wide heterozygosity and the facial traits (especially symmetry and averageness) using the ~2000 ALSPAC individuals who have whole genome sequencing data; and test the association between previously imputed classical HLA alleles and these facial traits separately for males and females. All GWA analyses will be conducted at the University of Bristol.

The second aim is to determine the association between health measures (exposure variables) and facial symmetry, averageness and sexual dimorphism (outcome variables). The health measures will be divided into prenatal risk factors (e.g. parental age, presence of gestational diabetes) and childhood health measures (e.g. body mass index, blood pressure and self-reported health). All three facial traits are generally assumed to indicate good health27, but studies testing these assumptions have suffered from various methodological drawbacks, including small sample sizes (~N=40-200). The size and quality of the ALSPAC data set, especially the wide range of physiological measurements, provides us with the ideal opportunity to test the association between health indices and these three facial traits in male and female faces respectively.

The third aim is to determine whether SNPs associated with these facial traits are also associated with traits proposed to indicate overall condition, specifically increased height and body mass index (BMI; within the healthy BMI range). To do so we will test the relationship between allelic scores of SNPs for the three facial traits, height and BMI.

To accomplish these aims we kindly request access to the following data in the ALSPAC dataset

Concept

Specific measure

Person

Source

Time point

Prenatal risk

Maternal age

Mother

Q

8-42 wks gestation

Prenatal risk

Paternal age

Mother

Q

12 wks gestation

Prenatal risk

Height & weight

Mother

C

1st trimester

Prenatal risk

Gestational diabetes

Mother

C

1st-3rd trimester

Prenatal risk

Blood pressure

Mother

C

1st-3rd trimester

Prenatal risk

Smoking

Mother

Q

18 wks gestation

Prenatal risk

Alcohol use

Mother

Q

18 wks gestation

Prenatal risk

General health

Mother

Q

32 wks gestation

Childhood health

Hospital admittance

Child

Q

4 wks-69 mths

Childhood health

General health

Child

Q

6-91 mths

Childhood health

Specific health problems (e.g. coughing)

Child

Q

4 wks-91 mths

Childhood health

Height & weight

Child

C

4 mths-17 yrs

Childhood health

Fat and lean mass from DXA scan

Child

C

9-17 yrs

Childhood health

Lung function

Child

C

61 mths-17 yrs

Childhood health

Grip strength

Child

C

11 yrs

Childhood health

Blood pressure & pulse rate

Child

C

37 mths-17 yrs

Childhood health

Blood pressure after exercise

Child

C

9-17 yrs

Confounding

Pubertal development

Child

Q

175 mths (or PUB7)

Images

Facial scans

Child

C

15 yrs

Genetic

Genome wide association data

Child

C

-

questionnaire=Q; clinic=C; weeks=wks; months=mths; years=yrs

References

1. Farkas LG, Katic MJ, Forrest CR, et al (2001) J Craniofac Surg, 12, 373-379.

2. Holm VA, Cassidy SB, Butler MG, et al (1993) Pediatrics, 91, 398-402.

3. Fang S, McLaughlin J, Fang J, et al (2008) Orthod Craniofac Res, 11, 162-171.

4. Hammond P. (2007) Arch Dis Child, 92, 1120-1126.

5. Hennessy RJ, Lane A, Kinsella A, et al (2004) Schizophr Res, 67, 261-268.

6. Hennessy RJ, Baldwin PA, Browne DJ, et al (2010) Schizophr Res, 122, 63-71.

7. Diewert V, Lozanoff S & Choy V. (1993) J Cran Genet Dev Bio, 13, 193.

8. Kohn L. (1991) Annu Rev Anthropol, 20, 261-278.

9. Hunter WS, Balbach DR & Lamphiear DE. (1970) Am J Orthod, 58, 128-134.

10. Nakata M, Yu P-I, Davis B, et al (1973) Am J Orthod, 63, 471-480.

11. Johannsdottir B, Thorarinsson F, Thordarson A, et al (2005) Am J Orthod Dentofac, 127, 200-207.

12. Paternoster L, Zhurov AI, Toma AM, et al (2012) Am J Hum Genet, 90, 478-485.

13. Tassabehji M, Read AP, Newton VE, et al (1993) Nat Genet, 3, 26-30.

14. Mather K. (1953) Heredity, 7, 297-336.

15. Van Valen L. (1962) Evolution, 16, 125-142.

16. Thornhill R & Moller AP. (1997) Biol Rev, 72, 497-548.

17. Sforza C, Dellavia C, Tartaglia GM, et al (2005) Int J Oral Max Surg, 34, 480-486.

18. Markow TA & Wandler K. (1986) Psychiat Res, 19, 323-328.

19. Hammond P, Forster-Gibson C, Chudley AE, et al (2008) Mol Psychiatr 13, 614-623.

20. Livshits G & Kobyliansky E. (1991) Hum Biol, 63, 441-446.

21. Rhodes G. (2006) Annu Rev Psychol, 57, 199-226.

22. Symons D. (1979) The evolution of human sexuality, Oxford University Press.

23. Thornhill R & Gangestad SW. (1993) Hum Nat, 4, 237-269.

24. Rhodes G, Zebrowitz LA, Clark A, et al (2001) Evol Hum Behav, 22, 31-46.

25. Farkas LG & Munro, IR (1988) Anthropometric Proportions in Medicine, Thomas, p29-56.

26. Mustanski BS, Viken RJ, Kaprio J, et al (2004) Dev Psychol, 40, 1188-1198.

27. Thornhill R & Gangestad SW. (1999) Trends Cogn Sci, 3, 452-460.

28. Scott IML, Clark AP, Boothroyd LG, et al (2012) Behav Ecol, 24, 579-589.

29. Rantala MJ, Coetzee V, Moore FR, et al (2013) P Roy Soc Lond B Bio, 280, 1471-2954.

30. Lie HC, Rhodes G & Simmons LW (2008) Evolution, 62, 2473-2486.

31. Rhodes G, Chan J, Zebrowitz LA, et al (2003) P Roy Soc Lond B Bio, 270, S93-S95.

32. Thornhill R & Gangestad SW. (2006) Evol Hum Behav, 27, 131-144.

33. Law Smith MJ, Perrett DI, Jones BC, et al (2006) P Roy Soc Lond B Bio, 273, 135-140.

34. Kamischke A, Baumgardt A, Horst J, et al (2003) J Androl, 24, 41-48.

35. Coetzee V, Re DE, Perrett DI, et al (2011) Body Image, 8, 190-193.

36. Feng S, Wang S, Chen C-C, et al (2011) BMC Genet, 12, 12.

37. Corvin A, Craddock N & Sullivan PF. (2010) Psychol Med, 40, 1063-1077.

38. Cantor RM, Lange K & Sinsheimer JS. (2010) Am J Hum Genet, 86, 6-22.

Background:

There are now many powerful GWAS based on large sets of meta-analysed data from multiple studies and the number of GWAS in literature is rapidly increasing. These studies provide robust effect estimates for the associations of single nucleotide polymorphisms (SNPs) across the genome with phenotypes of interest. For example, associations for all SNPs across the genome in relation to anthropometric phenotypes are available from the GIANT (Genetic Investigation of ANthropometric Traits) consortium involving meta-analysed data on up to 250,000 individuals (1-3). The GIANT GWAS observed many loci predicting variation in BMI (32 loci) and fat distribution (29 loci) at genome-wide significance level.

However, many loci will exist beyond those established using the stringent genome-wide cut-off level that are in reality reliably related to the phenotype of interest. Data from the GIANT height GWAS (N=183,727 individuals) identified 180 loci associated with height at genome-wide significance levels, explaining 10% of the phenotypic variation in height. However, the authors further estimated, based on recently developed methods (4), that there were in fact 697 loci (95% CI: 483-1040) with effects greater or equal to those identified from the GWAS which would explain a total of 15.7% of the phenotypic variation in height. Preliminary data from the GIANT consortium supports the conclusion that many of the associations that were observed but did not reach genome-wide significance are in fact real associations (J. Hirschhorn, Personal Communication).

Furthermore, when all SNPs across the genome are fitted simultaneously and the variance explained by all the SNPs together is estimated, 45% of the phenotypic variance in height can be explained (5).Together with the fact that for SNPs explaining 0.01% variation there is 99% power to get the direction of the association correct in a sample size of 129,000 individuals, this suggests that robust GWAS with large sample sizes can reliably inform the development of genome-wide polygenic instruments.

The inclusion of a potentially vast number of 'risk-alleles' across the genome would therefore explain significantly more phenotypic variation than instruments based on individual genetic variants or several established genetic variants together. As such, genome-wide polygenic scores provide a platform from which to develop a novel application of the Mendelian Randomization approach using genome-wide instruments that would incorporate a much larger proportion of genetic information than has otherwise been explored previously, resulting in considerably greater power for detecting causal effects for novel phenotypes of interest.

Aims:

The aims of this project are to develop and apply an original genome-wide Mendelian Randomization approach in order to: i) identify of novel causal risk factors for adult CVD, and ii) examine their impact on childhood and adolescent CVD-related traits

Hypotheses:

We hypothesise that the application of genome-wide allele scores for indexing traits known to be related to CVD-related phenotypes, such as adiposity, lipids and inflammation, will accurately index CVD risk and that these scores can subsequently be extended to explore more novel predictors such as iron status, fatty acid profile and uric acid.

Exposure variables:

genome-wide SNP data and phenotypic data for established CVD-related phenotypes (BMI, lipids, inflammatory markers) and novel predictors in CVD (iron status makers, fatty acids, uric acid)

Outcome variables:

Blood pressure, flow-mediated dilation, intima-media thickness

Confounders:

Maternal eduation, paternal education, family income, parental occupational class, child IQ, maternal smoking

References:

1. Speliotes EK, Willer CJ, Berndt SI, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 2010; 42(11): 937-48

2. Lango Allen H, Estrada K, Lettre G, et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 2010; 467(7317): 832-8

3.Heid IM, Jackson AU, Randall JC, et al. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat Genet 2010; 42(11): 949-60

4. Park JH, Wacholder S, Gail MH, et al. Estimation of effect size distribution from genome-wide association studies and implications for future discoveries. Nat Genet 2010; 42(7): 570-5

5.Yang J, Benyamin B, McEvoy BP, et al. Common SNPs explain a large proportion of the heritability for human height. Nat Genet 2010; 42(7): 565-9

The aim of this study is to examine the precision of perinatal depression screening, including interpersonal sensitivity, for identifying mother-infant relationship and maternal mental health problems that continue into or emerge in the second year of life.

The study will answer the research question: how much of the variance in mother-infant relational quality is explained by interpersonal sensitivity (IPSM) and perinatal depression secreening (EPDS) scores measured during pregnancy?

We aim to develop a method to investigate potential pleiotropy in causal estimates derived using the Mendelian randomization approach. The ALSPAC data will be used as an illustrative example.

Exposure: height, Outcome: FVC lung function (unadjusted for height), instrumental variables: 20 genotypes listed below.

Aims:

1. To determine whether visual and cognitive phenotypes cluster together within a normal population cohort of children.

2. To determine the underlying genetic associations of visual and cognitive phenotypes on a genome-wide basis in a normal population cohort.

3. To extrapolate datasets from animal models of associated visual and cognitive disorders, e.g. genes expressed in interneuron populations both in the retina and CNS, and investigate these candidate genes (within the datasets obtained as above) for associations with visual and cognitive phenotypes in a normal population cohort (this mouse data is already available from my lab).

Hypotheses:

Eye development in different organisms produces dramatically different structures, like the compound eye of insects and the camera-like eye of vertebrates. Nevertheless, the molecular mechanisms underlying eye specification are highly conserved (1), and the study of eye development in animal models has proven to be highly informative of the regulatory events that control human eye formation. For example, Pax6 was identified as a 'master' regulator, at the top of the hierarchical network of transcription factors (TFs) involved in eye development, since loss-of-function mutations of the eyeless gene (the Pax6 Drosophila homologue) lead to an eyeless phenotype (2) and over-expression can direct the formation of histologically normal ectopic eyes in flies (2) and in some vertebrates. Furthermore, recent work has established that the evolutionary conservation of the visual system extends beyond eye specification (by genes like Pax6) to include the visual system circuitry that connects the eye to the brain (1). Pax6 mutations in mice cause the small eye phenotype, and human PAX6 mutations lead to eye malformations including aniridia and other anomalies, while homozygotes demonstrate malformations of the central nervous system (CNS)(3). More recent work has shown that heterozygote PAX6 mutations are also associated with previously unrecognised and subtle structural brain abnormalities and cognitive deficits in humans (4). Furthermore, distant regulatory enhancer sequences influence transcription of Pax6 in mice, and consequently lead to eye abnormalities without any associated mutations of the Pax6 gene itself (5). While Pax6 is the most well-studied gene influencing eye development, several others are known to regulate interneuron development in the retina and CNS in an increasing number of animal models. Based on these data, we would predict that children carrying genetic polymorphisms and/or mutations in genomic regions encompassing so-called retinal and CNS "interneuron genes" would manifest deficits in vision and/or cognition.