Maciej Tomaszewski

Bernhard M Kaess

Timothy A Barnes

J Fadi

Klaus Charchar

Dawn Stark

Kijoung Waterworth

Peter Song

Gerard Vollenweider

Vincent Waeber

Mooser

Klaus Stark

Fadi J Charchar

Dawn Waterworth

Kijoung Song

William Ys Wang

Peter Vollenweider

Gerard Waeber

Vincent Mooser

Ewa Zukowska-Szczechowska

Nilesh J Samani

Christian Hengstenberg

FGF21 signalling pathway and metabolic traits -genetic association analysis

Keywords: fibroblast growth factor 21, fibroblast growth factor receptor 2, cholesterol, single nucleotide polymorphism, association

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Introduction

Genetic epidemiology has made significant progress over the last few years. With the availability of genotyping platforms that can analyse hundreds of thousands of single nucleotide polymorphisms (SNPs) it has become feasible to perform high resolution scans over the whole human genome (genome-wide association studies -GWAS), thereby identifying genetic variants associated with phenotypes of interest. The major advantage of GWAS is that they do not require a priori assumptions about a potential role of alleles, genes and pathways in regulation of the examined trait. However, because of the scale of such analysis the problem of substantial correction for multiple testing arises and, therefore, representative cohorts and replication are needed to reduce the probability of false positive reports. Furthermore, despite the extremely large number of SNPs on the platforms currently available, many single loci are poorly covered on a majority of platforms and therefore a significant proportion of potentially relevant variants may not be directly genotyped. Thus, GWAS are a tool of choice to discover disease genes that have not been suspected before, but may have limited value in analysis of candidate genes or pathways with strong functional potential to affect the phenotype of interest. For this type of investigation, systematic exploration of all common allelic variants in genes that encode molecules within the selected pathway is the method of choice. We used this approach to examine associations of common genetic variation in the fibroblast growth factor 21 (FGF21) signalling pathway with metabolic disturbances in white European population.

The FGF21 pathway is a novel metabolic signalling cascade recently identified as a master hormonal regulator of glucose, lipids and overall energy balance 1 . Specifically, endogenous FGF21 was shown to facilitate insulin-independent uptake of glucose in adipocytes [START_REF] Kharitonenkov | FGF-21 as a novel metabolic regulator[END_REF] and to promote lipolysis 3 contributing to reduction of fat storage [START_REF] Kurosu | Tissue-specific expression of betaKlotho and fibroblast growth factor (FGF) receptor isoforms determines metabolic activity of FGF19 and FGF21[END_REF] .

Exogenous FGF21 administration in experimental models was demonstrated to lower both glucose and triglyceride plasma levels and promote weight loss 2 . These effects may be mediated via 3 types of fibroblast growth factor receptor (termed FGFR1, FGFR2 and FGFR3, respectively) [START_REF] Ogawa | BetaKlotho is required for metabolic activity of fibroblast growth factor 21[END_REF][START_REF] Suzuki | bet}Klotho is required for fibroblast growth factor (FGF) 21 signaling through FGF receptor (FGFR) 1c and FGFR3c[END_REF][START_REF] Kharitonenkov | FGF-21/FGF-21 receptor interaction and activation is determined by betaKlotho[END_REF] . Activation of these FGFRs by FGF21 is conditional upon co-expression of klotho beta (KLB) [START_REF] Ogawa | BetaKlotho is required for metabolic activity of fibroblast growth factor 21[END_REF][START_REF] Suzuki | bet}Klotho is required for fibroblast growth factor (FGF) 21 signaling through FGF receptor (FGFR) 1c and FGFR3c[END_REF][START_REF] Kharitonenkov | FGF-21/FGF-21 receptor interaction and activation is determined by betaKlotho[END_REF] , a transmembrane protein that has been discovered through its structural similarity to klotho (which mediates receptor binding of FGF23).

We have hypothesised that common polymorphisms in 5 genes of FGF21 signalling pathway may be associated with determinants of metabolic risk in subjects of European origin.

Methods

Subjects

Primary association analyses were carried out in the Silesian Hypertension Study (SHS) that has been previously described [START_REF] Tomaszewski | Fibroblast growth factor 1 gene and hypertension: from the quantitative trait locus to positional analysis[END_REF] . In brief, SHS is a collection of 207 Polish Caucasian families (629 individuals, 49.8% men) recruited through essentially hypertensive offspring. Each individual was phenotyped according to a standard protocol (collection of clinical information by coded questionnaire, weight, height and blood pressure measurements, basic anthropometry, biochemical phenotyping). Blood samples were taken under fasting conditions. Total cholesterol (TC), HDL-C and triglycerides (TGs) were determined using standard enzymatic methods on a Cobas Bio-Autoanalyzer and LDL-C was calculated using the Friedewald formula. None of the subjects was treated pharmacologically with lipid-lowering medication.

Replication analyses were carried out in the Swiss CoLaus cohort and the German Myocardial Infarction Family Study (GerMIFS).

CoLaus is a random sample of 6,188 unrelated individuals (2,937 men) from the general population of Lausanne, Switzerland, and has been previously described in detail [START_REF] Firmann | The CoLaus study: a population-based study to investigate the epidemiology and genetic determinants of cardiovascular risk factors and metabolic syndrome[END_REF] . In short, randomly selected Caucasian Lausanne inhabitants aged 35-75 years were invited to join the study. Eligible subjects were phenotyped in the outpatient clinic of the Centre Hospitailier Universitaire Vaudois (CHUV). Phenotyping included taking clinical information, anthropometry and blood pressure measurements. Blood samples were taken after overnight fasting. LDL-C was calculated using the Friedewald formula (in subjects with triglycerides < 4.6 mmol/l) and was adjusted for lipid-lowering treatment by adding 1.5 mmol/l to measured values of subjects who were managed pharmacologically for hyperlipidaemia. [START_REF] Tobin | Adjusting for treatment effects in studies of quantitative traits: antihypertensive therapy and systolic blood pressure[END_REF] .

GerMIFS was based on 1,261 families (3,030 patients; 60.6% men) of white European ancestry. The details of this study have been previously described [START_REF] Broeckel | A comprehensive linkage analysis for myocardial infarction and its related risk factors[END_REF] . In brief, families were recruited through index patients with coronary artery disease (CAD) at 15 cardiac rehabilitation centres distributed throughout Germany. All index patients had MI before 60 years of age. If at least one sibling presented with MI or severe CAD before 70 years of age, the entire nuclear family (index patient, available parents, and all affected and unaffected siblings) was contacted and invited to participate in the study. Blood was drawn from non-fasting individuals. TC, LDL-C, HDL-C and TGs were determined directly using standard enzymatic methods. For the purpose of replication study of LDL-C, we excluded all patients on lipid-lowering treatment at the time of recruitment, leaving 3,115 patients available for analysis. After excluding patients with missing phenotypes, we were able to perform association analyses with LDL-C in 3,030 patients (1,261 families, 1,835 men).

The local Ethics Committees approved the study protocols, and all participants gave written, informed consent. All protocols conform to the principles outlined in the Declaration of Helsinki.

SNP selection

Tagging SNPs in FGF21, FGFR1, FGFR2, FGFR3 and KLB were selected from the HapMap database to reach a gene coverage of >90% with r 2 >0.8 for all SNPs with a minor allele frequency of ≥0.1 in the CEU population. In addition, each locus was saturated with additional common potentially functional polymorphisms (CpG islands, DNAse I hypersensitive sites, microRNA target sites) [START_REF] Tomaszewski | Association studies in current cardiovascular genetics -functional variants, tags or both?[END_REF] . Altogether, 63 SNPs were selected for genotyping in SHS. Of these, after genotyping, quality filters and association testing, 5 SNPs with high probability of a true association in SHS were selected for further replication.

Genotyping

Genotyping in SHS was carried out using iPLEX TM assays on the Sequenom MassArray system (Sequenom Inc, San Diego, CA, USA). SNPs in CoLaus cohort were retrieved from a genome-wide scan conducted using the Affymetrix Genome-Wide Human SNP Array 5.0. Specifically SNP rs2608819 was directly genotyped and the other 4 SNPs were imputed using the Quicktest imputation software (the quality metrics were: rs7012413: 0.999, rs4733946: 0.824, rs2071616: 0.302, rs7670903: 0.914). Replication genotyping of rs2071616 in GerMIFS was performed using the TaqMan assays (Applied Biosystems, Foster City, CA, USA) on ABI PRISM 7900HT Sequence Detection System (Applied Biosystems).

Statistical and bioinformatic analysis

The following quality control filters were applied to SNPs before the statistical analyses: call rate ≥ 0.9, no deviation from Hardy-Weinberg equilibrium (HWE) in the parental generation (HWE p>0.01) and number of Mendelian errors <3.

The primary association analyses in SHS were performed using family-based association testing (FBAT version 2.0.2) [START_REF] Horvath | The family based association test method: strategies for studying general genotype--phenotype associations[END_REF] under the null hypothesis of no linkage and no association. Replication analyses in CoLaus were based on linear regression models constructed in SAS v.9.1 (SAS Institute Inc., Cary, North Carolina). In GerMIFS association analysis was performed using generalised estimating equation (GEE) regression with an exchangeable familial correlation structure, as implemented in the geepack library in R [START_REF] Yan | Yet another Package for Generalized Estimating Equations[END_REF] .

We used FBAT for our exploratory primary analysis in SHS because this tends to produce less false positive results than GEE [START_REF] Cupples | The Framingham Heart Study 100K SNP genome-wide association study resource: overview of 17 phenotype working group reports[END_REF] and performs well in samples consisted of nuclear families with available parents (such as SHS). Generally, FBAT and GEE use different approaches to handle family structure: FBAT is based on Mendelian laws in families and therefore requires a certain minimum information content of the pedigree, which depends on family structure, genotypes and phenotypes. The GerMIFS cohort had initially been recruited for microsatellite linkage analyses and consists to a large extent mainly of sib-pairs. Given that about 50% of the pedigrees would not contribute to the FBAT statistic, we did not use FBAT in this cohort. GEE regression, on the other hand, is based on classical population-based association and therefore is more susceptible to bias due to stratification and admixture effects. However, in the GerMIFS study, the vast majority of index cases has been included into GWA studies [START_REF] Samani | Genomewide association analysis of coronary artery disease[END_REF][START_REF] Erdmann | New susceptibility locus for coronary artery disease on chromosome 3q22.3[END_REF] , showing no signs of population substructure.

All association tests were performed assuming an additive model of inheritance. All models were adjusted for age, age² and gender (where applicable). All p-values are given for two-tailed tests.

To correct for multiple testing in the primary family-based analysis we calculated false discovery rates (FDR) q-values. FDR describe the expected number of false-positives among all positive results. Q-values were calculated using the R-based tool q-value (smoother algorithm, λ range 0.0 -0.9) [START_REF] Storey | Statistical significance for genomewide studies[END_REF] , including all 216 performed association tests on the 4 traits analysed in SHS. At the replication stage we used Bonferroni correction. We genotyped rs2071616 in the third independent cohort -the GerMIFS study and found that rs2071616 was associated with LDL-C amongst men only (p=0.017), but not in the entire cohort of subjects (p=0.32). However, both the direction of allelic association and the magnitude of minor allele effect of rs2071616 on LDL-C in GerMIFs men were strikingly similar to CoLaus subjects (β=0.094, SE=0.039).

Discussion

The most important finding from our analysis is a suggestive association between a genetic variant of FGFR2 (SNP rs2071616) and LDL-C in white European subjects.

This association was identified through a systematic screening of all common variants in 5 genes of the FGF21 signalling cascade in Polish families and was replicated in subjects from the general Swiss population and German men from families with clustering of CAD. To the best of our knowledge, no associations between FGFR2 and lipids have been reported by GWAS or candidate gene studies.

The selection of FGF21 signalling cascade as a novel candidate pathway of metabolic regulation is supported by multiple lines of evidence from experimental models. The central molecule of this pathway -FGF21 -promotes glucose uptake into adipocytes and improves glucose tolerance in diabetic rodents without inducing hypoglycemia 2 .

Furthermore, mice overexpressing FGF21 have lower triglyceride plasma levels and are less prone to diet-induced obesity [START_REF] Kharitonenkov | FGF-21 as a novel metabolic regulator[END_REF] . When administered to diabetic monkeys, FGF21 improves glucose tolerance (without inducing hypoglycemia), lowers LDL-C and elevates HDL-C [START_REF] Kharitonenkov | The metabolic state of diabetic monkeys is regulated by fibroblast growth factor-21[END_REF] . Knockdown of FGF21 in mice leads to rapid hepatic fat accumulation and prominent lipid disturbances [START_REF] Badman | Hepatic fibroblast growth factor 21 is regulated by PPARalpha and is a key mediator of hepatic lipid metabolism in ketotic states[END_REF] . Acting downstream in the PPARα signalling cascade, FGF21 regulates metabolic adaptation to the fasting state. Under a ketogenic diet or in the fasting state, FGF21 stimulates lipolysis in white adipose tissue and hepatic ketogenesis [START_REF] Inagaki | Endocrine regulation of the fasting response by PPARalpha-mediated induction of fibroblast growth factor 21[END_REF][START_REF] Kurosu | Tissue-specific expression of betaKlotho and fibroblast growth factor (FGF) receptor isoforms determines metabolic activity of FGF19 and FGF21[END_REF][START_REF] Badman | Hepatic fibroblast growth factor 21 is regulated by PPARalpha and is a key mediator of hepatic lipid metabolism in ketotic states[END_REF] . These metabolic effects of FGF21 become apparent upon activation of at least 3 types of FGF receptor (FGFR1, FGFR2 and FGFR3) along with transmembrane mediator protein KLB [START_REF] Ogawa | BetaKlotho is required for metabolic activity of fibroblast growth factor 21[END_REF][START_REF] Suzuki | bet}Klotho is required for fibroblast growth factor (FGF) 21 signaling through FGF receptor (FGFR) 1c and FGFR3c[END_REF][START_REF] Kharitonenkov | FGF-21/FGF-21 receptor interaction and activation is determined by betaKlotho[END_REF] . The further signalling cascade has been shown to involve PPARγ-coactivator protein 1α [START_REF] Potthoff | FGF21 induces PGC-1alpha and regulates carbohydrate and fatty acid metabolism during the adaptive starvation response[END_REF] . Interestingly, both the FGF21induced tyrosine phosphorylation activity of FGFR2 and circulating FGF21 levels are augmented by the PPARγ-ligand rosiglitazone [START_REF] Christodoulides | Circulating fibroblast growth factor 21 is induced by peroxisome proliferator-activated receptor agonists but not ketosis in man[END_REF][START_REF] Moyers | Molecular determinants of FGF-21 activity-synergy and cross-talk with PPARgamma signaling[END_REF] , suggesting a feedback mechanism in metabolic regulation.

Our most significant association signal maps to FGFR2 (Figure 1) -a molecule known as a critical regulator of cell proliferation. Multiple splice variants of FGFR2 are expressed in many tissues, including vasculature and liver 26 , and bind to different FGF ligands including FGF21. The role of FGFR2 in metabolism is primarily determined by tissue-specific co-expression of KLB 4 : FGF receptors bind to the N-terminus of FGF21, while KLB interacts with the C-terminus [START_REF] Yie | FGF21 N-and C-termini play different roles in receptor interaction and activation[END_REF] . Genetic variation within FGFR2 has been linked to a wide range of phenotypes -from congenital syndromes of complex craniofacial malformations such as Crouzon syndrom (Dysostosis craniofacialis), Apert syndrome (acrocephalosyndactyly) and craniosynosthosis to breast cancer and other malignancies such as gastric, lung, ovarian and endometrial cancer [START_REF] Katoh | Cancer genomics and genetics of FGFR2 (Review)[END_REF] . The FGFR2 syntenic region in rats is located within a quantitative trait locus (QTL) for hypertriglyceridemia, type 2 diabetes and body weight (www.ucsc.org), but, to the best of our knowledge, associations between FGFR2 mutations and LDL-C have not been reported before.

The rs2071616 polymorphism is situated in intron 6 (approximately 100 bp from the nearest exon-intron boundary) of FGFR2 (isoform 2 precursor). The polymorphism is located within a DNA segment that shows a high level of conservation in mammalian evolution (Figure 1, panels C-D). There are not any known functional polymorphisms that could account for this association through high (r 2 ≥0.8) LD with rs2071616 (Supplementary Table 3, Supplementary Figure 1). The only statistically similar SNP (rs2912762 -in complete LD with rs2071616 in HapMap CEU individuals) maps to another intronic segment of FGFR2 and does not appear to exhibit any obvious biological potential (Supplementary Table 3) but is conserved (although to a lesser extent than rs2071616) in mammals. Further studies will be needed to clarify whether rs2071616, its proxy (rs2912762) or in fact an unidentified functional polymorphism in LD with rs2071616, is an actual driver of the association.

We should acknowledge several limitations of the study -first, unavailability of plasma glucose levels in SHS did not permit us to include this metabolic phenotype in the primary association analysis. Second, the replication studies in CoLaus cohort were based on data generated using imputation algorithms and rs2071616 was at the bottom end of what has been accepted as a valid imputation [START_REF] De Bakker | Practical aspects of imputation-driven meta-analysis of genome-wide association studies[END_REF] . Third, our cohorts were recruited by different strategies: SHS for hypertension, GerMIFS for familial clustering of CAD, CoLaus from general population. Fourth, we replicated our main association finding (rs2071616 and LDL-C) in German men only and not in women. Whether gender-specific effects may account (at least to some extent) for these findings will require further investigations in other cohorts. Finally, we should also acknowledge the limitations of multiple testing. We analysed over 50 SNPs and 4 different phenotypes, resulting in over 200 tests in SHS. A conventional adjustment for multiple testing such as Bonferroni correction would have been overly conservative given that neither genotyped SNP nor examined phenotypes were completely independent. Indeed, many SNPs were in LD with each other and lipid fractions showed high levels of correlation.

Although calculation of false discovery rate (q-values) does not account for this LDdriven or phenotypic non-independence we used a non-conservative threshold of 50% (q-value<0.5) to identify suggestive associations. The quantile-quantile plot of -log pvalues from all 216 tests performed in SHS revealed deviation in the extreme tail of association statistics distribution from the null distribution indicating that the findings above the point of the deviation (mainly our suggestive associations) could indeed represent genuine results (Supplementary Figure 2).

Conclusions

In summary, the identified suggestive genetic association between FGFR2 and LDL-C illuminates a novel potential mechanism of cholesterol regulation. We also demonstrate that systematic pathway-oriented genetic analyses may contribute to gene discovery in the era of GWAS. Further replication and functional analyses of FGFR2 in a metabolic context are warranted to validate the association identified through genetic screening of the relevant signalling pathway.

  Haplotypes and linkage disequilibrium (LD) in FGFR2 were constructed using the Haploview program 19 and visualized using the Locusview software. LocusView is a Java-based application that visualises chromosomal regions annotated with genomic features and experimental data (such as results from association analysis (http://www.broadinstitute.org/science/programs/medical-and-populationgenetics/locusview20). The software was developed by Petryshen, Kirby, and Ainscow and is freely available.The graphical representation of FGF21 signalling cascade (Figure1, panel B) was constructed using the GeneGo software (www.genego.com)ebi.ac.uk/Tools/clustalw2/index.html). Information on functional potential of SNPs was obtained in silico from SNP Function Annotation Portal http://brainarray.mbni.med.umich.edu/Brainarray/Database/SearchSNP/snpfunc.aspx. 0.05/5). The direction of the allelic effect on LDL-C was consistent with the findings from SHS -in both cohorts the minor allele was associated with higher LDL-C. The quantitative analysis in CoLaus showed that each minor allele copy of rs2071616 increased LDL-C by approximately 0.092 mmol/l (β= 0.092, SE=0.004).

Figure 1 :

 1 Figure 1: Fibroblast growth factor receptor 2 gene (FGFR2) -association and

Table 1 :

 1 General clinical characteristics of the study cohorts

	Phenotype	SHS	CoLaus	GerMIFS
	No. of subjects	629	5,478	3,030
	No. of families	207	0	1,261
	No. of men (%)	313 (49.8)	2,612 (47.7)	1,835 (60.6)
	Age, years	45.8 ± 15.7	53.2 ± 10.8	59.1 ± 10.7
	BMI, kg/m 2	26.8 ± 4.7	25.8 ± 4.6	27.2 ± 3.9
	LDL-cholesterol, mmol/L	4.0 ± 1.10	3.47 ± 1.01	4.17 ± 1.14
	HDL-cholesterol, mmol/L	0.98 ± 0.29	1.63 ± 0.44	1.36 ± 0.38
	Triglycerides, mmol/L	1.76 ± 1.10	1.40 ± 1.21	2.19 ± 1.42 *
	Lipid-lowering medication
		0	472 (8.6)	excluded
	(%)
	Data are means ± standard deviations or counts and percentages.

SHS: Silesian Hypertension Study; CoLaus: population-based cohort of Lausanne; GerMIFS: German Myocardial Infarction Family Study. *: non-fasting.

Table 2 :

 2 Nominally significant associations between SNPs of FGF21 signalling pathway and metabolic phenotypes in primary age-, age²-and gender-adjusted familybased analysis (SHS).

							Risk	Z-	p-	q-
	Trait	Gene	SNP	Alleles MA MAF
							allele	value	value	value
		FGFR3 rs3135859 G/T	T	0.22	G	2.13 0.033 0.68
			rs1982738 A/G	T	0.21	C	2.04 0.041 0.68
			rs7674434 G/T	G 0.29	G	2.30 0.021 0.59
	BMI
		KLB	rs12152703 G/T	T	0.23	T	2.11 0.035 0.68
			rs17431867 C/T	T	0.18	T	1.98 0.048 0.68
			rs2608819 C/T	T	0.11	C	2.55 0.011 0.42
			rs7012413 C/T	T	0.30	T	3.21 0.0013 0.14
		FGFR1
			rs4733946 G/T	T	0.09	T	3.42 0.0006 0.13
			rs3135766 C/T	G 0.45	G	2.09 0.037 0.68
	LDL-C
		FGFR2	rs3135763 G/T	A 0.18	A	2.05 0.041 0.68
			rs2071616 C/T	T	0.35	T	2.78 0.0055 0.39
		KLB rs7670903 A/G	A 0.38	A	2.57 0.011 0.47
	HDL-C	KLB rs2608819 C/T	T	0.11	C	-2.24 0.025 0.68
	triglycerides FGFR2 rs3135761 C/T	A 0.23	G	2.39 0.026 0.68

SHS -Silesian Hypertension

Study, BMI -body mass index, LDL-C -LDLcholesterol, HDL-C -HDL-cholesterol, SNP -single nucleotide polymorphism, MA -

Table 3 :

 3 Replication of suggestive associations identified in Silesian HypertensionStudy in CoLaus cohort.

						Risk	β-		p-
	Trait	Gene	SNP	MA MAF			SE
						allele	coefficient		value
			rs7012413	T	0.30	T	0.018	0.021	0.37
		FGFR1
			rs4733946	T	0.08	T	0.027	0.037	0.48
	LDL-C
		FGFR2	rs2071616	T	0.31	T	0.092	0.035	0.009
		KLB	rs7670309	A	0.42	G	0.024	0.019	0.21
	BMI	KLB	rs2608819	T	0.13	T	0.014	0.128	0.91

BMI -body mass index, LDL-C -LDL-cholesterol, SNP -single nucleotide polymorphism, MA -minor allele, MAF-minor allele frequency, β-coefficientestimated quantitative effect of each SNP minor allele copy on a metabolic phenotype (adjusted for age, age 2 and sex), SE -standard error

Acknowledgements

This project was supported by NIH Fogarty International Research Collaboration Award (R03 TW007165; to M.T.). BK's fellowship in Leicester was supported by the EU project Cardiogenics (LSHM-CT-2006-037593). T.B. was supported by a British Heart Foundation project grant (PG/06/097; to M.T.). N.J.S. holds a British Heart Foundation Chair of Cardiology. The CoLaus study was supported by research grants from GlaxoSmithKline and from the Faculty of Biology and Medicine of Lausanne, Switzerland, and is currently supported by the Swiss National Science Foundation (grant no: 33CSCO-122661). We are grateful to Dr Allan Beveridge for support in bioinformatic analysis.

Results

The brief clinical and anthropometric characteristics of the three study populations are listed in Table 1. Additional characteristics of the cohorts have been described elsewhere [START_REF] Firmann | The CoLaus study: a population-based study to investigate the epidemiology and genetic determinants of cardiovascular risk factors and metabolic syndrome[END_REF][START_REF] Broeckel | A comprehensive linkage analysis for myocardial infarction and its related risk factors[END_REF][START_REF] Tomaszewski | Essential hypertension and beta2adrenergic receptor gene: linkage and association analysis[END_REF] .

Primary association analysis -SHS

Of 63 selected and genotyped polymorphisms 9 SNPs were excluded before the association analysis due to poor genotyping quality. The mean call rate of the remaining 54 SNPs (Supplementary Table 2) was 97%. At r²=0.8, we reached a good tagging coverage of all HapMap SNPs in our candidate genes (100% for FGF21, 96% for FGFR1, 90% for FGFR2, 100% for FGFR3 and 78% for KLB). Under additive model of inheritance primary family-based analysis revealed 14 nominal genetic associations with metabolic phenotypes in SHS -6 with BMI (rs1982738, rs7674434, rs12152703, rs17431867, rs2608819, rs3135859), 6 with LDL-C (rs4733946, rs7012413, rs3135766, rs3135763, rs2071616, rs7670903), 1 with HDL-C (rs2608819) and 1 with TGs (rs3135761) (Table 2). Of those, only 5 (rs7012413, rs4733946, rs2071616, rs7670903 with LDL-C, rs2608819 with BMI) showed q-values <0.5 (suggestive associations) (Table 2). The details of this analysis are given in Supplementary Table 2. Replication 5 SNPs (rs7012413, rs4733946, rs2071616, rs7670903, rs2608819) with q-values< 0.5 were taken forward for in silico replication in the CoLaus cohort. Of those, FGFR2 rs2071616 polymorphism showed a significant association with LDL-C (p=0.009, Table 3), even after correcting for multiple testing (Bonferroni correction threshold: 0.01;

Disclosure

No conflicts of interest to disclose. minor allele, MAF -minor allele frequency in the parental generation of Silesian Hypertension Study, Z-value -family-based association test that illustrates the magnitude of the genetic association and the direction of the allelic association ("-" refers to the decrease in the quantity of the trait), p-value -level of statistical significance of family-based association test, q-value -false discovery rate.


Dr Paul Yannis

Robert A Pitsiladis

Mark E S Scott

Colin Neil Bailey

Richard H Moran

Noriyuki Wilson

Masashi Fuku

Athanasios Tanaka

Athanasios Z Tsiokanos

Eva Jamurtas

Grammatikaki

R A Scott

M E S Bailey

C N Moran

R H Wilson

N Fuku

M Tanaka

A Z Jamurtas

E Grammatikaki

G Moschonis

Y Manios

Y P Pitsiladis
email: y.pitsiladis@bio.gla.ac.uk

Pitsiladis

FTO genotype and adiposity in children: physical activity levels influence the effect of the risk genotype in adolescent males

Keywords:

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

INTRODUCTION

Childhood obesity is a leading risk factor for the metabolic syndrome and type 2 diabetes, as well as potentially for subsequent cardiovascular disease in adulthood [1][2][3] .

Obesity has a complex aetiology but is clearly influenced both by genetic variation and by environmental factors such as diet and lifestyle 4 . Physical activity, for example, is recognised as a prevention tool for obesity, is known to increase energy expenditure and also has the potential to regulate appetite 5 , although the relationship between physical activity and obesity is not clearly defined 6 .

Studies of the recently discovered fat mass and obesity associated (FTO) gene have provided the most compelling evidence to date of genetic variation in the general population that influences fat levels and risk of obesity 7;8 . A set of single nucleotide polymorphisms (SNPs) in the first intron of FTO are strongly associated with adiposity/obesity and related phenotypes [7][8][9][START_REF] Haworth | Increasing Heritability of BMI and Stronger Associations With the FTO Gene Over Childhood[END_REF][START_REF] Cornes | Replication of the association of common rs9939609 variant of FTO with increased BMI in an Australian adult twin population but no evidence for gene by environment (G x E) interaction[END_REF][START_REF] Do | Genetic variants of FTO influence adiposity, insulin sensitivity, leptin levels, and resting metabolic rate in the Quebec Family Study[END_REF][START_REF] Hubacek | The FTO Gene and Obesity in a Large Eastern European Population Sample: The HAPIEE Study[END_REF][START_REF] Hubacek | A common variant in the FTO gene is associated with body mass index in males and postmenopausal females but not in premenopausal females. Czech post-MONICA and 3PMFs studies[END_REF][START_REF] Scuteri | Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits[END_REF][START_REF] Chang | Common variation in the fat mass and obesity-associated (FTO) gene confers risk of obesity and modulates BMI in the Chinese population[END_REF][START_REF] Tan | FTO variants are associated with obesity in the Chinese and Malay populations in Singapore[END_REF] . It has been shown that adults homozygous for the risk allele at rs9939609 weigh on average ~3kg more than those homozygous for the other allele 7 . FTO genotype has also been reported to be associated with adiposity in children [7][8][9] , and the strength of its influence to increase during childhood [START_REF] Haworth | Increasing Heritability of BMI and Stronger Associations With the FTO Gene Over Childhood[END_REF] .

The function of FTO is currently unknown but is becoming clearer. It is widely expressed throughout the body and highly so in the arcuate nucleus, a region of the

RESULTS

ADOLESCENT COHORT

In a model-free ANCOVA analysis, FTO genotype was demonstrated to influence weight (P = 0.006) and BMI (P = 0.02) in the adolescent cohort (Table 1). A genetic model with dominant effects of the T-allele (T-dominant model) explained most of the genetic variance and all subsequent results were derived using this model (i.e. Tcarriers are compared to those homozygous for the G allele). Under the T-dominant model, FTO genotype influenced BMI (P = 0.007), weight (P = 0.001), triceps (P = 0.034) and subscapular skinfolds (P = 0.021), with GG individuals having higher adiposity than T-carriers for all phenotypes (Table 1). As can be seen in Table 1, those homozygous for the G-allele had measures of adiposity 0.18 -0.27 standard deviations higher than T-allele carriers. Interactions between FTO genotype, age, gender and PA on BMI were then investigated. Non-significant terms were removed from the model until the final model included the simplest significant three way interaction term (SNP*gender*PA: P = 0.028) and necessary sub-terms (Supplementary Table 1).

As can be seen in Figure 1, adolescent boys showed differential effects of genotype on BMI in the different physical activity groups, while, while girls did not. Given the significant SNP*gender*PA interactions, we performed gender-stratified analyses that showed that FTO genotype influenced BMI in males (P = 0.016in age-adjusted model not including SNP*PA interaction term) and that there was a significant SNP*PA interaction in males (P = 0.007) but there was no main effect of FTO genotype on BMI in females (P = 0.15), nor any SNP*PA interaction (P = 0.74).

Subsequent analyses in males, stratified by physical activity and adjusted for age, showed that inactive males homozygous for the G allele had higher mean BMI than inactive T-carriers (P = 0.0008), with FTO genotype explaining 10.1% of the variance in BMI amongst inactive males. These inactive male G homozygotes also had higher weight (P = 0.0009) and subscapular skinfolds (P = 0.03), while effects on triceps skinfolds tended in the same direction (P = 0.07). Inactive male G homozygotes had a mean BMI more than 3 units (3.25 kg/m 2 (95% C.I. 1.44 -5.06); P = 0.001, or 0.83 standard deviations) higher than inactive male T-carriers. However, when only active males were considered, FTO genotype had no effect on BMI (P = 0.52), weight (P = 0.32), triceps (P = 0.79) or subscapular skinfolds (P = 0.85). Age specific effects of FTO genotype are shown in Supplementary Table 2, where it can be seen that the direction of effect is consistent throughout the adolescent age range.

Looking at the question the other way round, we asked whether genotype influenced the relationship between physical activity level and the adiposity phenotypes.

Analysis of point biserial correlations between physical activity status and adiposity

phenotypes (Supplementary Table 3), showed weak but significant correlations in the whole cohort, whose magnitude was strengthened in GG homozygotes and weakened in T-carriers. For example, the correlation between physical activity level and BMI was -0.08 in the entire cohort, -0.06 in T-carriers, but increased in strength to -0.15 (with P = 0.042) in G allele homozygotes.

GENESIS COHORT (ages 1-5 years)

In the GENESIS cohort, we also tested the association between FTO genotype and energy intake, as well as measures of adiposity: BMI, weight, waist and hip circumference and bicep, tricep, subscapular and suprailiac skinfold, and birthweight.

As can be seen in Supplementary Table 4, none of these phenotypes showed any association with FTO genotype after adjustment for age and gender. We further tested for any age, gender or energy intake interactions to establish if any subgroup effects were being obscured. However, no significant interactions between FTO genotype and energy intake were observed on BMI (P > 0.1) using a backward stepwise approach. Furthermore, as can be seen in Supplementary Table 2, only the 3 year old children showed a significant effect of FTO genotype on age and sex-specific BMI zscores. While this was in a direction consistent with effects in the adolescent cohort, the other age groups showed no significant effects, and directions were inconsistent.

DISCUSSION

We report here that genotype at FTO SNP rs17817449 influences variation in obesityrelated measures in adolescents, and that T-allele carriers display lower levels of adiposity than G allele homozygotes. We also report an interaction between physical activity status, gender and FTO genotype, with the obesogenic influence of FTO largely confined to inactive males. In young children, in contrast, there was no association between FTO and adiposity phenotypes or energy intake.

In the adolescent cohort, weight and BMI, adjusted for age, were associated with FTO genotype (P < 0.01), while skinfold thicknesses showed genotypic differences with P values slightly higher than this (Table 1). This influence of genotype was strongest when analysed under a T-allele-dominant model. In terms of effect size, genotype alone explained 0.4-0.8% of the phenotypic variance for these traits. A significant interaction between FTO genotype and activity status was observed in males and the effect of genotype was stronger when analysed in inactive males, with up to 10% of variance explained (Table 1c). The proportion of females classed as inactive in the current study was greater than that of males (42% Vs. 20%). It has been shown

previously that adolescent females are less active than boys [START_REF] Brodersen | Trends in physical activity and sedentary behaviour in adolescence: ethnic and socioeconomic differences[END_REF] . However, if the subjective PA measurement method used in the present study is a less specific measure of inactivity in females than males, the inclusion of moderately active females in the inactive group may dilute any interaction, as it appears that the FTO effect is strongest in the least active individuals 17;26 . Nevertheless, the attenuation of the FTO effect by physical activity is in agreement with previous findings in adults 24- 26 . While some studies have failed to find such an effect 17;27 , a similar trend is in fact visible in the study of Tan et al. [START_REF] Tan | FTO variants are associated with obesity in the Chinese and Malay populations in Singapore[END_REF] . The equivocal findings may be attributable in part to the subjective measures of physical activity used in these studies, and further, large studies, using objective monitoring of PA are required. Inter-population differences in allele frequencies and patterns of linkage disequilibrium may also contribute to heterogeneity of findings. Previous studies have found that the influence of FTO on adiposity phenotypes is apparent from as young as 7 years old 7;32 and that the strength of the association increases into early adolescence [START_REF] Haworth | Increasing Heritability of BMI and Stronger Associations With the FTO Gene Over Childhood[END_REF] . In the GENESIS cohort we observed no association between FTO genotype and adiposity, which is in line with previous findings reporting no association of FTO genotype and childhood BMI in 2-5 year old children [START_REF] Zhao | The role of obesity-associated loci identified in genome-wide association studies in the determination of pediatric BMI[END_REF] , nor with energy intake (Supplementary Table 4). However, given the impact of physical activity on FTO associations in the adolescent cohort, if such interaction effects also operate in these younger age groups, the lack of reliable physical activity data in the GENESIS cohort may confound efforts to detect subgroup-specific genotype effects in the younger children.

Obesity phenotypes show a high degree of heritability in familial studies [START_REF] Haworth | Increasing Heritability of BMI and Stronger Associations With the FTO Gene Over Childhood[END_REF] . It has also been shown that having one or two overweight parents increases the risk of being overweight in childhood from a very young age 34;35 , further implicating genetic origins for a significant portion of the inter-individual variation in adiposity. However, evidence based on genetic association studies, particularly from genome-wide studies, does not account for a large proportion of this heritability. The FTO gene harbours variation that has the largest effect on adiposity phenotypes of any yet reported, accounting for around 1% of the total phenotypic variance in, for example, BMI in adults 7 . Here, we have shown that the influence of FTO may be small overall but it has a significantly greater influence in the physically inactive portion of the population, with FTO genotype explaining, after age adjustment, as much as 10% of phenotypic variance in inactive adolescent males (Table 1). Studies that do not consider physical activity levels may well underestimate the potential magnitude of the FTO effect on obesity in the most susceptible groups. Our results support the idea that certain genetic effects may be operating most strongly on groups in a particular environmental milieu and extend the finding that FTO acts most strongly in physically inactive subgroups to the pre-adult life stages. This supports the idea that some genetic influences may be missed in situations where unknown confounding environmental factors are operating to obscure real genetic effects acting on environmentally distinct subgroups.

As well as influencing risk of obesity, our results also suggest that FTO genotype modulates the relationship between adiposity and physical activity, since the genotype/activity interaction implies that, on one hand, genotypic effects are observed only in certain environments but also that, on the other, activity effects are different among genotype groups, Increasing physical activity should play a key role in attaining a negative energy balance by increasing energy expenditure, although the relationship between increasing physical activity and decreasing obesity is equivocal (reviewed by Wareham et al., 6 ). In the present study, we find that the weak relationship between physical activity and obesity is strengthened when examined only in those subjects homozygous for the risk allele (Supplementary Table 3). It must be acknowledged, however, that these findings arise from cross-sectional data with subjective physical activity measurement and with relatively modest sample size.

Whilst the direction of causality in this relationship is not evident from our crosssectional data [START_REF] Ekelund | Time spent being sedentary and weight gain in healthy adults: reverse or bidirectional causality?[END_REF] , the results do support the use of prospective studies with objective activity measurements to investigate the influence of activity on adiposity further. The influence of activity may have been obscured in previous studies of this type due to the inclusion of genetic 'non-responders'. Although limited evidence is available from lifestyle intervention studies to date 37;38 , future studies of the relationship between physical activity and obesity could consider a paradigm involving stratification by FTO genotype to reveal previously obscured environmental effects.

CONCLUSION

It has been suggested that moderate and controlled dietary restriction may prevent FTO-induced obesity 9 and previous studies on the genetic influence on obesity have highlighted the importance of genes regulating energy intake [START_REF] Willer | Six new loci associated with body mass index highlight a neuronal influence on body weight regulation[END_REF] . However, the finding that physical activity levels in adolescent males influence the obesogenic effects of FTO risk genotypes highlights that both sides of the energy balance equation: intake and expenditure, should be considered in combating obesity. In the present study, it was found that FTO genotype had no strong influence in physically active adolescents but did so in inactive males, with those homozygous for the risk, G allele having a mean BMI as much as 3 kg/m 2 higher than those who are physically active or Tcarriers. This is a promising advert for the benefit of a physically active lifestyle, particularly in those carrying FTO risk variants.

Figure 1. Effect of FTO genotype on BMI in adolescents and its interaction with Physical Activity and gender. Males showed a significant SNP*PA interaction (P interaction = 0.01 in model-free ANCOVA, as above, and P interaction = 0.0007 in Tdominant model). Females showed no significant SNP*PA interaction (P = 0.54 as above and P = 0.74 in T-dominant model). Data are presented as mean ± SEM (adjusted for age). N for each group as follows: Active males -GG: 145, GT: 189, TT: 67; Inactive males -GG: 29, GT: 43; TT: 26 Active females -GG: 96, GT: 118, TT: 44; Inactive females -GG: 61, GT: 91, TT: 39.

INTRODUCTION

Childhood obesity is a leading risk factor for the metabolic syndrome and type 2 diabetes, as well as potentially for subsequent cardiovascular disease in adulthood [1][2][3] .

Obesity has a complex aetiology but is clearly influenced both by genetic variation and by environmental factors such as diet and lifestyle 4 . Physical activity, for example, is recognised as a prevention tool for obesity, is known to increase energy expenditure and also has the potential to regulate appetite 5 , although the relationship between physical activity and obesity is not clearly defined 6 .

Studies of the recently discovered fat mass and obesity associated (FTO) gene have provided the most compelling evidence to date of genetic variation in the general population that influences fat levels and risk of obesity 7;8 . A set of single nucleotide polymorphisms (SNPs) in the first intron of FTO are strongly associated with adiposity/obesity and related phenotypes [7][8][9][START_REF] Haworth | Increasing Heritability of BMI and Stronger Associations With the FTO Gene Over Childhood[END_REF][START_REF] Cornes | Replication of the association of common rs9939609 variant of FTO with increased BMI in an Australian adult twin population but no evidence for gene by environment (G x E) interaction[END_REF][START_REF] Do | Genetic variants of FTO influence adiposity, insulin sensitivity, leptin levels, and resting metabolic rate in the Quebec Family Study[END_REF][START_REF] Hubacek | The FTO Gene and Obesity in a Large Eastern European Population Sample: The HAPIEE Study[END_REF][START_REF] Hubacek | A common variant in the FTO gene is associated with body mass index in males and postmenopausal females but not in premenopausal females. Czech post-MONICA and 3PMFs studies[END_REF][START_REF] Scuteri | Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits[END_REF][START_REF] Chang | Common variation in the fat mass and obesity-associated (FTO) gene confers risk of obesity and modulates BMI in the Chinese population[END_REF][START_REF] Tan | FTO variants are associated with obesity in the Chinese and Malay populations in Singapore[END_REF] . It has been shown that adults homozygous for the risk allele at rs9939609 weigh on average ~3kg more than those homozygous for the other allele 7 . FTO genotype has also been reported to be associated with adiposity in children [7][8][9] , and the strength of its influence to increase during childhood [START_REF] Haworth | Increasing Heritability of BMI and Stronger Associations With the FTO Gene Over Childhood[END_REF] .

The function of FTO is currently unknown but is becoming clearer. It is widely expressed throughout the body and highly so in the arcuate nucleus, a region of the hypothalamus involved in regulating energy balance [START_REF] Gerken | The obesity-associated FTO gene encodes a 2-oxoglutarate-dependent nucleic acid demethylase[END_REF] . Fischer et al. [START_REF] Fischer | Inactivation of the Fto gene protects from obesity[END_REF] reported recently that knocking out Fto in mice results in reduced adiposity via a mechanism apparently involving increased energy expenditure, sympathetic tone and levels of catecholamine hormones, which contrasts with studies to date in humans that implicate hyperphagia without altered energy expenditure 9;20-23;23 .

Despite having the largest influence on obesity-related phenotypes of any gene reported thus far, FTO genotype alone only accounts for a small proportion of the variance in these phenotypes. The full influence of FTO will only become clear through more thorough investigation of its interactions with environmental factors such as physical activity levels. Three studies have reported interactions between physical activity and FTO genotype in adults, with low activity levels being associated with enhanced genotype effects [START_REF] Rampersaud | Physical activity and the association of common FTO gene variants with body mass index and obesity[END_REF][START_REF] Andreasen | Low physical activity accentuates the effect of the FTO rs9939609 polymorphism on body fat accumulation[END_REF][START_REF] Vimaleswaran | Physical activity attenuates the body mass index-increasing influence of genetic variation in the FTO gene[END_REF] , while other studies have reported no interaction with physical activity 17;27 . However, to the best of our knowledge, no such studies have been conducted in pre-adults. We report here the influence of rs17817449 on adiposity in two cohorts of children, with ages ranging from one to eighteen years. In adolescent children we also investigated how physical activity levels modulate the FTO effects on adiposity-related phenotypes.

METHODS

SUBJECTS

Study participants were those of two previously published cohorts 28;29 . One cohort comprised subjects from the Growth, Exercise and Nutrition Epidemiological Study In preSchoolers (GENESIS) [START_REF] Manios | Design and descriptive results of the "Growth, Exercise and Nutrition Epidemiological Study In preSchoolers": the GENESIS study[END_REF] , which included 1980 healthy children aged 1-5 years from throughout Greece collected between April 2003 and July 2004. 52% were male.

The other cohort comprised 949 children aged 11-18 years, 53% of whom were male, collected from the Trikala region in central Greece between February 2000 and April 2001 [START_REF] Moran | Effects of interaction between angiotensin I-converting enzyme polymorphisms and lifestyle on adiposity in adolescent Greeks[END_REF] . Both studies were approved by the ethics committee of the University of Glasgow and by local authorities in Greece. Written informed consent was obtained from the parents of all participants prior to the study.

DATA COLLECTION

Data were collected as described below after a period of protocol standardization in both cohorts. In the GENESIS cohort, all measurements were made as previously described in detail [START_REF] Manios | Design and descriptive results of the "Growth, Exercise and Nutrition Epidemiological Study In preSchoolers": the GENESIS study[END_REF] . This included basic body composition measurements (weight, height, and waist, hip and mid-upper arm circumferences), as well as skinfolds at four sites (biceps, triceps, subscapular and suprailiac). Birthweight data for this cohort were also available from the child health booklet. Energy intake data were collected from all children for three days using a combination of weighed food records and 24 hr recall or food diaries, as previously described in detail [START_REF] Manios | Design and descriptive results of the "Growth, Exercise and Nutrition Epidemiological Study In preSchoolers": the GENESIS study[END_REF] and in the supplementary material. Physical activity (PA) data were not available in the GENESIS cohort. In the adolescent cohort, subjects were measured for height, weight, and triceps and subscapular skinfolds as previously described [START_REF] Moran | Effects of interaction between angiotensin I-converting enzyme polymorphisms and lifestyle on adiposity in adolescent Greeks[END_REF] and completed a questionnaire documenting their participation in physical activity both inside and outside of school.

Individuals reporting no additional physical activity outside of the compulsory weekly physical education class at school were classified as inactive (N = 289; 98 males and 191 females), while those reporting participation in further physical activity were classified as active (N = 660) [START_REF] Moran | Effects of interaction between angiotensin I-converting enzyme polymorphisms and lifestyle on adiposity in adolescent Greeks[END_REF] . Inactive individuals displayed higher subscapular skinfolds, after adjustment for age and gender, than those classified as active (P = 0.02) and displayed a trend towards higher BMI (P = 0.075) and triceps skinfold (P = 0.077) but not weight (P = 0.39). Dietary intake data were not available in the adolescent cohort. Genotyping was successful in 94% of subjects in the GENESIS cohort and in 96% of subjects in the adolescent cohort. G allele frequency at rs17817449 was 0.42 in the adolescent cohort and 0.43 in GENESIS. rs17817449 genotypes were in Hardy-Weinberg equilibrium (HWE) in both the adolescent (P = 0.17) and GENESIS cohorts (P = 0.14). rs17817449 is in complete linkage disequilibrium (in European populations) with the more commonly measured SNP rs9939609 (r 2 = 1).

STATISTICAL ANALYSIS

Groups were tested for normality using the Ryan-Joiner test, and each phenotype transformed (see Supplementary Material) so as to achieve as close to a normal distribution as possible across each cohort. After transformation, most phenotypes were normal, or approaching normality. Data were analyzed using R. We initially investigated the effect of FTO genotype on adiposity phenotypes by ANCOVA.

Hypothesizing that genotype effects might also differ in age or gender subgroups, we subsequently tested for interactions using a backward stepwise ANCOVA approach. No explicit multiple testing correction was carried out as we view FTO as having high prior likelihood of influencing all phenotypes investigated. In the adolescent cohort, point biserial correlations were calculated between physical activity status as a dichotomous variable (Active or Inactive) and adiposity phenotypes. For point biserial correlations, generally r lies between -0.8 and 0.8 [START_REF] Karabinus | The r-Point Biserial Limitation[END_REF] . It was used here to investigate the relationship between physical activity and adiposity in all subjects, then in risk and protective genotype groups.

RESULTS

ADOLESCENT COHORT

In a model-free ANCOVA analysis, FTO genotype was demonstrated to influence weight (P = 0.006) and BMI (P = 0.02) in the adolescent cohort (Table 1). A genetic model with dominant effects of the T-allele (T-dominant model) explained most of the genetic variance and all subsequent results were derived using this model (i.e. Tcarriers are compared to those homozygous for the G allele). Under the T-dominant model, FTO genotype influenced BMI (P = 0.007), weight (P = 0.001), triceps (P = 0.034) and subscapular skinfolds (P = 0.021), with GG individuals having higher adiposity than T-carriers for all phenotypes (Table 1). As can be seen in Table 1, those homozygous for the G-allele had measures of adiposity 0.18 -0.27 standard deviations higher than T-allele carriers. Interactions between FTO genotype, age, gender and PA on BMI were then investigated. Non-significant terms were removed from the model until the final model included the simplest significant three way interaction term (SNP*gender*PA: P = 0.028) and necessary sub-terms (Supplementary Table 1).

As can be seen in Figure 1, adolescent boys showed differential effects of genotype on BMI in the different physical activity groups, while, while girls did not. Given the significant SNP*gender*PA interactions, we performed gender-stratified analyses that showed that FTO genotype influenced BMI in males (P = 0.016 -in age-adjusted model not including SNP*PA interaction term) and that there was a significant SNP*PA interaction in males (P = 0.007) but there was no main effect of FTO genotype on BMI in females (P = 0.15), nor any SNP*PA interaction (P = 0.74).

Subsequent analyses in males, stratified by physical activity and adjusted for age, showed that inactive males homozygous for the G allele had higher mean BMI than inactive T-carriers (P = 0.0008), with FTO genotype explaining 10.1% of the variance in BMI amongst inactive males. These inactive male G homozygotes also had higher weight (P = 0.0009) and subscapular skinfolds (P = 0.03), while effects on triceps skinfolds tended in the same direction (P = 0.07). Inactive male G homozygotes had a mean BMI more than 3 units (3.25 kg/m 2 (95% C.I. 1.44 -5.06); P = 0.001, or 0.83 standard deviations) higher than inactive male T-carriers. However, when only active males were considered, FTO genotype had no effect on BMI (P = 0.52), weight (P = 0.32), triceps (P = 0.79) or subscapular skinfolds (P = 0.85). Age specific effects of FTO genotype are shown in Supplementary Table 2, where it can be seen that the direction of effect is consistent throughout the adolescent age range.

Looking at the question the other way round, we asked whether genotype influenced the relationship between physical activity level and the adiposity phenotypes.

Analysis of point biserial correlations between physical activity status and adiposity phenotypes (Supplementary Table 3), showed weak but significant correlations in the whole cohort, whose magnitude was strengthened in GG homozygotes and weakened in T-carriers. For example, the correlation between physical activity level and BMI was -0.08 in the entire cohort, -0.06 in T-carriers, but increased in strength to -0.15

(with P = 0.042) in G allele homozygotes.

GENESIS COHORT (ages 1-5 years)

In the GENESIS cohort, we also tested the association between FTO genotype and energy intake, as well as measures of adiposity: BMI, weight, waist and hip circumference and bicep, tricep, subscapular and suprailiac skinfold, and birthweight.

As can be seen in Supplementary Table 4, none of these phenotypes showed any association with FTO genotype after adjustment for age and gender. We further tested for any age, gender or energy intake interactions to establish if any subgroup effects were being obscured. However, no significant interactions between FTO genotype and energy intake were observed on BMI (P > 0.1) using a backward stepwise approach. Furthermore, as can be seen in Supplementary Table 2, only the 3 year old children showed a significant effect of FTO genotype on age and sex-specific BMI zscores. While this was in a direction consistent with effects in the adolescent cohort, the other age groups showed no significant effects, and directions were inconsistent.

DISCUSSION

We report here that genotype at FTO SNP rs17817449 influences variation in obesityrelated measures in adolescents, and that T-allele carriers display lower levels of adiposity than G allele homozygotes. We also report an interaction between physical activity status, gender and FTO genotype, with the obesogenic influence of FTO largely confined to inactive males. In young children, in contrast, there was no association between FTO and adiposity phenotypes or energy intake.

In the adolescent cohort, weight and BMI, adjusted for age, were associated with FTO genotype (P < 0.01), while skinfold thicknesses showed genotypic differences with P values slightly higher than this (Table 1). This influence of genotype was strongest when analysed under a T-allele-dominant model. In terms of effect size, genotype alone explained 0.4-0.8% of the phenotypic variance for these traits. A significant interaction between FTO genotype and activity status was observed in males and the effect of genotype was stronger when analysed in inactive males, with up to 10% of variance explained (Table 1c). The proportion of females classed as inactive in the current study was greater than that of males (42% Vs. 20%). It has been shown

previously that adolescent females are less active than boys [START_REF] Brodersen | Trends in physical activity and sedentary behaviour in adolescence: ethnic and socioeconomic differences[END_REF] . However, if the subjective PA measurement method used in the present study is a less specific measure of inactivity in females than males, the inclusion of moderately active females in the inactive group may dilute any interaction, as it appears that the FTO effect is strongest in the least active individuals 17;26 . Nevertheless, the attenuation of the FTO effect by physical activity is in agreement with previous findings in adults 24- 26 . While some studies have failed to find such an effect 17;27 , a similar trend is in fact visible in the study of Tan et al. [START_REF] Tan | FTO variants are associated with obesity in the Chinese and Malay populations in Singapore[END_REF] . The equivocal findings may be attributable in part to the subjective measures of physical activity used in these studies, and further, large studies, using objective monitoring of PA are required. Inter-population differences in allele frequencies and patterns of linkage disequilibrium may also contribute to heterogeneity of findings. Previous studies have found that the influence of FTO on adiposity phenotypes is apparent from as young as 7 years old 7;32 and that the strength of the association increases into early adolescence [START_REF] Haworth | Increasing Heritability of BMI and Stronger Associations With the FTO Gene Over Childhood[END_REF] . In the GENESIS cohort we observed no association between FTO genotype and adiposity, which is in line with previous findings reporting no association of FTO genotype and childhood BMI in 2-5 year old children [START_REF] Zhao | The role of obesity-associated loci identified in genome-wide association studies in the determination of pediatric BMI[END_REF] , nor with energy intake (Supplementary Table 4). However, given the impact of physical activity on FTO associations in the adolescent cohort, if such interaction effects also operate in these younger age groups, the lack of reliable physical activity data in the GENESIS cohort may confound efforts to detect subgroup-specific genotype effects in the younger children.

Obesity phenotypes show a high degree of heritability in familial studies [START_REF] Haworth | Increasing Heritability of BMI and Stronger Associations With the FTO Gene Over Childhood[END_REF] . It has also been shown that having one or two overweight parents increases the risk of being overweight in childhood from a very young age 34;35 , further implicating genetic origins for a significant portion of the inter-individual variation in adiposity. However, evidence based on genetic association studies, particularly from genome-wide studies, does not account for a large proportion of this heritability. The FTO gene harbours variation that has the largest effect on adiposity phenotypes of any yet reported, accounting for around 1% of the total phenotypic variance in, for example, BMI in adults 7 . Here, we have shown that the influence of FTO may be small overall but it has a significantly greater influence in the physically inactive portion of the population, with FTO genotype explaining, after age adjustment, as much as 10% of phenotypic variance in inactive adolescent males (Table 1). Studies that do not consider physical activity levels may well underestimate the potential magnitude of the FTO effect on obesity in the most susceptible groups. Our results support the idea that certain genetic effects may be operating most strongly on groups in a particular environmental milieu and extend the finding that FTO acts most strongly in physically inactive subgroups to the pre-adult life stages. This supports the idea that some genetic influences may be missed in situations where unknown confounding environmental factors are operating to obscure real genetic effects acting on environmentally distinct subgroups.

As well as influencing risk of obesity, our results also suggest that FTO genotype modulates the relationship between adiposity and physical activity, since the genotype/activity interaction implies that, on one hand, genotypic effects are observed only in certain environments but also that, on the other, activity effects are different among genotype groups, Increasing physical activity should play a key role in attaining a negative energy balance by increasing energy expenditure, although the relationship between increasing physical activity and decreasing obesity is equivocal (reviewed by Wareham et al., 6 ). In the present study, we find that the weak relationship between physical activity and obesity is strengthened when examined only in those subjects homozygous for the risk allele (Supplementary Table 3). It must be acknowledged, however, that these findings arise from cross-sectional data with subjective physical activity measurement and with relatively modest sample size.

Whilst the direction of causality in this relationship is not evident from our crosssectional data [START_REF] Ekelund | Time spent being sedentary and weight gain in healthy adults: reverse or bidirectional causality?[END_REF] , the results do support the use of prospective studies with objective activity measurements to investigate the influence of activity on adiposity further. The influence of activity may have been obscured in previous studies of this type due to the inclusion of genetic 'non-responders'. Although limited evidence is available from lifestyle intervention studies to date 37;38 , future studies of the relationship between physical activity and obesity could consider a paradigm involving stratification by FTO genotype to reveal previously obscured environmental effects.

CONCLUSION

It has been suggested that moderate and controlled dietary restriction may prevent FTO-induced obesity 9 and previous studies on the genetic influence on obesity have highlighted the importance of genes regulating energy intake [START_REF] Willer | Six new loci associated with body mass index highlight a neuronal influence on body weight regulation[END_REF] . However, the finding that physical activity levels in adolescent males influence the obesogenic effects of FTO risk genotypes highlights that both sides of the energy balance equation: intake and expenditure, should be considered in combating obesity. In the present study, it was found that FTO genotype had no strong influence in physically active adolescents but did so in inactive males, with those homozygous for the risk, G allele having a mean BMI as much as 3 kg/m 2 higher than those who are physically active or Tcarriers. This is a promising advert for the benefit of a physically active lifestyle, particularly in those carrying FTO risk variants.

10 .

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A common variant in the FTO gene is associated with body mass index in males and postmenopausal females but not in premenopausal females. Czech post-MONICA and 3PMFs studies. Clin Chem Lab Med 2009;47:387-90 15. Scuteri A, Sanna S, Chen WM, et al. Genome-wide association scan shows genetic variants in the FTO gene are associated with obesity-related traits. PLoS Genet 2007;3:e115 16. Chang YC, Liu PH, Lee WJ, et al. Common variation in the fat mass and obesity-associated (FTO) gene confers risk of obesity and modulates BMI in the Chinese population. Diabetes 2008;57:2245-52 17. Tan JT, Dorajoo R, Seielstad M, et al. FTO variants are associated with obesity in the Chinese and Malay populations in Singapore. Diabetes 2008;57:2851-57 18. Gerken T, Girard CA, Tung YC, et al. The obesity-associated FTO gene encodes a 2-oxoglutarate-dependent nucleic acid demethylase. Science 2007;318:1469-72 19. Fischer J, Koch L, Emmerling C, et al. Inactivation of the Fto gene protects from obesity. 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  GENOTYPING AND DATA ANALYSIS SNP variant rs17817449 was genotyped in both cohorts by a PCR-RFLP method, digesting the PCR product with AlwNI enzyme. The forward primer was 5. The G allele generated an undigested 223bp product while the T allele yielded 123bp and 100bp fragments after digestion.

Figure 1 .

 1 Figure 1. Effect of FTO genotype on BMI in adolescents and its interaction withPhysical Activity and gender. Males showed a significant SNP*PA interaction (P interaction = 0.01 in model-free ANCOVA, as above, and P interaction = 0.0007 in Tdominant model). Females showed no significant SNP*PA interaction (P = 0.54 as above and P = 0.74 in T-dominant model). Data are presented as mean ± SEM (adjusted for age). N for each group as follows: Active males -GG: 145, GT: 189, TT: 67; Inactive males -GG: 29, GT: 43; TT: 26 Active females -GG: 96, GT: 118, TT: 44; Inactive females -GG: 61, GT: 91, TT: 39.

  necessary sub-terms in adolescents and SNP*energy intake (Kcal/day)*gender*age in the GENESIS children. Age was classified into single year age categories. Interaction terms were left in the model if P < 0.1. The final model for each cohort consisted of all significant interaction terms and additional main effects not contained within those terms. Post-hoc comparisons of phenotypic differences between genotype groups in the adolescent male subgroup were calculated by ageadjusted GLM. To allow comparison of effects across the age range, age and sex specific z-scores were calculated for boys and girls separately in single year age strata in the GENESIS cohort, and in the same manner in adolescents, although 12 and 13 year olds were collapsed into one group, as were 17-18 year olds.

The starting model contained a four-way (SNP* PA*gender*age) interaction term and all hierarchically

ABSTRACT

Studies of the fat mass and obesity associated (FTO) gene provide compelling evidence of genetic variation in the general population that influences fat levels and obesity risk. Studies of the interaction between genetic and environmental factors such as physical activity will promote understanding of how lifestyle can modulate genetic contributions to obesity. In the current study, we investigated the effect of FTO genotype, and interactions with physical activity or energy intake, in young children and adolescents. 1-5 year old children from the GENESIS study (N =1980) and 11-18 year old Greek adolescents (N = 949) were measured for adiposity-related phenotypes and genotyped at the FTO SNP marker, rs17817449. Adolescents were classified as physically active or inactive based on self-reported levels of physical activity (PA). In adolescents, FTO genotype influenced weight (P = 0.001), and BMI (P = 0.007). There was also a significant SNP*PA*gender interaction (P = 0.028) on BMI, which reflected the association between FTO genotype and BMI in males (P = 0.016) but not females (P = 0.15) and significant SNP*PA interaction in males (P = 0.007) but not females (P = 0.74). The FTO genotype effect was more pronounced in inactive males than active. Inactive males homozygous for the G allele had a mean BMI of 3 kg/m 2 higher than T-carriers (P = 0.008). In the GENESIS study, no significant association between FTO genotype and adiposity was found. The present findings highlight physical activity as a key factor modifying the effect of FTO genotype. influence the effect of the risk genotype in adolescent males Tables Table 1. Association of FTO Genotype with adiposity phenotypes in the adolescent cohort. ANCOVA analysis in a) all participants. b) All participants under T-dominant model. c) Inactive males under T-dominant model. Effect sizes are expressed as age and sex specific z-scores. i.e. an effect size of 1 is equal to a 1 standard deviation increase in BMI.

Subjects