RESEARCH PAPER

Heritability and genetic correlations of obesity-related phenotypesamong Roma people

Alaitz Poveda, Ma Eugenia Ibanez & Esther Rebato

Department of Genetics, Physical Anthropology and Animal Physiology, Faculty of Science and Technology, University of theBasque Country (UPV/EHU), Bilbao 48080, Spain

Background: The Roma people are particularly vulnerable to

developing obesity and related diseases, due to their social

and ethnic backgrounds. However, little is known about the

genetic and/or environmental factors affecting the variability

of obesity-related traits among the Roma population.

Aim: The aim of the present study was to estimate heritabilities

and common genetic and environmental influences of obesity-

related phenotypes in a sample of Roma people living in the

Greater Bilbao region (Basque Country; Spain).

Subjects and methods: Three hundred and seventy-two

individuals from 50 large, extended and highly

consanguineous pedigrees were phenotyped for

anthropometric traits related to obesity. Heritability estimates

were assessed for all quantitative traits and bivariate analyses

were conducted to assess the phenotypic, genetic and

environmental correlations among these traits.

Results: Significant heritable components (p , 0.01) ranging

from 0.250.68 exist for the studied phenotypes. Heritability

for WHR (h 2 0.60) considerably surpasses the usualheritability estimates on family-based studies (,0.30).

Measures of overall fatness (BMI, CF and SF) show stronger

correlations with each other than body fat distribution traits

(WHR, CI and TER).

Conclusions: The study concluded that the Greater Bilbao Roma

population is genetically predisposed to abdominal fat

distribution. Variation in body mass is highly associated with

variation in adiposity. However, overall fatness and adiposity

distribution does not seem to share major common genetic

factors, although common environmental factors operate

between them.

Keywords: Adiposity, pleiotropy, ethnic groups, family studies

INTRODUCTION

In recent decades, obesity has reached epidemic proportionsin many developed countries, replacing under-nutrition and

infectious disease as the most significant cause of poorhealth (Kopelman 2000). The prevalence of obesity has morethan doubled in Western and westernizing countries overthe past decades (James 2004) and according to the WorldHealth Organization (WHO) it is estimated that, by 2015,,2.3 billion adults will be overweight and more than 700million will be obese. The causes of obesity have both geneticand environmental components, as shown in severalfamily (Dasgupta et al. 1997; Raychaudhuri et al. 2003),twin (Reis et al. 2007; Silventoinen et al. 2008) and adoptionstudies (Stunkard et al. 1999). However, the obesityphenotype is a complex and composite phenotype whichincludes various adiposity compartments. Although thesecompartments exhibit different biology and hence differentgenetic and environmental determination, some commondeterminants of the overall degree of fatness may exist(Hasselbalch et al. 2008). Very few studies have focused onthe existence of pleiotropic effects (i.e. genes simultaneouslyinfluencing several phenotypes) among obesity-relatedtraits. Livshits et al. (1998), in an attempt to discover thegenetic determinants responsible for the variability andcovariability of the adiposity traits, concluded thatpleiotropic gene effects play an important role in theirvariation. In addition to this, Choh et al. (2001) and Mathiaset al. (2009) found pleiotropic effects between BMI andskinfold thicknesses in Samoan and Danish populations.Thus, further research is needed in this field to lay down thebasis of the genetic and environmental determinantsresponsible for the overall fatness variability.

A promising approach for assessing the contributionof genetic and environmental factors affecting complexdiseases is the study of population isolates, as thesepopulations exhibit a relatively uniform genetic backgroundand low environmental variation (Heutink and Oostra 2002;Portas et al. 2010). European Gypsies, commonly referredto as Roma, constitute a genetically isolated population

Correspondence: Alaitz Poveda, PhD student, Department of Genetics, Physical Anthropology and Animal Physiology, Faculty of Science and

Technology, University of the Basque Country, Bilbao 48080, Spain, Tel: 34 94 6015405, Fax: 34 94 601 3145. E-mail: alaitz.poveda@ehu.es(Received 26 July 2011; revised 9 February 2012; accepted 20 February 2012 )

Annals of Human Biology, MayJune 2012; 39(3): 183189Copyright q Informa UK, Ltd.ISSN 0301-4460 print/ISSN 1464-5033 onlineDOI: 10.3109/03014460.2012.669794

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which, in contrast to other isolated populations, hasreceived little attention from the scientific community(Kalaydjieva et al. 2005). The Roma people originated inIndia, possibly in the modern Indian state of Rajasthan orin the Punjab Region, but it was not until the 14th centurythat the Roma population arrived in Europe (Kalaydjievaet al. 2005). Nowadays, they represent one of the largestethnic minorities in Europe, but also one of the mostexcluded, experiencing a wide range of social and healthinequalities. As a consequence, Roma people haveconsiderably poorer health status than the majoritypopulation and are considered to be a high risk group forobesity and cardiovascular diseases (Vozarova de Courtenet al. 2003; FSG 2009). The incorrect lifestyle of the Romanyminority, who have recently experienced a rapid transitionfrom a traditional lifestyle to a more sedentary lifestyle withhigher caloric intake (i.e. Westernized lifestyle), may haveplayed an important role in the increased prevalence ofobesity and related diseases (Simko and Ginter 2009).These particular features of the Roma population offer aunique opportunity to understand the role of geneticpredisposition and environmental influences on the riskof developing obesity. Therefore, in the present study, weestimate heritabilities, as well as genetic and environmentalcorrelations for a number of obesity-related traits in aRoma people sample and compare these results with thosefrom other populations. To the best of our knowledge, thisrepresents the first attempt to use quantitative geneticmethods to estimate the heritability of obesity phenotypesand their covariation in Roma nuclear families.

METHODS

SampleThe total number of individuals in this study is 655, of whom372 individuals were phenotyped. Of the 372 measuredindividuals, 354 belong to 50 extended pedigrees, includingsome very complex four-generation pedigrees. Onlyindividuals that self-identified as Roma people and whowere living in the Greater Bilbao region (Basque Country,Spain) were included in the study. In order to avoid possibleadmixture of different genetic backgrounds and to maintainthe homogeneity of the sample, only Roma people withSpanish origin were included in the sample; Rumanianorigin Roma people were excluded in the sample recruitment.The sample was randomly ascertained, the studied familieswere not selected for any specific feature or trait.

In order to avoid the confounding of disease statusand/or medical treatment on the quantitative geneticestimations, subjects having diagnosed cardiovasculardisease, hyperthyroidism, hypothyroidism or havingexperienced gastrointestinal surgery were excluded. Thenumber of each type of relative pair is listed in Table I.The overall sample includes 784 parentoffspring pairs,283 sibling pairs, 424 avuncular (aunt/uncle-nice/nephew)pairs and 231 first cousins pairs in addition to other moredistant relatives.

The data collection was carried out in marketplaces,health centres and public schools with high Roma peopleattendance and in family houses of Roma people, during20092011. The idiosyncrasy of the Roma populationmakes the access to this population very difficult; therefore,a tight collaboration with a Roma people association (KaleDor Kayiko; KDK) was established during the period ofsubject recruitment (20092011) to increase the partici-pation of the Roma people. Written informed consent wasobtained from all study participants and permission to carryout the study in the public centres was requested from thehead of each centre. The project was approved by the ethicscommittee of the University of the Basque Country.

Phenotypic dataUsing standard anthropometric techniques (Lohman et al.1988), measurements of height, weight, five circumferences(upper arm relaxed and contracted, waist, hip and medialcalf) and six skinfold thicknesses (biceps, triceps, sub-scapular, suprailiac, abdominal and medial calf) weretaken from each participant of the study. Height wasmeasured with a Siber-Hegner anthropometer (GPM,Zurich, Switzerland) accurate to 1 mm and a digital balance(accuracy of 0.1 kg) was used to measure body weight. Skin-folds were measured using a Lange caliper (CambridgeScientific Industries, Cambridge, MA) and circumferencesby using a Harpenden anthropometric tape (Holtain Ltd.,Crymych, Pembrokeshire, UK).

From these anthropometric measurements four adi-posity-related derived measurements were calculated;body mass index [BMI weight (kg)/height (m2)],which is widely considered an overall body mass indicatorand three body fat distribution indices: the waist-to-hip ratio (WHR waist circumference/hip circumference),the trunk-to-extremity skinfold ratio [TER(suprailiacsubscapularabdominal)/(medial calfbiceps triceps)]and the conicity index (CI). This index was calculatedaccording to Valdez et al. (1993) by using the following

Table I. Number of relationships between individuals.

Relative pairWhole

pedigrees (n)Phenotyped

individuals (n)

Parentoffspring 784 239Siblings 283 169Grandparentgrandchild 524 53Avuncular 424 153Half-siblings 10 6Great grandparentgrandchild 208 4Grand avuncular 128 16First cousins 231 172First cousins, once removed 174 117First cousins, twice removed 11 9Second cousins 54 44Second cousins, once removed 12 11Double first cousins 3 3Double first cousins, once removed 23 22Double second cousins 11 9Other 61 17Total 2941 1044

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formula: CI waist circumference (m)/(0.109) p[weight(kg)/height (m)].

While WHR and CI are indices traditionally employedto assess abdominal obesity, TER opposes trunk andextremities fat without regard specifically to abdominal fat.CI quantifies the deviation from the circumference of animaginary cylindrical shape. The CI ranges from 1.00(perfect cylinder) to 1.73 (perfect double cone) (Yasmin andMascie-Taylor 2000); therefore, the more central a person isin fat distribution, the higher the conicity index (Muelleret al. 1996). Unlike WHR, CI takes into account overallobesity as modelled from the height and weight of theindividual, independent of hip circumference, which couldbe an advantage when comparing men and women who varyin bone size (Wardle et al. 1996) and in the accumulation ofintra-abdominal fat mass (Yasmin and Mascie-Taylor 2000).

Statistical analysisBasic descriptive statistics (mean and standard deviation)were computed for all traits separated by sex and agegroup and Students t-test was used to evaluate differences inmean between males and females in each age group.

Two factor analyses were carried out using the principalcomponents extraction method for the circumference andskinfold categories in order to reduce the problem ofmultiple comparisons and redundancy of information(Livshits et al. 2002). The eigenvalue of one criterion wasimplemented to retain the factors and scores for eachindividual on the extracted two factors (CF and SF) wereused in further analysis.

Next, a stepwise multiple regression analysis wasimplemented to remove the effect of age (age, age2, age3)and sex on all anthropometric traits. In order to improve theadjustment, the sample was divided by sex and by two ageclasses: minors and adults (taking 18 years as the limit forthe two classes). Multiple determination coefficients, R 2, ofthese models were used as estimates of the amount of totaltrait variance attributable to a significant covariates effect(Livshits et al. 2005). Phenotypes were then generated foreach individual by retaining the residual regression scoreand then standardizing to a mean of 0 and a variance of 1within each group. After adjustment for age and sex, akurtosis test was applied to verify the normality of thesample distribution and, as none of the traits showed akurtosis above 1.96, no transformation was applied tothe data (Blangero et al. 2001). All statistical computationswere carried out using SPSS, versions 18.0 for Windows(SPSS Inc., Chicago, IL).

Quantitative genetic analysisIn order to quantify the relative additive geneticcontribution to the variation of the analysed traits, avariance component analysis was performed using theSOLAR programme (Sequential Oligogenic Linkage Anal-ysis Routines), available online (http:/www.sfbr.org/sfbr/public/software/solar/solar.html) (Almasy and Blangero1998). This analysis partitions the observed phenotypicvariance (VP) into additive genetic (VG) and environmental

components (VE), by maximum-likelihood methods(VP VG VE). The portion of the total phenotypicvariance accounted for by the additive genetic varianceis denoted by narrow sense heritability (h 2); h 2 VG/VP.The environmental component includes environmentalfactors, the non-additive genetic component and measure-ment errors. Significance of heritability estimates wastested formally by comparing the likelihoods for therestricted model, in which additive genetic variance wasconstrained to zero, to the likelihood for the general model,in which additive genetic variance was estimated.

Finally, a bivariate genetic analysis was carried out also inthe SOLAR programme as an extension of the univariateprocedure to determine the extent to which shared geneticand environmental effects influence the covariationamong pairs of phenotypes. This analysis decomposesthe phenotypic correlations (rP) between pairs of traits intothe underlying genetic (rG) and environmental correlation(rE), correcting for the use of related individuals, by thefollowing equation:

rP rGh2

p1

h2

p2 rE

12 h 21

q 12 h 22

q

where h 21 and h22 are the heritabilities of trait 1 and trait 2,

respectively (Falconer and Mackay 1996). A more detailedexplanation of this methodology can be found in Almasyand Blangero (1998). Significance of rP, rG and rE betweenany pair of traits was tested by comparison of thelog-likelihood values of the full models to a restrictedmodel, where the parameter of interest is fixed to zero.Complete pleiotropy was assessed by comparing the generalmodel, against a restricted model in which rG wasconstrained to 1.0 or 2 1.0.

RESULTS

Descriptive statisticsBasic descriptive statistics for all the anthropometric traitsand derived variables separated by sex and age group areshown in Table II. There were significant differences betweensexes for many traits. Men were significantly taller andheavier than women, whereas women and girls showedhigher adiposity than men and boys. This is reflected inthe higher mean values observed for women and girls inskinfolds thicknesses (except for abdominal skinfold).Significant differences were also found between men andwomen for adiposity distribution traits (WHR, TER, CI),their values being higher for men than for women.According to the WHO parameters, the present Romapopulation is classified as obese, as indicated by the meanBMIs for both sexes.

Factor decomposition analysesThe results of factor decomposition analyses are presentedin Table III. The KMO (Kaiser-Mayer-Olkin value) washigher than 0.6 and the Bartletts test was significant( p , 0.001) in both factors, indicating a good adequacy of

QUANTITATIVE GENETICS OF OBESITY IN ROMA PEOPLE 185

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the sample to the analyses (Suhr 2006). A single factor wasretained for each set of measures. The circumferences (CF)and skinfold (SF) factors accounted for 91.97% and 80.20%of the total variance in each group of traits, reflecting theoverall variation of body circumferences and skinfolds,respectively. Thus, CF could be considered as a measure ofoverall body mass and SF as an adiposity indicator. Bothsynthetic traits were used as summary variables in thesubsequent quantitative genetic analyses.

Univariate analysesHeritability estimates (h 2) with associated standard errorsfor the obesity-related phenotypes are given in Table IV. Theproportion of variance attributable to age effect (age, age2

and age3) varied considerably depending on trait and age orsex group, ranging from an R 2 of 0.08 for weight in womento 0.75 for weight in boys. Covariates explained a higherproportion of the variance in the minor age group (0.210.75) than in adults (0.080.51), reflecting the differences ingrowth stages among the individuals in the minor agegroup. After accounting for the significant covariates effects,heritability estimates were, in general, highly significant( p , 0.01), with moderate-to-high values ranging from0.250.60. WHR presented the highest heritability (0.60)followed by BMI, weight, CF and CI, which showedheritability estimates of 0.50 or higher and adiposityindicators (0.46 and 0.25 for SF and TER, respectively).

Bivariate analysesThe phenotypic, genetic and environmental correlations forobesity-related traits are listed in Table V. Eleven out of 21phenotypes shared significant genetic effects and all of themwere significantly different from 1.0 or 2 1.0, indicatingincomplete pleiotropy (i.e. shared and unique sets of genesinfluenced these traits). CF was the trait that showed thegreatest genetic and environmental correlations with the restof measurements. The measures of overall fatness (BMI, CF

and SF) were all highly correlated to each other (rG$ 0.87),whereas fat distribution traits (WHR, TER and CI) differedin the mode of inter-relation, with significant and non-significant and low-to-high correlations. BMI was the onlyoverall fatness trait that showed a significant geneticcorrelation with all fat distribution indicators. The numberand strength of environmental correlations between overallbody fat traits and fat distribution traits were higher thanthe genetic correlations. Among body distribution traits,whereas WHR and CI showed evidence of sharedenvironment with all the traits, TER did not show anyenvironmental correlation with any phenotype.

DISCUSSION

Isolated populations constitute a usually employed resourcefor the analysis of the genetic architecture of quantitativetraits (Portas et al. 2010). In the present study, high levelsof statistical significance were obtained with even lowheritabilities and a number of significant correlationswere detected in a genetically isolated Roma population.Consequently, the complex and unique structure of theseRoma people pedigrees seems to provide valid and robust

Table II. Phenotypic characteristics of participants and results of Students t-test.

Adults Minors

Traits Men (n 81) Women (n 134) t-test Boys (n 66) Girls (n 91) t-testAge 34.90 (11.35) 34.27 (11.59) n.s. 10.05 (3.67) 11.59 (3.83) n.s.Height (cm) 171.19 (6.65) 157.35 (5.78) *** 139.76 (20.38) 144.35 (18.41) n.s.Weight (kg) 89.52 (18.92) 75.62 (17.11) *** 42.60 (18.87) 48.69 (19.85) n.s.Arm rel. c. (cm) 33.83 (4.6) 33.48 (5.61) n.s. 24.05 (5.23) 25.87 (5.47) *Arm cont. c. (cm) 36.04 (4.70) 34.66 (5.64) n.s. 25.12 (5.55) 26.55 (5.40) n.s.Waist c. (cm) 99.54 (15.62) 87.81 (13.79) *** 69.50 (10.96) 69.09 (11.26) n.s.Hip c. (cm) 106.87 (9.97) 108.87 (12.58) n.s. 80.41 (14.62) 87.52 (16.26) **Medial calf c. (cm) 38.20 (3.59) 38.08 (4.32) n.s. 30.11 (5.10) 32.58 (5.64) **Biceps s. (mm) 15.65 (8.68) 25.49 (11.78) *** 12.50 (7.94) 15.02 (7.24) *Triceps s. (mm) 17.47 (8.27) 31.82 (11.33) *** 15.85 (8.30) 20.30 (7.95) **Subscapular s. (mm) 24.20 (8.25) 25.18 (10.10) n.s. 12.23 (6.36) 15.55 (7.89) **Suprailiac s. (mm) 22.30 (9.24) 27.72 (13.48) ** 14.30 (10.03) 19.33 (10.41) **Abdominal s. (mm) 40.38 (14.82) 39.21 (14.10) n.s. 21.12 (15.83) 26.50 (15.03) *Medial calf s. (mm) 21.67 (11.35) 37.20 (12.25) *** 18.80 (10.50) 25.98 (11.14) ***BMI (kg/m2) 30.59 (6.68) 30.55 (6.89) n.s. 20.65 (4.37) 22.32 (5.45) *WHR 0.93 (0.08) 0.81 (0.07) *** 0.87 (0.05) 0.80 (0.07) ***TER 1.74 (0.50) 0.98 (0.22) *** 0.98 (0.30) 0.97 (0.23) n.s.CI 1.26 (0.08) 1.17 (0.08) *** 1.18 (0.05) 1.13 (0.06) ***

Data are given in mean (standard deviation). N, number of individuals; b, breadth; c, circumference; s, skinfold; n.s., not significant. *p , 0.05,

**p , 0.01, *** p , 0.001.

Table III. Factor analysis of circumference and skinfold measurements.

CircumferenceFactorloading Skinfold

Factorloading

Arm relaxed 0.97 Biceps 0.93Arm contracted 0.98 Triceps 0.92Waist 0.92 Subscapular 0.86Hip 0.97 Suprailiac 0.90Medial calf 0.95 Abdominal 0.88

Medial calf 0.89Eigenvalue 4.60 4.81% of total variance 91.97 80.20KMO (Kaiser-Meyer-Olkin) 0.86 0.89Bartletts test 0.00 0.00

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information about the genetic and environmental contri-bution to variation in obesity-related traits. Our data arein agreement with previous studies, where heritability ofobesity-related phenotypes were estimated in Indians(Mathias et al. 2009; Zabaneh et al. 2009), WesternEurasians (Hasselbalch et al. 2008; Poveda et al. 2010;Jelenkovic et al. 2011), Mexicans (Bastarrachea et al. 2007)and Arabs (Bayoumi et al. 2007). The results of this study arealso comparable to those reported in other overweightpopulations such as Samoans (Choh et al. 2001) andMexican Americans (Comuzzie et al. 1994). Heritability ofbody weight in the present sample is very close to the valuesreported in American Indian (Choh et al. 2001), Spanish(Sanchez-Andres and Mesa 1994; Jelenkovic et al. 2011),Dutch (Zillikens et al. 2008) and Nigerian (Luke et al. 2001)populations. Concerning BMI, the most extensively usedindex for obesity assessment, cross-sectional twin and familystudies have shown a moderate-to-substantial geneticcomponent in its variation (Coady et al. 2002). The resultsof the present study suggest that BMI is significantlyinfluenced by additive genetic factors, with a heritabilityestimate of 50%, which is consistent with the resultsreported by other studies using family data (Luke et al. 2001;Zillikens et al. 2008; Mathias et al. 2009).

The most interesting fact is the high influence of additivegenetic factors on WHR found in this Roma sample(h 2 60%) which considerably surpass the usual herit-ability estimates in family-based studies (h 2 , 30%) (e.g.Mathias et al. 2009; Zabaneh et al. 2009). The highheritability found for WHR is indicative of a highly heritablefat distribution pattern in relation to abdominal obesityamong the Greater Bilbao Roma population. In addition,the high mean values observed for WHR and waistcircumference (see Table II) in this population suggeststhat the studied Roma people are genetically pre-disposed toabdominal fat accumulation. This fact could reflect adifferent environmental effect on genes that controlabdominal fat accumulation in the Roma population or itmay also indicate differences in allelic frequencies of thesegenes. The rapid transition to a westernized lifestyle andtheir particular social structure of small and endogamousgroups may have played an important role in the greater pre-disposition to abdominal obesity. Although BMI is the most

commonly used indicator of obesity, abdominal fataccumulation is considered to be the principal componentexplaining obesity-related health risks. In fact, WHR hasbeen frequently viewed as a better predictor of cardio-vascular disease risk than BMI in adults (Lee et al. 2010).Abdominal fat increases the risk of insulin resistanceand cardiovascular disease and is positively correlatedwith mortality, even within normal ranges of BMI(Hasselbalch et al. 2008; Mathias et al. 2009). Thus, thepresent sample of Roma people could help in theidentification of specific alleles or environmental effectsacting on the accumulation of abdominal fat whichhave important consequences for the development ofcardiovascular and other obesity-associated diseases.

Phenotypic correlations among the great majority ofobesity-related traits are shown in this sample. Pleiotropywas also found for an elevated number of pairs. Thephenotypic, genetic and environmental correlations esti-mated from the present study are mostly in line withprevious studies (Choh et al. 2001; Schousboe et al. 2004;Bastarrachea et al. 2007; Hasselbalch et al. 2008; Mathiaset al. 2009). The high phenotypic, genetic and environmen-tal correlation for BMI-SF reported in the present study,which is in agreement with the studies of Hasselbalchet al. (2008) and Schousboe et al. (2004), indicate thatvariation in body mass is highly associated with variation inadiposity. Livshits et al. (1998), in a study conducted onthree ethnically different populations, found that skinfoldindices measure a different dimension of fat distributionthan circumference indices, being to a great extentgenetically independent characteristics. However, the pre-sent study shows a high genetic correlation (0.87) betweenCF and SF, pointing to the high influence of pleiotropiceffects on the determination of both circumference andskinfold measurements.

Table V. Phenotypic (rP), genetic (rG) and environmental (rE)correlations between obesity-related phenotypes.

Weight CF SF BMI WHR TER

CF rP 0.95a

rG 0.96a

rE 0.93a

SF rP 0.84a 0.86a

rG 0.80a 0.87a

rE 0.88a 0.85a

BMI rP 0.93a 0.96a 0.86 a

rG 0.92a 0.96a 0.87a

rE 0.93a 0.97a 0.86a

WHR rP 0.29a 0.28a 0.32a 0.37a

rG 0.22 0.17 0.15 0.32a

rE 0.38b 0.42b 0.50a 0.44b

TER rP 0.11 0.10 0.10 0.16b 0.18b

rG 0.42 0.31 0.35 0.48c 0.48c

rE 20.07 20.02 20.03 20.02 20.01CI rP 0.37

a 0.37a 0.41a 0.42a 0.86a 0.21a

rG 0.31 0.25 0.24 0.36c 0.96a 0.43

rE 0.44b 0.51a 0.58a 0.49a 0.75a 0.09

p-values , 0.05 are marked in italics; a Estimated significant atp , 0.001; b Estimated significant at p , 0.01; c Estimated significant at

p , 0.05.

Table IV. Heritability estimates (h2), associated standards errors (S.E.)and proportion of variance attributed to covariate effects (R2) forobesity-related phenotypes.

Proportion of variance attributed

to covariate effects (R 2)

Phenotypes Men Women Boys Girls h 2 S.E. p-value

Weight 0.21 0.08 0.75 0.60 0.50 0.10 ,0.001

CF 0.23 0.10 0.59 0.52 0.52 0.09 ,0.001

SF 0.15 0.14 0.21 0.46 0.11 ,0.001

BMI 0.25 0.10 0.31 0.24 0.54 0.09 ,0.001

WHR 0.50 0.22 0.32 0.55 0.60 0.10 ,0.001

TER 0.41 0.25 0.11 0.006

CI 0.51 0.30 0.26 0.36 0.52 0.11 ,0.001

Only significant values are reported.

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In general, measurements of overall fatness (BMI, SF6,CF and SF) show stronger correlations with each other thanbody fat distribution traits (WHR, CI and TER), indicatinga higher influence of shared genetic and environmentalfactors on overall fatness than on adiposity distribution. Thestronger environmental than genetic correlations betweenoverall body fat traits and fat distribution traits show that,although common environmental factors, such as energyintake, operate among obesity-related traits, overall fatnessand adiposity distribution do not seem to share majorcommon genetic factors. Although BMI is weakly correlatedwith WHR, it shows high genetic and environmentalcorrelations (,0.90) with waist and hip circumferencesindependently (data not shown), as observed in previousstudies (Bastarrachea et al. 2007; Benyamin et al. 2007;Hasselbalch et al. 2008; Mathias et al. 2009). Thus, this factsuggests that while overall body fatness is strongly related tofat accumulation in body regions, overall body fatness isonly weakly correlated with fat distribution.

CONCLUSION

In conclusion, our findings have provided evidence for astrong genetic contribution to the variation of obesity-relatedphenotypes among the Greater Bilbao Roma populationwhich is genetically pre-disposed to abdominal obesity.Pleiotropic effects have a great influence among variousobesity-related traits, but none of the studied obesity-relatedtraits pairs show complete pleiotropy, indicating a significantresidual genetic influence specific for traits. In the presentRoma people sample, overall fatness and adipositydistribution do not seem to share major common geneticfactors although common environmental factors operateamong them. Understanding the complex relationshipsbetween the underlying genetic and environmental factors inthe variation of obesity phenotypes will help provide insightsinto the complex aetiology of obesity. Further study of thesefamilies may provide useful information for continuinggenetic research in this interesting anthropological popu-lation, including the identification of potentially uniquegenetic loci in Roma people.

ACKNOWLEDGEMENTS

We would like to thank the outpatient departments ofhealth centres and the education centres who allowed us tocarry out the study. We also would like to express ourgratitude to Kale Dor Kayiko for their collaboration. Specialthanks also go to the families enrolled in the study for theirparticipation.

Declaration of interest: This study was supported by grantsof the Bilbao Bizkaia Kutxa (BBK; 87014/97012/07007), ofthe Spanish Ministry of Science and Innovation (MICINN;GCL2010-15511), of the Industry Department of the BasqueGovernment (SAIOTEK; SA2010/00035) and by two pre-doctoral grants one of the Ministry of Education of Spain(for A.P) and the other from the University of the Basque

Country (for M.E.I.). The authors report no conflicts ofinterest. The authors alone are responsible for the contentand writing of the article.

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