FTO association and interaction with time spent sitting.

BACKGROUND/
OBJECTIVES: Multiple studies have revealed an interaction between a variant in the FTO gene and self-reported physical activity on body mass index (BMI). Physical inactivity, such as time spent sitting (TSS) has recently gained attention as an important risk factor for obesity and related diseases. It is possible that FTO interacts with TSS to affect BMI, and/or that FTO's putative effect on BMI is mediated through TSS. SUBJECTS/
METHODS: We tested these hypotheses in two cohorts of the Framingham Heart Study (FHS) (Offspring: n=3430 and Third Generation: n=3888), and attempted to replicate our results in the Women's Health Initiative (WHI; n=4756). Specifically, we examined whether an association exists between FTO and self-reported TSS, and whether an interaction exists between FTO and TSS on BMI, while adjusting for several important covariates such as physical activity.
RESULTS: In FHS, we find a significant positive association between the BMI-increasing FTO allele and TSS. We find a similar trend in WHI. Mediation analyses suggest that the effect of FTO on BMI is mediated through TSS. In FHS, we find a significant interaction of FTO and TSS on BMI, whereby the association of TSS with BMI is greatest among those with more FTO risk alleles. In WHI, we also find a significant interaction, although the direction is opposite to that in FHS. In a meta-analysis of the two data sets, there is no net interaction of FTO with TSS on BMI.
CONCLUSIONS: Our study suggests that FTO exerts its effect on BMI, at least partly, through energy expenditure mechanisms such as TSS. Further research into the intersection of genetics, sedentary behavior and obesity-related outcomes is warranted.

Introduction

Obesity, a major worldwide health problem, arises due to both genetic and environmental risk factors [1]. A variant in the fat mass and obesity associated (FTO) gene is the most well-established genetic risk factor for obesity [2,3]. The exact mechanism through which this variant is linked to obesity is still unknown, although there is some indication that it involves the hypothalamus, and thus potentially operates through energy expenditure and/or food intake [4-8].

Interaction analyses of FTO and physical activity have suggested that the putative effect of the FTO variant is diminished among individuals who are more physically active [9,10]. Although physical activity is an important lifestyle factor, another important factor which has received only recent attention is physical inactivity, such as time spent sitting (TSS) [11,12]. Prolonged and sustained sitting behavior has been identified as an independent risk factor for metabolic and cardiovascular disease [13], possibly by deactivating large postural muscles of the back and legs [14], leading to decreased lipoprotein lipase activity [15,16].

Despite the growing recognition of physical inactivity as an important risk factor for obesity and related diseases, little is known regarding how it may be linked to FTO as either 1) a potential mechanism connecting FTO to obesity, or 2) a modifier of the putative effect of FTO variation as has been discovered in the case of physical activity. Several studies have examined the interaction of BMI-associated genetic variants with sedentary behavior. Qi et al. examined the interaction of overall genetic risk to obesity and television watching, and found that the association of genetic risk with obesity was accentuated among those who spent more time watching television [17]. Graff et al. found two BMI-associated SNPs that interacted with screen time behavior, such that the association of these SNPs with BMI was accentuated among those who engaged in more screen time[18].

While television watching is often used as a proxy for sedentary behaviors, this variable does not fully capture total daily sitting time. TSS is an important and comprehensive lifestyle risk factor which has not yet been assessed directly in a genetic interaction study. Furthermore, the extent to which FTO may be associated with TSS, and thus potentially mediate the FTO-BMI association, has not been examined. Here, we tested the association of FTO with TSS, and the interaction of FTO with TSS on BMI in two studies, and in multiple ethnic/racial groups. We also use mediation analysis to determine the extent to which TSS mediates the association between FTO and BMI.

Methods

Studies

We used data from 7,318 European-American (EA) participants from the Offspring (Exam 4; n=3,430) and Third Generation (Exam 1; n=3,888) cohorts of the Framingham Heart Study (FHS), which is a prospective cohort study to examine the causes of heart disease [19]. The Offspring cohort initiated in 1971 includes the offspring of the Original cohort participants as well as the spouses of the offspring [20]. Third Generation participants includes children of the Offspring cohort participants as well as their spouses [21]. Our replication dataset consisted of 4,756 participants in the Women's Health Initiative (WHI) study [22]. Of these, 1,542 are self-identified Hispanic-Americans (HA) and 3,214 are self-identified African-Americans (AA). Based on the assumptions of our model described below, we have over 80% statistical power to detect an effect of FTO on a phenotype with an r2 as low as 0.35%, with a total sample size of 4,000, at an alpha of 0.05. An r2 of 0.35% corresponds to the proportion of BMI variation explained by FTO among individuals of European descent [3]. We have no a-priori expectation regarding the proportion of variation in TSS that variation in FTO might explain. Approval for this study was obtained from the University of Arizona Institutional Review Board. Data were obtained from the database of Genotypes and Phenotypes (dbGaP).

Phenotypic and lifestyle measurements

Body-mass index (BMI) was measured at baseline as weight (kg) divided by squared height (m). TSS in FHS was measured through the following question: “Number of hours typically sitting in a typical day?” In WHI, TSS was measured through the following question: “During a usual day and night, about how many hours do you spend sitting? Include the time you spend sitting at work, sitting at the table eating, driving or riding in a car or bus, and sitting up watching TV or talking.” In WHI, TSS was recorded as one of eight categories ranging from < 4 hrs to > 16 hrs, with the rest being in one-hour increments. We chose the mean for each time-interval category for the purpose of our analyses. Physical activity (PA) in FHS was measured using a questionnaire assessing the number of hours spent in slight, moderate, and heavy physical activity on a typical day. As previously described [23], the slight, moderate, and heavy activity were further multiplied by factors of 1.5, 2.4 and 5, respectively, to account for the contribution of levels of PA in enhancing cardiovascular disease-free life expectancy in adults aged >50 years. These quantities were subsequently summed to create an overall measure of PA in FHS. In WHI, PA was derived by summing metabolic equivalent (MET)-hours/week of energy expenditure on mild, moderate and hard exercise activity. Examples of each of these types of activities were provided in the questionnaire. MET-hours/week were calculated as the summed product of the frequency, duration and intensity of reported activities [24,25]. The intensity of the activities was assigned according to a standardized classification [26]. Dietary intake was assessed in WHI through estimating the amount of total dietary energy consumed (Kcal/day) on a normal day. Employment was dichotomized in both FHS (3rd Generation) and WHI to reflect the current employment status of each individual (0 if not employed, 1 if employed). Education was recoded into years of education in FHS (3rd Generation) and WHI. Cigarette smoking in both datasets was dichotomized to reflect any current or past smoking. Individuals with any missing values for the above variables were excluded from the analyses.

FTO genotype

We used a common variant, rs9939609 (A/T) repeatedly identified in many BMI GWAS in EA and AA [27-29]. Given the lack of association of the rs9939609 SNP in some studies of AA [30], we also considered other SNPs identified in fine-mapping efforts in AA: rs1421085, rs56137030, rs17817964, and rs8050136 [31,32]. Genotyping was originally performed using the Affymetrix 500 SNP Array in FHS, and the Affymetrix 6.0 SNP Array in WHI (Affymetrix Inc, Santa Clara, CA). In WHI the DNA was extracted from blood samples collected at enrollment, and genotyping QC included concordance rates for blinded and unblended duplicates [33]. In order to test multiple FTO SNPs in WHI, we used imputed genotypes which were available in dbGaP. Briefly, genotypes were imputed using BEAGLE software [34], and 1,000 Genomes data [35] as reference. Individuals with missing values of FTO genotypes were excluded from all analyses.

Statistical analyses

For our main analysis, we used the rs9939609 FTO SNP. However, other genetic variants listed above were also tested and showed similar results. The rs9939609 was chosen to maintain consistency across studies. Linear multiple regression models were used to assess the association between i) rs9939609 and BMI, adjusting for age, gender, smoking, and PA. ii) rs9939609 and TSS, adjusting for age, gender, BMI, smoking, and PA. These models were also run in our replication dataset of AA and HA in WHI, where we also adjusted for dietary intake. The distribution of all variables was visually examined in order to ensure no departure from normality. In WHI and the FHS 3rd Generation cohort, we also included employment and education as covariates. We performed a random-effects, inverse-variance weighted, meta-analysis of the association of rs9939609 with TSS, as well as for the interaction of rs9939609 with TSS, to combine estimates from FHS and WHI, using the metafor package in R[36]. We also examined the possible mediation effect of TSS and PA on the association between rs9939609 and BMI, by performing a causal mediation analysis using the “mediation” package in R [29, 30]. We included all covariates mentioned above in this analysis, and we included them additively, which is under the model assumption. Mediation analysis was carried out using a least squares regression to statistically test for the indirect effect or average causal mediation effect (ACME) of TSS and PA (tested separately) on the association of rs9939609 with BMI, and of BMI on the association of rs9939609 on TSS. A nonparametric bootstrap (simulations=1000) method was adopted to estimate parameter uncertainty. A sensitivity analysis was also conducted on the significant mediation results obtained in EA, with age, sex, PA, smoking and cohort type as covariates. Lastly, we also tested the interaction of TSS and rs9939609 on BMI by including in the regression model the product of these two variables. All analyses were conducted using R software (version 3.0.2 [37]) and two sided p-values less than 0.05 were considered statistically significant.

Results

Descriptive characteristics of the sample are shown in Table 1. The mean age of FHS participants is lower (mean≈45) than that of participants in WHI (mean≈61). Mean BMI is highest among African-Americans in WHI (mean=30.1 kg/m2). The frequency of the rs9939609-A allele does not differ greatly across racial/ethnic groups (see Table 1).

Table 1

Descriptive characteristics of the Framingham Heart Study (FHS) and Women's Health Initiative (WHI) samples. Mean and standard deviation are shown.

	FHS (N=7,318)	WHI (N=4,756)
	European Americans	African Americans (N=3,214)	Hispanic Americans (N=1,542)
Age	45.35±10.91	61.38±7.01	60.23±6.84
Sex (% female)	52%	100%	100%
BMI (kg/m2)	26.86±5.25	30.09±6.40	28.13±5.63
Sitting time (hours/day) *	6.63±3.27	6.80±3.72	5.86±3.36
Smoking (% smokers)	20.5%	49.5%	35.5%
rs9939609 (% A allele)	41%	47%	31%

Inferred from categorical variable in WHI sample (see Methods)

Association of rs9939609 with BM and TSS

We first confirmed that the A allele of rs9939609 is associated with a higher BMI, after adjustment for age, sex, and PA in the combined FHS (p=2.07 × 10-7) and WHI HA (p=8.79 × 10-3). Among AA, we do not observe a significant association of this allele with BMI (p=0.95) (see Table 2). The regression coefficient of rs9939609 with BMI is slightly smaller upon including TSS in the model (0.43 and 0.48 before inclusion of TSS, and 0.41 and 0.45 after inclusion of TSS, in the FHS Offspring and FHS 3rd Generation, respectively). In this same latter model, we also find that TSS is positively associated with BMI (p<1 × 10-6) in FHS. In WHI, the inclusion of TSS in the model with BMI as the outcome results in a decreased coefficient for rs9939609 in AA (0.01, before TSS inclusion vs. -0.11, after TSS inclusion), but an increased coefficient in HA (0.39 vs. 0.74). In the latter model, TSS is positively associated with BMI (p<1 × 10-6) in WHI.

Table 2

Association of FTO (rs9939609) with BMI (top half) and association of FTO with sitting time (bottom half). Coefficients followed by p-values in parentheses are shown for each of the covariates included in the model. Analyses were performed separately in each cohort of FHS, and in the combined FHS (Offspring + 3rd Generation), and separately in AA and HA of WHI in the combined WHI (AA+HA). Meta-analyses of the association of FTO with BMI and TSS were performed on the estimates of FHS-combined and WHI-combined.

	FHS			WHI			Meta-analysis
	Offspring	3rd Generation*	Combined	AA*	HA*	Combined*
BMI (kg/m2)
FTO	0.43(5.48E-4)	0.47(8.45E-5)	0.45(2.07E-7)	0.01(0.95)	0.38(8.79E-3)	0.12(0.16)	0.29(0.08)
Age	0.04(4.91E-7)	0.10(<2E-16)	0.07(<2E-16)	-0.11(<2E-16)	-0.05(2.48E-3)	-0.09(<2E-16)
PA	-0.03(4.46E-4)	-0.05(4.89E-11)	-0.03(4.73E-9)	-0.06(1.25E-15)	-0.06(1.98E-10)	-0.06(<2E-16)
Smoking	-0.47(0.01)	-0.23(0.32)	-0.15(0.30)	-0.02(0.89)	0.33(0.10)	0.11(0.36)
Gender	-1.55(<2E-16)	-1.98(<2E-16)	-1.79(<2E-16)	NA	NA	NA
Educ.	NA	-0.22(<2E-16)	NA	-0.22(<2E-16)	-0.12(2.62E-6)	-0.18(<2E-16)
Employed	NA	-0.05(0.48)	NA	0.69(4.69E-5)	-0.43(0.05)	-0.58(2.1E-5)
Diet	NA	NA	NA	9.00E-4(<2E-16)	7.89E-4(4.18E-14)	8.61E-4(<2E-16)
Race	NA	NA	NA	NA	NA	-2.62(<2E-16)
TSS (hrs/day)
FTO	0.12(8.7E-3)	0.09(0.03)	0.10(9.94E-4)	0.10(0.25)	0.19(0.14)	0.13(0.08)	0.11(2.3E-4)
Age	-0.01(7.2E-4)	-0.01(5.78E-4)	-0.01(1.53E-5)	-0.05(6.54E-7)	-0.00(0.64)	-0.03(6.24E-5)
BMI	0.01(0.02)	0.02(4.78E-6)	0.026(4.88E-9)	0.05(1.85E-6)	0.010(0.512)	0.04(2.47E-7)
PA	-0.25(<2E-16)	-0.25(<2E-16)	-0.254(<2E-16)	-0.01(0.02)	-0.02(0.13)	-0.01(4.3E-3)
Smoking	0.10(0.17)	0.08(0.31)	-0.11(0.05)	0.36(6.91E-3)	0.29(0.09)	0.33(1.87E-3)
Gender	-0.46(9.23E-13)	-1.00(<2E-16)	-0.79(<2E-16)	NA	NA	NA
Educ.	NA	-0.00(0.52)	NA	0.08(2.08E-6)	0.13(7.52E-11)	0.10(1.65E-14)
Employed	NA	0.66(2.78E-11)	NA	0.68(8.16E-6)	1.13(2.00-11)	0.91(7.77E-14)
Diet	NA	NA	NA	2.62E-4(2.79E-4)	3.05E-4(1.1E-13)	2.67E-4(3.32E-6)
Race	NA	NA	NA	NA	NA	-0.59(1.02E-6)

Abbreviations: AA - African-Americans, HA – Hispanic-Americans

additionally adjusted for cohort (FHS) or race (WHI)

additionally adjusted for total dietary intake, employment, and education, cigarette smoking

The A allele is also associated with greater TSS in the combined FHS (p=9.94 × 10-4) after adjustment for multiple covariates, including BMI. In the combined WHI dataset, we find a trend suggesting that the A allele is positively associated with TSS, although the association is not quite statistically significant (p=0.08), adjusting for all covariates, including employment and education (see Table 2). The meta-analyzed estimate of the rs9939609 association with TSS shows a significant positive association of rs9939609 with TSS (β=0.11, p=2.3 × 10-4), as shown in Table 2.

Mediation analysis revealed that the association of rs9939609 with BMI is partly mediated by TSS in FHS (p=0.02 and p=0.03, respectively in Offspring and 3rd Generation), but not by PA (p=0.78 and p=0.85, respectively in Offspring and 3rd Generation) (see Tables 3 & 4). In WHI, we find a similar trend, although the results are not statistically significant (see Tables 3 & 4). Table 5 shows the results of the mediation by BMI of rs9939609 on TSS. BMI appears to partly mediate the association between rs9939609 with TSS, although there also appears to be a direct effect of rs9939609 on TSS. As above, the results are stronger in FHS than in WHI.

Table 3

Mediation effect of TSS on the association of FTO (rs9939609) with BMI.

		Point Estimate	95% CI	P-value
FHS Offspring	Indirect Effect (FTO→TSS→BMI)	0.013	0.001, 0.033	0.02
	Direct Effect (FTO →BMI)	0.413	0.166, 0.658	<0.01
	Total effect	0.427	0.183, 0.671	<0.01
	Proportion of Total Effect by mediation	0.031	0.002,0.113	0.02
FHS 3rd Gen.	Indirect Effect (FTO→TSS→BMI)	0.021	0.003, 0.43	0.03
	Direct Effect (FTO →BMI)	0.454	0.211,0.684	<0.01
	Total effect	0.475	0.224,0.703	<0.01
	Proportion of Total Effect by mediation	0.044	0.008, 0.119	0.03
WHI AA	Indirect Effect	0.015	-0.015, 0.005	0.29
	Direct Effect	-0.112	-0.441, 0.200	0.48
	Total Effect	0.139	-0.400, 0.678	0.60
	Proportion of Total Effect by mediation	-0.161	-1.625,1.400	0.70
WHI HA	Indirect Effect	0.014	-0.006, 0.049	0.22
	Direct Effect	0.737	0.320, 1.186	<0.01
	Total Effect	0.752	0.332, 1.193	<0.01
	Proportion of Total Effect by mediation	0.019	-0.008, 0.082	0.22

Indirect Effect/ACME (FTO→TSS→BMI); Direct Effect (FTO →BMI)

Table 4

Mediation effect of PA on the association of FTO (rs9939609) with BMI.

		Point Estimate	95% CI	P-value
FHS Offspring	Indirect Effect (FTO→PA→BMI)	-0.001	0.009, 0.005	0.78
	Direct Effect (FTO →BMI)	0.413	0.167, 0.651	<0.01
	Total effect	0.412	0.168, 0.647	<0.01
	Proportion of Total Effect by mediation	-0.003	-0.029,0.019	0.79
FHS 3rd Gen.	Indirect Effect (FTO→PA→BMI)	-0.001	-0.007, 0.005	0.85
	Direct Effect (FTO →BMI)	0.454	0.228, 0.701	<0.01
	Total effect	0.453	0.228, 0.701	<0.01
	Proportion of Total Effect by mediation	-0.001	-0.018, 0.013	0.85
WHI AA	Indirect Effect	-0.012	-0.044,0.018	0.43
	Direct Effect	-0.112	-0.421, 0.191	0.52
	Total Effect	-0.125	-0.443, 0.191	0.49
	Proportion of Total Effect by mediation	0.097	-0.807, 1.265	0.70
WHI HA	Indirect Effect	-0.055	-0.125, 0.005	0.07
	Direct Effect	0.737	0.344, 1.178	<0.01
	Total Effect	0.682	0.287, 1.136	<0.01
	Proportion of Total Effect by mediation	-0.081	-0.303, 0.009	0.08

Indirect Effect/ACME (FTO→PA→BMI); Direct Effect (FTO →BMI)

Table 5

Mediation effect of BMI on the association of FTO (rs9939609) with TSS.

		Point Estimate	95% CI	P-value
FHS Offspring	Indirect Effect (FTO→BMI→TSS)	0.007	0.001, 0.015	0.03
	Direct Effect (FTO →TSS)	0.124	0.024, 0.213	0.01
	Total effect	0.129	0.035, 0.220	<0.01
	Proportion of Total Effect by mediation	0.049	0.004, 0.216	0.03
FHS 3rd Gen.	Indirect Effect (FTO→BMI→TSS)	0.013	0.006, 0.024	<0.01
	Direct Effect (FTO →TSS)	0.096	0.003, 0.188	0.05
	Total effect	0.109	0.017, 0.202	0.02
	Proportion of Total Effect by mediation	0.122	0.035, 0.558	0.02
WHI AA	Indirect Effect (FTO→BMI→TSS)	-0.005	-0.024,0.012	0.59
	Direct Effect (FTO→TSS)	0.108	-0.069, 0.285	0.27
	Total Effect	0.103	-0.073, 0.281	0.28
	Proportion of Total Effect by mediation	-0.053	-0.889, 0.674	0.71
WHI HA	Indirect Effect (FTO→BMI→TSS)	0.022	-0.003, 0.053	0.09
	Direct Effect (FTO→TSS)	0.196	-0.031, 0.455	0.09
	Total Effect	0.218	-0.008, 0.477	0.07
	Proportion of Total Effect by mediation	0.098	-0.284, 0.670	0.14

Indirect Effect/ACME (FTO→BMI→TSS); Direct Effect (FTO →TSS)

Interaction of rs9939609 and TSS

As shown in Table 6, we find a significant interaction of rs9939609 and TSS on BMI in both FHS (p=3.4 × 10-3) and WHI Hispanics (p=0.02). In FHS, the association of TSS with BMI is strongest among those homozygous for the rs9939609 risk allele (see Table 7). Conversely, the association of rs9939609 with BMI is strongest among those with high TSS, and weakest among those with low TSS. However, in WHI, we observed the opposite pattern of interaction whereby the association of TSS with BMI is strongest among those homozygous for the rs9939609 protective allele. As shown in Table 6, upon meta-analysis of the interaction estimate in WHI and in FHS, we find no significant interaction of rs9939609 with TSS on BMI (βinteraction=-0.29 × 10-4, pinteraction=0.99).

Table 6

Interaction of rs9939609 with TSS (hrs/day) on BMI (kg/m2), showing interaction coefficient and corresponding p-values in parentheses.

	FHS β(p-value)			WHI β(p-value)			Meta-analysis β(p-value)
	Offspring	3rd Gen.*	Combined**	AA*	HA*	Combined***
FTO * TSS	0.051 (0.21)	0.088 (0.01)	0.077 (3.4E-3)	-0.056 (0.19)	-0.140 (0.05)	-0.082 (0.02)	-0.29E-4 (0.99)

additionally adjusted dietary intake, education and employment status

additionally adjusted for cohort effect

additionally adjusted for race

Table 7

Association of rs9939609 with BMI in three strata of TSS.

	FHS* β (SE)	WHI** β (SE)
Low TSS	0.16(0.12); p=0.18	0.31(0.19); p=0.11
Moderate TSS	0.45(0.17); p=6.9E-3	0.29(0.23); p=0.19
High TSS	0.85(0.18); p=2.9E-6	-0.38(0.29); p=0.20

adjusted for cohort

adjusted for race, diet, income, education and employment status

Discussion

In a large sample of EA, we find that rs9939609 in FTO is associated with TSS, that TSS partly mediates the association of rs9939609 with BMI, and that the putative effect of sitting on BMI is greatest in those homozygous for the rs9939609 risk allele. In a replication sample of HA and AA, we find a similar, albeit not statistically significant, association of rs9939609 with TSS. In a meta-analysis of the two datasets we find a significant association of the SNP with TSS, that is independent of BMI, PA, and other covariates. The weaker findings in WHI may be related to the smaller sample size in HA as well as our finding of no association of FTO variants with BMI in AA. As mentioned, we tried other FTO variants previously found to be associated with BMI in AA, but we did not find any association in this cohort with these SNPs. Mediation analyses provided support for a model in which TSS mediates the association of rs9939609 with BMI, but also for a model in which in which BMI mediates the association of rs9939609 with TSS.

The mechanism through which FTO increases BMI is still unclear, although there is evidence from knockout mice models suggesting FTO could be functionally involved in energy homeostasis via regulation of energy expenditure [38]. FTO is also found to be strongly expressed in satiety centers within the hypothalamus region [39-41], where it could potentially influence increased energy intake [8,42], dietary preferences for increased intake of dietary fats [40,43], or protein [7,44], increased appetite and reduced satiety [45], as well as loss of control over eating [46]. However, the evidence that the FTO variant is associated with dietary intake is mixed, raising the alternative scenario in which FTO increases BMI through prolonged sedentary behavior. Previous studies of TSS and energy intake have shown that dramatic reductions in energy expenditure due to experimentally induced sitting do not lead to a reduction in appetite or a reduction in food intake [47,48]. Thus, it is possible that individuals with the higher risk alleles are not modulating energy intake following long-periods of reduced energy expenditures associated with high TSS.

Our results of interaction are in agreement with other studies suggesting that the effect of FTO is contingent on lifestyle factors such as physical activity [49]. The direction of the interaction in FHS is consistent with previous studies on physical activity interactions [9,10], in that the putative effect of FTO is reduced among those who report low TSS. The inconsistent direction of the interaction in the FHS and WHI datasets makes it difficult to draw any firm conclusions. However, our results do suggest that further exploration of interactions of FTO and/or other genetic factors with sedentary behavior is warranted, especially in diverse populations.

The strengths of our study include the use of overall sitting behavior as opposed to only screen/television time, a replication dataset, the use of mediation analysis, and the inclusion of many potential confounders as covariates in the statistical models. Limitations include the observational nature of the study, and the subjective and self-reported measurements of TSS, PA, and dietary intake. Another limitation is that the replication dataset is comprised of a different ethnic/racial group than the discovery dataset, as genetic and other risk factors may differ across these groups.

Further efforts in other datasets with objectively measured sitting behavior are needed to confirm our findings and to contribute to our understanding of the physiological mechanisms underlying specific genetic variants, and how these interact with our lifestyle.