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. Author manuscript; available in PMC: 2011 Mar 1.
Published in final edited form as: Med Sci Sports Exerc. 2009 Oct;41(10):1887–1895. doi: 10.1249/MSS.0b013e3181a2f646

Genome-wide Association Study of Exercise Behavior in Dutch and American Adults

Marleen HM De Moor 1,*,†, Yong-Jun Liu 2,*, Dorret I Boomsma 1, Jian Li 2, James J Hamilton 2, Jouke-Jan Hottenga 1, Shawn Levy 7, Xiao-Gang Liu 2, Yu-Fang Pei 2, Danielle Posthuma 1, Robert R Recker 6, Patrick F Sullivan 3, Liang Wang 2, Gonneke Willemsen 1, Han Yan 2, Eco JC De Geus 1,§, Hong-Wen Deng 2,4,5,§
PMCID: PMC2895958  NIHMSID: NIHMS131808  PMID: 19727025

Abstract

Introduction

The objective of this study was to identify genetic variants that are associated with adult leisure-time exercise behavior using genome-wide association in two independent samples.

Methods

Exercise behavior was measured in 1,772 unrelated Dutch and 978 unrelated American adults with detailed questions about type, frequency and duration of exercise. Individuals were classified into regular exercisers or non-exercisers using a threshold of 4 METhours (metabolic equivalents*hours per week). Regular exercisers were further divided into 5 categories of METhours, ranging from moderate (>=4 METhours) to highly vigorous (>=40 METhours) exercisers. Genome-wide association analyses with a total of 470,719 SNPs were conducted in both samples independently using regression-based techniques in SNPtest, including sex, age and BMI as covariates.

Results

SNPs located in SGIP1, DNASE2B, PRSS16, ERCC2 and PAPSS2 were associated with exercise participation (combined p-value between 0.0004 and 4.5*10-6 with the same direction of allelic effects in both samples). Associations of candidate genes based on existing literature were replicated for the LEPR gene in the American sample (rs12405556, p=0.0005) and for the CYP19A1 gene in the Dutch sample (rs2470158, 0.0098).

Conclusion

Two genes (SGIP1 and LEPR) are expressed in the hypothalamus and involved in the regulation of energy homeostasis. Their effects were independent of BMI, suggesting a direct role of hypothalamic factors in the drive to exercise.

Keywords: Physical activity, sports participation, genetics, genotype imputation, energy homeostasis

A sedentary lifestyle is an important risk factor for a variety of physical health problems, such as obesity, cardiovascular disease, type II diabetes and osteoporosis (2,4,23,35). Although lack of exercise participation is generally considered as an environmental risk factor, twin studies have found that genetic factors play a substantial role in adult exercise participation (5,33), suggesting that not all individuals have the same intrinsic drive to participate and persist in exercise. Based on a study conducted in over 85,000 adult twins from seven different countries Stubbe and co-authors (33) reported that between 48 to 71% of the variance in adult exercise behavior is explained by genetic factors. The remaining variance is accounted for by environmental factors that are not shared within families.

The heritability of exercise behavior in adults has been well established but not much is known about the genetic variants that are associated with this trait. So far, three genome-wide linkage studies and six candidate gene association studies have been conducted for exercise behavior or related physical activity phenotypes. The first linkage study for exercise behavior was conducted in 395 Caucasian adults from 207 families (31). Four physical activity phenotypes were measured, of which three reflected daily physical activity and one past year physical activity. For past year physical activity, suggestive linkage (p<0.01) was found on chromosomes 11p15 and 15q13.3. For daily physical activity promising linkage (p<0.0023) was found on chromosome 2p22-p16 and suggestive linkages were found for different loci on chromosomes 4q28.2, 7p11.2, 9q31.1, 13q22-q31 and 20q13.1. The second study consisted of 1,030 children from 319 Hispanic-American families (8). Significant linkage was found on chromosome 18q12-q21 (LOD=4.09) for daily physical activity. The third study was conducted in 1,432 adult Dutch sibling pairs from 622 families (11). Suggestive linkage was found for regular exercise participation on chromosome 19p13.3 (LOD=2.18).

Six studies tested for association of genetic variation in a number of candidate genes with exercise or physical activity phenotypes. In a study of adolescent girls (20), the calcium sensing receptor (CASR) gene was associated with weekly hours spent on physical activities. In a study of Pima Indians (32), the leptin receptor (LEPR) gene was associated with 24 hour energy expenditure and physical activity levels. In another study (30), the dopamine 2 receptor (DRD2) gene was associated with past year physical activity in women reporting European ancestry but not in subjects reporting African ancestry. In a sample of postmenopausal women (28), the aromatase (CYP19) gene was associated with physical activity. In a study of mild hypertensives (40), the angiotensin-converting enzyme (ACE) gene was associated with leisure-time physical activity. Finally, a study conducted in adults (19) showed that the melanocortin-4 receptor (MC4R) gene was associated with daily physical activity levels, independent of sex, age and BMI. The MC4R gene is located on chromosome 18, in the same region for which a significant linkage with child physical activity has been found (8).

This study is the first to report the results of a genome-wide association study for leisure-time exercise behavior, conducted in two independent samples comprising 1,772 Dutch and 978 American subjects genotyped on 470,719 SNP markers that passed quality controls. The aims of the study are 1) to identify new genetic variants that are associated with leisure-time exercise behavior, 2) to replicate the associations to previously reported candidate genes and linkage regions.

Materials and Methods

Subjects

The Netherlands

Dutch data on leisure-time exercise behavior were obtained from an on-going longitudinal study (1991-2004) on health, lifestyle and personality in twins and their family members registered at the Netherlands Twin Register (NTR) (6). A total of 1,860 unrelated individuals registered at the NTR were selected to be genotyped as part of the Genetic Association Information Network (GAIN) initiative (21), of which 1,703 served as controls and 160 as cases in a genome-wide association study for major depressive disorder (GAIN-MDD) (7,34). The study was approved by the Central Ethics Committee on Research Involving Human Subjects. All subjects provided written informed consent. After quality control of the genotype data (34) (see further below), data from 1,777 individuals were left for analysis, of whom 5 did not have valid data on exercise. For the remaining 1,772 individuals, we used their most recent exercise data. For 1,204 individuals (67.9%), data came from a survey sent out in 2004, for 325 individuals (18.3%) data came from a survey from 2002 and for the remaining individuals data came from earlier surveys (1991-2000). Mean age of the participants was 43.5 (sd=14.6, range=14.5-79.8) at the time of the survey collection. Mean BMI (defined as weight (kg) / height (m)2) was 24.3 (sd=3.6, range=14.3-42.0). There were 649 men (36.5%) and 1,128 women (63.5%).

United States of America

American data on leisure-time exercise behavior were collected as part of a larger study into the genetics of common human complex diseases/traits (e.g., osteoporosis, obesity, and height) in normal healthy subjects (13). The study was approved by the necessary Institutional Review Boards of involved institutions. Signed informed-consent was obtained from all study subjects before they entered the study. A random sample containing 978 unrelated Caucasian subjects was identified from our on-going study currently containing more than 6,000 individuals. All of the chosen subjects were of US Caucasians of Northern European origin living in Omaha, Nebraska and its surrounding areas. The inclusion and exclusion criteria were well defined (13). Briefly, subjects with chronic diseases and conditions involving vital organs (heart, lung, liver, kidney, and brain) and severe endocrinological, metabolic, and nutritional diseases were excluded from this study. Mean age of the participants was 49.96 (sd=18.3, range=19.1-87.2) at the time of the survey collection. Mean BMI was 27.3 (sd=5.2, range=14.2-49.4). There were 494 men (50.5%) and 484 women (49.5%).

Phenotypes

Leisure-time exercise behavior was measured in a comparable way in the Dutch and American samples, except for a minor difference in the first question asked. In the Dutch sample, the first question was “Do you participate in exercise regularly?” and in the American question this was “Do you take exercise for 60 minutes per week?”. These questions could be answered with ‘Yes’ or ‘No’. If the participants responded affirmative, they were asked to list all exercise activities, and to indicate on type, frequency and duration of each activity. All exercise activities were assigned a metabolic equivalent (MET) value according to the widely accepted Ainsworth's Compendium of physical activity (1). A MET score of 1 corresponds to the rate of energy expenditure when at rest (1 kcal/kg/h). Reported non-leisure time activities (biking to work, gardening, and work-related physical activity) were not counted as leisure time exercise. Over the remaining exercise activities, the total METhours were computed as MET*hours/week. Scores of non-exercising individuals were coded as zero. To keep consistent with existing epidemiological studies (15), we classified individuals as regular versus non-exercisers, based on a minimal threshold of at least 4 METhours weekly. For ancillary analysis, we further classified regular exercisers into five categories: 4-12 METhours, 13-21 METhours, 22-30 METhours, 31-39 METhours and >=40 METhours).

In the Dutch sample, all 1,772 individuals could be classified as regular (878 individuals, 49.5%) or non-exerciser (894 individuals, 50.5%). From the 878 regular exercisers, 817 individuals had valid data on type, frequency and duration of exercise and could be further classified into five categories of METhours: 4-12 METhours (N = 385, 47.1%), 13-21 METhours (N = 218, 26.7%), 22-30 METhours (N = 98, 12.0%), 31-39 METhours (N = 44, 5.4%) and >=40 METhours (N= 72, 8.8%).

In the American sample, the 978 individuals could be classified as regular (612 individuals, 62.6%) or non-exerciser (366 individuals, 37.4%). All the 612 regular exercisers had valid data on type, frequency and duration of exercise and could be further classified into five categories of METhours: 4-12 METhours (N = 57, 9.3%), 13-21 METhours (N = 329, 53.6%), 22-30 METhours (N = 194, 31.7%), 31-39 METhours (N = 19, 3.1%) and >=40 METhours (N= 14, 2.3%).

Genotypes

The Netherlands

DNA was extracted from frozen whole blood samples using the Puregene DNA Isolation kit (Gentra, Minneapolis, MN, USA). All procedures were performed according to the manufacturer's protocols. Genotyping was conducted by Perlegen Sciences, using a high-density oligonucleotide array-based platform (34). A total of 599,156 genotyped SNPs from 98.5% of all individuals participating in the GAIN-MDD study were returned. After quality control, 435,291 SNPs remained, of which 427,024 were autosomal SNPs. SNPs were excluded because of gross mapping errors (1,487 SNPs), duplicate errors (1,143), Mendelian inconsistencies (536 SNPs), minor allele frequency <0.01 (41,495 SNPs) and missing genotypes >0.05 (156,673 SNPs), or a combination of these reasons. Population stratification effects were examined with a subset of ∼127K SNPs in linkage equilibrium. First, a nearest neighbor approach based on genome-wide IBS estimates in PLINK was used to identify sample outliers. Second, a principal components analysis was carried out in the “smartpca” module in EigenSoft to compute two principal components that contrasted individuals with some Asian or African ancestry from individuals with clear European ancestry. Based on these two approaches, 58 individuals were removed. More details on genotyping procedures, genotype calling, and quality control checks of SNPs and samples in the GAIN-MDD sample can be found in a study from Sullivan and co-authors (34).

United States of America

Genomic DNA was extracted from whole human blood using a commercial isolation kit (Gentra systems, Minneapolis, MN, USA) following the protocols detailed in the kit. Genotyping with the Affymetrix Mapping 250K Nsp and 250K Sty arrays was performed using the standard protocol recommended by the manufacturer. Out of the initial full-set of 500,568 SNPs, we discarded 32,961 SNPs with sample call rate < 95%, another 36,965 SNPs with allele frequencies deviating from Hardy-Weinberg equilibrium (HWE) (p<0.001) and 51,323 SNPs with minor allele frequencies (MAF) < 1%. Thus, 381,100 SNPs remained. Population stratification effects were tested with the Structured Association method (25), performing 9 independent analyses assuming 2, 3, or 4 population strata and a set of 200, 2000, or 6000 randomly selected un-linked markers. The vast majority (>98%) of the subjects was tightly clustered together, with the exception of only six subjects, suggesting that there is essentially no population stratification in this sample. Exclusion of these six subjects resulted in almost identical association results (data not shown).

Genotype imputation

In order to be able to compare results at the SNP level, we imputed all SNPs that were observed in the Dutch sample but unobserved in the American sample and vice versa. Imputation was carried out in IMPUTE (22) using the HAPMAP phase II data available on the IMPUTE website http://www.stats.ox.ac.uk/∼marchini/software/gwas/impute.html#. IMPUTE computes the probabilities of each of the three possible genotypes for each unobserved SNP for each individual in the sample using the information of the surrounding observed genotypes for that individual and the LD information available from the HAPMAP data. IMPUTE also gives information about how well each SNP is imputed, quantified as the maximum posterior call averaged over all individuals for each SNP. The maximum posterior call averaged over all individuals over all SNPs that were imputed in the Dutch sample was 0.98 (median=0.99, minimum=0.44, maximum=1). 98.1% of the imputed SNPs had an average maximum posterior call of 0.90 or larger and 99.5% of 0.80 or larger. The mean, median, minimum and maximum average posterior call of all imputed SNPs in the American sample was, respectively, 0.90, 0.94, 0.46 and 1. 64.5% of the imputed SNPs had an average maximum posterior call of 0.90 or larger and 85.2% of 0.80 or larger.

We imputed 325,957 SNPs in the American sample and 280,033 SNPs in the Dutch sample. SNPs that were not well imputed (average maximum posterior probability < 0.80) in one of the samples were excluded in that sample (1.3% for the NTR and 2.9% for the American sample). We also excluded within each sample all SNPs with less or equal than 5 individuals in at least one of the genotype groups, based on a genotype calling threshold of 0.90 for the imputed SNPs. Given the size of our datasets, this roughly corresponded to excluding all SNPs with minor allele frequency smaller than 0.05 in the Dutch sample (9.3%) and 0.07 in the American sample (26.1%). This resulted in 632,044 SNPs in the Dutch sample (224,229 imputed and 407,815 observed) and 501 859 SNPs in the American sample (190,253 imputed and 311,606 observed). The overlap between the two samples was 470,719 SNPs that survived quality control in both samples.

Statistical analysis

Genome-wide association analyses were conducted using logistic regression in SNPtest (22). We used WGAviewer to create Manhattan and QQ plots and to annotate SNPs (14). Analyses were performed in both samples independently while taking the uncertainty of the imputed genotypes into account and including sex and age as covariates. Association of each SNP with the exercise participation phenotype was tested using a 2 degrees of freedom genotypic test.

Novel gene finding

for all 470,719 SNPs we computed the combined p-values across the two samples using Fisher's method in Haploview (3). To identify SNPs that might contribute to the heritability of exercise behavior we selected SNPs that 1) reached the genome-wide level for suggestive association (p<1/500,000=2.0*10-6) based on the combined p-value, or 2) that were nominally significant (p<0.01) in both samples. An additional criterion was that the direction of the effects was the same in the Netherlands and the USA.

Candidate gene replication

We inspected the association of all SNPs located in or in close vicinity (<10kb) of all candidate genes for physical activity phenotypes from previous candidate gene association studies, including the associated SNP reported in those studies if available. We used a replication strategy at the gene level, i.e. any SNP (not just the original SNP reported on) reaching a combined p-value smaller than 0.01 was considered to constitute a replication.

Linkage region replication

We inspected association of all SNPs located in the 95% confidence interval of all linkage peaks previously reported for exercise behavior and physical activity phenotypes. Because this involved thousands of SNPs with low average pairwise LD, only SNPs reaching a Bonferroni-corrected p-value of 0.01 / number of SNPs tested =∼ 1.0*10-5 in the combined sample was considered to be a replication.

For both novel identified SNPs as well as replicated SNPs, we performed checks for Hardy-Weinberg equilibrium (HWE tested at p<1.0*10-5), inspection of genotype calling cluster plots, and (if imputed) average maximum posterior call. Finally, to bolster our confidence in the relevance of the SNP for exercise behavior we performed an additional association analysis using weekly METhours as the phenotype and a combined p<0.01 as the criterion for significance.

Results

Novel gene finding

The Manhattan plot for exercise participation in the combined sample is given in Figure 1. The QQ plot is given in Figure 2. A list of the 20 most significant SNPs for exercise participation in the NTR and in the American sample are given, respectively, in Tables 1 and 2. In the Dutch sample, the lowest p-value was 2.8*10-6. In the American sample, the lowest p-value was 7.9*10-7. Table 3 displays the results of all SNPs that reach the threshold for genome-wide suggestive association (combined p<2.0*10-6) or that were nominally significant in both samples (p<0.01). The lowest combined p-value was 5.9*10-7. Only the 12 highlighted SNPs in Table 3 reached our a priori criteria of genome-wide significance in the combined sample or a nominal significance in both samples with the same direction of the allelic effects. Four of these SNPs are located in introns of genes; rs9633417 is located in the SH3-domain GRB2-like (endophilin) interacting protein 1 (SGIP1) gene, rs667923 in the deoxyribonuclease II beta (DNASE2B) gene, rs10946904 in the protease serine 16 (PRSS16) gene and rs238404 in the excision repair cross-complementing rodent repair deficiency, complementation group 2 (ERCC2) gene. SNP rs2762527 is located upstream of the 3′-phosphoadenosine 5′-phosphosulfate synthase 2 (PAPSS2) gene. The other seven SNPs are intergenic.

Figure 1.

Figure 1

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Manhattan plot of exercise participation for the combined p-values

Figure 2.

Figure 2

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QQ plot of exercise participation for the combined p-values

Table 1.

List of top 20 most significant SNPs for exercise participation in the Netherlands Twin Registry sample

SNP Chrom Bp Alleles P-value Beta-1 Beta-2 MAF Associated gene Location P-value American sample
rs10803261 1 241596482 C/G 3.2*10-5 -0.72 -0.89 0.27 EFCAB2 Intronic 0.37
rs11678571 2 47140345 C/G 3.8*10-5 -0.79 -0.69 0.31 TTC7A Intronic 0.65
rs10193648 2 134462763 A/G 1.6*10-5 -1.12 -0.89 0.22 - Intergenic 0.61
rs10197646 2 135596388 A/G 2.7*10-5 -1.66 -1.45 0.14 YSK4 Intronic 0.97
rs2159724 2 229817726 A/G 2.9*10-5 -0.17 0.47 0.41 PID1 Intronic 0.29
rs6550329 3 35211525 G/T 2.7*10-5 -0.48 -0.23 0.25 - Intergenic 0.50
rs3843917 6 154732923 C/G 2.1*10-5 0.40 0.59 0.38 NP_056368.1 Intronic 0.42
rs2891117 9 17977408 C/T 2.8*10-6 0.59 0.58 0.48 SH3GL2 Intergenic -
rs7919331 10 5541181 A/G 1.5*10-5 -0.03 -0.64 0.41 Q6ZR35_HUMAN Intergenic 0.07
rs8181407 10 72194444 C/T 1.5*10-5 -0.35 0.73 0.19 ADAMTS14 Downstream 0.67
rs17311303 11 119882018 A/G 2.5*10-5 0.82 0.43 0.34 Q3C1X2_HUMAN Intergenic 0.48
rs631750 12 3946116 C/T 3.0*10-5 0.19 -0.52 0.35 PARP11 Intergenic 0.82
rs11829154 12 3947492 A/G 3.4*10-5 0.65 0.33 0.34 PARP11 Intergenic -
rs7971136 12 12051742 A/G 2.8*10-6 0.01 0.66 0.41 BCL2L14 Intergenic 0.57
rs7982922 13 42771336 A/G 2.5*10-5 1.85 1.58 0.11 ENOX1 Intronic 0.80
rs10500278 19 43186344 A/G 2.3*10-5 0.69 0.81 0.30 SIPA1L3 Intronic 0.68
rs1433089 19 57198797 C/T 2.2*10-5 -0.57 -0.08 0.16 ZNF615 5prime_utr 0.49
rs2070513 21 34275744 G/T 1.6*10-5 0.46 -0.03 0.28 Q6ZRX0_HUMAN Intergenic 0.20
rs2834315 21 34277280 G/T 2.4*10-5 0.45 -0.08 0.28 Q6ZRX0_HUMAN Intergenic 0.31
rs11088265 21 34277488 A/G 2.5*10-5 0.53 0.09 0.28 Q6ZRX0_HUMAN Intergenic 0.24
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Chrom=Chromosome, Bp=Basepair position, Beta-1 = the log-odds ratio to be an exerciser of the heterozygous genotype group compared with the first homozygous genotype group (for example CG versus CC for the first SNP in the table), Beta-2 = the log-odds ratio to be an exerciser of second homozygous genotype group compared with the heterozygous genotype group (for example GG versus CG for the first SNP in the table), MAF=Minor allele frequency. Allele in bold is the minor allele. Note that sex and age were included as covariates in these analyses.

Table 2.

List of top 20 most significant SNPs for exercise participation in the American sample

SNP Chrom Bp Alleles P-value Beta-1 Beta-2 MAF Associated gene Location P-value Dutch sample
rs7521315 1 5195141 A/G 1.9*10-5 -0.33 -0.91 0.36 - Intergenic 0.95
rs878465 1 30916121 A/G 1.9*10-5 -0.60 0.07 0.49 LAPTM5 Intergenic 0.07
rs912216 1 30916182 A/G 1.9*10-5 -0.67 -0.07 0.49 LAPTM5 Intergenic 0.08
rs10737353 1 30920144 A/C 1.4*10-5 -0.66 -0.07 0.49 LAPTM5 Intergenic 0.08
rs17065829 3 62018255 C/G 1.3*10-5 1.82 1.02 0.11 PTPRG Intronic 0.78
rs32732 5 3509763 A/G 2.0*10-5 -0.68 -1.25 0.12 IRX1 Intergenic 0.27
rs7743055 6 145718922 C/T 1.3*10-5 0.73 1.20 0.11 EPM2A Intergenic 0.63
rs4896792 6 145727562 A/T 2.1*10-6 -0.92 -1.64 0.12 EPM2A Intergenic 0.67
rs4896794 6 145730006 C/G 2.0*10-6 0.72 1.63 0.12 EPM2A Intergenic 0.70
rs6926799 6 145741712 A/G 7.1*10-6 -1.22 -1.91 0.11 EPM2A Intergenic 0.65
rs6561985 13 58290774 C/G 4.4*10-6 -0.45 1.10 0.20 - Intergenic 0.53
rs2321884 13 58349733 C/T 2.1*10-6 -0.41 1.30 0.19 - Intergenic 0.36
rs7989735 13 109160955 C/T 2.4*10-5 -0.42 0.55 0.35 IRS2 Intergenic 0.03
rs17773922 19 34522469 C/T 2.8*10-5 0.90 0.48 0.38 UQCRFSL1 Intergenic 0.003
rs13051865 21 30531338 C/T 7.9*10-7 0.69 0.78 0.40 CLDN8 Intergenic 0.34
rs2832691 21 30537456 C/T 1.8*10-6 -0.09 -0.78 0.41 CLDN8 Intergenic 0.46
rs720901 21 30537905 A/G 2.2*10-5 -0.02 -0.64 0.36 CLDN8 Intergenic 0.72
rs2832700 21 30542823 A/T 1.6*10-5 0.63 0.66 0.36 CLDN8 Intergenic 0.83
rs2832741 21 30564220 A/C 1.9*10-6 -0.10 -0.78 0.41 KR241_HUMAN Intergenic 0.52
rs2835881 21 37955001 A/G 1.7*10-5 -0.82 -0.96 0.46 KCNJ6 Intronic 0.83
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See Table 1 for abbreviations.

Table 3.

List of SNPs for exercise participation that reach threshold for genome-wide suggestive association (combined p<2.0*10-6) or that are nominally significant in both samples (p<0.01)

SNP Chr Bp Alleles P-value combined P-value Dutch sample P-value American sample β-1 NTR β-2 NTR β-1 USA β-2 USA MAF NTR MAF USA Associated gene Location
rs9633417 1 66883433 A/C 0.0001 0.003 0.003 -1.65 -1.86 -2.41 -2.11 0.09 0.10 SGIP1 Intronic
rs667923 1 84583998 A/G 0.0003 0.006 0.005 0.72 0.46 0.18 0.61 0.18 0.19 DNASE2B Intronic
rs1766581 1 228754028 C/T 0.0008 0.009 0.009 0.25 0.40 0.06 0.58 0.44 0.44 SIPA1L2 Intergenic
rs4355145 2 75867660 A/G 0.0003 0.009 0.003 -0.18 -0.46 -1.09 -0.83 0.22 0.20 C2orf3 Intergenic
rs9789774 2 75905098 A/G 2.9*10-5 0.005 0.0004 -0.26 -0.53 -1.23 -0.95 0.22 0.20 C2orf3 Intergenic
rs3935887 2 75993336 A/G 0.0001 0.009 0.001 0.05 -0.26 -0.75 -0.53 0.29 0.28 C2orf3 Intergenic
rs1829618 2 212258831 A/G 0.0002 0.004 0.004 -0.16 0.19 -1.27 -1.01 0.20 0.17 ERBB4 Intronic
rs10203022 2 212284296 C/T 0.0004 0.004 0.009 0.25 0.59 -1.43 -1.06 0.13 0.13 ERBB4 Intronic
rs7601664 2 212318270 A/G 0.0005 0.006 0.007 -0.36 -0.42 -0.39 1.05 0.13 0.12 ERBB4 Intronic
rs13013897 2 228973701 A/G 6.1*10-6 0.002 0.0002 0.28 -0.07 0.10 -0.49 0.38 0.38 SKIP Intergenic
rs1283098 3 108450567 A/G 7.5*10-5 0.001 0.006 -0.21 0.26 0.20 0.61 0.45 0.45 CCDC54 Intergenic
rs2593943 3 110172911 A/G 0.0007 0.009 0.007 -0.05 0.28 0.39 -0.04 0.42 0.42 MORC1 Nonsynon
rs10946904 6 27325215 C/T 0.0004 0.008 0.004 0.19 0.59 0.41 0.50 0.25 0.24 PRSS16 Intronic
rs7793555a 7 80191396 A/G 5.9*10-7 0.0004 8.8*10-5 -0.05 0.35 0.60 0.02 0.26 0.28 SEMA3C Intronic
rs17837697 7 156120121 C/T 0.0004 0.004 0.009 -0.06 0.28 -0.81 -0.73 0.26 0.27 LMBR1 Intronic
rs1869075 8 530949 A/G 5.2*10-5 0.008 0.0005 0.30 -0.01 -0.90 -0.57 0.32 0.30 Q8NF75_HUMAN Intergenic
rs330075 8 9195294 A/G 8.0*10-6 0.001 0.0004 0.39 0.19 -0.42 0.35 0.40 0.38 Q6XYA8_HUMAN Intergenic
rs330069 8 9199283 A/G 7.9*10-5 0.004 0.002 0.23 -0.13 -0.64 -0.27 0.41 0.38 Q6XYA8_HUMAN Intergenic
rs330068 8 9199458 A/G 3.0*10-6 0.0005 0.0004 0.28 -0.13 -0.84 -0.50 0.37 0.33 Q6XYA8_HUMAN Intergenic
rs6988046 8 9985843 A/C 3.2*10-5 0.008 0.0003 -0.16 -0.67 0.04 1.33 0.22 0.23 MSRA Intronic
rs1149996 10 9395673 A/G 0.0004 0.006 0.005 0.28 0.40 0.06 -0.53 0.43 0.42 Q6ZMV0_HUMAN Intergenic
rs1149997 10 9395973 C/T 0.0004 0.005 0.008 -0.10 -0.40 0.56 0.52 0.43 0.42 Q6ZMV0_HUMAN Intergenic
rs1150001 10 9396637 A/G 0.0004 0.006 0.007 0.28 0.40 0.05 -0.53 0.43 0.42 Q6ZMV0_HUMAN Intergenic
rs1150002 10 9396735 A/G 0.0005 0.006 0.008 0.28 0.40 0.03 -0.53 0.43 0.42 Q6ZMV0_HUMAN Intergenic
rs10827786 10 37847539 A/G 0.0002 0.002 0.008 1.44 1.08 0.26 0.71 0.09 0.11 ZNF248 Intergenic
rs2762527 10 89408147 G/T 0.0001 0.005 0.002 -0.33 -0.30 -0.25 -0.72 0.37 0.39 PAPSS2 Upstream
rs10509410 10 89429981 A/G 5.1*10-5 0.003 0.001 -0.06 0.32 0.10 0.61 0.46 0.47 PAPSS2 Intronic
rs7908056 10 89434630 A/G 1.8*10-5 0.003 0.0004 -0.06 0.32 0.10 0.64 0.46 0.46 PAPSS2 Intronic
rs1626521 11 73391987 A/G 0.0007 0.007 0.009 -0.47 -0.19 0.16 -0.23 0.27 0.24 UCP3 Intronic
rs12101846 15 59358620 G/T 0.0003 0.003 0.008 -0.35 0.04 -0.11 0.67 0.30 0.29 RORA Intergenic
rs12905612 15 91581352 A/T 0.0002 0.004 0.004 0.12 0.55 0.45 0.07 0.32 0.34 Q6UXP9_HUMAN Intergenic
rs9944863 18 1590972 C/T 0.0002 0.002 0.0096 -0.35 -0.62 0.22 -0.22 0.23 0.22 C18orf2 Intergenic
rs2166030 18 61340512 C/T 0.0003 0.002 0.0096 -0.22 0.17 0.18 0.48 0.45 0.44 CDH7 Intergenic
rs8108111 19 34501691 A/G 3.2*10-5 0.002 0.001 -0.19 0.27 0.32 -0.36 0.43 0.40 UQCRFSL1 Intergenic
rs10415631 19 34502402 C/G 9.1*10-5 0.002 0.004 -0.47 -0.28 0.62 0.35 0.43 0.40 UQCRFSL1 Intergenic
rs10423930 19 34502527 G/T 4.2*10-5 0.002 0.002 -0.47 -0.28 0.65 0.34 0.43 0.41 UQCRFSL1 Intergenic
rs10425007 19 34504237 C/T 4.6*10-5 0.002 0.002 -0.47 -0.28 0.64 0.33 0.43 0.40 UQCRFSL1 Intergenic
rs10424492 19 34505271 A/G 5.2*10-5 0.002 0.002 -0.47 -0.28 0.64 0.34 0.43 0.40 UQCRFSL1 Intergenic
rs7253662 19 34507562 C/T 0.0001 0.002 0.006 -0.19 0.28 0.32 -0.22 0.43 0.42 UQCRFSL1 Intergenic
rs10423259 19 34511945 A/G 4.6*10-5 0.002 0.002 -0.45 -0.28 0.63 0.28 0.44 0.41 UQCRFSL1 Intergenic
rs8113496 19 34512369 A/G 7.0*10-5 0.002 0.002 -0.17 0.28 0.35 -0.25 0.44 0.41 UQCRFSL1 Intergenic
rs8102393 19 34512400 C/T 3.3*10-5 0.002 0.001 -0.45 -0.28 0.65 0.29 0.44 0.41 UQCRFSL1 Intergenic
rs8107784 19 34516147 C/T 9.7*10-6 0.003 0.0002 -0.17 0.30 0.43 -0.28 0.42 0.41 UQCRFSL1 Intergenic
rs1122022 19 34517054 A/G 9.1*10-6 0.002 0.0003 -0.45 -0.28 0.73 0.35 0.44 0.41 UQCRFSL1 Intergenic
rs17773922a 19 34522469 C/T 1.4*10-6 0.003 2.8*10-5 -0.44 -0.26 0.90 0.48 0.44 0.38 UQCRFSL1 Intergenic
rs10415713 19 34523381 C/T 4.7*10-6 0.002 0.0001 -0.17 0.28 0.41 -0.33 0.44 0.41 UQCRFSL1 Intergenic
rs10423829 19 34524171 C/G 7.9*10-6 0.002 0.0002 -0.45 -0.28 0.71 0.31 0.44 0.41 UQCRFSL1 Intergenic
rs10425546 19 34524238 C/T 9.9*10-6 0.002 0.0003 -0.17 0.28 0.38 -0.32 0.44 0.41 UQCRFSL1 Intergenic
rs10406567 19 34524595 C/T 9.5*10-6 0.002 0.0003 -0.45 -0.28 0.70 0.31 0.44 0.41 UQCRFSL1 Intergenic
rs13346397 19 34542618 A/T 7.0*10-6 0.002 0.0002 -0.17 0.28 0.40 -0.33 0.44 0.41 UQCRFSL1 Intergenic
rs7508455 19 34542918 A/C 1.1*10-5 0.002 0.0003 -0.17 0.28 0.38 -0.36 0.44 0.40 UQCRFSL1 Intergenic
rs7508469 19 34543002 A/C 1.3*10-5 0.002 0.0004 -0.17 0.28 0.39 -0.30 0.44 0.41 UQCRFSL1 Intergenic
rs8107741 19 34550442 G/T 7.7*10-5 0.002 0.003 -0.46 -0.28 0.58 0.22 0.44 0.42 UQCRFSL1 Intergenic
rs11880983 19 34551705 C/T 1.3*10-5 0.002 0.0004 -0.46 -0.28 0.69 0.28 0.44 0.41 UQCRFSL1 Intergenic
rs6509387 19 34553547 A/T 2.4*10-5 0.002 0.0007 -0.46 -0.28 0.68 0.33 0.44 0.41 UQCRFSL1 Intergenic
rs6509388 19 34553722 A/G 8.6*10-6 0.004 0.0002 -0.17 0.28 0.41 -0.32 0.45 0.41 UQCRFSL1 Intergenic
rs1558364 19 34557185 A/G 7.6*10-6 0.004 0.0001 -0.45 -0.28 0.75 0.34 0.45 0.40 UQCRFSL1 Intergenic
rs17624933 19 34559723 C/T 1.8*10-5 0.004 0.0003 -0.18 0.27 0.40 -0.29 0.45 0.41 UQCRFSL1 Intergenic
rs238404 19 50557530 C/Tb 4.5*10-6 8.8*10-5 0.003 0.26 -0.41 0.21 -0.32 0.49 0.45 ERCC2 Intronic
rs4816397 21 31766413 A/C 0.0001 0.009 0.0010 0.63 0.67 -0.97 -1.15 0.22 0.22 TIAM1 Intronic
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a

genome-wide suggestive association,

b

minor allele in Dutch sample is T and in American sample C. In grey: SNPs have same direction of effect in both samples. See Table 1 for abbreviations.

Candidate gene replication

Table 4 presents the association of SNPs in the six candidate genes with exercise participation in the two samples. Two genes (the CYP19A1 and LEPR gene) reached our a priori criterion for replication. The lowest combined p-value in the CYP19A1 gene is 9.8*10-3 for SNP rs2470158. This SNP is nominally significant in the Dutch sample (p=7.2*10-3), but not in the American sample (p=0.18). There are no other SNPs in the CYP19A1 gene that are nominally significant. Inspecting the LD structure in this gene, it is apparent that SNP rs2470158 is not in high LD with any surrounding SNPs. The lowest combined p-value in the LEPR gene is 1.8*10-3 for SNP rs12405556, which is only 4604 basepair away from the significant SNP that was reported in the previous candidate gene study (rs1137101) (32). SNP rs12405556 is in moderate LD with SNP rs1137101 (r2=0.44). SNP rs12405556 was significant in the American sample (p=5.0*10-4) but not in the Dutch sample (p=0.39). In total, 14 of the tested SNPs in the LEPR gene have a p-value smaller than 0.01 in the American sample, but none of these SNPs were nominally significant in the Dutch sample.

Table 4.

Testing association with 6 candidate genes for exercise participation

Candidate gene Study Chrom Lowest p-value combineda SNP #SNPs in gene Coverage Lowest p Dutch sample SNP Lowest p American sample SNP
ACE Winnicki et al (2004) 7q23.3 0.38 rs8066114 7 0.50 0.39 rs8066114 0.21 rs4611524
CASR Lorentzon et al. (2001) 3q21.1 0.06 rs9831894 42 0.68 0.05 rs9831894 0.08 rs10934578
CYP19A1 Salmen et al. (2003) 15q21.2 0.0098 rs2470158 43 0.70 0.007 rs2470158 0.18 rs2470158
DRD2 Simonen et al. (2003a) 11q23 0.21 rs2234689 22 0.82 0.14 rs2470158 0.07 rs2471857
LEPR Stefan et al. (2002) 1p31.3 0.002 rs12405556 59 0.88 0.01 rs11585329 0.0005 rs12405556
MC4R Loos et al. (2005) 18q21.32 0.10 rs9965495 5 0.69 0.10 rs17066829 0.04 rs9965495
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a

in gene or 10kb around gene. Chrom=Chromosome, Coverage = Number of observed SNPs in gene + Number of tagged common SNPs (R2>0.80) divided by the total number of common SNPs in HAPMAP. In grey: p<0.01, Note that sex and age were included as covariates in these analyses.

Linkage region replication

Table 5 presents an overview of the most significant SNPs in the linkage regions that have been reported for exercise and physical activity phenotype in previous studies (8,11,31). None of the SNPs located in the linkage regions reached our a priori criterion for replication. Moreover, most SNPs with the lowest combined p-values were often not located near the peak markers.

Table 5.

Testing association in 10 linkage regions for exercise participation

Linkage region Study Marker at peak Flanking markers a Lowest p-value combined SNP Distance of SNP to peak marker (bp) #SNPs in region Gene associated with SNP Lowest p Dutch sample Lowest p American sample
19p13.3 De Moor et al. (2007) D19S247 D19S591 – D19S865 0.005 rs2118706 5.524.705 447 ADAMTS10 0.004 0.002
18q12.2 Cai et al. (2006) D18S1102 D18S1102 – D18S474 0.0009 rs10853477 3.728.030 2702 - 0.0004 0.001
18q21.2 Cai et al. (2006) D18S474 D18S474 – D18S61 0.0003 rs2166030 14.391.594 3814 CDH7 0.0004 0.0007
2p22.3 Simonen et al. (2003b) D2S2347 - 0.002 rs17010549 2.081.010 1269 GALNT14 0.34 0.0007
4q31.21 Simonen et al. (2003b) UCP1 - 0.008 rs10440457 359.319 1021 SCOC 0.0009 0.004
7p13-p12 Simonen et al. (2003b) IGFBP1 - 0.0001 rs11763891 2.572.126 1230 AC004455.2 0.0003 7.8 *10-5
9q31 Simonen et al. (2003b) D9S938 - 0.0004 rs11792745 1.280.865 1313 SMC2 4.2*10-5 0.003
13q22 Simonen et al. (2003b) D13S317 - 0.002 rs7938327 1.331.016 2602 - 0.0008 0.002
15q13 Simonen et al. (2003b) D15S165 - 0.0009 rs11161249 5.367.880 2018 ATP10A 0.0009 0.001
20q12 Simonen et al. (2003b) PLCG1 - 0.0001 rs2425456 186.970 2023 TOP1 0.0009 0.0001
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- = Not available,

a

In the study by Simonen et al. (2003b) no confidence intervals or flanking regions are given. For regions reported in this study we used a 6 Mb interval around the peak. Note that the peak at the marker C11P15_3 reported by Simonen et al. (2003b) is omitted from this table, since this marker could not be identified. Sex and age were included as covariates in these analyses.

Additional checks and association analyses for the surviving SNPs

The 12 surviving novel SNPs and the 2 remaining SNPs in candidate genes were all in Hardy Weinberg equilibrium in both the group of non-exercisers and exercisers. The imputation quality was very good for the 13 SNPs imputed in either sample (average maximum posterior probability>0.96). Table 6 presents additional association analyses on exercise participation for the 14 surviving SNPs using BMI as an additional covariate,. Notably, all associations remained significant after correction for BMI. Table 6 also presents the association of these SNPs to the 6-category METhours phenotype. Nine of the surviving SNPs were associated with exercise intensity expressed as weekly METhours at a nominal significant level.

Table 6.

Additional association analyses for exercise participation (also correcting for BMI) and for METhours for the remaining SNPs

Association with exercise participation
(covariates sex, age and BMI)		 Association with METhours
(covariates sex and age)
P value P value
NTR USA Combined NTR USA Combined
rs9633417 SGIP1 0.006 0.003 0.0002 0.06 0.007 0.003
rs667923 DNASE2B 0.01 0.005 0.0005 0.06 0.02 0.009
rs1766581 INTERGENIC 0.005 0.007 0.0004 0.0004 0.001 8.00*10-6
rs4355145 INTERGENIC 0.006 0.0009 6.60*10-5 0.64 0.0008 0.004
rs9789774 INTERGENIC 0.004 0.002 7.34*10-5 0.76 0.0005 0.004
rs13013897 SKIP 0.003 0.004 0.0001 0.08 0.001 0.001
rs10946904 PRSS16 0.005 0.01 0.0005 0.25 0.02 0.03
rs10827786 INTERGENIC 0.003 0.02 0.0006 0.04 0.01 0.03
rs2762527 PAPSS2 0.01 0.002 0.0002 0.02 0.01 0.003
rs12101846 INTERGENIC 0.004 0.0001 7.08*10-6 0.04 0.08 0.02
rs12905612 INTERGENIC 0.003 0.01 0.0004 0.40 0.004 0.01
rs238404 ERCC2 0.0007 0.003 2.82*10-5 0.0003 0.02 0.0001
rs2470158 CYP19A1 0.006 0.18 0.008 0.0062 0.23 0.01
rs12405556 LEPR 0.37 0.003 0.009 0.76 3.10*10-5 0.0003
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Discussion

This study is the first to report the results from a genome-wide association study for leisure-time exercise behavior, using data from 1,772 unrelated Dutch adults and 978 unrelated American adults with on 470,719 SNPs. The genome-wide association analyses revealed several novel SNPs in the genes SGIP1, DNASE2B, PRSS16, ERCC2 and PAPSS2 that are associated with exercise participation. Inspection of for the DNASE2B, PRSS16 and ERCC2 genes on the Ensembl and Entrez Gene websites revealed no immediate relevance for exercise behavior. The PAPSS2 gene encodes a protein that is involved in the sulfation of compounds such as lipids, carbohydrates and exogenous drugs and has been related to skeletal development and arthrosis (16,38).

The SGIP1 gene is primarily expressed in the hypothalamus of the brain and is implicated in the regulation of energy homeostasis. A study in rodents (36) suggests that the SGIP1 protein encoded by this gene has a physiological role in the hypothalamic neuronal systems that promotes positive energy balance and weight gain. Suppression of the mRNA product of the SGIP1 gene in rodents was shown to result in decreased food intake and increased metabolic rate. The SGIP1 gene is highly conserved between species and may therefore also be of importance for energy homeostasis in humans. In our combined samples the SGIP1 gene was associated with exercise behavior independent of BMI. This suggests that the gene is involved also in energy expenditure and the drive to exercise.

Of the candidate genes for exercise and physical activity phenotypes extracted from the literature (19,20,28,30,32,40), we replicated association with the CYP19A1 gene in the Dutch sample and with the LEPR gene (rs12405556) in the American sample. The CYP19A1 gene is involved in the biosynthesis of estrogens from androgens and is suggested to play a role in bone mineral density and body fat regulation (26,28,37). The CYP19A1 gene has previously been related to physical activity in a study of early postmenopausal women (28). Leptin is a hormone that has an important role in the regulation of energy balance. The LEPR gene, like the SGIP1 gene, is expressed in the hypothalamus (24). The LEPR gene has frequently been linked to obesity and type 2 diabetes mellitus and is thought to influence food intake and energy expenditure (17,24). As for the SGIP1 gene, the effects of LEPR were independent of BMI. This corroborates a study in a sample of 268 non-diabetic Pima Indians, in which the LEPR gene was found to be associated with 24 hour energy expenditure and physical activity, independent of adiposity (32).

Associations of SNPs within several candidate linkage regions (8,11,31) were not replicated after multiple testing within each region had been taken into account. Still, one nominally significant SNP (rs10440457, p=7.9*10-3) in the 4q31.21 region is located only 359 kb away from the UCP1 gene. Uncoupling proteins are involved in metabolic energy expenditure (27). The 4q31.21 region has also been implicated to influence exercise ability (12).

A number of recent genome-wide association studies show that the effect sizes of most loci that are identified for complex human diseases so far are small and very large samples including ten thousands of individuals instead of thousands of individuals are needed to detect these effects (9,18,39). Although we combined full GWA datasets from two relatively large samples, larger samples are likely to be needed to more robustly detect the small polygenic effects that account for the heritability of exercise behavior. A second limitation of our study is that parts of the relevant genomic variation may not have been captured. Variation in the final SNP set used may not have tagged all of the common variation in the genome. Moreover, our analyses were restricted to the autosomal genome, which excludes genes on the sex chromosomes and the mitochondrial DNA. The mitochondrial DNA is especially of interest because of its well-known involvement in energy metabolism and ATP generation (29).

To conclude, this study found an association between genetic variants in the SGIP1, CYP19A1 and LEPR genes and voluntary exercise behavior. These effects on the drive to exercise were independent of BMI. We previously hypothesized that genetic effects on exercise ability, acute psychological effects of exercise and personality traits would account for the heritability of adult exercise behavior (10,11). The results of the current study suggest an important role for a different pathway, the hypothalamic regulation of the energy balance. Larger GWA studies are needed to identify the full palette of genetic variants influencing voluntary exercise behavior.

Acknowledgments

Disclosure of funding: For the Dutch sample, we acknowledge financial support from the Netherlands Organization for Scientific Research (NWO): Database Twin Register (575-25-006), Twin-family database for behavior genetics and genomic studies (480-04-004), genetic basis of anxiety and depression (904-61-090); resolving cause and effect in the association between exercise and well-being (904-61-193); twin-family database for behavior genomics studies (480-04-004); twin research focusing on behavior (400-05-717), Center for Medical Systems Biology (NWO Genomics); Spinozapremie (SPI 56-464-14192); Centre for Neurogenomics and Cognitive Research (CNCR-VU); genomewide analyses of European twin and population cohorts (EU/QLRT-2001-01254); genome scan for neuroticism (NIMH R01 MH059160); Geestkracht program of ZonMW (10-000-1002); matching funds from universities and mental health care institutes involved in NESDA (GGZ Buitenamstel-Geestgronden, Rivierduinen, University Medical Center Groningen, GGZ Lentis, GGZ Friesland, GGZ Drenthe). Genotyping was funded by the Genetic Association Information Network (GAIN) of the Foundation for the US National Institutes of Health, and analysis was supported by grants from GAIN and the NIMH (MH081802). Genotype data were obtained from dbGaP (http://www.ncbi.nlm.nih.gov/dbgap, accession number phs000020.v1.p1). Statistical analyses were carried out on the Genetic Cluster Computer (http://www.geneticcluster.org) which is financially supported by the NWO (480-05-003).

For the American sample, we acknowledge financial support from the National Institute of Health (NIH) of USA (R01 AR050496, R21 AG027110, R01 AG026564, P50 AR055081 and R21 AA015973). The study was also benefited from grants from National Science Foundation of China, Huo Ying Dong Education Foundation, HuNan Province, Xi'an Jiaotong University, and the Ministry of Education of China.

The results of this study do not constitute endorsement by ACSM.

Footnotes

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