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Distribution of allele frequencies for genes associated with physical activity and/or physical capacity in a homogenous Norwegian cohort- a cross-sectional study



There are large individual differences in physical activity (PA) behavior as well as trainability of physical capacity. Heritability studies have shown that genes may have as much impact on exercise participation behavior as environmental factors. Genes that favor both trainability and participation may increase the levels of PA. The present study aimed to assess the allele frequencies in genes associated with PA and/or physical capacity, and to see if there is any association between these polymorphisms and self-reported PA levels in a cohort of middle-aged Norwegians of Scandinavian descent (n = 831; mean age mean age (± SD) 55.5 ± 3.8 years).


The genotype distributions of the ACTN3 R577X, ACE I/D and MAOA uVNTR polymorphisms were similar to other populations of European descent. When comparing the genotype distribution between the low/medium level PA group (LMPA) and high level PA groups (HPA), a significant difference in ACTN3 577X allele distribution was found. The X allele frequency was 10% lower in the HPA level group (P = 0.006). There were no differences in the genotype distribution of the ACE I/D or MAOA uVNTR polymorphism. Education and previous participation in sports or outdoor activities was positively associated with the self-reported PA levels (P ≤ 0.001).


To the best of our knowledge, this is the first study to report association between ACTN3 R577X genotype and PA level in middle-aged Scandinavians. Nevertheless, the contribution of a single polymorphism to a complex trait, like PA level, is likely small. Socioeconomic variables, as education and previous participation in sports or outdoor activities, are positively associated with the self-reported PA levels.


Physical activity (PA) is a complex behavior [1], influenced by both genetic and environmental variables [2,3,4]. The health effects of PA are well described [5,6,7], as are the negative consequences of inactivity [8,9,10]. Insufficient PA levels have been linked to an increased risk of many chronic diseases [11]. Physical inactivity is a modifiable risk factor meaning that increased PA may have a positive effect on several diseases, e.g. diabetes and hypertension [5, 12]. Recommendation for maintaining good health is aerobic PA for a minimum of 150 min per week at moderate intensity or a minimum of 75 min per week at high intensity [10]. Despite strong evidence for genetic influence on PA [1], it is complex and not yet fully understood.

There are large inter-individual differences in PA levels [13] and trainability [14, 15]. Genes influence response to exercise as well as intrinsic behavior like motivation for activity [16, 17]. Thus, genes that favor both trainability and participation may increase the levels of PA [4, 14]. With increased age, heredity may have an even larger impact on exercise participation behavior [18]. Twin studies have shown that up to 62% of PA levels may be explained by genetic factors [4]. However, PA level is affected by the interaction of many genes, most of them with only a small effect each [13].

The two genes most studied in relation to trainability of cardiovascular traits [7, 19], physical function [20] and muscle strength [21] are α-actinin-3 (ACTN3) and angiotensin-converting enzyme (ACE). ACTN3 is a member of the alpha-actin binding protein family. It is predominately expressed in fast twitch muscle fibers [22]. The wild type RR genotype has been reported to be more common among athletes in sprint/power sporting disciplines [23, 24]. The bases for many previous studies have been ethnically highly heterogenous cohorts [25]. Around 18% of the European population are homozygous for the minor allele R577X polymorphism, a premature stop codon [23], with large differences in the minor allele frequency among populations. The frequency of the X allele covaries with the latitude gradient [26]. Absence of the α-actinin-3 protein has been associated with a range of alterations in muscle function [27], including more efficient muscle metabolism [28], increased post-exercise muscle damage and risk of injuries [25]. The R577X polymorphism is one of only two known loss-of-function polymorphisms in humans known to have a selective advantage [27], it is more frequently observed in athletes participating in endurance disciplines [23, 24].

ACE codes for the angiotensin-converting enzyme and plays a role in blood pressure regulation [29]. It also influences skeletal muscle metabolism [30] and thus aerobic capacity [31, 32]. Aerobic capacity has been shown to be an important determinant for PA levels [33]. The ACE insertion/deletion (I/D) polymorphism is a length polymorphism, where a 287-bp Alu repeat is either present or absent [34]. The D allele is mostly associated with sprinting performance ability [35], while the I allele is associated with endurance performance ability [35, 36]. Studies conducted on non-athletes have revealed that the ACE I/D polymorphism may also influence responses to strength training [37]. Even though it has been suggested to influence the PA level [38, 39], the results are inconclusive [40].

In addition to genes influencing physiological exercise responses, genes altering PA motivation may also influence the PA level [16]. The dopaminergic system has been a subject to a number of studies on voluntary PA due to its role in reward systems and motor movement, [16, 41,42,43] and it has been shown to influence the inherent motivation to run in female mice [42].

Monoamine oxidase A (MAOA) is one of the genes in the dopaminergic pathways [44] that have been found to influence sedentary behavior [16]. It codes for an enzyme involved in oxidation of neurotransmitters, especially, serotonin, norepinephrine and dopamine [45], and may thus play a role in behavior [42, 44]. The MAOA gene is located on the X chromosome and a variable number of tandem repeat sequence upstream from MAOA (MAOA uVNTR) has been shown to influence the transcription levels of the enzyme [44]. Six alleles have previously been reported [46] with the major MAOA allele 3 having lower transcriptional activity (TA) than the other common alleles 3.5 and 4 (high TA alleles) [44, 47] It has therefore been hypothesized that individuals with MAOA high TA alleles degrade monoamine neurotransmitters more rapidly ultimately leading to lower PA levels [16]. Also the ACE gene may play a role in the dopaminergic pathways [48, 49], as the renin-angiotensin system, which the enzyme is a part of, and the dopaminergic system interacts [50]. Thus, ACE might be involved in the neurobiological regulation of exercise motivation [13, 16]. However, evidence for such relationships is still weak.

Identifying the role of genes and investigating their effect on PA behavior may contribute to further understanding of the large individual differences in PA behavior. Since many studies have been performed on either athletes, well-trained participants or patient groups, they generally have a low number of participants, and seldom represent the general population. In addition, there are large variations in allele frequencies between populations. In order to advance the knowledge, studies on larger and more homogenous cohorts are needed.

The functional ACTN3 R577X polymorphism has been associated with a range of different exercise and performance related phenotypes, but few studies, if any, have investigated its relation to PA levels. On the other hand, both ACE I/D and MAOA uVNTR polymorphisms have been studied in relation to PA phenotypes, however, the results have been inconsistent. Therefore, the aim of the present study was to assess ACTN3, ACE and MAOA allele frequencies in a cohort of middle-aged Norwegians of mainly Scandinavian descent, and to investigate any associations between these genes and self-reported PA levels. In addition, the authors wanted to look for associations between socioeconomic variables, such as education and previous participation in sports or outdoor activities, and self-reported PA levels.



Blood samples and questionnaire data for 416 males and 415 females were available in this study. The mean age of the subjects was 55.5 ± 3.8 years. Participants were slightly overweight, as the mean BMI was 26.1 ± 3.8. However, the BMI in the HPA group was significantly lower than in the LMPA level group (P = 0.001; Table 1). The proportion of the cohort with higher education was 24.7%.

Table 1 Anthropometric data according to self-reported PA level

Physical activity data

Of the 831 participants, 25.9 and 74.1% reported LMPA and HPA, respectively. Females reported a significantly higher PA level compared to males (P < 0.01). Regular participation in sports or outdoor activities at a younger age was reported to be 51.7%, and participation was higher among males (59.4%) than females (44.0%; P < 0.01). Prior participation in sports and outdoor activities was positively associated with reported PA level later in life (P < 0.01). Similarly, higher education was positively associated with higher PA levels (P < 0.01).

Genotype and allele frequency distribution for ACE, ACTN3 and MAOA

Out of the 831 participants, 822 were successfully genotyped for the ACTN3, 721 for the MAOA and 616 for the ACE gene. For the ACE gene, 24.5% (n = 151) were homozygous for the D allele, 52.6% (n = 324) were heterozygous, and 22.9% (n = 141) were homozygous for the I allele (Table 2). Allele frequencies for the ACE gene were 50.8 and 49.2% for the D and I allele, respectively. Genotype distribution for the ACTN3 gene was 30.8% (n = 253), 50.5% (n = 415), 18.7% (n = 154) for the RR, RX and XX genotype, respectively (Table 2). The frequency for the R allele was 56.0, and 44.0% for the X allele. The observed genotype frequencies were in HWE for all genotypes. Genotype frequencies did not differ significantly between male and female subjects for either ACTN3 R577X or ACE I/D polymorphisms. For the MAOA uVNTR polymorphism, five alleles were found: 2, 3, 3.5, 4 and 5 repeat alleles. Frequencies of the rare alleles were as follows: allele 2 was found in one heterozygous female; allele 5 in one hemizygous male and four heterozygous females; allele 3.5 in two hemizygous males and seven heterozygous females (1%). Common allele frequencies were 38.6 and 61.4% for the alleles 3 and 4, respectively. The observed heterozygosity for the common alleles was 42.5%.

Table 2 Genotype distribution for the ACTN3, ACE and MAOA gene according to PA level

The ACTN3 X allele frequency was 10.0% lower in individuals with HPA than those with LMPA level (P = 0.006, Fig. 1). When stratified by sex, significant difference in the X allele was only seen in males (P = 0.013).

Fig. 1
figure 1

ACTN3 R577X allele distribution differences between the low/medium and high physical activity groups. *significantly different from the high PA level group (P = 0.006). LMPA-low/medium physical activity group; HPA- high physical activity group

No associations were found between the ACE I/D (Fig. 2) or MAOA uVNTR (Fig. 3) polymorphisms and the PA level (Table 2).

Fig. 2
figure 2

ACE I/D genotype distribution in the low/medium and high physical activity groups. LMPA-low/medium physical activity group; HPA- high physical activity group

Fig. 3
figure 3

MAOA uVNTR genotype distribution in the low/medium and high physical activity groups among female (a) and male (b) participants. a TA- transcriptional activity genotype. Low TA represents 3-repeat allele homozygotes; High TA represents 3.5- or 4-repeat allele homozygotes and heterozygotes carrying one of each high TA alleles; heterozygotes- carriers of one low TA and one high TA alleles. b TA- transcriptional activity genotype. Low TA represents 3-repeat allele hemizygotes; High TA represents 3.5- or 4-repeat allele hemizygotes

Logistic regression models

Gender was found to significantly influence the likelihood of belonging to one of the two PA level groups, i.e. either to LMPA or to HPA level group (P < 0.001; Table 3). Males were less likely (OR: 0.47) to belong into the HPA level group compared to females. The BMI was more likely to be lower among subjects in the HPA level group compared to the LMPA level group counterparts (p < 0.01; OR = 0.92). Education level showed a statistically significant association (P < 0.01) with the PA level. Participants having completed higher education were 2.2 times more likely to belong to the HPA group than the participants with secondary (or lower) education level. Also, subjects who participated in sports/outdoor activities earlier in life were 1.8 times more likely to belong to the HPA level group compared to those that had not (P < 0.01).

Table 3 Variables entered on step 1: gender, age, BMI, education (2 categories) and participation in sports/outdoor activities earlier in life (2 categories)

In the second logistic regression model, the genotype data were added to the socioeconomic factors tested previously. Those socioeconomic variables that contributed significantly to the PA level, remained significantly associated in the second model (Table 4). In addition, the ACTN3 R577X polymorphism was significantly associated with the PA levels (P < 0.01). Subjects with the RX genotype were more likely to belong to the LMPA level group (P = 0.001; OR = 0.43) compared to the RR genotype subjects. Neither ACE I/D nor MAOA uVNTR polymorphism showed a significant association with the PA levels.

Table 4 Variables entered on step 2: gender, age, BMI, education (2 categories), participation in sports/outdoor activities earlier in life (2 categories), ACE I/D polymorphism (3 categories), ACTN3 R577X (3 categories) and MAOA uVNTR (3 categories)


Allele frequencies for the ACTN3 R577X and the ACE I/D polymorphisms has been reported to be highly variable between different ethnic groups, and the X allele has previously been reported to be much more common in Japanese (55%) [51] than it is in Kenyans (9%) [52]. Prevalence of the ACTN3 X allele in the present study was similar to populations of European descent, with around 45% of individuals being carriers of the minor allele [23].

The frequency distributions for the ACE alleles in the present study are also in line with other populations of European descent [30] i.e. 25–50% - 25% for the II, ID and DD alleles respectively. For the ACE I/D polymorphism, distribution of the D allele ranges from 10% for the D allele in Samoans [53], to around 60% in African Americans [54]. Genotype frequencies in other studies on Norwegian subjects were comparable with the frequencies in the present study [55, 56]. However, in one of the studies, the DD genotype was reported to be more prevalent than in other European populations. This is likely due to the preferential amplification of the D allele in heterozygotes, leading to mistyping of some heterozygotes as homozygotes for D allele [57]. That particular study did not used the insertion-specific primers to avoid mistyping of the ID genotype. The large variations in allele frequencies among different ethnicities is important to take into account when doing candidate gene studies [58]. Thus, analyzing homogenous cohorts [54], or accounting for the stratification [58] may improve study power. For the MAOA uVNTR polymorphism the allele frequencies were similar to those previously observed in Europeans [44, 47].

The present study found differences in the ACTN3 R577X allele distribution between the LMPA and the HPA level group. The logistic regression model indicated that the RX allele carriers were more likely to belong to the LMPA level group compared to the RR counterparts. Furthermore, individuals reporting HPA demonstrated higher frequency of the R allele compared to those reporting LMPA. Interestingly, when analyzed by gender, only males demonstrated significant differences in allele distributions between the two PA level groups. Although the authors have not found other studies reporting a relationship between the ACTN3 gene and PA levels in the general public, it has been suggested to be a potential candidate gene for PA behavior in mice models [13]. The ACTN3 R577X polymorphism has been linked to trainability of various cardiovascular traits [7, 19] which could, in turn, influence PA behavior [4]. Furthermore, the polymorphism has been associated with traits like sarcopenia [59], muscle function [51] and strength [60]. Previous research indicates that these may be important correlates of PA phenotypes [61,62,63,64]. Animal studies have shown changes in signaling and metabolism, among other things [27], which might help to explain the differences in phenotypes between the different genotypes.

Only few studies have been performed on ACE I/D polymorphism and PA levels in adults. Similarly to Fuentes et al. [40], the present study could not find any relationship between the ACE I/D polymorphism and PA levels, although some previous studies have found an association [38, 39].

The MAOA uVNTR polymorphism has a potential to be a candidate gene for influencing PA due to the phenotypic differences in transcriptional activity. Higher TA is expected to lead to higher monoamine oxidase activity and thus lower levels of monoamine neurotransmitters [44]. This, in turn, may lead to different PA level phenotypes [16]. However, the present study could not confirm the findings of Good et al. [16] who observed higher levels of PA in girls homozygous for the low TA allele compared to the high TA allele counterparts. It is still unclear whether the high and low TA alleles influence the monoamine oxidase A enzyme activity in the brain, as Fowler [65]. was not able to measure significant enzyme activity differences in brains of healthy male participants.

Results from the present study showed a strong association between the present PA level and PA at younger ages (P < 0.01). Education also correlated with PA levels in the present study (P < 0.01). Both education level [66, 67], and PA activity level at younger ages [66, 68], have in previous studies been shown to correlate positively with present PA levels.

A large proportion of the present cohort reported high PA level (74%). This could be, in part, due to the use of questionnaires as a method for determining the PA levels in the present study. Questionnaire-based methods have been shown to over or underestimate PA behavior compared to the objectively measured PA. The subjective nature of questionnaires may explain the large variation in PA levels observed between different studies [69]. Nevertheless, due to cost efficiency, questionnaires are often used in large epidemiologic studies, including genetic studies [1]. Another questionnaire-based study on a large European cohort of older subjects reported relatively low proportion of participants with no vigorous/moderate physical activity. The overall prevalence of inactivity in the cohort was reported to be 12.5%, with the Scandinavian countries demonstrating some of the lowest rates, i.e. 4.9 and 7.5% in Sweden and Denmark, respectively [64].

The present cohort was randomly drawn from NORCCAP, a homogenous Scandinavian population study with a high attendance rate [70]. The data from the questionnaires allowed the authors to map the ethnicity of the participants. Out of the 831 participants, only two did not have grandparents of Scandinavian descent or lacked the information about ethnicity. The remaining 829 (99,8%) had grandparents of Scandinavian descent. According to Marchini, Cardon [71], a well-known problem with genetic association studies is the undetected population structure such as heterogeneous ethnicity. In the present study, the material may be regarded as ethnically homogenous based on the results from the questionnaires. This can be regarded as one of the strengths of the present study, as a homogenous cohort reduces the chances of both false positive results and failures to detect genuine associations [71]. A further stratification based on ethnicity was therefore not necessary or possible.

Although study population was overweight, the differences in the BMI between the LMPA and HPA groups may also indicate that the self-reported PA levels are reliable [72]. Increase in adiposity (BMI) has been reported to be the cause of decrease in PA levels, as opposed to being the consequence of inactivity [43, 73, 74]. The design of the present study would have been strengthened by including direct measurements of the PA levels to validate the questionnaire data [69].


The present study demonstrates a novel finding that the X allele of the ACTN3 gene is underrepresented among participants reporting high PA levels. Genotype data from the present study can be used as a control population in future intervention studies on subjects of European descent. Consistently with previous reports, PA levels in adulthood are associated with factors like education and participation in exercise or outdoor activities earlier in life.



Blood samples and self-reported PA data were available for 831 individuals from the cohort “Kolorektal cancer, Arv og Miljø (KAM)”, a molecular epidemiological study partly based on the screening group of the Norwegian Colorectal Cancer Prevention Study (The NORCCAP study) in the county of Telemark, Norway [70, 75]. The study design, inclusion/exclusion criteria, participation rates and other relevant information about the NORCCAP study is described in Bretthauer, Gondal [70]. The study was approved by the Regional Medical Ethics Committee of South-Eastern Norway and the Data Inspectorate (REK 3087, S-98052 and S-98190), and is registered in Clinical Trials [76] with the identifier NCT00119912. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee, and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed written consent was obtained from all individual participants included in the study. Only control subjects (polyp free or polyps with mild grade dysplasia) were included in the present study. Socioeconomic data, including education data, were available for all the subjects. Characteristics of the participants are presented in Table 1.

Assessment of PA level

PA data was obtained by questionnaire used in the KAM study [75]. The following questions had graded responses on weekly frequency of the activity: “In the last five years, have you walked or bicycled to or from work?”, “Do you hike (cross country)?”, “How often do you exercise for at least 20 minutes?”. Questions “If you exercise, do you perspire?” and “Were you regularly participating in sports or outdoor activities at a younger age?” were dichotomous.

Since the demand for energy differs between various types of activities [77, 78], the different activities were scaled as for example: Hiking = 1 (used as reference, and representing moderate to vigorous intensity), Walking/bicycling = 0.5 (representing low to moderate intensity), Exercise = 1.5 (representing vigorous intensity). By summing the frequencies per week of the scaled activities, each person achieved an activity score. The American College of Sports Medicine (ACSM) has recommended moderate-intensity cardiorespiratory exercise training for at least 30 min, at least five days per week, or at least 20 min of vigorous-intensity cardiorespiratory exercise training for at least three days per week, or a combination of the two training modalities [79]. The activity score of 3 in the present study thus represents the minimum for accomplishing the ACSM physical activity recommendations. For example, a person performing exercise training of 45 min two times per week, or hiking three times per week, or performing walking or cycling six times per week, will reach the activity score 3. Therefore, participants who achieved a score under 3 were defined as inactive or untrained (possessing LMPA) and those who achieved a score ≥ 3 was defined as active/trained (possessing HPA). These PA level groups were assessed for any associations with the ACE I/D, ACTN3 R577X and MAOA uVNTR genotypes, as well as for relationships with gender, education level and previous participation in sports or outdoor activities.

DNA collection and genotyping

The genomic DNA was extracted from venous EDTA blood stored at -20 °C by using a salting out procedure [80] with minor modifications [75].

The ACE I/D polymorphism was genotyped using the Eppendorf Mastercycler Gradient (Eppendorf AG, Germany). Each reaction mixture of 25.5 μl contained 2% DMSO, 1 x PCR buffer, 0.2 mM dNTP, 2 mM MgCl, 0.2 pmol/μl of each primer, 0.5 U/μl Taq polymerase, and 1 μl of DNA (~ 100 ng). Forward and reverse primer were 5′-CTGGAGACCACTCCCATCCTTTCT-3′ and 5′-GATGTGGCCAT-CACATTCGTCAGAT-3′, respectively [34]. Initial denaturation at 95 °C for 3 min was followed by 30 cycles of denaturation (95 °C; 15 s), hybridization (53 °C; 45 s) and extension (72 °C; 30 s). After final elongation (72 °C; 5 min) the PCR products were stored at 4 °C. These were separated by 6% polyacrylamide gel electrophoresis (PAGE) for 30 min at 150 V, and resulted in three possible outcomes (DD, ID and II).

The ACE I allele is often weakly amplified in heterozygotes. Samples with the DD genotype were therefore re-analyzed by using a different PCR reaction in order to avoid mistyping of heterozygotes as DD. Insertion specific forward primer 5′-TTTGAGACGGAGTCTCGCTC-3′ and standard reverse primer [57] were used. Each reaction of 25.0 μl contained 12.5 μl AmpliTaq Gold® PCR Master Mix (Thermo Fisher Scientific, Inc.; MA, USA), 5% DMSO, 0.2 pmol/μl of each primer, and ~ 100 ng template DNA. PCR reaction conditions were as follows: initial denaturation at 95 °C for 3 min followed by 30 cycles of denaturation (92 °C), hybridization (61 °C) and extension (72 °C) for 1 min each. After final elongation (72 °C; 7 min) the PCR products were visualized by 6% PAGE. Insertion specific PCR reaction yielded a 408 bp long DNA fragment in carriers of the I-allele and no PCR product in DD subjects. 215 (25.8%) samples yielded no genotype results ever after repeated PCR runs.

Genotyping of the ACTN3 R577X polymorphism was carried out with TaqMan® SNP Genotyping Assay, assay ID C____590093_1 (Applied Biosystems®, CA, USA) on the StepOnePlus™ Real-Time PCR System (Applied Biosystems®, CA, USA). Genotype calling was performed by StepOne Software v2.0. Each 15 μl reaction genotyping mixture contained 8.44 μl Genotyping Master Mix, 0.42 μl Assay mix (40x), 6.33 μl distilled H20 and ~ 150 ng of DNA template. Reaction conditions were as follows: 30 s at 60 °C was followed by initial denaturation stage for 10 min at 95 °C; denaturation at 95 °C for 15 s followed by annealing at 60 °C for 1 min in cycling stage with 40 cycles altogether; finally post read temperature was kept at 60 °C for 30 s. Nine samples (1.1%) yielded no genotype results.

The MAOA promoter polymorphism was amplified by PCR followed by capillary electrophoresis on an Applied Biosystems 3130xl genetic analyzer using GeneMapper® (Applied Biosystems®, CA, USA) Software 5. Each 15 μl reaction contained 5% DMSO, 1x PCR buffer, 0.2 mM dNTP, 2.5 mM MgCl, 0.4 mM of each primer, 1 U/μl Taq polymerase. The PCR conditions were as follows: initial denaturation at 95 °C (2 min) followed by 35 cycles of denaturation at 95 °C (1 min), annealing at 55.5 °C (1 min) and elongation at 72 °C (2 min), and a final elongation at 72 °C (5 min). Primer sequences have been described earlier [44], and were as follows: a FAM labeled forward primer 5′-ACAGCCTGACCGTGGAGAAG-3′ and a reverse primer 5′-GAACGGACGCTCCATTCGGA-3′. 110 (13.2%) samples yielded no results even after repeated PCR run.

In order to check for reproducibility for ACE I/D and MAOA uVNTR polymorphisms, approximately 10% of the samples were re-analyzed. In addition, those samples that yielded inconclusive results were re-run. They were excluded from further data analysis if the samples did not yield any genotype or if they remained inconclusive. For ACTN3 R577X, all samples were run as duplicates.

Statistical analysis

The material was tested for normality and corrected for multiple testing (Bonferroni method), where appropriate. Association between the BMI and PA level groups was analyzed by using two-tailed independent sample t-test. Pearson’s Chi-square test (χ2) was applied to test for the Hardy-Weinberg equilibrium (HWE) for the ACTN3 R577X, ACE I/D genotype, and the differences in categorical variables, including genotype and allelic frequencies between the PA level groups. MAOA genotypes were analyzed by dividing genotypes into groups, based on the TA of the alleles [44, 47]. Males carrying the 3-repeat allele and homozygous females for the 3-repeat allele were grouped into low TA, while males carrying either 3.5- or 4-repeat alleles were grouped into high TA group. Similarly, females homozygous for either 3.5 or 4-repeat-alleles and females heterozygous for 3.5 or 4-repeat-alleles were grouped into high TA group. Heterozygous females carrying one 3-repeat and either 3.5- or 4-repeat allele were placed into the heterozygous group. Individuals carrying the rare alleles were excluded from the analysis.

To test the contribution of socioeconomic factors (Gender, Age, BMI, Education, Participation in sports/outdoor activities earlier in life) and genetic variables (ACTN3 R577X, ACE I/D and MAOA uVNTR genotypes) to the PA level, binomial logistic regression was used. For this purpose, two models were analyzed: 1. socioeconomic factors only; 2. socioeconomic and genotype data together. Odds ratio (OR) were calculated for the significant associations in the logistic regression. Significance was set at 0.05 for all tests. Results are presented as mean ± SD. All statistical analysis was performed in IBM SPSS Statistics, version 25 (Chicago, IL, USA).

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to Norwegian legislation but are available from the corresponding author on reasonable request.



Angiotensin-converting enzyme




Body mass index


High physical activity


Hardy-Weinberg equilibrium


«Kolorektal cancer, Arv og Miljø» study


Low/medium physical activity


Monoamine oxidase A


Physical activity


Polyacrylamide gel electrophoresis


Polymerase chain reaction


Transcriptional activity


  1. Bray MS, Fulton JE, Kalupahana NS, Lightfoot JT. Genetic Epidemiology, Physical Activity, and Inactivity. Genet Mol Aspects Sport Performance. 2011:81–9.

  2. De Moor MH, Liu YJ, Boomsma DI, Li J, Hamilton JJ, Hottenga JJ, et al. Genome-wide association study of exercise behavior in Dutch and American adults. Med Sci Sports Exerc. 2009;41(10):1887–95.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Lightfoot JT. Current understanding of the genetic basis for physical activity. J Nutr. 2011;141(3):526–30.

    Article  CAS  PubMed  Google Scholar 

  4. Stubbe JH, Boomsma DI, Vink JM, Cornes BK, Martin NG, Skytthe A, et al. Genetic Influences on Exercise Participation in 37.051 Twin Pairs from Seven Countries. PloS one. 2006;1(1):e22.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Bird SR, Hawley JA. Exercise and type 2 diabetes: new prescription for an old problem. Maturitas. 2012;72(4):311–6.

    Article  PubMed  Google Scholar 

  6. Ehsani AA, Martin WH 3rd, Heath GW, Coyle EF. Cardiac effects of prolonged and intense exercise training in patients with coronary artery disease. Am J Cardiol. 1982;50(2):246–54.

    Article  CAS  PubMed  Google Scholar 

  7. Myerson SG, Montgomery HE, Whittingham M, Jubb M, World MJ, Humphries SE, et al. Left ventricular hypertrophy with exercise and ACE gene insertion/deletion polymorphism: a randomized controlled trial with losartan. Circulation. 2001;103(2):226–30.

    Article  CAS  PubMed  Google Scholar 

  8. Levine JA. Lethal sitting: homo sedentarius seeks answers. Physiology (Bethesda, Md). 2014;29(5):300–1.

    Google Scholar 

  9. McGuire DK, Levine BD, Williamson JW, Snell PG, Blomqvist CG, Saltin B, et al. A 30-year follow-up of the Dallas Bedrest and training study: I. effect of age on the cardiovascular response to exercise. Circulation. 2001;104(12):1350–7.

    Article  CAS  PubMed  Google Scholar 

  10. World Health Organization. Global recommendations on physical activity for health. Geneva: World Health Organization; 2010.

    Google Scholar 

  11. Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U, et al. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet. 2012;380(9838):247–57.

    Article  PubMed  Google Scholar 

  12. Hagberg JM, Park JJ, Brown MD. The role of exercise training in the treatment of hypertension - an update. Sports Med. 2000;30(3):193–206.

    Article  CAS  PubMed  Google Scholar 

  13. Lightfoot JT. Can you be born a couch potato? The genomic regulation of physical activity. Exercise Genomics: Springer; 2011. p. 45–72.

  14. Nicklas BJ. Heterogeneity of physical function responses to exercise in older adults: possible contribution of variation in the Angiotensin-1 converting enzyme (ACE) gene? Perspect Psychol Sci. 2010;5(5):575–84.

    Article  PubMed  Google Scholar 

  15. Sarzynski MA, Rankinen T, Bouchard C. Twin and Family Studies of Training Responses. 2011. In: genetic and molecular aspects of sport performance [internet]. Wiley-Blackwell.

  16. Good DJ, Li M, Deater-Deckard K. A genetic basis for motivated exercise. Exerc Sport Sci Rev. 2015;43(4):231–7.

    Article  PubMed  Google Scholar 

  17. Simonen RL, Rankinen T, Perusse L, Leon AS, Skinner JS, Wilmore JH, et al. A dopamine D2 receptor gene polymorphism and physical activity in two family studies. Physiol Behav. 2003;78(4–5):751–7.

    Article  CAS  PubMed  Google Scholar 

  18. Stubbe JH, Boomsma DI, De Geus EJ. Sports participation during adolescence: a shift from environmental to genetic factors. Med Sci Sports Exerc. 2005;37(4):563–70.

    Article  PubMed  Google Scholar 

  19. Deschamps CL, Connors KE, Klein MS, Johnsen VL, Shearer J, Vogel HJ, et al. The ACTN3 R577X Polymorphism Is Associated with Cardiometabolic Fitness in Healthy Young Adults. PloS one. 2015;10(6):e0130644.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Pereira A, Costa AM, Leitao JC, Monteiro AM, Izquierdo M, Silva AJ, et al. The influence of ACE ID and ACTN3 R577X polymorphisms on lower-extremity function in older women in response to high-speed power training. BMC Geriatr. 2013;13:131.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Pereira A, Costa AM, Izquierdo M, Silva AJ, Bastos E, Marques MC. ACE I/D and ACTN3 R/X polymorphisms as potential factors in modulating exercise-related phenotypes in older women in response to a muscle power training stimuli. Age. 2013;35(5):1949–59.

    Article  CAS  PubMed  Google Scholar 

  22. Mills M, Yang N, Weinberger R, Vander Woude DL, Beggs AH, Easteal S, et al. Differential expression of the actin-binding proteins, alpha-actinin-2 and -3, in different species: implications for the evolution of functional redundancy. Hum Mol Genet. 2001;10(13):1335–46.

    Article  CAS  PubMed  Google Scholar 

  23. Yang N, Garton F, North K. alpha-actinin-3 and performance. Med Sport Sci. 2009;54:88–101.

    Article  CAS  PubMed  Google Scholar 

  24. Ahmetov II, Egorova ES, Gabdrakhmanova LJ, Fedotovskaya ON. Genes and athletic performance: an update. Med Sport Sci. 2016;61:41–54.

    Article  PubMed  Google Scholar 

  25. Pickering C, Kiely J. ACTN3: more than just a gene for speed. Front Physiol. 2017;8:1080.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Friedlander SM, Herrmann AL, Lowry DP, Mepham ER, Lek M, North KN, et al. ACTN3 allele frequency in humans covaries with global latitudinal gradient. PLoS One. 2013;8(1):e52282.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Lee FXZ, Houweling PJ, North KN, Quinlan KGR. How does α-actinin-3 deficiency alter muscle function? Mechanistic insights into ACTN3, the ‘gene for speed’. Biochim Biophys Acta. 2016;1863(4):686–93.

    Article  CAS  PubMed  Google Scholar 

  28. Head SI, Chan S, Houweling PJ, Quinlan KGR, Murphy R, Wagner S, et al. Altered Ca2+ Kinetics Associated with alpha-Actinin-3 Deficiency May Explain Positive Selection for ACTN3 Null Allele in Human Evolution. PLoS genetics. 2015;11(1).

  29. Coates D. The angiotensin converting enzyme (ACE). Int J Biochem Cell Biol. 2003;35(6):769–73.

    Article  CAS  PubMed  Google Scholar 

  30. Jones A, Woods DR. Skeletal muscle RAS and exercise performance. Int J Biochem Cell Biol. 2003;35(6):855–66.

    Article  CAS  PubMed  Google Scholar 

  31. Goh KP, Chew K, Koh A, Guan M, Wong YS, Sum CF. The relationship between ACE gene ID polymorphism and aerobic capacity in Asian rugby players. Singap Med J. 2009;50(10):997–1003.

    CAS  Google Scholar 

  32. Tamburus NY, Verlengia R, Kunz VC, Cesar MC, Silva E. Apolipoprotein B and angiotensin-converting enzyme polymorphisms and aerobic interval training: randomized controlled trial in coronary artery disease patients. Braz J Med Biol Res. 2018;51(8):e6944.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Novak CM, Escande C, Gerber SM, Chini EN, Zhang M, Britton SL, et al. Endurance capacity, not body size, determines physical activity levels: role of skeletal muscle PEPCK. PLoS One. 2009;4(6):e5869.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Rigat B, Hubert C, Corvol P, Soubrier F. PCR detection of the insertion/deletion polymorphism of the human angiotensin converting enzyme gene (DCP1) (dipeptidyl carboxypeptidase 1). Nucleic Acids Res. 1992;20(6):1433.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Myerson SG, Hemingway H, Budget R, Martin J, Humphries S, Montgomery H. Human angiotensin I-converting enzyme gene and endurance performance. J Appl Physiol. 1999;87(4):1313–6.

    Article  CAS  PubMed  Google Scholar 

  36. Hruskovicova H, Dzurenkova D, Selingerova M, Bohus B, Timkanicova B, Kovacs L. The angiotensin converting enzyme I/D polymorphism in long distance runners. J Sports Med Physical Fitness. 2006;46(3):509–13.

    CAS  Google Scholar 

  37. Folland J, Leach B, Little T, Hawker K, Myerson S, Montgomery H, et al. Angiotensin-converting enzyme genotype affects the response of human skeletal muscle to functional overload. Exp Physiol. 2000;85(5):575–9.

    Article  CAS  PubMed  Google Scholar 

  38. Maestu J, Latt E, Raask T, Sak K, Laas K, Jurimae J, et al. Ace I/D polymorphism is associated with habitual physical activity in pubertal boys. J Physiol Sci. 2013;63(6):427–34.

    Article  CAS  PubMed  Google Scholar 

  39. Winnicki M, Accurso V, Hoffmann M, Pawlowski R, Dorigatti F, Santonastaso M, et al. Physical activity and angiotensin-converting enzyme gene polymorphism in mild hypertensives. Am J Med Genet A. 2004;125A(1):38–44.

    Article  PubMed  Google Scholar 

  40. Fuentes RM, Perola M, Nissinen A, Tuomilehto J. ACE gene and physical activity, blood pressure, and hypertension: a population study in Finland2002 2002-06-01 00:00:00. 2508–12 p.

  41. Knab AM, Bowen RS, Hamilton AT, Gulledge AA, Lightfoot JT. Altered dopaminergic profiles: implications for the regulation of voluntary physical activity. Behav Brain Res. 2009;204(1):147–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Park YM, Kanaley JA, Padilla J, Zidon T, Welly RJ, Will MJ, et al. Effects of intrinsic aerobic capacity and ovariectomy on voluntary wheel running and nucleus accumbens dopamine receptor gene expression. Physiol Behav. 2016;164(Pt A):383–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Friend DM, Devarakonda K, O'Neal TJ, Skirzewski M, Papazoglou I, Kaplan AR, et al. Basal ganglia dysfunction contributes to physical inactivity in obesity. Cell Metab. 2017;25(2):312–21.

    Article  CAS  PubMed  Google Scholar 

  44. Sabol SZ, Hu S, Hamer D. A functional polymorphism in the monoamine oxidase a gene promoter. Hum Genet. 1998;103(3):273–9.

    Article  CAS  PubMed  Google Scholar 

  45. Shih JC, Thompson RF. Monoamine oxidase in neuropsychiatry and behavior. Am J Hum Genet. 1999;65(3):593–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Huang YY, Cate SP, Battistuzzi C, Oquendo MA, Brent D, Mann JJ. An association between a functional polymorphism in the monoamine oxidase a gene promoter, impulsive traits and early abuse experiences. Neuropsychopharmacology. 2004;29(8):1498–505.

    Article  CAS  PubMed  Google Scholar 

  47. Deckert J, Catalano M, Syagailo YV, Bosi M, Okladnova O, Di Bella D, et al. Excess of high activity monoamine oxidase a gene promoter alleles in female patients with panic disorder. Hum Mol Genet. 1999;8(4):621–4.

    Article  CAS  PubMed  Google Scholar 

  48. Jenkins TA, Mendelsohn FAO, Chai SY. Angiotensin-converting enzyme modulates dopamine turnover in the striatum. J Neurochem. 1997;68(3):1304–11.

    Article  CAS  PubMed  Google Scholar 

  49. Nyberg F, Terenius L. Degradation of Neuropeptides In: Henriksen JH, editor. Degradation of Bioactive Substances: Physiology and Pathophysiology Boca Raton, Florida: CRC Press, Inc; 1991. p. 189–200.

  50. Labandeira-Garcia JL, Rodriguez-Pallares J, Rodriguez-Perez AI, Garrido-Gil P, Villar-Cheda B, Valenzuela R, et al. Brain angiotensin and dopaminergic degeneration: relevance to Parkinson's disease. Am J Neurodegenerative Dis. 2012;1(3):226–44.

    Google Scholar 

  51. Kikuchi N, Yoshida S, Min SK, Lee K, Sakamaki-Sunaga M, Okamoto T, et al. The ACTN3 R577X genotype is associated with muscle function in a Japanese population. Appl Physiol Nutr Metabol. 2015;40(4):316–22.

    Article  CAS  Google Scholar 

  52. Yang N, MacArthur DG, Wolde B, Onywera VO, Boit MK, Lau SY, et al. The ACTN3 R577X polymorphism in east and west African athletes. Med Sci Sports Exerc. 2007;39(11):1985–8.

    Article  PubMed  Google Scholar 

  53. Barley J, Blackwood A, Carter ND, Crews DE, Cruickshank JK, Jeffery S, et al. Angiotensin converting enzyme insertion/deletion polymorphism: association with ethnic origin. J Hypertens. 1994;12(8):955–7.

    Article  CAS  PubMed  Google Scholar 

  54. Mathew J, Basheeruddin K, Prabhakar S. Differences in frequency of the deletion polymorphism of the angiotensin-converting enzyme gene in different ethnic groups. Angiology. 2001;52(6):375–9.

    Article  CAS  PubMed  Google Scholar 

  55. Bohn M, Berge KE, Bakken A, Erikssen J, Berg K. Insertion/deletion (I/D) polymorphism at the locus for angiotensin I-converting enzyme and myocardial infarction. Clin Genet. 1993;44(6):292–7.

    Article  CAS  PubMed  Google Scholar 

  56. Tronvik E, Stovner LJ, Bovim G, White LR, Gladwin AJ, Owen K, et al. Angiotensin-converting enzyme gene insertion/deletion polymorphism in migraine patients. BMC Neurol. 2008;8:4.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Shanmugam V, Sell KW, Saha BK. Mistyping ACE heterozygotes. PCR Methods Applications. 1993;3(2):120–1.

    Article  CAS  PubMed  Google Scholar 

  58. Gordish-Dressman H, Devaney JM. Statistical and methodological considerations in exercise genomics. Exercise Genomics: Springer; 2011. p. 23–43.

  59. Cho J, Lee I, Kang H. ACTN3 gene and Susceptibility to sarcopenia and osteoporotic status in older Korean adults. Biomed Res Int. 2017;2017:4239648.

    PubMed  PubMed Central  Google Scholar 

  60. Erskine RM, Williams AG, Jones DA, Stewart CE, Degens H. The individual and combined influence of ACE and ACTN3 genotypes on muscle phenotypes before and after strength training. Scand J Med Sci Sports. 2014;24(4):642–8.

    Article  CAS  PubMed  Google Scholar 

  61. Viken H, Aspvik NP, Ingebrigtsen JE, Zisko N, Wisloff U, Stensvold D. Correlates of objectively measured physical activity among Norwegian older adults: the generation 100 study. J Aging Phys Act. 2016;24(2):369–75.

    Article  PubMed  Google Scholar 

  62. Leblanc A, Pescatello LS, Taylor BA, Capizzi JA, Clarkson PM, Michael White C, et al. Relationships between physical activity and muscular strength among healthy adults across the lifespan. SpringerPlus. 2015;4:557.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Rojer AGM, Reijnierse EM, Trappenburg MC, van Lummel RC, Niessen M, van Schooten KS, et al. Instrumented assessment of physical activity is associated with muscle function but not with muscle mass in a general population. J Aging Health. 2018;30(9):1462–81.

    Article  PubMed  Google Scholar 

  64. Gomes M, Figueiredo D, Teixeira L, Poveda V, Paúl C, Santos-Silva A, et al. Physical inactivity among older adults across Europe based on the SHARE database. Age Ageing. 2017;46(1):71–7.

    Article  PubMed  Google Scholar 

  65. Fowler JS, Alia-Klein N, Kriplani A, Logan J, Williams B, Zhu W, et al. Evidence that brain MAO a activity does not correspond to MAO a genotype in healthy male subjects. Biol Psychiatry. 2007;62(4):355–8.

    Article  CAS  PubMed  Google Scholar 

  66. Bauman AE, Reis RS, Sallis JF, Wells JC, Loos RJ, Martin BW. Correlates of physical activity: why are some people physically active and others not? Lancet. 2012;380(9838):258–71.

    Article  PubMed  Google Scholar 

  67. Lubs L, Peplies J, Drell C, Bammann K. Cross-sectional and longitudinal factors influencing physical activity of 65 to 75-year-olds: a pan European cohort study based on the survey of health, ageing and retirement in Europe (SHARE). BMC Geriatr. 2018;18(1):94.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Wichstrom L, von Soest T, Kvalem IL. Predictors of growth and decline in leisure time physical activity from adolescence to adulthood. Health Psychol. 2013;32(7):775–84.

    Article  PubMed  Google Scholar 

  69. Prince SA, Adamo KB, Hamel ME, Hardt J, Connor Gorber S, Tremblay M. A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. Int J Behav Nutr Phys Activity. 2008;5:56.

    Article  Google Scholar 

  70. Bretthauer M, Gondal G, Larsen K, Carlsen E, Eide TJ, Grotmol T, et al. Design, organization and management of a controlled population screening study for detection of colorectal neoplasia: attendance rates in the NORCCAP study (Norwegian colorectal Cancer prevention). Scand J Gastroenterol. 2002;37(5):568–73.

    Article  CAS  PubMed  Google Scholar 

  71. Marchini J, Cardon LR, Phillips MS, Donnelly P. The effects of human population structure on large genetic association studies. Nat Genet. 2004;36(5):512–7.

    Article  CAS  PubMed  Google Scholar 

  72. Piirtola M, Kaprio J, Waller K, Heikkila K, Koskenvuo M, Svedberg P, et al. Leisure-time physical inactivity and association with body mass index: a Finnish twin study with a 35-year follow-up. Int J Epidemiol. 2016.

  73. Ekelund U, Brage S, Besson H, Sharp S, Wareham NJ. Time spent being sedentary and weight gain in healthy adults: reverse or bidirectional causality? Am J Clin Nutr. 2008;88(3):612–7.

    Article  CAS  PubMed  Google Scholar 

  74. Metcalf BS, Hosking J, Jeffery AN, Voss LD, Henley W, Wilkin TJ. Fatness leads to inactivity, but inactivity does not lead to fatness: a longitudinal study in children (EarlyBird 45). Arch Dis Child. 2011;96(10):942–7.

    Article  CAS  PubMed  Google Scholar 

  75. Skjelbred CF, Saebo M, Wallin H, Nexo BA, Hagen PC, Lothe IM, et al. Polymorphisms of the XRCC1, XRCC3 and XPD genes and risk of colorectal adenoma and carcinoma, in a Norwegian cohort: a case control study. BMC Cancer. 2006;6:67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Clinical Trials. NORCCAP: Norwegian Colorectal Cancer Prevention Trial: U.S. National Library of Medicine; [Available from:

  77. Ainsworth BE, Haskell WL, Leon AS, Jacobs DR Jr, Montoye HJ, Sallis JF, et al. Compendium of physical activities: classification of energy costs of human physical activities. Med Sci Sports Exerc. 1993;25(1):71–80.

    Article  CAS  PubMed  Google Scholar 

  78. Ainsworth BE, Haskell WL, Herrmann SD, Meckes N, Bassett DR Jr, Tudor-Locke C, et al. 2011 compendium of physical activities: a second update of codes and MET values. Med Sci Sports Exerc. 2011;43(8):1575–81.

    Article  PubMed  Google Scholar 

  79. Garber CE, Blissmer B, Deschenes MR, Franklin BA, Lamonte MJ, Lee IM, et al. American College of Sports Medicine position stand. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: guidance for prescribing exercise. Med Sci Sports Exerc. 2011;43(7):1334–59.

    Article  PubMed  Google Scholar 

  80. Miller SA, Dykes DD, Polesky HF. A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res. 1988;16(3):1215.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Authors thank Andrew Jenkins for his contribution to the English proofreading. The authors would also like to thank the University of South-Eastern Norway Publication funds for providing the financial support to cover Article Processing Charges.


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SGF carried out the genetic studies, interpreted the study results and drafted the manuscript, AMB converted the questionnaire responses to PA level scores, interpreted the study results and also drafted the manuscript, EHK, IKL, MS and ØS participated in designing the study, interpreting the results, writing and supervising the writing of the manuscript. All authors have read and approved the final version of the manuscript, and agree with the order of presentation of the authors.

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Correspondence to Sannija Goleva-Fjellet.

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The Regional Medical Ethics Committee of South-Eastern Norway and the Data Inspectorate (REK 3087, S-98052 and S-98190) approved the study. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee. Informed written consent was obtained from all individual participants included in the study.

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Goleva-Fjellet, S., Bjurholt, A.M., Kure, E.H. et al. Distribution of allele frequencies for genes associated with physical activity and/or physical capacity in a homogenous Norwegian cohort- a cross-sectional study. BMC Genet 21, 8 (2020).

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  • Genes
  • Polymorphism
  • ACTN3
  • Physical activity