Genetics of Physical Activity Level
Regular physical activity is a central component of current public health recommendations. While psychological, social, and environmental factors contribute significantly to physical activity behavior, it is important to recognize that activity behavior also has a biological basis and that genetic variation could affect individuals’ propensity to be physically active or sedentary. Twin studies, as well as studies in nuclear and extended families, have provided maximal heritability estimates ranging from 15% to 60% for total physical activity level as well as for sedentarism, leisure-time activity, and sport participation.
At this point it would be helpful to introduce genome-wide association studies (GWAS). Rapid technical improvements in microarray-based high-throughput genotyping methods have made it possible to assay hundreds of thousands of SNPs in a single reaction, allowing detailed GWAS. A typical GWAS relies on a large number of SNPs that are distributed evenly across the genome, using either a case–control or a cohort study design. Associations between the trait of interest and each SNP are tested using standard statistical methods. However, because of the large number of tests in a GWAS, criteria for statistical significance have to be modified. For example, in a GWAS with 1 million SNPs, the threshold of genome-wide statistical significance is p < 5 × 10−8.
Data on the molecular genetics of physical activity levels in humans are still scarce, although the first GWAS for activity level was published in 2009 (De Moor et al. 2009). The report included results from two cohort studies: 1,644 unrelated individuals from the Netherlands Twin Register and 978 subjects living in Omaha, Nebraska. None of the 1.6 million SNPs reached the commonly used threshold of genome-wide significance (p = 5 × 10−8), although SNPs in three genomic regions showed p-values less than 1 × 10−5. The strongest associations were observed on chromosome 10q23.2 at the 3’-phosphoadenosine 5’-phosphosulfate synthase 2 (PAPSS2) gene locus: The odds ratio (OR) for being an exerciser was 1.32 (p = 3.81 × 10−6) for the common T-allele of SNP rs10887741. The other two SNPs with p < 1 × 10−5 were rs12612420 (p = 7.61 × 10−6, OR = 1.43), located about 12 kilobases (kb) upstream of the first exon of the DNA polymerase-transactivated protein 6 (DNAPTP6) gene, and rs8097348 (p = 6.68 × 10−6, OR = 1.36), which is located about 236 kb upstream of chromosome 18 open reading frame 2 (C18orf2). The associations with previously reported physical activity candidate genes were explored as well. The strongest candidate gene association was detected with SNP rs12405556 (p = 9.7 × 10−4, OR = 1.24) at the leptin receptor (LEPR) gene locus.
The major advantage of the GWAS strategy is that it is not restricted by a priori hypotheses, as is the case in candidate gene studies. Moreover, with millions of measured and imputed SNPs, a GWAS covers the entire genome uniformly and has sufficient sensitivity to detect small to moderate gene effects of relatively common sequence variants if the sample size is large. A critical feature of genetic studies is replication: Findings of an individual study should be tested in other large cohorts with a similar phenotype and study design. If the associations are replicated, the case for the contribution of a gene and DNA sequence variant to the trait of interest becomes considerably stronger. Given that several large cohort studies with physical activity questionnaire data available have also recently completed GWAS SNP genotyping, we could have interesting new data with replication panels in the near future.
Individual Differences in Response to Regular Exercise
There are marked interindividual differences in the adaptation to exercise training. For example, in the HERITAGE Family Study, 742 healthy but sedentary participants followed an identical, well-controlled endurance training program for 20 weeks. Despite the identical training program, increases in .VO2max varied from no change to increases of more than 1 L/min (figure 24.9). This high degree of heterogeneity in responsiveness to a fully standardized exercise program in the HERITAGE Family Study was not accounted for by age, gender, or ethnic differences. A similar pattern of variation in training responses was observed for several other phenotypes, such as plasma high-density lipoprotein (HDL) cholesterol levels and submaximal exercise heart rate and blood pressure changes (Bouchard and Rankinen 2001). These data underline the notion that the effects of endurance training on cardiovascular and other relevant traits should be evaluated not only in terms of mean changes, but also in terms of response heterogeneity.
A number of questions come to mind as a result of observations such as those depicted in figure 24.9 and the others discussed previously. Are the high and low responses to regular exercise characterized by significant familial aggregation; that is, are there families with mainly low responders and others in which all family members show significant improvements? Is individual variability a normal biological phenomenon reflecting genetic diversity? Can we identify SNPs, genes, and alleles that predict the ability to respond positively or adversely to regular exercise?
Genes and Responses to Exercise
We now turn our attention to the evidence for a role of specific gene and sequence variants in the range of responses to regular exercise. Blood pressure, lipids and lipoproteins, glucose and insulin, and cardiorespiratory endurance response phenotypes are discussed. For editorial considerations, studies reviewed from this point onward in this chapter are not referenced individually. However, the interested reader can find these references in the latest version of the Human Gene Map for Performance and Health-Related Fitness Phenotypes (Bray et al. 2009).
Another example of response heterogeneity relates to skeletal metabolism indicators and comes from the HERITAGE Family Study. The levels of nine enzymes involved in phosphocreatine metabolism, glycolysis, and oxidative metabolism were measured in muscle biopsies obtained before and after a 20-week endurance training program in 78 individuals from 19 nuclear families (Rico-Sanz et al. 2003). Exercise training induced statistically significant increases in all enzyme levels. Furthermore, all training responses showed significant familial aggregation: The between-family variance was 1.85 to 4.0 times greater than the variance within families.
Although exercise-related traits are mainly polygenic and multifactorial in nature, much can be learned from some monogenic disorders characterized by compromised exercise capacity or exercise intolerance. These disorders affect only a few individuals, but they provide interesting examples of genetic defects that have profound effects on the ability to perform physical activity, usually attributable to compromised energy metabolism. Although these genetic defects compromise exercise capacity, there is no evidence that overexpression of these genes leads to improved physical performance. However, it is important to understand the molecular mechanisms contributing to both ends of the distribution of cardiorespiratory endurance and its trainability. Table 24.3 lists some of the genes that have been associated with a decreased exercise capacity (Rankinen et al. 2004).
Genes and Blood Pressure Response to Regular Exercise
The 2007 update of the Human Gene Map for Performance and Health-Related Fitness Phenotypes included 17 genes from 23 studies that have been investigated in relation to exercise training–induced changes in hemodynamic phenotypes (Bray et al. 2009). Findings for 13 candidate genes (AGTR1, AMPD1, APOE, BDKRB2, CHRM2, EDN1, FABP2, GNB3, HBB, KCNQ1, NFKB1, PPARA, TTN) were based on a single study. However, with four candidate genes, the positive associations were reported in at least two studies. For example, in both the HERITAGE Family Study and the DNASCO Study cohorts, the angiotensinogen (AGT) Met235Thr polymorphism (in which threonine is substituted for methionine) was associated with endurance training–induced changes in diastolic blood pressure in men.
Similarly, an association between the angiotensin I converting enzyme (ACE) gene I/D (insertion or deletion of a sequence) polymorphism and training-induced left ventricular (LV) growth has been reported in two studies (figure 24.10) (Montgomery et al. 1997; Myerson et al. 2001). In 1997, Montgomery and coworkers reported that the ACE D-allele was associated with greater increases in LV mass and with septal and posterior wall thickness after 10 weeks of physical training in British Army recruits (figure 24.10a). A similar training paradigm was repeated a few years later, and the training-induced increase in LV mass was 2.7 times greater in the D/D genotype compared with the I/I homozygotes (figure 24.10b).
A third candidate gene with positive evidence of associations from multiple studies is endothelial nitric oxide synthase 3 (NOS3). In the HERITAGE Family Study, homozygotes for the glutamine allele at codon 298 (Glu298) had a reduction in submaximal exercise diastolic blood pressure that was more than three times greater than that of the homozygotes for the asparagine allele (Asp298Asp) after the training program. A similar pattern was evident with the systolic blood pressure and rate–pressure product training responses (Rankinen et al. 2000). In coronary artery disease patients, exercise training significantly improved acetylcholine-induced change in average peak velocity of coronary arteries. However, the training response was significantly blunted in the carriers of the NOS3 –786C allele of a polymorphism located in the 5’-UTR of the NOS3 gene compared with the patients who were homozygotes for the –786T-allele (Erbs et al. 2003).