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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Neurosci. Author manuscript; available in PMC 2013 July 12.
Published in final edited form as:
PMCID: PMC3710129
NIHMSID: NIHMS431167

DRD4 genotype predicts longevity in mouse and human

Abstract

Longevity is influenced by genetic and environmental factors. The brain's dopamine system may be particularly relevant, since it modulates traits (e.g., sensitivity to reward, incentive motivation, sustained effort) that impact behavioral responses to the environment. In particular, the dopamine D4 receptor (DRD4) has been shown to moderate the impact of environments on behavior and health. We tested the hypothesis that the DRD4 gene influences longevity and that its impact is mediated through environmental effects. Surviving participants of a 30 year-old population-based health survey (N=310, age range 90–109; the 90+ Study) were genotyped/resequenced at the DRD4 gene, and compared to a European ancestry-matched younger population (N=2902, age range 7–45). We found that the oldest-old population had a 66% increase in individuals carrying the DRD4 7R allele relative to the younger sample (p=3.5 × 10−9), and that this genotype was strongly correlated with increased levels of physical activity. Consistent with these results, DRD4 knockout mice, when compared to wild-type and heterozygous mice, displayed a 7–9.7% decrease in lifespan, reduced spontaneous locomotor activity, and no lifespan increase when reared in an enriched environment. These results support the hypothesis that DRD4 gene variants contribute to longevity in humans and in mice, and suggest that this effect is mediated by shaping behavioral responses to the environment.

Introduction

Individuals 90 years or older constitute the fastest growing segment of our population (Figure 1) (Day, 1996), yet little is known about the factors that contribute to their extreme longevity. Addressing this gap would help develop smarter strategies to increase the number of people who reach old age in good health.

Figure 1
Survival curves of the oldest old cohort

Undoubtedly, genetic factors contribute to both longevity (Sebastiani et al., 2012) and health among the oldest-old (Martin et al., 2007). Surprisingly, little attention has been directed toward genes that influence personality as “longevity genes” (Eaton et al., 2012). This is puzzling not only because the impact of life-style choices on longevity is self evident (e.g., smoking and early death from pulmonary disease or cancer), but also because of the strong genetic contributions to human personality (Bouchard and Loehlin, 2001).

A good example is the human dopamine receptor D4 (DRD4) gene, whose associations with the personality trait of novelty seeking (MIM 601696; Kluger et al., 2002; Gillespie et al., 2003) and with attention deficit hyperactivity disorder (ADHD) (MIM 126452; Faraone et al., 2001; Grady et al., 2003; Grady et al., 2005; Li et al., 2006) have been replicated numerous times. The DRD4 gene exhibits an unusually high amount of expressed polymorphism (Figure 1 inset). Most of this variation is the result of a 48-bp variable number tandem repeat (VNTR), located in exon 3 that encodes the third intracellular loop of the receptor. Alleles containing from 2 to 11 repeats (2R to 11R) have been identified, with the three most common variants (2R, 4R, 7R) accounting for over 90% of the allelic diversity (Ding et al., 2002; Wang et al., 2004).

The functional significance of these length/sequence changes in the DRD4 protein, in a region that couples to G proteins, has been studied extensively. The 7R variant shows a blunted response to dopamine when compared to the 4R variant (Asghari et al., 1995; Jovanovic et al., 1999; Wang et al., 2004) and does not form heteromers with the short isoform of the presynaptic dopamine D2 receptor (Borroto-Escuelo et al., 2011; Gonzalez et al., 2011). The DRD4 protein is expressed in several brain regions, with high levels in prefrontal cortex (Oak et al. 2000). DRD4 knockout mice display better performance on complex motor tasks and reduced exploration of novel stimuli (Rubinstein et al., 1997; Dulawa et al., 1999). ADHD individuals with a 7R allele have faster reaction times than non-7R individuals (Swanson et al., 2000; Langley et al., 2004).

Given that the DRD4 7R allele is associated with increased activity/novelty-seeking in children and young adults (Faraone et al., 2001; Kluger et al., 2002; Grady et al., 2003; Grady et al., 2005; Li et al., 2006), we hypothesized that this allele would be associated with increased physical activity in the elderly. In addition, since physical activity is associated with increased lifespan and health (Adlard et. al. 2005; Larson et al., 2006; Wilcox et al., 2006; Paganini-Hill et al., 2011), we hypothesized that the DRD4 7R allele would be overrepresented in the oldest old.

Materials and Methods

Study participants

The 90+ Study cohort (N=1,151) consists of all participants aged 90 or over who were alive on January 1, 2003 and were part of the initial Leisure World Cohort Study (Corrada et al., 2008; Paganini-Hill et al., 2011). The Leisure World Cohort Study was established in 1981, when a health survey was mailed to all residents who at that time owned homes in the Leisure World retirement community, Laguna Woods, California. A total of 13,979 residents (61%) returned the questionnaire and constitute this cohort. The median age of subjects when they joined the cohort was 73 years (range 44–107). Reflecting the characteristics of the community, the cohort was primarily of European ancestry (98%) and highly educated (67% some college to advanced degree). An additional questionnaire on current (2003-present) activity level was obtained for 233 participants, using questions appropriate for this age group. The longitudinal data in this large cohort has been used to conclude that spending more time in physical activities is significantly associated with lower mortality risks (15–35%), and is not confounded by smoking, alcohol intake, caffeine consumption, body mass index (BMI), or a history of hypertension, angina, heart attack, stroke, diabetes, rheumatoid arthritis or cancer (Paganini-Hill et al., 2011).

Cell lines/DNA and genotyping/resequencing

Genotypes were obtained from DNA isolated from blood cell pellets from a subset of 310 subjects (91 males and 219 females, approximately 60% of individuals still alive) representative of the entire 90+ cohort with respect to gender, education, BMI, exercise, drinking or medical histories. 270 participants had complete information available from the Leisure World Cohort Study (Paganini-Hill et al., 2011). Lymphoblastoid cell lines were established for all 310 participants, using a modified protocol involving up to 5 months of cell culture (Grady et al, 2003). Detailed ancestry information was collected from the 90+ Study participants to confirm self-declared ancestry for matching to European ancestry population-based control sample DRD4 genotypes (N=2902) from ongoing studies (Ding et al., 2002; Grady et al., 2003; Wang et al., 2004; Grady et al., 2005). Controls ranged in age from 7 to 45 years of age (mostly adolescents and college students). Previously established methods to genotype and resequence the DRD4 VNTR variant were used (Ding et al., 2002; Grady et al., 2003; Wang et al., 2004; Grady et al., 2005). Single nucleotide polymorphisms (SNPs) in other genes used as controls were genotyped by DNA sequencing. The IRB of the University of California, Irvine, approved all procedures.

Genotypes obtained for the 90+ sample were: 6 2R/2R, 27 2R/4R, 4 2R/7R, 4 3R/3R, 25 3R/4R, 7 3R/7R, 135 4R/4R, 1 4R/5R, 3 4R/6R, 82 4R/7R, 1 4R/11R, 1 5R/6R, 1 5R/7R, 1 6R/6R, 11 7R/7R, and 1 7R/8R. The genotypes of the control individuals were: 60 2R/2R, 16 2R/3R, 345 2R/4R, 1 2R/5R, 58 2R/7R, 1 2R/8R, 4 3R/3R, 127 3R/4R, 17 3R/7R, 1715 4R/4R, 5 4R/5R, 1 4R/6R, 493 4R/7R, 1 4R/8R, 1 4R/9R, 2 5R/5R, 2 5R/7R, 2 6R/7R, 51 7R/7R. There were no significant deviations from expected Hardy-Weinberg equilibrium. Observed allele frequencies for the oldest-old sample were 2R=0.07, 3R=0.06, 4R=0.66, 5R=0.005, 6R=0.009, 7R=0.189, and 8R=0.0016. Observed allele frequencies for the population and ancestry-matched controls were 2R=0.09, 3R=0.03, 4R=0.76, 5R=0.002, 6R<0.001, 7R=0.12, 8R=<0.001, and 9R<0.001. The DRD4 7R/x category contains individuals with at least one 7R (or derived 7R, i.e., 5R,6R,8R,9R,10R and 11R) allele, and the non-7R/x category the remaining individuals (Ding et al., 2002; Wang et al., 2004). A representative sample of 140 oldest-old individuals was resequenced at the DRD4 exon 3 polymorphism to determine the haplotype (273 alleles). The common 7R(1-2-6-5-2-5-4) haplotype or 7R derivatives were the predominant 7R sequences, with the fraction of common to rare haplotypes similar to that observed in prior population studies (Ding et al., 2002; Wang et al., 2004).

Mouse longevity studies

DRD4 wildtype (WT), heterozygous (HT) and homozygous knockout (KO) mice (Rubinstein et al., 1997) were assigned to either an enriched environment or EE (housed with other mice and with access to toys and running wheels) or a deprived environment or DE (housed in isolation in standard cages) and maintained under these conditions for their lifespan. Mice were divided into 6 groups distinguished by their genotype (WT, HT, and KO) and environmental exposure (EE and DE) with the following sample sizes: WT (DE=66, EE=51), HT (DE=31, EE=26) and KO (DE=34, EE=34). Age at time of death was recorded for all animals following guidelines previously outlined (Ullman-Cullere and Foltz, 1999). Locomotor activity was tested bimonthly for 90 minutes (Mini Mitter activity system). Difference in longevity and locomotor activity between groups were assessed using SAS 9.1 and STRATA 7.0. Experiments followed the Guide for the Care and Use of Laboratory Animals (Quimby et al., 2010).

Results

To test our hypothesis we determined DRD4 exon 3 VNTR genotypes/sequences for 310 90+ study individuals (the “oldest-old”; mean age 95.2 years; age range 90–109; 91 males) (Figure 1). These individuals represent approximately 1.4%–2.9% of their initial birth cohort and, by definition, should have altered allele frequencies of genes that impact health and longevity. When compared to matched European ancestry controls of individuals 50–90 years younger (N=2902; range 7–45 years of age), the oldest-old had a 66% increase in observed 7R/x genotype (Figure 2; 0.364 versus 0.219, χ2 = 33.28, p=3.5 × 10−9; ϕ2=0.026). This extremely small p-value would be significant at a genome-wide level following Bonferroni correction. Comparing allele frequency rather than genotype, the frequency of the 7R allele is greater in the oldest-old population by 72% (0.205 versus 0.119, χ2 = 38.27, p=6.15 × 10−10). This increase was preferentially observed in females (0.393 versus 0.297 males, χ2 = 9.65, p= 0.002), among whom the difference was significant at a genome-wide level (0.393 versus 0.219, χ2 = 34.66, p=1.7 × 10−9). In contrast, the difference in males, while significant, did not reach genome-wide level significance (0.297 versus 0.219, χ2 = 3.11, p=0.034) (Figure 2). This gender difference could reflect potentially negative (and often risky) behaviors associated with DRD4 7R alleles earlier in life, such as ADHD and drug abuse, which have a significant male bias (Swanson et al., 2000; Grady et al., 2003; Olsson et al., 2011). Allele frequencies at 20 randomly chosen polymorphisms in 9 unrelated genomic regions (CFTR, ERCC8, ERBB4, FANCC, HTERT, NRF1, RTN1, SLC6A3, UCP3) did not differ significantly between the oldest-old individuals, younger control individuals, or HapMap European ancestry populations (IHMC, 2007).

Figure 2
Comparison of expected to observed DRD4 genotypes

In the oldest-old we also observed a significant association between the 7R/x genotype and higher reported activity levels in both males and females (Figure 3). This association was observed not only for measures obtained in 1981, when the mean of self-reported activity level differed by 45% (1.42 hours versus 0.98 hours; χ2 = 5.07, p=0.024 when measured as a categorical variable and t=2.40, p=0.018 when measured as a continuous variable), but also when the measured variable was current self-reported activity (χ2=4.43, p=0.033). Oldest-old participants with the DRD4 7R/x genotype were twice as likely to report exercising 2 or more hours/day in 1981 (top 20%; χ2 = 17.33, p=1.64 × 10−5; Figure 3), The correlation of higher activity with DRD4 7R/x genotype was not confounded by age, gender, alcohol intake, BMI, or medical history, similar to results obtained in the larger Leisure World cohort (Paganini-Hill et al., 2011). The proportion of the variance in physical activity explained by DRD4 7R in this oldest old population was r2=0.023.

Figure 3
DRD4 genotype and self-reported activity

In order to test the association of the DRD4 gene with longevity and responses to the environment directly, we compared the lifespan of mice that expressed DRD4 (HT and WT) versus that of KO mice (Rubinstein et al., 1997) that lacked the DRD4 gene, when reared in either deprived (DE; standard laboratory cage) or enriched (EE; group cages with multiple options for physical activity and exploration) environments (Ullman-Cullere and Foltz, 1999; Quimby et al., 2010). DRD4 genotype significantly affected the lifespan (F=5.74, p=0.004, η2=0.03; Figure 4). DRD4 KO mice (N=68) had a 9.7% shorter lifespan (Mean and standard errors of: 94.8 ±2.08 weeks, CI95% 91–99) than HT mice (N=57; 104 ±1.82 weeks, CI95% 100–108; p<0.05) and 7% shorter than WT mice (N=117; 101.4 ±2.38 weeks, CI95% 97–106; p<0.05). The rearing environment also influenced longevity (F=10.58, p=0.001, η2=0.03; Figure 4). Mice in EE (N=111; 102.2 ±1.76 weeks, CI95% 98–106) lived 5.7% longer than mice in DE (N=131; 96.7 ±1.54 weeks, CI95% 94–100; p<0.05), but these effects were moderated by the DRD4 genotype, as shown by a significant environment × genotype interaction (F=8.05, p<0.001, η2=0.04; Figure 4). Specifically, whereas the lifespan was increased in an EE for HT (9% increase) and WT (16% increase), the longevity of DRD4 KO mice did not differ between DE and EE rearing conditions (p=0.15; Figure 4 inset).

Figure 4
Kaplan-Meier survival curves of mice as a function of genotype and exposure to an enriched (EE) or a deprived environment (DE). Insert. Lifespan per group (mean and standard error)

Discussion

We have shown that a human DRD4 variant associated with activity in younger individuals is at higher observed frequency in the oldest-old (Figures 13), and that the DRD4 gene in mice moderated the beneficial effects of an enriched environment on lifespan (Figures 45). These results are consistent with human studies showing that sensitivity to environmental factors, both positive and negative, is modulated by the DRD4 7R variant (Sheese et. al., 2007; Belsky et al., 2009; Das et al., 2011; Olsson et al., 2011). The reduced spontaneous locomotor activity observed in DRD4 KO mice compared to WT and HT (Figure 5) supports this interpretation, as well as the higher reported physical activity levels in oldest-old individuals with a DRD4 7R variant (Figure 3). However, from our experimental design it can not be determined if the beneficial effects of an enriched environment on longevity in WT and HT mice is moderated through social factorsrather than increased locomotor activity per se.

Figure 5
Spontaneous locomotor activity for different ages in WT, HT and KO mice reared in deprived (DE) and enriched environments (EE)

The mechanism by which the DRD4 variants would differentially influence environmental responses is unclear. The variant DRD4 7R protein has a blunted response for cAMP reduction compared with the 4R protein (Asghari et al., 1995; Jovanovic et al., 1999; Wang et al., 2004, Swanson et al., 2000). Also the DRD4 7R protein, unlike the DRD4 4R protein, does not form functional heteromers with the short presynaptic isoform of the dopamine D2 receptor in the striatum (Borroto-Escuela et al., 2011; Gonzalez et al., 2011). Dysfunctional D2–D4 7R heteromers may impair dopaminergic control of corticostriatal glutamatergic neurotransmission. Thus, it is plausible that either a dopamine signaling deficit or enhanced glutamate release, mediated by DRD4 7R, could enhance reactivity to environmental stimuli.

While the biochemical/physiological significance of DRD4 variants is under continuing investigation, there is solid molecular evolutionary evidence that functional differences exist at this locus. Extensive linkage disequilibrium (LD) surrounds the DRD4 7R allele in individuals sampled worldwide (in comparison to the random LD at the ancestral 4R allele), as determined by direct DNA resequencing of the entire gene and genotyping of polymorphisms extending 100kb in both directions (Ding et al., 2002; Wang et al., 2004; Wang et al., 2006). The high frequency of an allele coupled with extensive LD is a powerful indicator of recent adaptive selection (LDD test; Wang et al., 2006; Hawks et al., 2007). The pattern of LD decay surrounding the DRD4 7R allele, with the highest LD near the VNTR decaying in both directions, suggests that the DRD4 7R VNTR is the “target” of this Darwinian selection, with a recent evolutionary origin (approximately 40,000–50,000 years ago). Consistent with this recent evolutionary origin and the known biochemical differences noted above, we have speculated that a “response ready” adaptation influenced by the DRD4 7R allele might have played a role in the Out of Africa human exodus (Ding et al., 2002; Wang et al., 2004).

In further discussing our findings it is important to note their limitations. The ideal control for the genotype/haplotype frequencies of DRD4 alleles would have been the nonliving birth cohorts of our oldest-old population, which is clearly not possible. However, human gene frequencies cannot change radically in a few generations, except following extreme population bottlenecks, which have not occurred in the last hundred years in European ancestry populations. A possible selective force that could have introduced intergenerational variation is the high mortality due to infectious disease experienced early in life by this oldest-old cohort, which has been largely eliminated through the use of immunization and antibiotics in the younger cohort (Figure 1). Thus, any hypothetical “resistance” allele might exhibit higher frequencies in the oldest-old population than currently observed, due to this environmental intervention. However, it is unlikely that the DRD4 gene (or any of the three closest genes in modest LD with it: DEAF1, SCT, and MUPCDH) (IHGSC, 2004) would confer such resistance. Moreover, allele frequencies at 20 randomly chosen polymorphisms in 9 unrelated genomic regions (CFTR, ERCC8, ERBB4, FANCC, HTERT, NRF1, RTN1, SLC6A3, UCP3) did not differ significantly between the oldest-old individuals, younger control individuals, or HapMap European ancestry populations (IHMC, 2007); nor was the observed DRD4 7R male/female allele frequency bias (Figure 2) found at any of these randomly chosen sites. It is difficult to conceive of a mechanism that explains the variations in allele frequencies between males and females except for one that involves the functional significance of the underlying variant locus. We cannot, however, rule out alternative behavioral possibilities to explain why 7R/x individuals are disproportionately found in our specific cohort, such as the ability/desire to live in a planned retirement community with good access to leisure activities (see below). Clearly, further studies of the allele frequencies of DRD4 throughout the lifespan, rather than just at the extremes (Figure 1) are warranted.

A strength and a weakness of the current study is that the oldest-old population studied is of predominantly European ancestry. We specifically chose this population to reduce the genetic diversity and allow for better case-control matching, essential for genetic studies. It should be cautioned, therefore, that it is unclear how broadly such results can be inferred beyond the ancestry group examined. The ubiquitous presence of DRD4 7R (and it's derived 2R allele) in most human populations (Ding et al., 2002; Wang et al., 2004), however, make it likely that this study can be generalized (and potentially replicated) in individuals whose ancestry is from different geographic origins. While a prospective design would allow for much stronger inferences, the logistics and cost of a prospective study to assess the effects of genotype on longevity are enormous and data would not be available for analysis until the cohorts died.

The current study was conducted on a population with high educational achievement and socioeconomic status (SES), again, to minimize the effect of these variables on our genetic studies. SES is known to influence both longevity and research participation (Seeman et al., 2004). While this homogeneity is an advantage, making it less likely that individuals with different genotypes would be differentially recruited, replicate studies in other SES populations should be pursed. Indeed, one could hypothesize that the “enriched environment” found in the currently investigated 90+ population (Paganini-Hill et al., 2011) would not be available to individuals with lower SES, and hence DRD4 7R frequency might not be elevated in such populations.

Finally, it would be useful to obtain measures of physical activity and DRD4 genotype in younger populations. DRD4 variation is not associated with higher BMI (except in African American populations; Guo et al., 2007; Fuemmeler et al., 2010), but few studies on DRD4/activity have been conducted except for individuals diagnosed with ADHD (Swanson, et al., 2000). Measurements of the heritability of physical activity vary widely, with a value of approximately 0.30 often reported (Perusse et al., 1989; Horimoto, et al., 2011). The proportion of the variance in physical activity explained by DRD4 7R in the oldest old population (Figure 3) is r2=0.023, or approximately 7.7% of the variance attributable to genes measured in heritability studies. However, heritability studies assume multiple additive genes contribute to the phenotype, but an interacting gene model, as proposed for the DRD4 7R/ADHD association (Grady et al., 2003) would make the contribution of DRD4 7R to the variance in activity level significantly greater (Zuk et al., 2012). Clearly, further studies of DRD4 7R contributions to the variance of activity level in other populations should be pursed. Unfortunately, there are no genetic markers on commercially available genotyping arrays that are in strong LD with the DRD4 7R VNTR (Saccone et al., 2009).

For genetic studies of complex phenotypes, most associated variants are observed to have relatively small effect sizes (<1%), as expected for polygenic traits (Manolio et al., 2009). Studies of longevity in European ancestry populations similar to the oldest-old population studied here have indicated that longevity has modest heritability (approximately 25%; Herskind et al., 1996; Mitchell et al., 2001). Given the estimated effect size for the DRD4 7R/longevity association (ϕ2=0.026; Figure 2), we can estimate that approximately 10.2% of the variance attributable to genes, measured in heritability studies, is associated with DRD4 7R in our oldest-old population. The effect sizes of DRD4 variants on longevity, estimated in our mouse studies, are comparable (η2=0.03–0.04; Figure 4). These values are quite large for a phenotype expected to be associated with many genes, and suggests that variants of DRD4 make a significant impact on longevity.

In conclusion, we observed a striking increase in DRD4 7R allele frequency in the oldest-old, and a strong association of this allele with increased activity levels. These findings are consistent with our animal model results showing that the DRD4 gene moderates the beneficial effects of an enriched environment in increasing the rodent's lifespan. We propose that DRD4 gene variants moderate longevity by altering behavioral responses to environmental factors. It is likely that such gene-environmental interactions underlie much human variability and the common disorders afflicting humankind (Wang et al., 2006; Manolio et al., 2009).

Acknowledgements

We dedicate this paper to Hubert HM Van Tol, whose pioneering work on the DRD4 protein will not be forgotten. We thank the participants, especially those of the 90+ Study, for their generous involvement in this project, Hao-Yuan Cheng and Simin Hakim for technical assistance, and Ruben Baler for editorial assistance. This work was supported by grants from the U.S. Department of Energy and National Institute of Aging (to RKM), a National Library of Medicine National Research Service Award (to ETW), the National Institute of Aging (to CK), the 111 Gene/Brain/Behavior Project from the Ministry of Education of China (to QD), and the NIAAA Intramural Program (to NDV).

Footnotes

Financial or conflict of interests: None

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