The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 62:301-307 (2007)
© 2007 The Gerontological Society of America
Genetic Variation and Human Aging: Progress and Prospects
David Melzer,
Alison J. Hurst and
Tim Frayling
Peninsula Medical School, University of Exeter, United Kingdom.
Address correspondence to David Melzer, MB, BCh, Peninsula Medical School, RD&E Wonford Site, Barrack Road, Exeter, EX2 5DW, United Kingdom. E-mail: david.melzer{at}pms.ac.uk
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Abstract
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The genetics of aging has seen extraordinary progress over the last few decades, with animal models suggesting key roles for a number of metabolic pathways. However, humans outlive laboratory models many times over, and only evidence from humans can ultimately identify the drivers of human aging. In this article we thematically review progress in identifying human genetic variants associated with longevity. We also look at the bigger picture of progress in identifying genetic associates of disease and functioning and healthy aging in older people. Although much of the existing evidence is fragmentary, recent exciting findings and robust methods are taking the field rapidly forward.
THE aging process, at least in lower organisms, appears to be affected by several biochemical pathways, including insulin and insulin-like growth factor 1 (IGF1) signaling (1,2), growth hormone (3), oxidative damage, and lipids (4). In addition, maintenance and repair of DNA (5), including mitochondrial DNA (6), has proved important. Control of apoptosis and cell senescence (7,8), often in response to DNA damage, appears to affect aging through a mechanism shared with protection from malignancy (9). Identifying robust and replicated effects for these candidate genes and pathways in humans has been very challenging. Dealing with these challenges forms the basis of this review.
It is worth noting at the outset why identifying gene variants for aging in humans might be productive. Although some dream of arresting aging itself (10), the more immediately practical objective is to understand the mechanisms involved in premature aging or early onset of disabling conditions. Understanding how gene variants lead to extended healthy life expectancy could lead to new interventions to avoid early disability and premature mortality (11). It could also clarify the role of environmental exposures and provide natural experiments from which the possible causative roles of intermediates can be deduced (12).
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THE ROLES OF GENES IN AGING
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There are a number of distinct roles that genes and genetic variations may play in aging (5), disease, and longevity. First, there may be genes that cause aging—i.e., that evolved to trigger an aging process, although most gerontologists doubt the existence of this hypothetical category. Genes may also alter longevity, rather than aging, by being linked to early onset of specific pathology and disease. Similarly, genes for nonfatal conditions may merely influence what changes develop in old age (e.g., onset of gray hair), changing the type of older person you become (e.g., making sarcopenia more marked than vascular changes). In practice, distinctions between true aging and age-related disease genes in humans are likely to be difficult to define; even in apparently healthy centenarians, disease appears to play a role in death in all cases (13), and low cardiovascular disease susceptibility appears to be key to becoming a centenarian (4,14,15).
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THE GENETIC COMPONENT OF HUMAN LONGEVITY, BIOLOGICAL AGE, AND FUNCTIONING
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An estimated one quarter to one third (16–19) of differences in life expectancy in humans are explained by genetic factors. Exceptional longevity appears to have an especially strong genetic basis (20,21). Extreme survival is only one aspect of aging, and for humans the notion of successful aging (22) or aging well with good functioning in old age is probably of more interest than extreme longevity. Biological age is defined by how well a person functions in comparison with others of the same chronological age (23): Those persons functioning relatively well are deemed biologically younger. Many aspects of biological age have substantial heritability (24). Measures of systolic blood pressure (25), pulmonary function (26), fasting glucose (27), diabetes (28), and bone degeneration (24) have all been reported to show significant heritability.
In terms of overall functioning, muscle strength (usually measured as grip strength) shows substantial heritability in older people (29–31). Various aspects of physical functioning, including walk speed, also show high heritability (32), suggesting a major role for genetic variation in the timing of the development of physical impairments, limitations, and disabilities. Tested physical functioning has also been shown to be a sensitive phenotype for the effects of the apolipoprotein E (ApoE) e4 variant in older people (33). There is also strong evidence for heritability of overall cognitive function (34) with estimates of 76% in Danish twins aged 70+ (35) and 62% for general cognitive ability (36) in Swedish twins.
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METHODS FOR IDENTIFYING GENETIC VARIANTS
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Given the substantial heritability of so many aging traits, it should be possible to identify at least some of the genetic polymorphisms involved, if robust study methods are used. In this section we will review the main methods available, which include linkage studies within families and association studies in unrelated samples.
Investigations of single gene disorders have been productive in identifying rare mutations that result in severe, accelerated aging syndromes (37). These progeria disorders include Werner's syndrome (caused by mutations in a Helicase gene) and Hutchinson Guilford syndrome (caused by mutations in a specific region of the Lamin A/C gene). However, the progeria disorders provide models of only certain aspects of aging, and have a debatable relationship to normal aging (38).
Linkage Studies
Finding the genetic variants that are common in the general population and contribute to aging processes in large numbers of people has proven very difficult. This is because there are likely to be many individual genes, each of small effect, that contribute to different aspects of aging. This problem is typical of common, partly genetic, partly environmental, traits.
A popular way of trying to identify alleles contributing to common conditions is to look for regions of the human genome shared more often than expected by chance between close relatives who also share the target condition. Until recently, however, this approach has not been very successful at narrowing the regions to specific alleles in specific genes. An example of linkage analyses in aging is provided by Puca and colleagues (39), who scanned the whole genome using 308 individuals belonging to 137 sibships with exceptional longevity. The study identified a region on chromosome 4 linked to human longevity. Subsequent fine mapping using association study methods implicated the MTP gene (40), although attempts to replicate this have been problematic. Similar work has been reported from the Framingham study (41).
A drawback of linkage is an inability to map a gene of modest relative risk (genotype relative risk < 2) except in unrealistically large samples (42). As aging in humans, like most complex traits, is likely to be influenced by many genes of small effect, linkage studies have limited power to detect such genes (43).
Genetic Association Studies
Genetic association studies use populations of unrelated individuals, and aim to detect statistical association between one or more genetic polymorphisms and a trait. Traits might be a quantitative characteristic or a discrete attribute or disease (44). Association study methods can be applied in a wide variety of specific designs. Although cross-sectional and longitudinal population studies are possible (43), the most common association study design in aging is a case–control format, comparing allele frequencies in centenarians or nonagenarians to frequencies in younger control populations.
There are many reported associations between gene variants and longevity; however, few of the findings have been consistent. Four example polymorphisms are summarized in Table 1, showing both statistically positive and negative findings for all four.
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Table 1. Details of Published Genetic Association Studies Comparing Long-Lived to Younger Individuals, of Four Candidate Genes for Longevity.
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There is a strong temptation to interpret failure to replicate in independent studies and other populations as a reflection of true biological variation. However, Ioannidis and colleagues (63) reviewed allele markers of risk thought to vary in effect across traditional racial boundaries and found that, whereas prevalence of the markers did vary, their biological impact on the risk for the common diseases examined were usually consistent.
Genetic association studies have suffered from a disconcerting lack of replication of findings in many fields. In a review in 2002, Hirschhorn and colleagues (64) examined over 600 reported positive associations between common gene variants and disease, including 166 reported three or more times. Evidence of consistent replication was found for only six of these associations. With little improvement since then, Ioannidis and colleagues (65) recently described the gene–disease literature as plagued with problems, dominated by many small, underpowered studies, often with flawed designs, and by selective reporting of positive results.
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SAMPLE SIZE
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Perhaps the most common reason for lack of replication has simply been inadequate sample size. As there are up to 30,000 genes in the human genome, and it is unlikely that more than a few hundred drive variation in any single complex phenotype, the a priori probability that any gene influences a given trait is very low (66). In addition, for multifactorial traits, relatively small effect sizes are to be expected. In most recent cases, odds ratios have been < 1.5 for allele phenotype associations in general population samples. With small studies of complex traits, claims of big effect sizes have generally disappeared as larger studies have reported more conservative estimates (67). In fact, a rapid early sequence of extreme, opposite results (68) often appears, with the magnitude of the effect reported in early studies failing to predict the eventual size of association or even whether a significant association exists.
An important element in the unreliability of early reports of association was that authors undertook exploratory analyses, too often arising from post hoc subdivision and stratification of the data, in an effort to produce at least one p value that reaches nominal significance (66). Inevitably such repeated analyses inflate the chances of Type 1 error. Thus exploratory associations should always be regarded as hypothesis generating, irrespective of the apparent size of the effect in the analysis.
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CONFOUNDING AND ADMIXTURE
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Problems can arise from confounded comparisons between groups with different allele frequencies and disease prevalences. For example, Reiner and colleagues (69) found gene–disease associations in the Cardiovascular Health Study, but these were linked to the different gene prevalence in African Americans. When individual ethnicity and socioeconomic deprivation was accounted for, associations disappeared.
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RECENT SUCCESSES
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Although the field has faced problems, larger-scale resources and robust methods are now in place to identify many more genes. Several common alleles have recently been found to alter diseases related to aging. These include a common variant in the TCF7L2 gene (70) in which the 9% of persons homozygous for the T allele are approximately twice as likely to get diabetes compared to the 47% of persons homozygous for the C allele. This is the most important type 2 diabetes gene variant yet found, and initial evidence from an older population suggests that T allele carriers are less likely to have features of metabolic syndrome, though they may be at greater risk of micro-vascular complications (71). There has also been very exciting progress in understanding the risk factors for age-related macular degeneration, the most common form of blindness in developed countries. Several studies have, in a very short time, confirmed a whole genome association scan (72) finding of an exceptionally strong link with the Y402H polymorphism in Complement Factor H. This gene was not previously suspected of contributing to age-related macular degeneration, and so will offer new opportunities for disease prevention and treatment.
In the related field of maturity onset diabetes of the young [possibly a kind of progeria, according to Martin (38)] identification of the responsible genes has led to improved treatment, with patients better controlled on sulphylureas than on insulin (73).
As the search for genetic variants associated with complex traits intensifies due to recent successes (Table 2) and reduced genotyping costs, it will clearly be necessary to address the methodological problems the field has faced.
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THE WAY FORWARD
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If study design (especially sample size) is not addressed, the tide of unreplicated associations will continue and genuine associations will be missed, with research prematurely abandoned because of underpowered negative studies (75).
Clearly a move is needed away from the headline p value of a gene–phenotype association to an appraisal of experimental quality. If new associations are found, particularly from multiple or subgroup analyses, the key to credibility must be confirmation in subsequent directed (hypothesis driven) analyses in other study populations. Study quality should be key, using heritable and accurately measured phenotypes, carefully matched large samples with sufficient statistical power to detect the relatively small risks usually found, well-chosen genetic markers, and adequate standards in genotyping, analysis, and interpretation (76). Thus we should see a move from studies of a few hundred cases and controls to studies involving several thousands of respondents.
A powerful solution to sample size and quality problems is to build up evidence over several studies in meta-analyses. For example, a post hoc meta-analysis (Figure 1) of the apparently conflicting ApoE published findings from Table 1 shows a net odds ratio for extreme longevity of 0.51 (0.42–0.61) for the e3/e4 against the common e3/e3 genotype. Formal testing for heterogeneity proved negative: The apparent variability with negative reports could have arisen by chance. Similarly, in a recent review, Christensen and colleagues (77) stated that other than APOE all reported polymorphism longevity associations had not been replicated. Although this is true in a narrow sense, the apparently inconsistent findings for the ACE gene insertion/deletion polymorphism across studies may in fact be consistent with an overall effect on meta-analysis, although the pooled estimate narrowly misses significance (Figure 2).
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Figure 1. Meta-analysis of reported associations of apolipoprotein E (ApoE) showing odds ratios for e3/e4 vs e3/e3 genotype for long-lived compared to younger individuals, p <.0001
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Figure 2. Meta-analysis of reported associations of angiotensin I converting enzyme(ACE) showing odds ratios for DD vs II genotype in long lived compared to younger individuals, p =.068
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Meta-analyses are preferably built in a preplanned way, ensuring study standardization and quality and, crucially, avoiding publication bias from the tendency for negative findings to be absent from the literature. Formalized standards for meta-analysis in genetic epidemiology are emerging (65), and support for meta-analysis groups in aging could extend methodological work and combine existing older population data sets to take genetic association studies forward.
To collect sufficiently large numbers of new samples, large scale collaborative efforts will be needed such as the pan-European "Genetics of Healthy Aging" (GEHA) consortium (http://www.geha.unibo.it), which is recruiting 5600 old siblings and 2800 young controls (78). Another example of collaborations across epidemiological studies is the United States National Institute on Aging–supported (79) Longevity Consortium. Such initiatives should also lead to promising associations in one study format being explored in others; thus, candidates found in centenarian studies have recently been explored in younger aging cohorts (80–82).
Candidates Versus Genome-Wide Scan
In the past, high genotyping costs have limited the numbers of polymorphisms that could be tested, but with new techniques, high densities of single-nucleotide polymorphisms can now be tested across the genome at feasible costs. As a first step, investigators can economically type more single-nucleotide polymorphisms in each gene of interest, using publicly available databases (e.g., HapMap) to choose an efficient set to capture the majority of common variation. The whole genome association approach (41,83) is likely to prove the most productive in helping to understand mechanisms, because they offer the chance to identify genes not previously thought of as biologically important (84).
Enlarging the Focus From Centenarians—Considering Wider Phenotypes of Aging
The search for alleles of interest in aging in humans has thus far been dominated by case–control association studies of long-lived individuals. In addition to the methodological and practical limitations of such studies, ultimately very few people will become centenarians, and the overall relevance of the phenotype to ordinary older people is unclear (24). A number of authors have therefore argued for enlarging the focus to study protective factors for the rate of aging or for common age-related diseases (85). George Martin (86) has argued that what is really required are studies beginning in middle age, to identify alleles linked to "elite" aging in one or more physiological systems. Similarly Karasik and colleagues (24) have advocated association studies using markers of biological age. Although many aging phenotypes show moderate to high heritability, identifying a single comprehensive biomarker of aging has proved difficult thus far [see Karasik and colleagues (24) for a review]. It may be that different aging phenotypes are affected differently by different metabolic pathways, and more than one will emerge empirically as being useful in replicated polymorphism–phenotype association studies.
Conclusion
Genetic variation has been shown to play an important role, alongside environmental factors, in producing variations in a wide range of markers of human aging and longevity. Only studies in human populations can show whether specific genetic variants in humans are linked to human diseases or aging, although good candidates can be chosen from animal work. Identifying which variants are important will clarify causal pathways in humans and may provide opportunities to delay disability and premature aging.
The existing genetic linkage and association study literature has often produced apparently contradictory findings. However, progress is being made with important variants being identified, notably for diseases of aging including macular degeneration and diabetes. Standards for good study design, with sufficient sample size, are emerging. Studies of a wider variety of aging phenotypes may add to the popular comparison of long lived with younger individuals. Falling genotyping costs and good study design are likely to combine to produce rapid progress in identifying polymorphisms of importance in human aging in the coming decade.
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Acknowledgments
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This work is supported in part by National Institutes of Health/National Institute on Aging (NIH/NIA) Grant R01 AG24233-01. DM is supported by a National Health Service (NHS) Executive National Public Health Career Scientist Award (Ref: PHCSA/00/002).
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Footnotes
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Decision Editor: Luigi Ferrucci, MD, PhD
Received May 13, 2006
Accepted August 5, 2006
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The Roles of Genes...
