Affiliations of authors: V. Bataille, Dermatology Department and Imperial Cancer Research Fund (ICRF), Skin Tumour Laboratory, St. Bartholomew's and Royal London School of Medicine, U.K., and St. John's Institute of Dermatology and Twin Research and Genetic Epidemiology Unit, St. Thomas Hospital. London; H. Snieder, A. J. MacGregor, T. D. Spector, Twin Research and Genetic Epidemiology Unit, St. Thomas Hospital; P. Sasieni, ICRF Mathematics, Statistics and Epidemiology Department, London.
Correspondence to: Veronique Bataille, M.D., M.R.C.P., Ph.D., Twin Research and Genetic Epidemiology Unit, St. Thomas Hospital, London SE1 7EH, U.K. (e-mail: v.bataille{at}icrf.icnet.uk).
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ABSTRACT |
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INTRODUCTION |
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Common and atypical nevi are the most powerful predictors of risk for melanoma in population-based as well as family studies (10-16). However, the predictive value of nevi as a marker of risk does vary with age, since nevi do show a statistically significant decrease with age, although it cannot be ruled out that this may be because of cohort effects (14,15). Individuals who belong to melanoma families often have large numbers of common and atypical nevi (17-19). This melanoma-associated phenotype is known as the atypical mole syndrome phenotype (AMS) and was first described simultaneously by Clark et al. (20) and Lynch et al. (21) in 1978. Most family studies that investigate potential linkage with the p16 tumor suppressor gene locus in melanoma families have concentrated on the melanoma phenotype. However, there is some recent evidence from two European melanoma family studies (22,23) that the p16 locus (CDKN2) may have a role in expression of nevi. Although most family studies have concentrated on studying the rare melanoma kindreds with multiple cases of melanoma, more power could potentially be derived from studying the "precursor phenotype" or the "intermediate phenotype." Nevi are both precursors and markers for melanoma, since there is strong evidence that melanoma arises in pre-existing nevi, especially in patients with a genetic susceptibility to the disease (24). By studying nevi, one may be able to discover genes involved in the early steps of melanocytic differentiation. Studying a large, population-based cohort rather than rare families is more likely to yield results that are relevant to the whole population studied in terms of genes involved in nevus expression. Freckles are not precursor lesions for melanoma but are associated with a fair skin type that is a well-recognized risk factor for melanoma (25). Environmental factors, particularly sun exposure, are also involved in the expression of nevi. Comparisons between Australia and the U.K. have shown, both in children and in adults, that high numbers of nevi are associated with high levels of sun exposure (26-28). However, whether this environmental effect only influences genetically susceptible individuals is not known.
Family studies investigate familial aggregation of disease but cannot easily distinguish between shared environmental and genetic factors. The twin model, by comparing similarities between identical and nonidentical (i.e., fraternal) twin pairs, is better suited to separate environmental and genetic influences on the liability to a trait or disease (29). This U.K. twin study is the first to investigate the relative contribution of genetic and environmental factors in the expression of nevi and freckles in adults through the study of twins.
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SUBJECTS AND METHODS |
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The subjects consisted of 127 monozygotic twin pairs and 323 dizygotic twin pairs, all
Caucasian and all female, aged 18 through 72 years, who were recruited from January 1997
through December 1997 from the St. Thomas U.K. Adult Twin Register in London. For
historical reasons, the register consists mainly of female twins. Twins were not aware of the
hypotheses being tested because they were part of a large study investigating many diseases and
traits. Each underwent a detailed, nurse-administered questionnaire covering demographic
information as well as days of exposure to natural and artificial sunlight throughout a lifetime.
The data on sun and sunbed exposure will be part of a separate publication. A standardized skin
examination, including a full-body nevus countdivided into 17 body sites (excluding the
genital area and posterior scalp)was performed by nurses trained by one of us (V.
Bataille) for 4 weeks before the start of the study. The nevus count protocol had been validated in
two previous case-control studies of melanoma from our group (14,15).
All twin pairs were examined on the same day under the same circumstances in a well-lit
examination room. Five research nurses performed the nevus counts, but each twin within a pair
was always examined by the same nurse. Possible interobserver bias was assessed before the start
of the study by having two different observers count nevi on 20 volunteers on two separate visits,
yielding a correlation coefficient of .95 between the two examiners. Not all five nurses were
assessed for interobserver bias. Nevi were recorded by size in three categories (>2 mm and
5 mm, >5 mm and
10 mm, and >10 mm). The total-body nevus count was defined
as the sum of all nevi greater than 2 mm in diameter. Large nevi were defined for the purpose of
this study as melanocytic lesions of 5 mm in diameter or above (excluding congenital nevi). The
counts of large nevi can be considered as a surrogate for atypical nevi, since most benign nevi at
least 5 mm in diameter are atypical, with irregular edges and/or an irregular border. For the
purpose of this study, nevi on sun-exposed sites were all nevi on the face, arms, and lower legs
and nevi on sun-protected sites were nevi on the back, chest, abdomen, buttocks, and thighs. Skin
type was assessed according to the classification of Fitzpatrick et al. (30)type I: always burn and never tan; type II: often burn and tan lightly; type III: burn
moderately and tan gradually; type IV: burn minimally and tan easily; and type V: never burn
and tan deeply. Hair and eye color was also recorded for each twin. Freckle counts on the face,
arms, and back were performed by use of the scoring by Gallagher et al. (31). The freckle score was stratified into 10 categories from 0 to 100; for the analyses,
the total freckle count was used, combining scores on the face, arms, and back. The Ethics
Committee approval for this study was obtained at the Guy's and St. Thomas Hospital
Trust, London, U.K.
Statistical Methods
Associations between categorical traits were tested by use of 2 statistics.
For continuous traits (mole counts), intraclass correlations within monozygotic and dizygotic
twin pairs were calculated by use of analysis of variance in Stata Statistical Software (Release
5.0, 1997; Stata Corporation, College Station, TX). The P values were two-sided, and
the cutoff for statistical significance was P<.05. For categorical data (freckle counts
and skin type), a continuous underlying liability distribution was assumed in which thresholds
divide the distribution into categories (32). The correlation in liability
within twins is called the tetrachoric (for two categories) or polychoric (for more than two
categories) correlation. Polychoric correlations for monozygotic and dizygotic pairs were
obtained with the statistical package Mx (33). Quantitative genetic
analyses on both continuous and categorical data were performed to quantify the genetic and
environmental components of the variance (see the "Appendix" section).
All modeling was done with the software package Mx (33).
For the genetic analyses, nevus counts were summarized into 2 x 2
variance-covariance matrices. For the analysis investigating the influence of age,
variance-covariance matrices for four age-by-zygosity groups were calculated: monozygotic and
dizygotic less than 45 years of age and monozygotic and dizygotic aged 45 years or older. The
age of 45 years was selected as a cutoff because this was the mean age of the twins. Similarly, to
investigate the influence of sun exposure, a four-group analysis was performed (sun-exposed
versus sun-protected sites in monozygotic and dizygotic twin pairs). In view of the highly
skewed nevus counts, the logs of total nevus counts were used. For freckle counts and skin type,
the model was directly fitted to the monozygotic and dizygotic contingency tables, with
dimensions of 10 x 10 for freckle counts and 5 x 5 for skin type. Models were fitted
to these variance-covariance matrices and contingency tables by the method of maximum
likelihood that yields parameter estimates (a, d, c, and e) and 95%
confidence intervals (CIs), a 2 test for the goodness of fit of the model, and
the Akaike's information criteria (AIC) calculated as (
2 - 2 df ). The overall
2 test measures the agreement between the
observed and predicted variances and covariances in the different zygosity groups. Submodels
were compared with hierarchic
2 tests, in which the
2
value for a reduced model is subtracted from that of a full model. The degrees of freedom for this
are equal to the difference between the degrees of freedom for the full and reduced model.
Another aim of the model-fitting procedure is to explain the pattern of covariances and variances
by use of as few parameters as possible. Therefore, AIC was used to evaluate the fit of the
models. The model with the lowest AIC reflects the best balance of fit and parsimony (34).
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RESULTS |
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The mean age of the twins was 48 years (range, 19-73 years) for the
monozygotic pairs and 44 years (range, 18-69 years) for the dizygotic
pairs. Most twins were skin type II or III, and the distribution of
skin types in monozygotic and dizygotic is shown in Table
1. The mean number of nevi was 35, with a median of
22 (range, 0-324). Fifty-five percent of the twins had fewer than 25
nevi, and 22% had more than 100 nevi (Table
1
). The mean number of
nevi decreased with age (P<.0001) (Fig.
1
). The mean number of large nevi (melanocytic
lesions with a diameter of at least 5 mm) was 1.4 (range, 0-88); 14%
of the twins had more than two large nevi.
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There was a highly statistically significant association between the number of common nevi and large nevi: 31% of the twins with 100 or more common nevi had two or more large nevi compared with 6% of the twins with 25 or fewer common nevi (P<.0001). Large nevi were more frequent in individuals with skin type II or III compared with individuals with skin type I or IV.
Heritability of Nevus Counts
The intraclass correlation coefficient for total nevus count was .83
in monozygotic twins compared with .51 in dizygotic twins. The effect
of age was examined in more detail, separating the twins into two age
groups (less than and at least 45 years of age) by use of the mean age
as a cutoff point. Differences between intraclass correlation
coefficients for total nevus count were higher for older twins than for
younger twins: .67 and .58, respectively, in monozygotic and dizygotic
below the age of 45 years and .86 and .42, respectively, in monozygotic
and dizygotic pairs for twins aged 45 years or older (Fig.
2). The best-fitting model for total nevus count in
the two age groups is shown in Table 2
. In twins aged
less than 45 years, 36% of the variance in nevus counts was explained
by additive genetic effects, with the remainder due to both unique and
shared environmental effects. For twins 45 years old or older, the
contribution of shared environmental variance was no longer
statistically significant and could be set to zero
(
2[1] = .28; P = .60). As a result, the
additive genetic variance was much greater in the older group (84%;
95% CI = 77%-88%) than in the younger group (36%;
95% CI =
0.8%-63%). Nevus numbers on sun-protected sites were more highly
correlated within monozygotic and dizygotic pairs than for nevi on
sun-exposed sites: intraclass correlations of .72 and .49,
respectively, for sun-protected sites compared with .56 and .33 for
sun-exposed sites. Estimates of variance components could not be set
equal for sun-exposed and sun-protected sites (
2[3] =
18.99; P<.0003). Table 3
shows estimates
of the best-fitting model. The additive genetic variance for nevus
counts on sun-exposed sites was smaller than that for nevus counts on
sun-protected sites: 42% (95% CI = 17%-66%) versus
62% (95% CI =
44%-81%), respectively. Accordingly, both shared and unique
environmental factors had a greater effect on the variance for nevus
counts on sun-exposed sites.
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Polychoric correlation coefficients for skin type were .76 in
monozygotic twins compared with .38 in dizygotic twins. The
best-fitting model included additive genetic effects (A = .78;
95% CI = 69%-85%) and unique environmental effects (E
=
22%; 95% CI = 15%-31%). For total freckle count, the
best-fitting
model was an AE model with the lowest AIC, and 91%
(95% CI = 86%-94%) of the variance was explained by additive
genetic effects (Table 4). No statistically
significant differences in the heritability of freckles was observed
when analyzing body site of freckles separately.
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DISCUSSION |
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In this study, twins were Caucasian females recruited from all over the U.K. via the media. The twins were not aware of the hypotheses tested regarding skin phenotypes, since they were unselectively recruited. The mean number of common nevi recorded by trained research nurses was also very similar to another non-twin, U.K. population-based nevus count study performed by two dermatologists, so that the data can be generalized (15). Although intraobserver variability was not assessed in this study, it is unlikely that the small differences in nevus counts due to intraobserver bias had a substantial effect on the results, since these differences should have been similar for the monozygotic and dizygotic pairs and this study is comparing intrapair differences for monozygotic compared with dizygotic twins. Furthermore, the intraclass correlation coefficient for total nevus count in this study of .83 in monozygotic pairs can be regarded as a lower limit of reproducibility for nevus counts. Nevus count can be defined as a complex trait, since it is influenced by many factors and is extremely common in the normal population. Monozygotic and dizygotic pairs were similar regarding prevalence, mean number, and variance for nevus and freckle counts, which makes any unexpected biases in recruiting monozygotic compared with dizygotic twin pairs highly unlikely.
Quantitative genetic model fitting was used for this study, since estimates of genetic and environmental effects based on comparisons of intraclass correlations alone have relatively low power, large standard errors, and do not make full use of the information available in the variances and covariances (34). It involves solving a series of simultaneous structural equations to estimate genetic and environmental parameters that best fit the observed twin variances and covariances. In many instances, this model fitting has several advantages over the classic twin methodology, as seen in other twin studies (35). It allows for the separation of the observed phenotypic variance into its genetic and environmental components. However, large datasets are needed to confidently accept or reject a tested model, especially when attempting to quantify shared environmental factors. If we speculate that sun exposure is the most important environmental factor influencing nevi, the shared environment would be the sun exposure shared by both twins (such as family holidays), while the unique environment would be the sun exposure unique to one twin within a pair. For example, one twin may be a keen swimmer or keen tennis player, while the other may not be.
The variance in freckle count was almost entirely determined by genetic factors in this study, with no measurable common environmental effects. Freckles were counted on the face, back, and arms, and care was taken not to include solar lentigines on sun-exposed areas. The strong genetic influence on freckle counts may, in part, be explained by polymorphisms in the melanocortin 1 receptor (MC1R). In a twin study of children, an association study (37) has supported the role of MC1R polymorphisms in determining hair color and skin types. As expected, red hair and skin type I were strongly associated with freckle count in the study reported here. Furthermore, MC1R polymorphisms have been associated with melanoma susceptibility in sporadic and familial melanomas, and this is likely to reflect the increased risk of melanoma in fair-skinned individuals (38).
The higher degree of concordance in total nevus count in monozygotic compared with dizygotic twins confirms that the expression of nevi is statistically significantly influenced by genetic factors. The number of nevi decreases with age, as has been previously reported in the U.K. and Australia (14,15). The greater additive genetic variance for total nevus counts in older twins, with a complete disappearance of common environmental factors, is likely to provide insight into the genetics of nevi over a lifetime. Older twins are likely to have a less-shared environment than younger twins because they are more likely to live apart, and the effects of education, diet, and upbringing may be less important with increasing age. Higher correlation coefficients for nevus counts with age may also be due to the influences of genes involved in the involution of nevi in older age groups. This disappearance of nevi with age may, for example, suggest the involvement of senescence genes. A possible candidate would be p16 (also known as CDKN2), a tumor suppressor gene associated with melanoma that has been shown to have an important role in cell senescence (39). Melanocytes in culture, taken from a melanoma-prone individual carrying a homozygous deletion of p16, appear to grow for more than 20 months, which is longer than ever reported for human melanocytes (Bennett D: personal communication). Furthermore, among melanoma families in the U.K., p16 mutation carriers have higher nevus counts than noncarriers (22). The CDKN2 locus has also been shown to contribute toward the expression of the nevus phenotype in Dutch melanoma families (23). However, segregation analyses of the nevus phenotype in French families with at least one case of melanoma suggest that the mode of inheritance of nevi is likely to be very complex (40).
A variety of genes may be involved in expression of nevi throughout a lifetime, but gene-environment interactions are likely to occur, since sun exposure is also important in the induction of nevi. Comparison studies (26-28) have shown that nevus numbers are greater in Australian children and adults that in age-matched individuals in the U.K. Nevi on sun-protected sites were under greater genetic influence than nevi on sun-exposed sites for twins of all ages in this study, which supports the role of sun exposure in the induction and involution of nevi. The fact that the upper arm had a higher median number of nevi than did the lower armwith similar results for the legssuggests that sun exposure may have a role in the involution of nevi on chronically sun-exposed sites. Indeed, data from our group, where a single examiner was involved in both studies, showed that the difference in nevus counts between Australia and the U.K. (i.e., with higher numbers of nevi in Australia in young age groups) is reversed in older age groups, with lower median nevus counts in Australians 45 years old or older compared with age-matched U.K. control subjects (28). This again implies that sun exposure may be involved in the involution of nevi in older age groups and that this response to sun with age may also be, in part, genetically determined.
The discovery of genes influencing expression of nevi is important and clinically relevant, since nevi are powerful predictors of risk for melanoma. Common and atypical nevi are not only markers of risk but, also unlike freckles, can be precursor lesions. Melanoma tumors may show a wide variety of chromosome losses, especially at the 9p21 chromosomal locus (CDKN2 locus), but germline mutations of p16 or CDKN2 are relatively rare, so that other melanoma genes remain to be discovered (41). Common regions of losses in melanoma tumors are chromosomes 1, 10, and 6but none of these regions have, as yet, been shown to harbor a new melanoma tumor suppressor gene (42). Genetic changes in atypical nevi, studied by use of microsatellite markers and polymerase chain reaction analysis, are too rare to provide clues about early changes in precursor lesions (43). Furthermore, chromosome loss is unlikely to be the only mechanism leading to tumor initiation in melanoma, and other search strategies need to be implemented to discover melanoma genes or unravel the role of existing genes. Association studies using the intermediate or precursor phenotype in families as well as in population-based, case-control studies may, therefore, be very useful to discover genes involved in nevus expression.
This U.K. twin study confirms that genetic factors are very important in the expression of cutaneous traits associated with an increased melanoma risk. This study has shown that age has a significant effect on the heritability of nevi and highlights the need to carefully adjust or match for age in future genetic linkage or association studies involving nevus counts. Analyses of sun-exposed sites versus sun-protected sites confirm that environmental factors also play a substantial role in the expression of nevi and that the environmental influence on total nevus count varies statistically significantly over a lifetime. Future association or linkage studies by use of single or multiple cutaneous traits not only will shed some light on the role of existing candidate genes, such as p16, p19 ARF, CDK4, and MC1R on nevus expression and skin type, but also may lead to the discovery of new genes involved in skin pigmentation and melanocyte differentiation (44). With data on polymorphisms in known and novel genes, gene-environment interactions could also be investigated further. Moreover, once populations at risk of melanoma are characterized genetically, primary prevention of melanoma may be specifically targeted toward these "genotypically at risk" individuals.
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APPENDIX |
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The genetic model can be represented by the following linear structural equations (32) : 1) Pi = aA i + dD i + cC i + eEI and 2) VP = a2 + d2 + c2 + e2, where P is the phenotype of the ith individual scaled as a deviation from zero. A, D, C, and E can be conceived of as uncorrelated latent factors with zero mean and unit variance, while a, c, d, and e are regression coefficients of the observed variable on the latent factors, inasmuch as they indicate the degree of relationship between the latent factors and the phenotype. VP is the phenotypic variance. Squaring the regression coefficients yields the (unstandardized) variance components (VA = a2, VD = d2, VC = c2, and VE = e2), whose sum is equal to the total phenotypic variance. Each of these components divided by the total variance VP yields the different standardized component of variance. Heritability can be defined as the ratio of additive genetic variance to the total phenotypic variance (h2 = VA/VP). For monozygotic twins, correlations of the additive and dominance genetic factors between a twin and their co-twin are unity. For dizygotic twins, these values are .5 and .25, respectively. By definition, in both monozygotic and dizygotic same-sex pairs, correlations for common environmental factors are unity and for unique environmental factors are zero. The covariances in monozygotic and dizygotic twin pairs are thus equal to 1) COVMZ (P1,P2) = a2 + d2 + c2 and 2) COVDZ (P1,P2) = .5 a2 + 0.25d2 + c2.
The model further assumes random mating and absence of gene-environment correlation or interaction.
In twin studies, the effects of D and C are confounded, which means that they cannot be included in the same univariate model. However, D and C have opposite effects on the patterns of monozygotic and dizygotic twin correlations. D tends to produce dizygotic correlations that are less than 50% of the monozygotic correlations, and C inflates the dizygotic correlations to be above 50% of the monozygotic correlations. Models constraining all genetic effects to be nonadditive are considered unlikely because they lack a plausible biologic interpretation.
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NOTES |
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We thank the twins who took part in this research project.
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Manuscript received August 6, 1999; revised November 29, 1999; accepted January 4, 2000.
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