Does the Addition of Information on Genotype Improve Prediction of the Risk of Melanoma and Nonmelanoma Skin Cancer beyond That Obtained from Skin Phenotype?

Terence Dwyer1, James M. Stankovich1, Leigh Blizzard1 , Liesel M. FitzGerald1, Joanne L. Dickinson1, Anne Reilly1, Jan Williamson2, Rosie Ashbolt1, Marianne Berwick3 and Michèle M. Sale1,4,5

1 Menzies Research Institute, University of Tasmania, Hobart, Tasmania, Australia.
2 Department of Pathology, Royal Hobart Hospital, Hobart, Tasmania, Australia.
3 Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY.
4 Center for Human Genomics, Wake Forest University School of Medicine, Winston-Salem, NC.
5 Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC.

Received for publication July 22, 2003; accepted for publication November 25, 2003.


    ABSTRACT
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
The authors quantified improvement in predicting cutaneous malignant melanoma, basal cell carcinoma, and squamous cell carcinoma of the skin made possible by information on common variants of the melanocortin-1 receptor gene (MC1R) in a 1998–1999 population-based case-control study of subjects aged 20–59 years of northern European ancestry in Tasmania, Australia. Melanin density at the upper inner arm was estimated by spectrophotometry. DNA samples were genotyped for five MC1R variants: Val60Leu, Asp84Glu, Arg151Cys, Arg160Trp, and Asp294His. Among controls (n = 267), variant carriers, versus noncarriers, had lower (p < 0.01) mean melanin concentrations. Increased risk conferred by genotype was restricted mainly to those with the darkest skins: for subjects with at least 2% melanin, the odds of carrying each additional variant were higher for cutaneous malignant melanoma (n = 39; odds ratio = 1.45, 95% confidence interval: 0.87, 2.44), basal cell carcinoma (n = 35; odds ratio = 1.86, 95% confidence interval: 1.14, 3.02), and squamous cell carcinoma (n = 42; odds ratio = 2.67, 95% confidence interval: 1.50, 4.74) cases than for controls (n = 135). Adding MC1R information to prediction based on age, sex, and cutaneous melanin increased the area under the receiver operating characteristic curve by 1.4% (cutaneous malignant melanoma), 3.2% (basal cell carcinoma), or 2.0% (squamous cell carcinoma). The improvement in prediction was probably too small to be valuable in a clinical setting.

case-control studies; epidemiologic factors; genetics; melanins; polymorphism (genetics); receptor, melanocortin, type 1

Abbreviations: Abbreviations: CMM, cutaneous malignant melanoma; MC1R, melanocortin-1 receptor gene; PCR, polymerase chain reaction; ROC, receiver operating characteristic.


    INTRODUCTION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Variants of the melanocortin-1 receptor gene (MC1R) are associated with phenotypic features such as red hair (17), light skin color (4, 7), and the skin type (8) associated with burning rather than tanning after sun exposure (1, 3, 57). These skin phenotypes carry a higher risk for cutaneous malignant melanoma (CMM) (9) and nonmelanoma skin cancer (10). Several studies have now shown that risk of CMM (4, 6, 11) and nonmelanoma skin cancer (3, 57) is higher among MC1R variant carriers than among noncarriers, with the strongest effects for carriers of multiple variants (46).

There has been investigation of whether the effect of MC1R variants on risk of CMM and nonmelanoma skin cancer can be captured by measuring the phenotypic features that this gene influences: red hair, light skin color, and sun-sensitive skin type. The clear finding is that some association with MC1R remains after stratifying by these phenotypic features (1, 47). This finding is true of studies (57) that have measured a large number of variants, leaving less leeway for the remaining association to be an artifact of unmeasured variants, suggesting that the effect of MC1R is not exerted entirely through hair and skin color.

What is not clear from these results is whether the association of MC1R variants with skin cancer is restricted to subjects with particular skin types. In some studies (1, 57), similar increases in risk for MC1R variants were found irrespective of skin type. In contrast, despite finding no overall increase in risk of CMM associated with the Asp84Glu, Val92Met, and Asp294His variants in an English population, Ichii-Jones et al. (12) found a strong association for subjects with Fitzpatrick (8) skin types III (sometimes burn, average tan) or IV (rarely burn, tan easily). They concluded that some MC1R alleles influence susceptibility to melanoma in subjects who tan but not in subjects with sun-sensitive skin types I (always burn, never tan) or II (usually burn, tan with difficulty). Then, in an Australian study, Palmer et al. (4) stratified by self-reported skin color and found that increased risk for carriers of the Arg151Cys, Arg160Trp, or Asp294His variants was restricted to those with medium or olive/dark skin color. They concluded that particular MC1R variants increase risk for persons with darker complexions. Such persons tend to tan rather than burn (8), and these were the subjects who Ichii-Jones et al. found to be at increased risk of CMM for carrying an MC1R variant.

While it has not yet been resolved whether the association for MC1R is independent of skin type, the findings to date indicate a possible contribution to risk prediction from including genotype in risk models. To this point, however, the extra contribution has not been quantified. We chose to examine this question by using a recently developed noninvasive method of measuring skin phenotype: spectrophotometry of cutaneous melanin density (13). This measure has been shown to be strongly associated with risk of CMM and nonmelanoma skin cancer (14). Particularly for men, it is a stronger predictor than the alternative skin phenotypic measures that rely principally on observation of eye color, self-report of natural hair color, assessment of apparent skin color, or self-assessment of tendency to burn or to tan. We had access to data collected for a population-based case-control study of CMM and nonmelanoma skin cancer, in which cases and controls were drawn, with high participation rates, from the same source population by using probability sampling. These data enabled us to assess the prevalence of MC1R variants in a representative population sample and to directly estimate the contributions of MC1R and cutaneous melanin to risk of CMM and nonmelanoma skin cancer. Our purpose was to determine whether there is clinical value in collecting genotypic as well as phenotypic information from persons when estimating susceptibility to skin cancer.


    MATERIALS AND METHODS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Subjects
The ethics committee of the University of Tasmania approved this study. All participants provided informed consent. Eligible subjects were participants in a population-based case-control study of persons aged 20–59 years of northern European ancestry in Tasmania, Australia; the study has been described in detail elsewhere (14). Briefly, notification of cancer is a legal requirement of pathology laboratories in Tasmania, and cases were ascertained from registrations of histologically confirmed diagnoses by the Tasmanian Cancer Registry. Cases of CMM were registered in 1998–1999. The basal cell carcinoma and squamous cell carcinoma cases were chosen at random from registrations for July 1998–June 1999 and were frequency matched to CMM cases by sex, 5-year age group, and month of diagnosis. Participation was 90.1 percent (245/272) for eligible CMM cases, 88.2 percent (224/254) for eligible basal cell carcinoma cases, and 88.1 percent (199/226) for eligible squamous cell carcinoma cases. Controls were chosen at random from the State Electoral Roll, a comprehensive population listing, and were frequency matched to CMM cases by sex and 5-year age group. Participation was 80.9 percent (490/606) for eligible persons. After exclusions principally for medical conditions, the subjects for analysis were reduced to 244 CMM cases, 220 basal cell carcinoma cases, 195 squamous cell carcinoma cases, and 483 controls.

Buccal mucosa swabs were provided by 243 CMM cases, all 220 basal cell carcinoma cases, 194 squamous cell carcinoma cases, and 470 controls. Genomic DNA was successfully extracted for 179 CMM cases, 181 basal cell carcinoma cases, 165 squamous cell carcinoma cases, and 317 controls. Genotyping for the presence or absence of at least one MC1R variant was successful for 164 CMM cases, 160 basal cell carcinoma cases, 147 squamous cell carcinoma cases, and 291 controls. Not all five variants were able to be genotyped for all subjects. The basal cell carcinoma and squamous cell carcinoma cases included five subjects considered as both a basal cell carcinoma and a squamous cell carcinoma case (n = 3) or as a subsequent CMM case (n = 2). Hence, the 471 (164 + 160 + 147) cases consisted of 466 (471 – 5) different subjects.

Measurements
Melanin density at the upper inner arm was estimated from skin reflectance of light of 400-nm and 420-nm wavelengths (13). The research assistants assessed eye color and gathered information on other study factors by using a standardized, interviewer-administered questionnaire.

Genomic DNA was extracted from the buccal mucosa swabs by using Puregene DNA isolation kits (Gentra Systems, Minneapolis, Minnesota). The buccal swabs had been stored at 4°C for some time (average, 2.5 years), and the DNA extractions from some specimens resulted in template DNA of insufficient quality for PCR (polymerase chain reaction) and/or the SNaPshot procedure (Applied Biosystems, Foster City, California). Moreover, the yields from the buccal swabs were highly variable, and sufficient DNA was not always available to repeat failed reactions.

To measure MC1R genotype, we chose five variants shown to be associated with skin cancer risk in the Australian population (4, 7): Val60Leu, Asp84Glu, Arg151Cys, Arg160Trp, and Asp294His. Initially, an MC1R gene fragment of 1,016 base pairs, encompassing all five variants, was amplified by using a PC960C thermal cycler (Corbett Research, Sydney, Australia). The primer sequences were as follows: 5'-AGGCCTCCAACGACTCCTT-3' (MC1R-F) and 5'-CACTTAAAGCGCGTGCACC-3' (MC1R-R). For individual amplifications, 20–50 ng of genomic DNA template was combined with 10 mM of Tris-HCl (pH 8.3), 50 mM of KCl, 1.5 mM of MgCl2, 1 µM each of the four dNTPs, 5 µl of Q solution (Qiagen, Valencia, California), 0.8 µM of each primer, and 2.5 U of Taq polymerase (Qiagen) in a 25-µl reaction. Samples were denatured for 2 minutes at 94°C and were amplified by using 38 cycles consisting of 40 seconds at 94°C, 40 seconds at 63°C, and 1 minute at 72°C, followed by a final elongation step for 5 minutes at 72°C.

We treated 16 µl of the MC1R PCR product with 4 U of shrimp alkaline phosphatase (SAP) (Amersham Biosciences, Amersham, United Kingdom) and 4 U of Exonuclease 1 (Exo 1; Amersham Biosciences) for 1 hour at 37°C and 15 minutes at 72°C. We combined 4 µl of treated PCR product with 5 µl of SNaPshot Ready Reaction Premix (Applied Biosystems) and 1 µM of 0.15 µM of variant primer to detect the single nucleotide polymorphisms (SNPs) of interest. The polyacrylamide gel electrophoresis–purified SNaPshot primers were as follows: Val60Leu, 5'-GTGGAGAACGCGCTGGTG-3'; Asp84Glu, 5'-CTGCCTGGCCTTGTCGGA-3'; Arg151Cys, 5'-ATCTCCATCTTCTACGCACTG-3'; Arg160Trp, 5'-AGCATCGTGACCCTGCCG-3'; and Asp294His, 5'-CATCTGCAATGCCATCATC–3'. SNaPshot primers for Val60Leu, Asp84Glu, and Arg160Trp were used at 1 µM, whereas primers for Arg151Cys and Asp294His were used at 0.15 µM. Samples were amplified by using 25 cycles of 10 seconds at 96°C, 5 seconds at 50°C, and 30 seconds at 60°C. The 10 µl SNaPshot reaction was then treated with 0.5 U of shrimp alkaline phosphatase for 1 hour at 37°C and 15 minutes at 72°C. A total of 2 µl of treated SNaPshot reaction was denatured in 10 µl of formamide for 5 minutes at 95°C and was analyzed by using an ABI PRISM 310 Genetic Analyzer (Applied Biosystems) with the Fast Native Protocol (SNaPshot ddNTP Primer Extension Kit Protocol; Applied Biosystems). Results were visualized by using GeneScan software (Applied Biosystems) and genotyping data independently scored by two persons. Ambiguous data were resolved by a third investigator or were removed from the data set.

To validate the SNaPshot genotypes, 19 samples were randomly selected for sequencing. The 1,016–base pair MC1R gene fragment was amplified as described above. Individual PCR reactions were purified by using an UltraClean PCR Clean-up Kit (Mo Bio Laboratories, Inc., Carlsbad, California). Forward and reverse sequences were amplified by using a CEQ 2000 Dye Terminator Cycle Sequencing with Quick Start Kit (Beckman Coulter, Fullerton, California). Individual sequencing reactions of 20 µl contained 50–65 ng of DNA template, 1.6 pmol/µl of sequencing primer (MC1R-F or MC1R-R), and 8 µl of DTCS Quick Start Master Mix (Beckman Coulter). Samples were amplified by using 30 cycles of 20 seconds at 96°C, 20 seconds at 50°C, and 4 minutes at 60°C. Sequencing reactions were ethanol precipitated as described in the CEQ 2000 Dye Terminator Cycle Sequencing with Quick Start Kit protocol (BCI P/N 608118.AA; Beckman Coulter) and were resuspended in 40 µl of the Sample Loading Solution from the CEQ 2000 Dye Terminator Cycle Sequencing with Quick Start Kit (Beckman Coulter). Samples were analyzed on a Beckman Coulter CEQ2000XL automated sequencer by using the LFR-1 sequencing protocol. Both forward and reverse sequences were analyzed by using Sequencher 4.1 (Gene Codes Corporation, Ann Arbor, Michigan). All MC1R sequencing results were consistent with the SNaPshot genotyping data.

Data analysis
Odds ratio estimates of the relative risk of skin cancer associated with the presence of the MC1R variants and for categories of cutaneous melanin density were estimated by using logistic regression. Ninety-five percent confidence intervals were calculated from the standard errors of the estimated coefficients of the binary (0/1) predictors used. The estimates were adjusted for age by including binary predictors for the 5-year age groups used in frequency matching the controls to CMM cases, but data for subjects younger than age 40 years were combined because of a paucity of numbers. Tests of trend were undertaken by replacing the binary predictors for a study factor with a single linear predictor, taking rank scores for the categories. Statistical interaction between two study factors was assessed from the coefficient and standard error of a product term formed from their binary predictors. To assess the relative contributions of MC1R genotyping and melanin density measurement to risk prediction based on calculated probabilities from logistic regression models, areas under receiver operating characteristic (ROC) curves were calculated (15). The area under the ROC curve is equal to the probability that, for one case and one control chosen at random from the data set, the estimated risk of disease is higher for the case than for the control. This area ranges from 0.5 for a model with no discriminating ability to 1 for a model that discriminates perfectly between cases and controls.


    RESULTS
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
Information on the presence of five variants of the MC1R gene was obtained for 66.9 percent (757/1,132) of the study participants. The phenotypic characteristics of those subjects are displayed in table 1. Greater proportions of cases than of controls had the phenotypes that previous epidemiologic studies found to predict risk of skin cancer: blonde or red hair color, blue/grey eyes, an inability to tan, a tendency to sunburn, heavy freckling, and at least one large nevus on the left arm. Spectrophotometric measurements of the percentage of melanin in the skin of the upper inner arm were made as an objective determination of skin phenotype, and cases were found to have less melanin (lighter skin color) than controls (men, p < 0.01; women, p < 0.01). When we compared the phenotypic characteristics of subjects not genotyped and of subjects who were, we found only minor differences for male controls and for female subjects generally. There were some differences for men with CMM, however. Compared with male CMM cases with MC1R genotyping, those not genotyped tended to have lighter skin color (mean melanin concentration, 1.32 percent (standard deviation, 0.84 percent) compared with 1.62 percent (standard deviation, 0.91 percent), p = 0.12), lighter hair color (p = 0.09), and less of an ability to tan (p = 0.04).


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TABLE 1. Characteristics of subjects studied to assess whether genotype information improves the prediction of risk of melanoma and nonmelanoma skin cancer, Tasmania, Australia, 1998–1999
 
MC1R genotype frequencies are shown in table 2 for the five MC1R polymorphisms included in this study. Subjects (95 cases, 24 controls) for whom genotyping was incomplete and for whom no variants were detected were excluded from the noncarrier group. The frequencies did not differ by gender for either cases (p = 0.92) or controls (p = 0.70). When, additionally, we did not find gender differences in estimates of the relative risk of skin cancer for variant status, results for men and women were combined.


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TABLE 2. MC1R* genotypic frequencies in cases and controls, Tasmania, Australia, 1998–1999
 
The associations of variant status with risk of each of the three types of skin cancer—CMM, basal cell carcinoma, and squamous cell carcinoma—are displayed in table 3. The odds ratios compare the odds for cases of having an additional MC1R variant, or an additional allele of a variant in the single-variant analyses, with the corresponding odds for controls. In this sample, variant status was more strongly associated with risk of basal cell carcinoma and squamous cell carcinoma, the two types of nonmelanoma skin cancer, than with risk of CMM. The Arg151Cys and Asp294His variants did not confer an additional risk of CMM, and carriers of the Val60Leu variant were not at a substantially increased risk of any type of skin cancer.


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TABLE 3. Estimated per-variant increase in risk of cutaneous malignant melanoma and of basal cell carcinoma and squamous cell carcinoma of the skin associated with the presence of five MC1R* variants, Tasmania, Australia, 1998–1999
 
Table 4 shows estimates of the concentration of melanin in the skin of the upper inner arm for controls with the MC1R genotype. Compared with noncarriers, variant carriers had significantly (p < 0.01) lower mean concentrations of melanin (lighter skin color). Those with two copies of the same variant had the lowest concentrations of all, with clear evidence of a dose response in trend (p < 0.01). In parallel with the relative risk estimates, Val60Leu carriers and noncarriers had a similar melanin density. Subjects carrying "red hair color" variants (Arg151Cys, Arg160Trp, and Asp294His) had some of the lowest melanin concentrations. Among controls, 91 percent (10/11) of those who had red hair as a teenager or young adult had a red hair color variant compared with 40 percent (34/85) of those with mousy brown or blonde hair, 28 percent (32/114) of those with light brown hair, and 30 percent (24/81) of those with black or dark brown hair.


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TABLE 4. Estimated concentration of cutaneous melanin in the skin of the upper inner arm of controls, Tasmania, Australia, 1998–1999
 
To investigate whether the increased risk for MC1R variant carriers differed by melanin phenotype, we dichotomized cutaneous melanin at 2 percent (approximately the median value for controls) and repeated the relative risk estimation for each subgroup of subjects. The results per variant for darker-skinned subjects with at least 2 percent melanin are shown in the first two rows of table 5. The results for lighter-skinned subjects with less than 2 percent melanin are not displayed directly in the table; their estimated relative risks per variant were as follows: odds ratio = 0.77 (95 percent confidence interval: 0.51, 1.16) for CMM, odds ratio = 1.32 (95 percent confidence interval: 0.88, 1.99) for basal cell carcinoma, and odds ratio = 1.00 (95 percent confidence interval: 0.65, 1.54) for squamous cell carcinoma. As a test of whether the odds ratios for variant carriers differed by MC1R genotype, the probability values for statistical interaction were p = 0.06 (CMM), p = 0.30 (basal cell carcinoma), and p = 0.01 (squamous cell carcinoma). Our interpretation of this information is that, particularly for squamous cell carcinoma, the increase in estimated risk for variant carriers was confined mostly to darker-skinned subjects in our sample, and lighter-skinned persons were at increased risk irrespective of variant status.


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TABLE 5. Estimated per-variant increase in risk of cutaneous malignant melanoma and of basal cell carcinoma and squamous cell carcinoma of the skin associated with the presence of an MC1R* variant among subjects stratified by density (%) of melanin in their skin, Tasmania, Australia, 1998–1999
 
To assess and compare the additional value of phenotypic and genotypic information in categorizing risk of skin cancer, we calculated the increase in the area under the ROC curve contributed by cutaneous melanin measurements and MC1R variant information to prediction by age and sex (figure 1). For CMM, melanin was the major contributor to the area under the curve. For basal cell carcinoma and for squamous cell carcinoma, melanin and MC1R genotype made similar contributions. Adding MC1R genotype to a model containing age, sex, and melanin increased the area under the curve by 1.4 percent (CMM), 3.2 percent (basal cell carcinoma, without interaction term), and 2.0 percent (squamous cell carcinoma).



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FIGURE 1. Relative contributions of measurement of cutaneous melanin density and melanocortin-1 receptor (MC1R) genotyping to risk prediction for cutaneous malignant melanoma (CMM), basal cell carcinoma (BCC), and squamous cell carcinoma (SCC) in Tasmania, Australia, 1998–1999. From top to bottom, the diagrams show area under the receiver operator characteristic curves and increases ({Delta}) in area as additional predictors are added to the model. The term "+ Product" means that a dichotomous (melanin x MC1R) product term for multiplicative interaction was added at the final step.

 

    DISCUSSION
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
In this study of subjects of northern European origin living at latitudes 41–44°S in Tasmania, Australia, we investigated whether MC1R variants were associated with risk of skin cancer and, if so, whether adding a measure of MC1R genotype would improve the prediction of risk beyond that achieved with a relatively accurate measure of skin phenotype. The measure of skin phenotype was density of melanin in the skin of the upper inner arm estimated by spectrophotometry. We have previously shown that the model used here to estimate melanin density from skin reflectance accurately predicts this skin property (13) and is in turn associated with risk of CMM and of basal cell carcinoma and squamous cell carcinoma of the skin (14).

We found that the Asp84Glu, Arg151Cys, Arg160Trp, and Asp294His variants were associated with increased risk of basal cell carcinoma and squamous cell carcinoma. For CMM, increases in risk were restricted to Arg160Trp and the rare Asp84Glu variant. Subjects with MC1R variants were expected to have lower cutaneous melanin density, which was the case. Consistent with the relative risk estimates, the association with melanin was much weaker for the Val60Leu variant than for the four less common variants studied (Asp84Glu, Arg151Cys, Arg160Trp, and Asp294His).

Stratified by melanin density, our results resembled those from previous studies of CMM in Caucasian populations of England (12) and Queensland, Australia (4). In those studies, the presence of a variant appeared to appreciably increase risk of CMM for only the least sun-sensitive (12) or darkest (4) subjects. We were unable to replicate the very high odds ratios for variant carriers with self-reported olive or dark skins in the Queensland study (4). Most of our darkest-skinned subjects would have fit into the medium skin color category in that study, however, and the estimated elevation in risk for that intermediate category was less pronounced. For basal cell carcinoma, we found a nonsignificant risk increase per variant of about 30 percent for our lightest-skinned subjects that, if real, would support a hypothesis proposed by previous investigators (57, 11) that possession of an MC1R variant is related to skin phenotype but has an additional, independent association with risk of skin cancer.

Our results suggest that only part of the risk of each type of skin cancer conferred by MC1R variants can be captured by measuring the skin phenotype it influences. This finding suggests that there should be value in using genotypic information in addition to phenotypic information when predicting individual risk. This was the case in our study, although the increase in the area under the ROC curve achieved by adding MC1R was only modest (1–3 percent). The final prediction model, containing MC1R genotype and melanin density in addition to age and sex, forecasted as high a proportion of the area under the ROC curve as do the best models currently available for predicting risk of breast cancer (16), for example. On the basis of rates of occurrence in our setting, the model including age and sex alone would predict 42 of the 640 cases of basal cell carcinoma that would occur in a cohort of 10,000 adults aged 20–59 years during a 10-year period of follow-up. Adding the melanin phenotype measure would permit estimation of a further 14 cases (56 in total); adding MC1R would predict another two cases (58 in total).

The limitations of this study should be borne in mind. Although we were able to sample cases and controls from the population in an unbiased way and we achieved high response rates, our inability to obtain genotypic data from all subjects is a possible source of bias in the estimates we obtained. This limitation could have placed into question the validity of the findings, but it does not appear to have been a source of material error in this study. To reach that conclusion, we recalculated estimates of disease associations by using genotypic information imputed where missing from the available measures of skin phenotype. We found that the general inferences were not affected. It is noteworthy that the variant frequencies in our sample were remarkably similar to those in samples from populations of similar descent in England and Ireland (17) and in Queensland (4).

In conclusion, four of the five MC1R variants we studied were associated with increased risk of skin cancer, but the improvement in prediction over that provided by melanin phenotype was modest and probably too small to be valuable in a clinical setting. Replication of the finding that MC1R variants increase risk of skin cancer principally in those with dark skins should prompt further research aimed at elucidating the causal pathways through which MC1R mutations contribute to risk.


    ACKNOWLEDGMENTS
 
The National Health and Medical Research Council of Australia funded this study. Financial assistance for the research program was received from The Medical Benefits Fund of Australia Limited, a registered health benefits organization.

The authors thank the research nurses who undertook the field measurements.


    NOTES
 
Correspondence to Dr. Leigh Blizzard, Menzies Research Institute, University of Tasmania, Private Bag 23, Hobart 7001, Australia (e-mail: Leigh.Blizzard{at}utas.edu.au). Back


    REFERENCES
 TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 

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