Predicting skin cancer risk from facial images with an explainable artificial intelligence (XAI) based approach: a proof-of-concept study.

Background: Efficient identification of individuals at high risk of skin cancer is crucial for implementing personalized screening strategies and subsequent care. While Artificial Intelligence holds promising potential for predictive analysis using image data, its application for skin cancer risk prediction utilizing facial images remains unexplored. We present a neural network-based explainable artificial intelligence (XAI) approach for skin cancer risk prediction based on 2D facial images and compare its efficacy to 18 established skin cancer risk factors using data from the Rotterdam Study. Methods: The study employed data from the Rotterdam population-based study in which both skin cancer risk factors and 2D facial images and the occurrence of skin cancer were collected from 2010 to 2018. We conducted a deep-learning survival analysis based on 2D facial images using our developed XAI approach. We subsequently compared these results with survival analysis based on skin cancer risk factors using cox proportional hazard regression. Findings: Among the 2810 participants (mean Age = 68.5 ± 9.3 years, average Follow-up = 5.0 years), 228 participants were diagnosed with skin cancer after photo acquisition. Our XAI approach achieved superior predictive accuracy based on 2D facial images (c-index = 0.72, 95% CI: 0.70-0.74), outperforming that of the known risk factors (c-index = 0.59, 95% CI 0.57-0.61). Interpretation: This proof-of-concept study underscores the high potential of harnessing facial images and a tailored XAI approach as an easily accessible alternative over known risk factors for identifying individuals at high risk of skin cancer. Funding: The Rotterdam Study is funded through unrestricted research grants from Erasmus Medical Center and Erasmus University, Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. G.V. Roshchupkin is supported by the ZonMw Veni grant (Veni, 549 1936320).

Targeted DNA methylation analysis and prediction of smoking habits in blood based on massively parallel sequencing.

Tobacco smoking is a frequent habit sustained by > 1.3 billion people in 2020 and the leading preventable factor for health risk and premature mortality worldwide. In the forensic context, predicting smoking habits from biological samples may allow broadening DNA phenotyping. In this study, we aimed to implement previously published smoking habit classification models based on blood DNA methylation at 13 CpGs. First, we developed a matching lab tool based on bisulfite conversion and multiplex PCR followed by amplification-free library preparation and targeted paired-end massively parallel sequencing (MPS). Analysis of six technical duplicates revealed high reproducibility of methylation measurements (Pearson correlation of 0.983). Artificially methylated standards uncovered marker-specific amplification bias, which we corrected via bi-exponential models. We then applied our MPS tool to 232 blood samples from Europeans of a wide age range, of which 90 were current, 71 former and 71 never smokers. On average, we obtained 189,000 reads/sample and 15,000 reads/CpG, without marker drop-out. Methylation distributions per smoking category roughly corresponded to previous microarray analysis, showcasing large inter-individual variation but with technology-driven bias. Methylation at 11 out of 13 smoking-CpGs correlated with daily cigarettes in current smokers, while solely one was weakly correlated with time since cessation in former smokers. Interestingly, eight smoking-CpGs correlated with age, and one displayed weak but significant sex-associated methylation differences. Using bias-uncorrected MPS data, smoking habits were relatively accurately predicted using both two- (current/non-current) and three- (never/former/current) category model, but bias correction resulted in worse prediction performance for both models. Finally, to account for technology-driven variation, we built new, joint models with inter-technology corrections, which resulted in improved prediction results for both models, with or without PCR bias correction (e.g. MPS cross-validation F1-score > 0.8; 2-categories). Overall, our novel assay takes us one step closer towards the forensic application of viable smoking habit prediction from blood traces. However, future research is needed towards forensically validating the assay, especially in terms of sensitivity. We also need to further shed light on the employed biomarkers, particularly on the mechanistics, tissue specificity and putative confounders of smoking epigenetic signatures.

Younger facial looks are associate with a lower likelihood of several age-related morbidities in the middle-aged to elderly.

BACKGROUND: Looking older for one's chronological age is associated with a higher mortality rate. Yet it remains unclear how perceived facial age relates to morbidity and the degree to which facial ageing reflects systemic ageing of the human body. OBJECTIVES: To investigate the association between ΔPA and age-related morbidities of different organ systems, where ΔPA represents the difference between perceived age (PA) and chronological age. METHODS: We performed a cross-sectional analysis on data from the Rotterdam Study, a population-based cohort study in the Netherlands. High-resolution facial photographs of 2679 men and women aged 51.5-87.8 years of European descent were used to assess PA. PA was estimated and scored in 5-year categories using these photographs by a panel of men and women who were blinded for chronological age and medical history. A linear mixed model was used to generate the mean PAs. The difference between the mean PA and chronological age was calculated (ΔPA), where a higher (positive) ΔPA means that the person looks younger for their age and a lower (negative) ΔPA that the person looks older. ΔPA was tested as a continuous variable for association with ageing-related morbidities including cardiovascular, pulmonary, ophthalmological, neurocognitive, renal, skeletal and auditory morbidities in separate regression analyses, adjusted for age and sex (model 1) and additionally for body mass index, smoking and sun exposure (model 2). RESULTS: We observed 5-year higher ΔPA (i.e. looking younger by 5 years for one's age) to be associated with less osteoporosis [odds ratio (OR) 0.76, 95% confidence interval (CI) 0.62-0.93], less chronic obstructive pulmonary disease (OR 0.85, 95% CI 0.77-0.95), less age-related hearing loss (model 2; B = -0.76, 95% CI -1.35 to -0.17) and fewer cataracts (OR 0.84, 95% CI 0.73-0.97), but with better global cognitive functioning (g-factor; model 2; B = 0.07, 95% CI 0.04-0.10). CONCLUSIONS: PA is associated with multiple morbidities and better cognitive function, suggesting that systemic ageing and cognitive ageing are, to an extent, externally visible in the human face.

Prediction of Smoking Habits From Class-Imbalanced Saliva Microbiome Data Using Data Augmentation and Machine Learning.

Human microbiome research is moving from characterization and association studies to translational applications in medical research, clinical diagnostics, and others. One of these applications is the prediction of human traits, where machine learning (ML) methods are often employed, but face practical challenges. Class imbalance in available microbiome data is one of the major problems, which, if unaccounted for, leads to spurious prediction accuracies and limits the classifier's generalization. Here, we investigated the predictability of smoking habits from class-imbalanced saliva microbiome data by combining data augmentation techniques to account for class imbalance with ML methods for prediction. We collected publicly available saliva 16S rRNA gene sequencing data and smoking habit metadata demonstrating a serious class imbalance problem, i.e., 175 current vs. 1,070 non-current smokers. Three data augmentation techniques (synthetic minority over-sampling technique, adaptive synthetic, and tree-based associative data augmentation) were applied together with seven ML methods: logistic regression, k-nearest neighbors, support vector machine with linear and radial kernels, decision trees, random forest, and extreme gradient boosting. K-fold nested cross-validation was used with the different augmented data types and baseline non-augmented data to validate the prediction outcome. Combining data augmentation with ML generally outperformed baseline methods in our dataset. The final prediction model combined tree-based associative data augmentation and support vector machine with linear kernel, and achieved a classification performance expressed as Matthews correlation coefficient of 0.36 and AUC of 0.81. Our method successfully addresses the problem of class imbalance in microbiome data for reliable prediction of smoking habits.

Smoking-related changes in DNA methylation and gene expression are associated with cardio-metabolic traits.

BACKGROUND: Tobacco smoking is a well-known modifiable risk factor for many chronic diseases, including cardiovascular disease (CVD). One of the proposed underlying mechanism linking smoking to disease is via epigenetic modifications, which could affect the expression of disease-associated genes. Here, we conducted a three-way association study to identify the relationship between smoking-related changes in DNA methylation and gene expression and their associations with cardio-metabolic traits. RESULTS: We selected 2549 CpG sites and 443 gene expression probes associated with current versus never smokers, from the largest epigenome-wide association study and transcriptome-wide association study to date. We examined three-way associations, including CpG versus gene expression, cardio-metabolic trait versus CpG, and cardio-metabolic trait versus gene expression, in the Rotterdam study. Subsequently, we replicated our findings in The Cooperative Health Research in the Region of Augsburg (KORA) study. After correction for multiple testing, we identified both cis- and trans-expression quantitative trait methylation (eQTM) associations in blood. Specifically, we found 1224 smoking-related CpGs associated with at least one of the 443 gene expression probes, and 200 smoking-related gene expression probes to be associated with at least one of the 2549 CpGs. Out of these, 109 CpGs and 27 genes were associated with at least one cardio-metabolic trait in the Rotterdam Study. We were able to replicate the associations with cardio-metabolic traits of 26 CpGs and 19 genes in the KORA study. Furthermore, we identified a three-way association of triglycerides with two CpGs and two genes (GZMA; CLDND1), and BMI with six CpGs and two genes (PID1; LRRN3). Finally, our results revealed the mediation effect of cg03636183 (F2RL3), cg06096336 (PSMD1), cg13708645 (KDM2B), and cg17287155 (AHRR) within the association between smoking and LRRN3 expression. CONCLUSIONS: Our study indicates that smoking-related changes in DNA methylation and gene expression are associated with cardio-metabolic risk factors. These findings may provide additional insights into the molecular mechanisms linking smoking to the development of CVD.

Validated inference of smoking habits from blood with a finite DNA methylation marker set.

Inferring a person's smoking habit and history from blood is relevant for complementing or replacing self-reports in epidemiological and public health research, and for forensic applications. However, a finite DNA methylation marker set and a validated statistical model based on a large dataset are not yet available. Employing 14 epigenome-wide association studies for marker discovery, and using data from six population-based cohorts (N = 3764) for model building, we identified 13 CpGs most suitable for inferring smoking versus non-smoking status from blood with a cumulative Area Under the Curve (AUC) of 0.901. Internal fivefold cross-validation yielded an average AUC of 0.897 ± 0.137, while external model validation in an independent population-based cohort (N = 1608) achieved an AUC of 0.911. These 13 CpGs also provided accurate inference of current (average AUCcrossvalidation 0.925 ± 0.021, AUCexternalvalidation0.914), former (0.766 ± 0.023, 0.699) and never smoking (0.830 ± 0.019, 0.781) status, allowed inferring pack-years in current smokers (10 pack-years 0.800 ± 0.068, 0.796; 15 pack-years 0.767 ± 0.102, 0.752) and inferring smoking cessation time in former smokers (5 years 0.774 ± 0.024, 0.760; 10 years 0.766 ± 0.033, 0.764; 15 years 0.767 ± 0.020, 0.754). Model application to children revealed highly accurate inference of the true non-smoking status (6 years of age: accuracy 0.994, N = 355; 10 years: 0.994, N = 309), suggesting prenatal and passive smoking exposure having no impact on model applications in adults. The finite set of DNA methylation markers allow accurate inference of smoking habit, with comparable accuracy as plasma cotinine use, and smoking history from blood, which we envision becoming useful in epidemiology and public health research, and in medical and forensic applications.

Publisher Correction: Novel pleiotropic risk loci for melanoma and nevus density implicate multiple biological pathways.

The original version of this Article contained errors in the spelling of the authors Fan Liu and M. Arfan Ikram, which were incorrectly given as Fan Lui and Arfan M. Ikram. In addition, the original version of this Article also contained errors in the author affiliations which are detailed in the associated Publisher Correction.

Novel pleiotropic risk loci for melanoma and nevus density implicate multiple biological pathways.

The total number of acquired melanocytic nevi on the skin is strongly correlated with melanoma risk. Here we report a meta-analysis of 11 nevus GWAS from Australia, Netherlands, UK, and USA comprising 52,506 individuals. We confirm known loci including MTAP, PLA2G6, and IRF4, and detect novel SNPs in KITLG and a region of 9q32. In a bivariate analysis combining the nevus results with a recent melanoma GWAS meta-analysis (12,874 cases, 23,203 controls), SNPs near GPRC5A, CYP1B1, PPARGC1B, HDAC4, FAM208B, DOCK8, and SYNE2 reached global significance, and other loci, including MIR146A and OBFC1, reached a suggestive level. Overall, we conclude that most nevus genes affect melanoma risk (KITLG an exception), while many melanoma risk loci do not alter nevus count. For example, variants in TERC and OBFC1 affect both traits, but other telomere length maintenance genes seem to affect melanoma risk only. Our findings implicate multiple pathways in nevogenesis.

Genome-wide association study in 176,678 Europeans reveals genetic loci for tanning response to sun exposure.

The skin's tendency to sunburn rather than tan is a major risk factor for skin cancer. Here we report a large genome-wide association study of ease of skin tanning in 176,678 subjects of European ancestry. We identify significant association with tanning ability at 20 loci. We confirm previously identified associations at six of these loci, and report 14 novel loci, of which ten have never been associated with pigmentation-related phenotypes. Our results also suggest that variants at the AHR/AGR3 locus, previously associated with cutaneous malignant melanoma the underlying mechanism of which is poorly understood, might act on disease risk through modulation of tanning ability.

Investigating the Epigenetic Discrimination of Identical Twins Using Buccal Swabs, Saliva, and Cigarette Butts in the Forensic Setting.

Monozygotic (MZ) twins are typically indistinguishable via forensic DNA profiling. Recently, we demonstrated that epigenetic differentiation of MZ twins is feasible; however, proportions of twin differentially methylated CpG sites (tDMSs) identified in reference-type blood DNA were not replicated in trace-type blood DNA. Here we investigated buccal swabs as typical forensic reference material, and saliva and cigarette butts as commonly encountered forensic trace materials. As an analog to a forensic case, we analyzed one MZ twin pair. Epigenome-wide microarray analysis in reference-type buccal DNA revealed 25 candidate tDMSs with >0.5 twin-to-twin differences. MethyLight quantitative PCR (qPCR) of 22 selected tDMSs in trace-type DNA revealed in saliva DNA that six tDMSs (27.3%) had >0.1 twin-to-twin differences, seven (31.8%) had smaller (<0.1) but robustly detected differences, whereas for nine (40.9%) the differences were in the opposite direction relative to the microarray data; for cigarette butt DNA, results were 50%, 22.7%, and 27.3%, respectively. The discrepancies between reference-type and trace-type DNA outcomes can be explained by cell composition differences, method-to-method variation, and other technical reasons including bisulfite conversion inefficiency. Our study highlights the importance of the DNA source and that careful characterization of biological and technical effects is needed before epigenetic MZ twin differentiation is applicable in forensic casework.

Automated human skull landmarking with 2D Gabor wavelets.

Landmarking of CT scans is an important step in the alignment of skulls that is key in surgery planning, pre-/post-surgery comparisons, and morphometric studies. We present a novel method for automatically locating anatomical landmarks on the surface of cone beam CT-based image models of human skulls using 2D Gabor wavelets and ensemble learning. The algorithm is validated via human inter- and intra-rater comparisons on a set of 39 scans and a skull superimposition experiment with an established surgery planning software (Maxilim). Automatic landmarking results in an accuracy of 1-2 mm for a subset of landmarks around the nose area as compared to a gold standard derived from human raters. These landmarks are located in eye sockets and lower jaw, which is competitive with or surpasses inter-rater variability. The well-performing landmark subsets allow for the automation of skull superimposition in clinical applications. Our approach delivers accurate results, has modest training requirements (training set size of 30-40 items) and is generic, so that landmark sets can be easily expanded or modified to accommodate shifting landmark interests, which are important requirements for the landmarking of larger cohorts.

Lifestyle and Physiological Factors Associated with Facial Wrinkling in Men and Women.

Facial wrinkling is one of the most notable signs of skin aging. Men and women show different wrinkling patterns yet the lifestyle and physiological factors underlying these sex-specific patterns are relatively unknown. Here, we investigated sex-specific determinants for facial wrinkles. Wrinkle area was quantified digitally using facial photographs of 3,831 northwestern Europeans (51-98 years, 58% female). Effect estimates from multivariable linear regressions are presented as the percentage difference in the mean value of wrinkle area per unit increase of a determinant (%Δ). Wrinkle area was higher in men (median 4.5%, interquartile range: 2.9-6.3) than in women (3.6%, interquartile range: 2.2-5.6). Age was the strongest determinant, and current smoking (men: 15.5%Δ; women: 30.9%Δ) and lower body mass index (men: 1.7%Δ; women: 1.8%Δ) were also statistically significantly associated with increased wrinkling. Pale skin color showed a protective effect (men: -21.0%Δ; women: -28.5%Δ) and, in men, sunburn tendency was associated with less wrinkling. In women, low educational levels and alcohol use were associated with more wrinkling, whereas female pattern hair loss and a higher free androgen index were associated with less wrinkling. In summary, we validated known and identified additional determinants for wrinkling. Skin aging-reducing strategies should incorporate the sex differences found in this study.

Novel quantitative pigmentation phenotyping enhances genetic association, epistasis, and prediction of human eye colour.

Success of genetic association and the prediction of phenotypic traits from DNA are known to depend on the accuracy of phenotype characterization, amongst other parameters. To overcome limitations in the characterization of human iris pigmentation, we introduce a fully automated approach that specifies the areal proportions proposed to represent differing pigmentation types, such as pheomelanin, eumelanin, and non-pigmented areas within the iris. We demonstrate the utility of this approach using high-resolution digital eye imagery and genotype data from 12 selected SNPs from over 3000 European samples of seven populations that are part of the EUREYE study. In comparison to previous quantification approaches, (1) we achieved an overall improvement in eye colour phenotyping, which provides a better separation of manually defined eye colour categories. (2) Single nucleotide polymorphisms (SNPs) known to be involved in human eye colour variation showed stronger associations with our approach. (3) We found new and confirmed previously noted SNP-SNP interactions. (4) We increased SNP-based prediction accuracy of quantitative eye colour. Our findings exemplify that precise quantification using the perceived biological basis of pigmentation leads to enhanced genetic association and prediction of eye colour. We expect our approach to deliver new pigmentation genes when applied to genome-wide association testing.

Pigmentation-Independent Susceptibility Loci for Actinic Keratosis Highlighted by Compound Heterozygosity Analysis.

Actinic keratosis (AK) is a skin disease frequently found in European elderly, and it represents the precursor of cutaneous squamous cell carcinoma. Our recent genome-wide association study highlighted DNA variants in two pigmentation genes, IRF4 and MC1R, that confer AK risk in Europeans. Here, we performed a genome-wide search for relaxed forms of compound heterozygosity in association with AK using our recently developed software CollapsABEL. In a discovery dataset of 3,193 Dutch Europeans, a total of 15 genetic loci showed genome-wide significant association with AK (P < 1.25 × 10-10). Of those, three loci (6p21.2, 6p12.2, and 6q13) were confirmed in a replication dataset that included 624 additional Dutch Europeans (P < 0.05). These replicated loci harbored six genes (KCNK5/KCNK17, PAQR8/GSTA2, and KCNQ5/KHDC1), none of them known to be involved in pigmentation. A candidate compound heterozygosity analysis for 12 pigmentation loci highlighted SLC24A4 at 14q32.12 as showing significant association with AK (P = 8.83 × 10-9). The four significantly AK-associated compound heterozygosity single-nucleotide polymorphism pairs together explained 4.37% of the total AK variation, which was 2.62 times greater than the two top-associated individual single nucleotide polymorphisms together (1.67%) identified in the previous conventional genome-wide association study. In conclusion, CollapsABEL showed compound heterozygosity in non-pigmentation- and pigmentation-related loci conferring genetic risk of AK.

Genetics of skin color variation in Europeans: genome-wide association studies with functional follow-up.

In the International Visible Trait Genetics (VisiGen) Consortium, we investigated the genetics of human skin color by combining a series of genome-wide association studies (GWAS) in a total of 17,262 Europeans with functional follow-up of discovered loci. Our GWAS provide the first genome-wide significant evidence for chromosome 20q11.22 harboring the ASIP gene being explicitly associated with skin color in Europeans. In addition, genomic loci at 5p13.2 (SLC45A2), 6p25.3 (IRF4), 15q13.1 (HERC2/OCA2), and 16q24.3 (MC1R) were confirmed to be involved in skin coloration in Europeans. In follow-up gene expression and regulation studies of 22 genes in 20q11.22, we highlighted two novel genes EIF2S2 and GSS, serving as competing functional candidates in this region and providing future research lines. A genetically inferred skin color score obtained from the 9 top-associated SNPs from 9 genes in 940 worldwide samples (HGDP-CEPH) showed a clear gradual pattern in Western Eurasians similar to the distribution of physical skin color, suggesting the used 9 SNPs as suitable markers for DNA prediction of skin color in Europeans and neighboring populations, relevant in future forensic and anthropological investigations.

IRF4, MC1R and TYR genes are risk factors for actinic keratosis independent of skin color.

Actinic keratosis (AK) is a pre-malignant skin disease, highly prevalent in elderly Europeans. This study investigates genetic susceptibility to AK with a genome-wide association study (GWAS). A full body skin examination was performed in 3194 elderly individuals from the Rotterdam Study (RS) of exclusive north-western European origin (aged 51-99 years, 45% male). Physicians graded the number of AK into four severity levels: none (76%), 1-3 (14%), 4-9 (6%) and ≥10 (5%), and skin color was quantified using a spectrophotometer on sun-unexposed skin. A GWAS for AK severity was conducted, where promising signals at IRF4 and MC1R (P < 4.2 × 10(-7)) were successfully replicated in an additional cohort of 623 RS individuals (IRF4, rs12203592, Pcombined = 6.5 × 10(-13) and MC1R, rs139810560, Pcombined = 4.1 × 10(-9)). Further, in an analysis of ten additional well-known human pigmentation genes, TYR also showed significant association with AK (rs1393350, P = 5.3 × 10(-4)) after correction for multiple testing. Interestingly, the strength and significance of above-mentioned associations retained largely the same level after skin color adjustment. Overall, our data strongly suggest that IRF4, MC1R and TYR genes likely have pleiotropic effects, a combination of pigmentation and oncogenic functions, resulting in an increased risk of AK.

A Genome-Wide Association Study Identifies the Skin Color Genes IRF4, MC1R, ASIP, and BNC2 Influencing Facial Pigmented Spots.

Facial pigmented spots are a common skin aging feature, but genetic predisposition has yet to be thoroughly investigated. We conducted a genome-wide association study for pigmented spots in 2,844 Dutch Europeans from the Rotterdam Study (mean age: 66.9±8.0 years; 47% male). Using semi-automated image analysis of high-resolution digital facial photographs, facial pigmented spots were quantified as the percentage of affected skin area (mean women: 2.0% ±0.9, men: 0.9% ±0.6). We identified genome-wide significant association with pigmented spots at three genetic loci: IRF4 (rs12203592, P=1.8 × 10(-27)), MC1R (compound heterozygosity score, P=2.3 × 10(-24)), and RALY/ASIP (rs6059655, P=1.9 × 10(-9)). In addition, after adjustment for the other three top-associated loci the BNC2 locus demonstrated significant association (rs62543565, P=2.3 × 10(-8)). The association signals observed at all four loci were successfully replicated (P<0.05) in an independent Dutch cohort (Leiden Longevity Study n=599). Although the four genes have previously been associated with skin color variation and skin cancer risk, all association signals remained highly significant (P<2 × 10(-8)) when conditioning the association analyses on skin color. We conclude that genetic variations in IRF4, MC1R, RALY/ASIP, and BNC2 contribute to the acquired amount of facial pigmented spots during aging, through pathways independent of the basal melanin production.

MC1R variants increased the risk of sporadic cutaneous melanoma in darker-pigmented Caucasians: a pooled-analysis from the M-SKIP project.

The MC1R gene is a key regulator of skin pigmentation. We aimed to evaluate the association between MC1R variants and the risk of sporadic cutaneous melanoma (CM) within the M-SKIP project, an international pooled-analysis on MC1R, skin cancer and phenotypic characteristics. Data included 5,160 cases and 12,119 controls from 17 studies. We calculated a summary odds ratio (SOR) for the association of each of the nine most studied MC1R variants and of variants combined with CM by using random-effects models. Stratified analysis by phenotypic characteristics were also performed. Melanoma risk increased with presence of any of the main MC1R variants: the SOR for each variant ranged from 1.47 (95%CI: 1.17-1.84) for V60L to 2.74 (1.53-4.89) for D84E. Carriers of any MC1R variant had a 66% higher risk of developing melanoma compared with wild-type subjects (SOR; 95%CI: 1.66; 1.41-1.96) and the risk attributable to MC1R variants was 28%. When taking into account phenotypic characteristics, we found that MC1R-associated melanoma risk increased only for darker-pigmented Caucasians: SOR (95%CI) was 3.14 (2.06-4.80) for subjects with no freckles, no red hair and skin Type III/IV. Our study documents the important role of all the main MC1R variants in sporadic CM and suggests that they have a direct effect on melanoma risk, independently on the phenotypic characteristics of carriers. This is of particular importance for assessing preventive strategies, which may be directed to darker-pigmented Caucasians with MC1R variants as well as to lightly pigmented, fair-skinned subjects.

Intrinsic and extrinsic risk factors for sagging eyelids.

IMPORTANCE: Sagging eyelids, or dermatochalasis, are a frequent concern in older adults. It is considered a feature of skin aging, but risk factors other than aging are largely unknown. OBJECTIVE: To study nongenetic and genetic risk factors for sagging eyelids. DESIGN: Upper eyelid sagging was graded in 4 categories of severity using digital photographs. Dermatochalasis was defined as the eyelid hanging over the eyelashes. Age, sex, skin color, tanning ability, hormonal status in women, current smoking, body mass index, and sun protection behavior were analyzed in a multivariable multinomial logistic regression model. Genetic predisposition was assessed using heritability analysis and a genome-wide association study. SETTING AND PARTICIPANTS: The study was performed in 2 independent population-based cohorts. The Rotterdam Study included older adults from one district in Rotterdam, the Netherlands, and the UK Adult Twin Registry (TwinsUK) included twins from all over the United Kingdom. Participants were 5578 unrelated Dutch Europeans (mean age, 67.1 years; 44.0% male) from the Rotterdam Study and 2186 twins (mean age, 53.1 years; 10.4% male) from the TwinsUK. MAIN OUTCOMES AND MEASURES: Sagging eyelid severity levels, ranging from 1 (normal control) to 4 (severe sagging). RESULTS: Among 5578 individuals from the Rotterdam Study, 17.8% showed dermatochalasis (moderate and severe sagging eyelids). Significant and independent risk factors for sagging eyelids included age, male sex, lighter skin color, and higher body mass index. In addition, current smoking was borderline significantly associated. Heritability of sagging eyelids was estimated to be 61% among 1052 twin pairs from the TwinsUK (15.6% showed dermatochalasis). A meta-analysis of genome-wide association study results from 5578 Rotterdam Study and 1053 TwinsUK participants showed a genome-wide significant recessive protective effect of the C allele of rs11876749 (P = 1.7 × 10(-8)). This variant is located close to TGIF1 (an inducer of transforming growth factor ß), which is a known gene associated with skin aging. CONCLUSIONS AND RELEVANCE: This is the first observational study to date demonstrating that other risk factors (male sex, genetic variants, lighter skin color, high body mass index, and possibly current smoking) in addition to aging are involved in the origin of sagging eyelids.

Direct evidence for positive selection of skin, hair, and eye pigmentation in Europeans during the last 5,000 y.

Pigmentation is a polygenic trait encompassing some of the most visible phenotypic variation observed in humans. Here we present direct estimates of selection acting on functional alleles in three key genes known to be involved in human pigmentation pathways--HERC2, SLC45A2, and TYR--using allele frequency estimates from Eneolithic, Bronze Age, and modern Eastern European samples and forward simulations. Neutrality was overwhelmingly rejected for all alleles studied, with point estimates of selection ranging from around 2-10% per generation. Our results provide direct evidence that strong selection favoring lighter skin, hair, and eye pigmentation has been operating in European populations over the last 5,000 y.