Early warning systems (EWS) have been proposed as a measure for controlling and preventing dengue fever outbreaks in countries where this infection is endemic. A vaccine is not available and has yet to reach the market due to the economic burden of development, introduction and safety concerns. Understanding how dengue spreads and identifying the risk factors will facilitate the development of a dengue EWS, for which a climate-based model is still needed. An analysis was conducted to examine emerging spatiotemporal hotspots of dengue fever at the township level in Taiwan, associated with climatic factors obtained from remotely sensed data in order to identify the risk factors. Machinelearning was applied to support the search for factors with a spatiotemporal correlation with dengue fever outbreaks. Three dengue fever hotspot categories were found in southwest Taiwan and shown to be spatiotemporally associated with five kinds of sea surface temperatures. Machine-learning, based on the deep AlexNet model trained by transfer learning, yielded an accuracy of 100% on an 8-fold cross-validation test dataset of longitudetime sea surface temperature images.

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Background: Germline variants in MC1R may increase risk of childhood/adolescent melanoma, but a clear conclusion is challenging because of the limited number of studies and cases. We evaluated the association of MC1R variants and childhood/adolescent melanoma in a large study comparing the prevalence of MC1R variants of childhood/adolescent melanoma patients to that among adult melanoma cases and unaffected controls. Methods: Phenotypic and genetic data on 233 childhood/adolescent (≤20 years) and 932 adult melanoma patients, and 932 unaffected controls, were gathered through the M-SKIP Project, the Italian Melanoma Intergroup and European centers. We calculated odds ratios (OR) for childhood/adolescent melanoma associated with MC1R variants by multivariable logistic regression. Subgroup analysis was done for children aged ≤18 and ≤14 years. Findings: Children and adolescents had a higher odds of carrying MC1R r variants than adults (OR:1•54; 95%CI:1•02-2•33), also when analysis was restricted to cases ≤18 years (OR:1•80; 95%CI:1•06-3•07). All the investigated variants except R160W showed a higher frequency in childhood/adolescent melanoma compared to adult melanoma, with significant results for V60L (OR:1•60; 95%CI:1•05-2•44) and D294H (OR:2•15; 95%CI:1•05-4•40). Compared to unaffected controls, childhood/adolescent melanoma patients had significantly higher frequencies of any MC1R variants.. Interpretation: Our pooled-analysis of childhood/adolescent patients with MC1R genetic data revealed that MC1R r variants were more prevalent in childhood/adolescent compared to adult melanoma especially in children ≤18 years. Our findings support the role of MC1R in childhood/adolescent melanoma susceptibility with a potential clinical relevance in developing early melanoma detection and preventive strategies.

This study aimed to identify single nucleotide polymorphism (SNP) alleles at multiple loci associated with racial differences in skin color using SNP genotyping. A total of 122 Caucasians in Toledo, Ohio and 100 Mongoloids in Japan were genotyped for 20 SNPs in 7 candidate genes, encoding the Agouti signaling protein (ASIP), tyrosinase-related protein 1 (TYRP1), tyrosinase (TYR), melanocortin 1 receptor (MC1R), oculocutaneous albinism II (OCA2), microphthalmia-associated transcription factor (MITF), and myosin VA (MYO5A). Data were used to analyze associations between the 20 SNP alleles using linkage disequilibrium (LD). Combinations of SNP alleles were jointly tested under LD for associations with racial groups by performing a χ2 test for independence. Results showed that SNP alleles at multiple loci can be considered the haplotype that contributes to significant differences between the two population groups and suggest a high probability of LD. Confirmation of these findings requires further study with other ethnic groups to analyze the associations between SNP alleles at multiple loci and skin color variation among races.

BackgroundFor complex diseases like cancer, pooled-analysis of individual data represents a powerful tool to investigate the joint contribution of genetic, phenotypic and environmental factors to the development of a disease. Pooled-analysis of epidemiological studies has many advantages over meta-analysis, and preliminary results may be obtained faster and with lower costs than with prospective consortia.Design and methodsBased on our experience with the study design of the Melanocortin-1 receptor (MC1R) gene, SKin cancer and Phenotypic characteristics (M-SKIP) project, we describe the most important steps in planning and conducting a pooled-analysis of genetic epidemiological studies. We then present the statistical analysis plan that we are going to apply, giving particular attention to methods of analysis recently proposed to account for between-study heterogeneity and to explore the joint contribution of genetic, phenotypic and environmental factors in the development of a disease. Within the M-SKIP project, data on 10,959 skin cancer cases and 14,785 controls from 31 international investigators were checked for quality and recoded for standardization. We first proposed to fit the aggregated data with random-effects logistic regression models. However, for the M-SKIP project, a two-stage analysis will be preferred to overcome the problem regarding the availability of different study covariates. The joint contribution of MC1R variants and phenotypic characteristics to skin cancer development will be studied via logic regression modeling.DiscussionMethodological guidelines to correctly design and conduct pooled-analyses are needed to facilitate application of such methods, thus providing a better summary of the actual findings on specific fields.