Computational protocol: Common Genetic Risk for Melanoma Encourages Preventive Behavior Change

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Protocol publication

[…] All data analysis included in the current study was performed in 2014. We recoded the four preventive behaviors into a single yes/no binary trait reflecting whether participants increased preventive behavioral change (requiring one or more of the following: decreased sun exposure, increased use of sunscreen, increased use of protective clothing, increased frequency of skin self-exams). We also constructed a categorical reported risk variable that captured whether a participant specified that they received a risk report including one or two of the genetic risk variants (the rs910873 T allele) and/or family history risk. Since only a small fraction of participants (20/718) reported having two copies of the genetic risk variant, we combined any participant reporting at least one genetic risk variant into a single genetic risk category resulting in a total of four risk categories: no risk, genetic only risk, family history only risk, both genetic and family history risk.We used binomial logistic regression as implemented in the glm function in R [] to evaluate the contributions of anxiety and reported risk after correcting for demographic covariates (age, gender, and recruitment cohort). To generate all pairwise comparisons among the four reported risk categories we ran a smaller model (behavioral_change ~ reported risk) using glm in combination with the glht function in the multcomp R library [].The mediation model was implemented with an SPSS macro procedure, PROCESS [,,] with the logistic regression function. […]

Pipeline specifications

Software tools multcomp, SPSS
Applications Miscellaneous, GWAS
Organisms Homo sapiens
Diseases Melanoma, Skin Neoplasms