Patrick G Meirmans

Since the data resulting from modern genotyping tools are astoundingly complex, genotyping studies require great care in the sampling design, genotyping, data analysis and interpretation. Such care is necessary because, with datasets containing thousands of loci, small biases can easily become strongly significant patterns. Such biases may already be present in routine tasks that are present in almost every genotyping study. Here, I discuss seven common mistakes that can be frequently encountered in the genotyping literature: (i) giving more attention to genotyping than to sampling; (ii) failing to perform or report experimental randomisation in the lab; (iii) equating geopolitical borders with biological borders; (iv) testing significance of clustering output; (v) misinterpreting Mantel's r statistic; (vi) only interpreting a single value of k; (vii) forgetting that only a small portion of the genome will be associated with climate. For every of those issues, I give some suggestions how to avoid these mistakes. Overall, I argue that genotyping studies would benefit from establishing a more rigorous experimental design, involving proper sampling design, randomisation and better distinction of a priori hypotheses and exploratory analyses. This article is protected by copyright. All rights reserved.