title: Seven common mistakes in population genetics and how to avoid them.
abstract: As 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 data sets 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 randomization in the laboratory, (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 and (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 the mistake. Overall, I argue that genotyping studies would benefit from establishing a more rigorous experimental design, involving proper sampling design, randomization and better distinction of a priori hypotheses and exploratory analyses.
authors.affiliation: Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, P.O. Box 94248, 1090GE, Amsterdam, Netherlands.
authors.forename: Patrick G
authors.initials: PG
authors.lastname: Meirmans
mesh_terms: D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D014644:Genetic Variation; D005828:Genetics, Population / Q000379:methods*; D060005:Genotyping Techniques / Q000379:methods*; D005843:Geography; D012107:Research Design
keywords: Mantel test; amova; clustering; genome scan; population structure; unicorns