P
Patrick G. Meirmans
Researcher at University of Amsterdam
Publications - 51
Citations - 6232
Patrick G. Meirmans is an academic researcher from University of Amsterdam. The author has contributed to research in topics: Population & Introgression. The author has an hindex of 25, co-authored 48 publications receiving 5430 citations. Previous affiliations of Patrick G. Meirmans include Canadian Forest Service & University of Lausanne.
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genotype and genodive: two programs for the analysis of genetic diversity of asexual organisms
TL;DR: Two programs, GENOTYPE and GENODIVE, developed for analyses of clonal diversity in asexually reproducing organisms, show that genotype can be used for detecting genotyping errors in studies of sexual organisms.
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Assessing population structure: FST and related measures
TL;DR: In this paper, the relationship between the three classes of statistics (F(ST), F'(ST) and D), their estimation and their properties is discussed, and the authors illustrate the relationships between the statistics using a data set of estimates from 84 species taken from the last 4 years of Molecular Ecology.
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Using the amova framework to estimate a standardized genetic differentiation measure
TL;DR: A method to estimate a standardized measure of population differentiation based on the analysis of molecular variance framework is presented, which can be readily expanded to include different hierarchical levels in the tested population structure.
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The trouble with isolation by distance
TL;DR: It is argued that all analyses in a study should take the spatial dependence in the data into account, unless it can be shown that there is no spatial autocorrelation in the allele frequency distribution that is under investigation, and it is urgent to develop additional statistical approaches that are based on a spatially explicit null model instead of the non‐spatial Island model.
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Seven common mistakes in population genetics and how to avoid them
TL;DR: Overall, it is argued 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.