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Seven common mistakes in population genetics and how to avoid them

Overview of attention for article published in Molecular Ecology, June 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

blogs
3 blogs
twitter
97 tweeters
facebook
1 Facebook page

Citations

dimensions_citation
177 Dimensions

Readers on

mendeley
964 Mendeley
Title
Seven common mistakes in population genetics and how to avoid them
Published in
Molecular Ecology, June 2015
DOI 10.1111/mec.13243
Pubmed ID
Authors

Patrick G. Meirmans

Abstract

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.

Twitter Demographics

The data shown below were collected from the profiles of 97 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 964 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 17 2%
France 8 <1%
Germany 7 <1%
Brazil 7 <1%
United Kingdom 5 <1%
Spain 5 <1%
Canada 5 <1%
Portugal 4 <1%
Switzerland 4 <1%
Other 22 2%
Unknown 880 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 294 30%
Researcher 189 20%
Student > Master 173 18%
Student > Bachelor 64 7%
Student > Doctoral Student 54 6%
Other 144 15%
Unknown 46 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 658 68%
Biochemistry, Genetics and Molecular Biology 119 12%
Environmental Science 77 8%
Earth and Planetary Sciences 7 <1%
Computer Science 4 <1%
Other 23 2%
Unknown 76 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 79. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 May 2020.
All research outputs
#253,706
of 15,020,800 outputs
Outputs from Molecular Ecology
#62
of 4,780 outputs
Outputs of similar age
#4,842
of 232,039 outputs
Outputs of similar age from Molecular Ecology
#4
of 129 outputs
Altmetric has tracked 15,020,800 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,780 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.3. This one has done particularly well, scoring higher than 98% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 232,039 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 129 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.