Testing for Spatially Divergent Selection: Comparing Q ST to F ST
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Q(ST) is a standardized measure of the genetic differentiation of a quantitative trait among populations. The distribution of Q(ST)'s for neutral traits can be predicted from the F(ST) for neutral marker loci. To test for the neutral differentiation of a quantitative trait among populations, it is necessary to ask whether the Q(ST) of that trait is in the tail of the probability distribution of neutral traits. This neutral distribution can be estimated using the Lewontin-Krakauer distribution and the F(ST) from a relatively small number of marker loci. We develop a simulation method to test whether the Q(ST) of a given trait is consistent with the null hypothesis of selective neutrality over space. The method is most powerful with small mean F(ST), strong selection, and a large number (>10) of measured populations. The power and type I error rate of the new method are far superior to the traditional method of comparing Q(ST) and F(ST).
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When Qst is much greater than Fst, trait divergence exceeds neutral expectations and can be attributed to divergent selection.
“Thus, when Qst is much greater than Fst, trait divergence exceeds neutral expectations and can be attributed to divergent selection [11].”
From Investigating the case of human nose shape and climate adaptation (2017) · cites this paper as [11]
The Qst of a neutrally evolving trait is expected to follow the Fst distribution of neutrally evolving loci.
“In principle, the Qst of a neutrally evolving trait is expected to follow the Fst distribution of neutrally evolving loci [11].”
From Investigating the case of human nose shape and climate adaptation (2017) · cites this paper as [11]
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- Authors
- Whitlock, M. C., & Guillaume, F.
- Affiliations
- University of British Columbia (CA)
- Department of Zoology , University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
- Corresponding author
- Whitlock, Michael C
- ORCID
- Whitlock, Michael C
- Venue
- Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 2009-11-01
- Volume
- 183
- Issue
- 3
- Pages
- 1055-1063
- ISSN
- 0016-6731 (ISSN-L)
- 1943-2631
- PMC
- PMC2778959
- OpenAlex
- W2131395947
- Cited by
- 180
- References
- 22
- Citations by year
- 2026: 4 · 2025: 9 · 2024: 7 · 2023: 3 · 2022: 7 · 2021: 10 · 2020: 14 · 2019: 15 · 2018: 21 · 2017: 6 · 2016: 15 · 2015: 15 · 2014: 13 · 2013: 15 · 2012: 12
- FWCI
- 5.07
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Genetics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (115383)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- quantitative sensory testing, FST, type I error rate, statistical power, population genetics
- MeSH terms
- Algorithms, Animals, Computer Simulation, Genetics, Population, Humans, Models, Genetic, Selection, Genetic, Genetic Variation, Reproducibility of Results, Quantitative Trait, Heritable, Quantitative Trait Loci
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