Molecular EcologyGenomicsPeer-reviewed journal article
Estimating recent and historical effective population size of marine and freshwater sticklebacks
Abstract
Effective population size (Ne) is a quantity of central importance in evolutionary biology and population genetics, but often notoriously challenging to estimate. Analyses of Ne are further complicated by the many interpretations of the concept and the alternative approaches to quantify Ne utilising different properties of the data. However, the alternative methods are also informative over different time scales, suggesting that a combination of approaches should allow piecing together the entire continuum of Ne, spanning from the recent to more distant past. To test this in practice, we inferred the Ne continuum for 45 populations of nine-spined sticklebacks (Pungitius pungitius) using whole-genome data with both LD- and coalescent-based methods. Our results show that marine populations exhibit the highest Ne values in contemporary, recent, and historical times, followed by coastal and freshwater populations. They also demonstrate the impact of both recent and historical gene flow on Ne estimates and show that simple summary statistics are informative in comprehending the events in the very recent past and aid in more accurate estimation of NeC, the contemporary Ne, as well as in reconstruction and interpretation of recent demographic histories. Although our sample size for large populations is limited, we found that GONE can provide reasonable Ne estimates. However, due to challenges in detecting subtle genetic drift in large populations, these estimates may represent the lower bound of Ne. Finally, we show that combining GONE and CurrentNe2, both sensitive to population structure, with MSMC2 provides a meaningful interpretation of Ne dynamics over time.
- assay: whole-genome sequencing
- research focus: variant calling
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: benchmarking
- approach: statistical model
- approach: matrix factorization
- Citations
- 7
- FWCI
- 2.92
- Field percentile
- top 9%
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