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      Effects of Overlapping Generations on Linkage Disequilibrium Estimates of Effective Population Size

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          Abstract

          Use of single-sample genetic methods to estimate effective population size has skyrocketed in recent years. Although the underlying models assume discrete generations, they are widely applied to age-structured species. We simulated genetic data for 21 iteroparous animal and plant species to evaluate two untested hypotheses regarding performance of the single-sample method based on linkage disequilibrium (LD): (1) estimates based on single-cohort samples reflect the effective number of breeders in one reproductive cycle ( N b), and (2) mixed-age samples reflect the effective size per generation ( N e). We calculated true N e and N b, using the model species’ vital rates, and verified these with individual-based simulations. We show that single-cohort samples should be equally influenced by N b and N e and confirm this with simulated results: N ^ b was a linear ( r 2 = 0.98) function of the harmonic mean of N e and N b. We provide a quantitative bias correction for raw N ^ b based on the ratio N b/ N e, which can be estimated from two or three simple life history traits. Bias-adjusted estimates were within 5% of true N b for all 21 study species and proved robust when challenged with new data. Mixed-age adult samples produced downwardly biased estimates in all species, which we attribute to a two-locus Wahlund effect (mixture LD) caused by combining parents from different cohorts in a single sample. Results from this study will facilitate interpretation of rapidly accumulating genetic estimates in terms of both N e (which influences long-term evolutionary processes) and N b (which is more important for understanding eco-evolutionary dynamics and mating systems).

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          Most cited references37

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          Estimation of effective population size from data on linkage disequilibrium

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            Sibship reconstruction from genetic data with typing errors.

            Likelihood methods have been developed to partition individuals in a sample into full-sib and half-sib families using genetic marker data without parental information. They invariably make the critical assumption that marker data are free of genotyping errors and mutations and are thus completely reliable in inferring sibships. Unfortunately, however, this assumption is rarely tenable for virtually all kinds of genetic markers in practical use and, if violated, can severely bias sibship estimates as shown by simulations in this article. I propose a new likelihood method with simple and robust models of typing error incorporated into it. Simulations show that the new method can be used to infer full- and half-sibships accurately from marker data with a high error rate and to identify typing errors at each locus in each reconstructed sib family. The new method also improves previous ones by adopting a fresh iterative procedure for updating allele frequencies with reconstructed sibships taken into account, by allowing for the use of parental information, and by using efficient algorithms for calculating the likelihood function and searching for the maximum-likelihood configuration. It is tested extensively on simulated data with a varying number of marker loci, different rates of typing errors, and various sample sizes and family structures and applied to two empirical data sets to demonstrate its usefulness.
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              simuPOP: a forward-time population genetics simulation environment.

              simuPOP is a forward-time population genetics simulation environment. The core of simuPOP is a scripting language (Python) that provides a large number of objects and functions to manipulate populations, and a mechanism to evolve populations forward in time. Using this R/Splus-like environment, users can create, manipulate and evolve populations interactively, or write a script and run it as a batch file. Owing to its flexible and extensible design, simuPOP can simulate large and complex evolutionary processes with ease. At a more user-friendly level, simuPOP provides an increasing number of built-in scripts that perform simulations ranging from implementation of basic population genetics models to generating datasets under complex evolutionary scenarios. simuPOP is freely available at http://simupop.sourceforge.net, distributed under GPL license.
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                Author and article information

                Journal
                Genetics
                Genetics
                genetics
                genetics
                genetics
                Genetics
                Genetics Society of America
                0016-6731
                1943-2631
                June 2014
                8 April 2014
                8 April 2014
                : 197
                : 2
                : 769-780
                Affiliations
                [* ]Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, Washington 98112
                []Vector Biology Department, Liverpool School of Tropical Medicine, Liverpool, L3 5QA United Kingdom
                []Flathead Lake Biological Station, Fish and Wildlife Genomics Group, Division of Biological Sciences, University of Montana, Polson, Montana 59860
                Author notes

                Available freely online through the author-supported open access option.

                [1 ]Corresponding author: Northwest Fisheries Science Center, 2725 Montlake Blvd. E., Seattle, WA 98112. E-mail: robin.waples@ 123456noaa.gov
                Article
                164822
                10.1534/genetics.114.164822
                4063931
                24717176
                cefcaaf5-e8a1-4a90-804f-d0ee0da54f78
                Copyright © 2014 by the Genetics Society of America

                Available freely online through the author-supported open access option.

                History
                : 17 January 2014
                : 02 April 2014
                Page count
                Pages: 12
                Categories
                Investigations
                Population and Evolutionary Genetics
                Custom metadata
                v1

                Genetics
                age structure,life history,iteroparity,ldne,computer simulations,agene,effective number of breeders

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