Population genomics has the potential to improve studies of evolutionary genetics, molecular ecology and conservation biology, by facilitating the identification of adaptive molecular variation and by improving the estimation of important parameters such as population size, migration rates and phylogenetic relationships. There has been much excitement in the recent literature about the identification of adaptive molecular variation using the population-genomic approach. However, the most useful contribution of the genomics model to population genetics will be improving inferences about population demography and evolutionary history.
Effective population size (N e ) is an important genetic parameter because of its relationship to loss of genetic variation, increases in inbreeding, accumulation of mutations, and effectiveness of selection. Like most other genetic approaches that estimate contemporary N e , the method based on linkage disequilibrium (LD) assumes a closed population and (in the most common applications) randomly recombining loci. We used analytical and numerical methods to evaluate the absolute and relative consequences of two potential violations of the closed-population assumption: (1) mixture LD caused by occurrence of more than one gene pool, which would downwardly biasN e , and (2) reductions in drift LD (and hence upward bias inN e ) caused by an increase in the number of parents responsible for local samples. The LD method is surprisingly robust to equilibrium migration. Effects of mixture LD are small for all values of migration rate (m), and effects of additional parents are also small unless m is high in genetic terms. LD estimates of N e therefore accurately reflect local (subpopulation) N e unless m . 5-10%. With higher m,N e converges on the global (metapopulation) N e . Two general exceptions were observed. First, equilibrium migration that is rare and hence episodic can occasionally lead to substantial mixture LD, especially when sample size is small. Second, nonequilibrium, pulse migration of strongly divergent individuals can also create strong mixture LD and depress estimates of local N e . In both cases, assignment tests, Bayesian clustering, and other methods often will allow identification of recent immigrants that strongly influence results. In simulations involving equilibrium migration, the standard LD method performed better than a method designed to jointly estimate N e and m. The above results assume loci are not physically linked; for tightly linked loci, the LD signal from past migration events can persist for many generations, with consequences for N e estimates that remain to be evaluated. INTEREST in estimating the contemporary effective size (N e ) of natural populations using genetic methods is growing apace (reviewed by Leberg 2005;Wang 2005;Luikart et al. 2010), spurred by several major factors: (1) difficulty of collecting sufficient demographic information to calculate N e directly, (2) rapidly increasing availability (and declining costs) of polymorphic genetic markers, and (3) increased development of software implementing new statistical methods. Until very recently, most genetically based estimates of contemporary N e have used the temporal method, which requires at least two samples spaced in time (Nei and Tajima 1981;Waples 1989;Wang 2001;Anderson 2005).Notably, a recent review of genetic estimates of N e (Palstra and Ruzzante 2008) included only the temporal method because so few published estimates were available for other methods. In the last few years, however, considerable interest has focused on estimators that require only a single sample (Nomura 2008;Tallmon et al. 20...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.