Analysis of gene associated tandem repeat markers in Atlantic salmon (Salmo salar L.) populations: implications for restoration and conservation in the Baltic Sea

Abstract

Patterns of genetic diversity and differentiation among five wild and four hatchery populations of Atlantic salmon in the Baltic Sea were assessed based on eight assumedly neutral microsatellite loci and six geneassociated markers, including four expressed sequence tag (EST) linked and two major histocompatibility complex (MHC) linked tandem repeat markers (micro-and mini-satellites). The coalescent simulations based on the method of Beaumont and Nichols (1996, Proc. R. Soc. Lond. Ser. B -Biol. Sci., 263, 1619-1626) indicated that two loci (MHCIIa and Ssa171, with the lowest and highest overall F ST estimates, respectively) exhibited significant departures (P<0.05) from the neutral expectations. Another coalescentbased test for selective neutrality (Vitalis et al. 2001, Genetics, 158, 1811-1823 further supported the outlier status of the Ssa171 microsatellite locus but not of the MHCIIa linked minisatellite. In addition, actin related protein linked microsatellite locus was identified with this test as an outlier in six pairwise population comparisons. All genetic diversity estimates revealed more genetic variation in hatchery stocks than in the small wild salmon populations from the Gulf of Finland. However, the wild populations possessed alleles at gene-associated markers (e.g. MHCI and IGF) not found in the hatchery stocks, which together with moderate genetic differentiation and distinctive environmental conditions justifies the special conservation measures for the last remaining native salmon populations in the Gulf of Finland.

A Comparison of Biallelic Markers and Microsatellites for the Estimation of Population and Conservation Genetic Parameters in Atlantic Salmon (Salmo salar)

“…Therefore, further studies are warranted in order to determine the true homology of this gene sequence in salmon and whether the effects of selection might have implications for its use as a reference gene in expression studies. Interestingly, the MHC II beta locus, which was selected as an a priori candidate for selection, did not deviate from the simulated neutral expectations (Figure 8), despite the fact that the locus has been identified as an outlier in several population genetic studies of Atlantic salmon (Landry and Bernatchez 2001;Vasemägi, Gross, et al 2005) and has also been demonstrated to directly affect the survival in several disease trials (Langefors et al 2001;Lohm et al 2002) and during empirical experiments in natural environment (de Eyto et al 2007). To conclude, the main limitation on the use of biallelic markers in ecology and population genetics of nonmodel species continues to be the lack of markers, and this, together with the low information content compared with microsatellites (Kalinowski 2002), hinders their application in nonmodel organisms.…”

Spatially and temporally fluctuating selection at non-MHC immune genes: evidence from TAP polymorphism in populations of brown trout (Salmo trutta, L.)

“…This method builds the expected neutral distribution of F ST as a function of heterozygosity, based on coalescent simulations, to which the observed distribution of F ST at Adaptive genetic variation in brown trout LF Jensen et al individual loci can be compared. Beaumont and Nichols (1996) showed that this procedure is rather robust to many violations in assumptions used to generate the expected distribution, for example model of population structure and mutation rate, and the approach has been applied to detect selection in several studies (Storz and Dubach, 2004;Vasemägi et al, 2005;Aguilar and Garza, 2006). Specifically, all simulations were based on 50 000 realizations assuming a stepwise mutation model.…”

Historical and recent genetic bottlenecks in European grayling, Thymallus thymallus

“…Grayling exhibit very low overall genetic diversity (Koskinen et al 2002c;Susnik et al 2004;Gum et al 2006) compared to other salmonids (e.g. Vasemägi et al 2005;Gross et al 2007;Fave and Turgeon 2008;Lehtonen et al 2009). The population structure of grayling has been recently characterized throughout Finland and three previously unanticipated population groups have been described, corresponding to the northern, Baltic and southeastern geographic regions (Swatdipong et al 2009).…”

“…An increasing number of studies have detected such bottleneck footprints in various species groups, including insects (Dhuyvetter et al 2005;Chen et al 2006), mollusks (Bouza et al 2007), amphibians (Beebee and Rowe 2001), reptiles (Russello et al 2007), birds (Lambert et al 2005), fish (Vasemägi et al 2005;Vähä et al 2007) and mammals (Lucchini et al 2004;Fernandez-Stolz et al 2007). However, it has been shown that bottlenecks generate detectable genetic footprints only in extreme cases when effective population sizes decreases rapidly to tens rather than to hundreds of individuals (Luikart and Cornuet 1998).…”

“…Moreover, the strength of the bottleneck footprint depends on multiple factors including duration (Nei et al 1975) and the time elapsed since the population reduction (Maruyama and Fuerst 1985), which relate to the generation time of the studied species (Dinerstein and McCracken 1990). As a result, it is not surprising that only a relatively small number of studies to date have found strong footprints of genetic bottlenecks in natural populations (Abdelkrim et al 2005;Vasemägi et al 2005;Wang et al 2005;Spear et al 2006).…”