Genome‐wide SNP analysis reveals a genetic basis for sea‐age variation in a wild population of Atlantic salmon (Salmo salar)

Abstract

Delaying sexual maturation can lead to larger body size and higher reproductive success, but carries an increased risk of death before reproducing. Classical life history theory predicts that trade-offs between reproductive success and survival should lead to the evolution of an optimal strategy in a given population. However, variation in mating strategies generally persists, and in general, there remains a poor understanding of genetic and physiological mechanisms underlying this variation. One extreme case of this is in the Atlantic salmon (Salmo salar), which can show variation in the age at which they return from their marine migration to spawn (i.e. their 'sea age'). This results in large size differences between strategies, with direct implications for individual fitness. Here, we used an Illumina Infinium SNP array to identify regions of the genome associated with variation in sea age in a large population of Atlantic salmon in Northern Europe, implementing individual-based genome-wide association studies (GWAS) and population-based FST outlier analyses. We identified several regions of the genome which vary in association with phenotype and/or selection between sea ages, with nearby genes having functions related to muscle development, metabolism, immune response and mate choice. In addition, we found that individuals of different sea ages belong to different, yet sympatric populations in this system, indicating that reproductive isolation may be driven by divergence between stable strategies. Overall, this study demonstrates how genome-wide methodologies can be integrated with samples collected from wild, structured populations to understand their ecology and evolution in a natural context.

A genome scan for selection signatures comparing farmed Atlantic salmon with two wild populations: Testing colocalization among outlier markers, candidate genes, and quantitative trait loci for production traits

“…In addition, our hierarchical Arlequin analysis of TOB_WILD versus AQUA identified an outlier locus in a similar position (ESTNV_34703_1491 on Ssa 09: 27.5 Mb and 52.7 cM female map) to one previously associated with sea age in wild Atlantic salmon populations (Johnston et al., 2014) that was not significant in a second GWAS after accounting for population structure (Barson et al., 2015). It also identified a consistent outlier GCR_cBin15233_Ctg1_136_V2 on Ssa 15 near the TSHR (the thyrotropin receptor) (Table 4).…”

Genomic tools for new insights to variation, adaptation, and evolution in the salmonid fishes: a perspective for charr

“…The markers were localised on reference genomes and therefore an extremely powerful approach that trait mapped individuals in natural populations. The authors detected genomic regions significantly associated with differences in sea age and these were distributed across several regions of the genome (Johnston et al, 2014). Thus, across species, the consistency of the genetic mechanisms for migration remains to be identified.…”

“…Johnston and colleagues used an array of 6000 SNPs to study genomic patterns associated with differences in sea age (Johnston et al, 2014). The markers were localised on reference genomes and therefore an extremely powerful approach that trait mapped individuals in natural populations.…”

Ecological characteristics andin situgenetic associations for yield-component traits of wildMiscanthusfrom eastern Russia

“…Analyses of phenotypic, ecological and genetic data were conducted with the following objectives: (1) to identify potential geographic, climatic, ecological and anthropogenic influences on the ranges of M. sinensis and M. sacchariflorus; (2) to quantify phenotypic variation for biomass traits; (3) to understand population structure of Russian M. sinensis and M. sacchariflorus in order to identify genetic groups for germplasm conservation, association analysis and potential sources of heterosis; (4) to compare genetic diversity of Russian Miscanthus with previously characterized Miscanthus populations in order to assess its relative utility for breeding; and (5) to investigate the potential to identify quantitative trait loci (QTLs) for traits of agronomic interest via GWAS of in situ phenotypic data obtained during germplasm collection. Although it is currently unusual to perform GWAS for crop germplasm without phenotypic data from replicated field trials, there are previous examples of successful association studies using phenotypic data from natural populations, particularly in forestry (Parchman et al, 2012;Budde et al, 2014) and animal ecology (Johnston et al, 2011(Johnston et al, , 2014Narum et al, 2013). Here we demonstrate that value can be added to a crop germplasm collection by combining in situ phenotype data and inexpensive genotyping data in a GWAS.…”