Search

Additive and dominance genetic variances underlying the expression of quantitative traits are important quantities for predicting short‐term responses to selection, but they are notoriously challenging to estimate in most non‐model wild populations. Specifically, large‐sized or panmictic populations may be characterized by low variance in genetic relatedness among individuals which, in turn, can prevent accurate estimation of quantitative genetic parameters. We used estimates of genome‐wide identity‐by‐descent (IBD) sharing from autosomal SNP loci to estimate quantitative genetic parameters for ecologically important traits in nine‐spined sticklebacks (Pungitius pungitius) from a large, outbred population. Using empirical and simulated datasets, with varying sample sizes and pedigree complexity, we assessed the performance of different crossing schemes in estimating additive genetic variance and heritability for all traits. We found that low variance in relatedness characteristic of wild outbred populations with high migration rate can impair the estimation of quantitative genetic parameters and bias heritability estimates downwards. On the other hand, the use of a half‐sib/full‐sib design allowed precise estimation of genetic variance components and revealed significant additive variance and heritability for all measured traits, with negligible dominance contributions. Genome‐partitioning and QTL mapping analyses revealed that most traits had a polygenic basis and were controlled by genes at multiple chromosomes. Furthermore, different QTL contributed to variation in the same traits in different populations suggesting heterogeneous underpinnings of parallel evolution at the phenotypic level. Our results provide important guidelines for future studies aimed at estimating adaptive potential in the wild, particularly for those conducted in outbred large‐sized populations. [ABSTRACT FROM AUTHOR]

Heritable variation in traits under natural selection is a prerequisite for evolutionary response. While it is recognized that trait heritability may vary spatially and temporally depending on which environmental conditions traits are expressed under, less is known about the possibility that genetic variance contributing to the expected selection response in a given trait may vary at different stages of ontogeny. Specifically, whether different loci underlie the expression of a trait throughout development and thus providing an additional source of variation for selection to act on in the wild, is unclear. Here we show that body size, an important life-history trait, is heritable throughout ontogeny in the nine-spined stickleback (Pungitius pungitius). Nevertheless, both analyses of quantitative trait loci and genetic correlations across ages show that different chromosomes/loci contribute to this heritability in different ontogenic time-points. This suggests that body size can respond to selection at different stages of ontogeny but that this response is determined by different loci at different points of development. Hence, our study provides important results regarding our understanding of the genetics of ontogeny and opens an interesting avenue of research for studying age-specific genetic architecture as a source of non-parallel evolution. [ABSTRACT FROM AUTHOR]

Repeated and independent adaptation to specific environmental conditions from standing genetic variation is common. However, if genetic variation is limited, the evolution of similar locally adapted traits may be restricted to genetically different and potentially less optimal solutions or prevented from happening altogether. Using a quantitative trait locus (QTL) mapping approach, we identified the genomic regions responsible for the repeated pelvic reduction (PR) in three crosses between nine‐spined stickleback populations expressing full and reduced pelvic structures. In one cross, PR mapped to linkage group 7 (LG7) containing the gene Pitx1, known to control pelvic reduction also in the three‐spined stickleback. In the two other crosses, PR was polygenic and attributed to 10 novel QTL, of which 90% were unique to specific crosses. When screening the genomes from 27 different populations for deletions in the Pitx1 regulatory element, these were only found in the population in which PR mapped to LG7, even though the morphological data indicated large‐effect QTL for PR in several other populations as well. Consistent with the available theory and simulations parameterized on empirical data, we hypothesize that the observed variability in genetic architecture of PR is due to heterogeneity in the spatial distribution of standing genetic variation caused by >2× stronger population structuring among freshwater populations and >10× stronger genetic isolation by distance in the sea in nine‐spined sticklebacks as compared to three‐spined sticklebacks. see also the Perspective by Yo Y. Yamasaki and Jun Kitano. [ABSTRACT FROM AUTHOR]

The degree to which adaptation to similar selection pressures is underlain by parallel versus non‐parallel genetic changes is a topic of broad interest in contemporary evolutionary biology. Sticklebacks provide opportunities to characterize and compare the genetic underpinnings of repeated marine‐freshwater divergences at both intra‐ and interspecific levels. While the degree of genetic parallelism in repeated marine‐freshwater divergences has been frequently studied in the three‐spined stickleback (Gasterosteus aculeatus), much less is known about this in other stickleback species. Using a population transcriptomic approach, we identified both genetic and gene expression variations associated with marine‐freshwater divergence in the nine‐spined stickleback (Pungitius pungitius). Specifically, we used a genome‐wide association study approach, and found that ~1% of the total 173,491 identified SNPs showed marine‐freshwater ecotypic differentiation. A total of 861 genes were identified to have SNPs associated with marine‐freshwater divergence, but only 12 of these genes have also been reported as candidates associated with marine‐freshwater divergence in the three‐spined stickleback. Hence, our results indicate a low degree of interspecific genetic parallelism in marine‐freshwater divergence. Moreover, 1,578 genes in the brain and 1,050 genes in the liver were differentially expressed between marine and freshwater nine‐spined sticklebacks, ~5% of which have also been identified as candidates associated with marine‐freshwater divergence in the three‐spined stickleback. However, only few of these (e.g., CLDND1) appear to have been involved in repeated marine‐freshwater divergence in nine‐spined sticklebacks. Taken together, the results indicate a low degree of genetic parallelism in repeated marine‐freshwater divergence both at intra‐ and interspecific levels. [ABSTRACT FROM AUTHOR]

Quantitative traits important to organismal function and fitness, such as brain size, are presumably controlled by many small-effect loci. Deciphering the genetic architecture of such traits with traditional quantitative trait locus (QTL) mapping methods is challenging. Here, we investigated the genetic architecture of brain size (and the size of five different brain parts) in nine-spined sticklebacks ( Pungitius pungitius) with the aid of novel multilocus QTL-mapping approaches based on a de-biased LASSO method. Apart from having more statistical power to detect QTL and reduced rate of false positives than conventional QTL-mapping approaches, the developed methods can handle large marker panels and provide estimates of genomic heritability. Single-locus analyses of an F2 interpopulation cross with 239 individuals and 15 198, fully informative single nucleotide polymorphisms (SNPs) uncovered 79 QTL associated with variation in stickleback brain size traits. Many of these loci were in strong linkage disequilibrium (LD) with each other, and consequently, a multilocus mapping of individual SNPs, accounting for LD structure in the data, recovered only four significant QTL. However, a multilocus mapping of SNPs grouped by linkage group (LG) identified 14 LGs (1-6 depending on the trait) that influence variation in brain traits. For instance, 17.6% of the variation in relative brain size was explainable by cumulative effects of SNPs distributed over six LGs, whereas 42% of the variation was accounted for by all 21 LGs. Hence, the results suggest that variation in stickleback brain traits is influenced by many small-effect loci. Apart from suggesting moderately heritable ( h2 ≈ 0.15-0.42) multifactorial genetic architecture of brain traits, the results highlight the challenges in identifying the loci contributing to variation in quantitative traits. Nevertheless, the results demonstrate that the novel QTL-mapping approach developed here has distinctive advantages over the traditional QTL-mapping methods in analyses of dense marker panels. [ABSTRACT FROM AUTHOR]