Bolstad, G. H. | bioRxiv Science

bioRxiv2021

Dissecting the loci underlying maturation timing in Atlantic salmon using haplotype and multi-SNP based association methods

Resolving the genetic architecture of fitness-related traits is key to understanding the evolution and maintenance of fitness variation. However, well-characterized genetic architectures of such traits in wild populations remain uncommon. In this study, we used haplotype-based and multi-SNP Bayesian association methods with sequencing data for 313 individuals from wild populations to further characterize known candidate regions for sea age at maturation in Atlantic salmon (Salmo salar). We detected an association at five loci (on chromosomes ssa06, ssa09, ssa21, and ssa25) out of 116 candidates previously identified in an aquaculture strain with maturation timing in wild Atlantic salmon. We found that at each of these five loci, variation explained by the locus was predominantly driven by a single SNP suggesting the genetic architecture of Atlantic salmon maturation includes multiple loci with simple, non-clustered alleles. This highlights the diversity of genetic architectures that can exist for fitness-related traits. Furthermore, this study provides a useful multi-SNP framework for future work using sequencing data to characterize genetic variation underlying phenotypes in wild populations.

evolutionary biology↗

bioRxiv2020

Fly wing evolutionary rate is a near-isometric function of mutational variation

If there is abundant mutational and standing genetic variation, most expect that the rate of evolution would be driven primarily by natural selection, and potentially be independent of current variability or variation. Contrary to this expectation, we (H17: Houle et al. 2017 Nature 548:447) found surprisingly strong scaling relationships with slopes near one between mutational variance, standing genetic variance and macro-evolutionary rate in Drosophilid wing traits. Jiang and Zhang (J&Z20: 2020 Evolution https://doi.org/10.1111/evo.14076) have challenged these results and our interpretation of them. J&Z20 showed that the method used in H17 to estimate the scaling relationship between variation at different biological levels is uninformative. Using an alternative method, they estimated that the scaling relationship has a slope substantially less than one, and propose a variant of our neutral subset hypothesis to explain this. Here we use simulations to confirm J&Z20s finding that the H17 method for estimating scaling of variances is uninformative. The simulations also show their alternative method for estimating scaling is likely to be seriously biased towards lower scaling relationships. We propose and verify an alternative approach to calculating scaling relationships based on independently estimated variance matrices, which we call the Q method. Simulations and reanalyses of the Drosophilid data set using the Q method suggests that our original estimates of the scaling relationship were close to the true value. We propose an analytical version of the neutral subset model, and show that it can indeed explain any scaling slope by varying assumptions about the pattern of pleiotropy. We continue to regard neutral subset models as implausible for wing shape in Drosophilids due to the likelihood that wing shape is subject to direct selection. Hybrid models in which pleiotropy reduces the available genetic and mutational variation, and a combination of selection and drift controls the change in species means seem more biologically promising.

evolutionary biology↗