Thermal Adaptation of Westslope Cutthroat Trout

Populations of westslope cutthroat trout (Oncorhynchus clarkii lewisi), a State species of special concern, have declined throughout their native range. Genetic introgressions, mainly from rainbow trout (O. mykiss), but also from Yellowstone cutthroat trout (O. c. bouvieri), and habitat loss are believed to be the leading causes of this decline. Populations that remain are often small and isolated, thereby increasing their risk of inbreeding depression and extinction. Translocation projects may offer a solution by infusing new genetic material into populations and potentially increasing their probability of persistence. However, local adaptations must be considered when selecting a donor population. We investigated thermal adaptations of four wild populations of westslope cutthroat trout from the Missouri River drainage and one hatchery population from the Washoe Park Trout Hatchery, Anaconda, Montana. Two wild populations were deemed to be from warm streams and two from cold streams. Fish were spawned streamside and at the hatchery. The resulting embryos were placed in experimental systems at 8, 10, and 14 °C. Survival was monitored throughout incubation. Post-embryonic growth was measured 90 days after hatching. Relationships between population performance and natal stream thermal characteristics were examined for adaptive differences

Data from: Identification of genomic regions associated with sex in Pacific halibut

Understanding and identifying the genetic mechanisms responsible for sex-determination are important for species management, particularly in exploited fishes where sex biased harvest could have implications on population dynamics and long-term persistence. The Pacific halibut (Hippoglossus stenolepis) supports important fisheries in the North Pacific Ocean. The proportion of each sex in the annual harvest is currently estimated using growth curves, but genetic techniques may provide a more accurate method. We used restriction-site associated DNA (RAD) sequencing to identify RAD-tags that were linked to genetic sex, based on differentiation (FST) between the sexes. Identified RAD-tags were aligned to the Atlantic halibut (Hippoglossus hippoglossus) linkage map, the turbot (Scophthalmus maximus) genome, and the half-smooth tongue sole (Cynoglossus semilaevis) genome to identify genomic regions that may be involved in sex determination. In total, 56 RAD-tags (70 single nucleotide polymorphisms) were linked to sex, and three RAD-tags were identified in only females. Sex-linked loci aligned to three linkage groups in the Atlantic halibut (LG07: 7 loci, LG15: 1 locus, and LG24: 1 locus), three chromosomes in the turbot (LG12: 13 loci, LG01: 1 locus, and LG05: 1 locus), and one chromosome in the half-smooth tongue sole (ChrZ: 9 loci). Results add support to the hypothesis that Pacific halibut genetic sex is determined in a ZW system. Two sex-linked loci were further developed into sex identification assays, and their efficacy was tested on individuals that had been morphologically sexed. The accuracy of each assay on its own was 97.5% compared to morphological sex

supplemental_material_199_sex_assays

Results of two TaqMan assays linked to sex

supplemental_materials_alignments

Alignments between Pacific halibut loci and 1) Atlantic halibut linkage map, 2) turbot genome, and 3) half-smooth tongue sole genome

halibut_genepop

File containing all genotypes. File in Genepop format. Locus names are formatted with an underscore between the RADtag name and SNP position

Data from: Integration of Random Forest with population-based outlier analyses provides insight on the genomic basis and evolution of run timing in Chinook salmon (Oncorhynchus tshawytscha)

Anadromous Chinook salmon populations vary in the period of river entry at the initiation of adult freshwater migration, facilitating optimal arrival at natal spawning. Run timing is a polygenic trait that shows evidence of rapid parallel evolution in some lineages, signifying a key role for this phenotype in the ecological divergence between populations. Studying the genetic basis of local adaptation in quantitative traits is often impractical in wild populations. Therefore, we used a novel approach, Random Forest, to detect markers linked to run timing across 14 populations from contrasting environments in the Columbia River and Puget Sound, USA. The approach permits detection of loci of small effect on the phenotype. Divergence between populations at these loci was then examined using both principle component analysis and FST outlier analyses, to determine whether shared genetic changes resulted in similar phenotypes across different lineages. Sequencing of 9107 RAD markers in 414 individuals identified 33 predictor loci explaining 79.2% of trait variance. Discriminant analysis of principal components of the predictors revealed both shared and unique evolutionary pathways in the trait across different lineages, characterized by minor allele frequency changes. However, genome mapping of predictor loci also identified positional overlap with two genomic outlier regions, consistent with selection on loci of large effect. Therefore, the results suggest selective sweeps on few loci and minor changes in loci that were detected by this study. Use of a polygenic framework has provided initial insight into how divergence in a trait has occurred in the wild

samples_barcodes_coordinates

Samples description, barcodes for demultiplexing and GPS coordinates of sampling locations

RandomForest_code_input_files

Code and input files for the random forest analysi

Kernel_smoothing_code_input file

Code and input files for the kernel smoothing analysi

Data from: A practical introduction to random forest for genetic association studies in ecology and evolution

Large genomic studies are becoming increasingly common with advances in sequencing technology, and our ability to understand how genomic variation influences phenotypic variation between individuals has never been greater. The exploration of such relationships first requires the identification of associations between molecular markers and phenotypes. Here we explore the use of Random Forest (RF), a powerful machine learning algorithm, in genomic studies to discern loci underlying both discrete and quantitative traits, particularly when studying wild or non-model organisms. RF is becoming increasingly used in ecological and population genetics because, unlike traditional methods, it can efficiently analyze thousands of loci simultaneously and account for non-additive interactions. However, understanding both the power and limitations of Random Forest is important for its proper implementation and the interpretation of results. We therefore provide a practical introduction to the algorithm and its use for identifying associations between molecular markers and phenotypes, discussing such topics as data limitations, algorithm initiation and optimization, as well as interpretation. We also provide short R tutorials as examples, with the aim of providing a guide to the implementation of the algorithm. Topics discussed here are intended to serve as an entry point for molecular ecologists interested in employing Random Forest to identify trait associations in genomic data sets