Jon E. Hess

Comparison of F ST outlier tests for SNP loci under selection

Genome scans with many genetic markers provide the opportunity to investigate local adaptation in natural populations and identify candidate genes under selection. In particular, SNPs are dense throughout the genome of most organisms and are commonly observed in functional genes making them ideal markers to study adaptive molecular variation. This approach has become commonly employed in ecological and population genetics studies to detect outlier loci that are putatively under selection. However, there are several challenges to address with outlier approaches including genotyping errors, underlying population structure and false positives, variation in mutation rate and limited sensitivity (false negatives). In this study, we evaluated multiple outlier tests and their type I (false positive) and type II (false negative) error rates in a series of simulated data sets. Comparisons included simulation procedures (FDIST2, arlequin v.3.5 and BAYESCAN) as well as more conventional tools such as global FST histograms. Of the three simulation methods, FDIST2 and BAYESCAN typically had the lowest type II error, BAYESCAN had the least type I error and Arlequin had highest type I and II error. High error rates in Arlequin with a hierarchical approach were partially because of confounding scenarios where patterns of adaptive variation were contrary to neutral structure; however, Arlequin consistently had highest type I and type II error in all four simulation scenarios tested in this study. Given the results provided here, it is important that outlier loci are interpreted cautiously and error rates of various methods are taken into consideration in studies of adaptive molecular variation, especially when hierarchical structure is included.

Population genomics of Pacific lamprey: adaptive variation in a highly dispersive species.

Unlike most anadromous fishes that have evolved strict homing behaviour, Pacific lamprey (Entosphenus tridentatus) seem to lack philopatry as evidenced by minimal population structure across the species range. Yet unexplained findings of within‐region population genetic heterogeneity coupled with the morphological and behavioural diversity described for the species suggest that adaptive genetic variation underlying fitness traits may be responsible. We employed restriction site–associated DNA sequencing to genotype 4439 quality filtered single nucleotide polymorphism (SNP) loci for 518 individuals collected across a broad geographical area including British Columbia, Washington, Oregon and California. A subset of putatively neutral markers (N = 4068) identified a significant amount of variation among three broad populations: northern British Columbia, Columbia River/southern coast and ‘dwarf’ adults (FCT = 0.02, P ≪ 0.001). Additionally, 162 SNPs were identified as adaptive through outlier tests, and inclusion of these markers revealed a signal of adaptive variation related to geography and life history. The majority of the 162 adaptive SNPs were not independent and formed four groups of linked loci. Analyses with matsam software found that 42 of these outlier SNPs were significantly associated with geography, run timing and dwarf life history, and 27 of these 42 SNPs aligned with known genes or highly conserved genomic regions using the genome browser available for sea lamprey. This study provides both neutral and adaptive context for observed genetic divergence among collections and thus reconciles previous findings of population genetic heterogeneity within a species that displays extensive gene flow.

Comparison of SNPs and microsatellites for fine-scale application of genetic stock identification of Chinook salmon in the Columbia River Basin

Genetic stock identification (GSI) is an important tool in fisheries management. Microsatellites (μSATs) have been the dominant genetic marker for GSI; however, increasing availability and numerous advantages of single‐nucleotide polymorphism (SNP) markers make them an appealing alternative. We tested performance of 13 μSAT vs. 92 SNP loci in a fine‐scale application of GSI, using a new baseline for Chinook salmon consisting of 49 collections (n = 4014) distributed across the Columbia River Basin. In GSI, baseline genotypes for both marker sets were used independently to analyse a real fishery mixture (n = 2731) representing the total run of Chinook salmon passing Bonneville Dam in the Columbia River. Marker sets were evaluated using three criteria: (i) ability to differentiate reporting groups, (ii) proportion of correct assignment in mixture simulation tests and baseline leave‐one‐out analyses and (iii) individual assignment and confidence intervals around estimated stock proportions of a real fishery mixture. The μSATs outperformed the SNPs in resolving fine‐scale relationships, but all 105 markers combined provided greatest power for GSI. SNPs were ranked by relative information content based on both an iterative procedure that optimized correct assignment to the baseline and ranking by minor allele frequency. For both methods, we identified a subset of the top 50 ranked loci, which were similar in assignment accuracy, and both reached maximum available power of the total 92 SNP loci (correct assignment = 73%). Our estimates indicate that between 100 and 200 highly informative SNP loci are required to meet management standards (correct assignment > 90%) for resolving stocks in finer‐scale GSI applications.

Use of genotyping by sequencing data to develop a high-throughput and multifunctional SNP panel for conservation applications in Pacific lamprey.

Next‐generation sequencing data can be mined for highly informative single nucleotide polymorphisms (SNPs) to develop high‐throughput genomic assays for nonmodel organisms. However, choosing a set of SNPs to address a variety of objectives can be difficult because SNPs are often not equally informative. We developed an optimal combination of 96 high‐throughput SNP assays from a total of 4439 SNPs identified in a previous study of Pacific lamprey (Entosphenus tridentatus) and used them to address four disparate objectives: parentage analysis, species identification and characterization of neutral and adaptive variation. Nine of these SNPs are FST outliers, and five of these outliers are localized within genes and significantly associated with geography, run‐timing and dwarf life history. Two of the 96 SNPs were diagnostic for two other lamprey species that were morphologically indistinguishable at early larval stages and were sympatric in the Pacific Northwest. The majority (85) of SNPs in the panel were highly informative for parentage analysis, that is, putatively neutral with high minor allele frequency across the species’ range. Results from three case studies are presented to demonstrate the broad utility of this panel of SNP markers in this species. As Pacific lamprey populations are undergoing rapid decline, these SNPs provide an important resource to address critical uncertainties associated with the conservation and recovery of this imperiled species.

Environmental adaptation in Chinook salmon (Oncorhynchus tshawytscha) throughout their North American range

Landscape genomics is a rapidly growing field with recent advances in both genotyping efficiency and statistical analyses that provide insight towards local adaptation of populations under varying environmental and selective pressure. Chinook salmon (Oncorhynchus tshawytscha) are a broadly distributed Pacific salmon species, occupying a diversity of habitats throughout the northeastern Pacific with pronounced variation in environmental and climate features but little is understood regarding local adaptation in this species. We used a multivariate method, redundancy analysis (RDA), to identify polygenic correlations between 19 703 SNP loci and a suite of environmental variables in 46 collections of Chinook salmon (1956 total individuals) distributed throughout much of its North American range. Models in RDA were conducted on both rangewide and regional scales by hierarchical partitioning of the populations into three distinct genetic lineages. Our results indicate that between 5.8 and 21.8% of genomic variation can be accounted for by environmental features, and 566 putatively adaptive loci were identified as targets of environmental adaptation. The most influential drivers of adaptive divergence included precipitation in the driest quarter of the year (Rangewide and North Coastal Lineage, anova P = 0.002 and 0.01, respectively), precipitation in the wettest quarter of the year (Interior Columbia River Stream‐Type Lineage, anova P = 0.03), variation in mean diurnal range in temperature (South Coastal Lineage, anova P = 0.005), and migration distance (Rangewide, anova P = 0.001). Our results indicate that environmental features are strong drivers of adaptive genomic divergence in this species, and provide a foundation to investigate how Chinook salmon might respond to global environmental change.

A centralized model for creating shared, standardized, microsatellite data that simplifies inter-laboratory collaboration

We demonstrate an efficient model for standardizing microsatellite DNA data among laboratories studying Oncorhynchus mykiss. Eight laboratories standardized 13 microsatellite loci following allele nomenclature of a central laboratory (average inter-laboratory genotyping concordance >98%). Following this central model, we have currently standardized 298 alleles from throughout the species native range. Although we focus here on O. mykiss, our experiences and recommendation apply equally to other broadly distributed species that may benefit from multi-laboratory collaborative data collection.

Major Lineages and Metapopulations in Columbia River Oncorhynchus mykiss Are Structured by Dynamic Landscape Features and Environments

Abstract It is widely recognized that genetic diversity within species is shaped by dynamic habitats. The quantitative and molecular genetic patterns observed are the result of demographics, mutation, migration, and adaptation. The populations of rainbow trout Oncorhynchus mykiss in the Columbia River basin (including both resident and anadromous forms and various subspecies) present a special challenge to understanding the relative roles of those factors. Standardized microsatellite data were compiled for 226 collections (15,658 individuals) from throughout the Columbia and Snake River basins to evaluate the genetic patterns of structure and adaptation. The data were primarily from fish of the anadromous life history form, and we used a population grouping procedure based on principal components and hierarchical k-means clustering to cluster populations into eight aggregates or groups with similar allele frequencies. These aggregates approximated geographic regions, and the two largest principal componen...

Resolving Adaptive and Demographic Divergence among Chinook Salmon Populations in the Columbia River Basin

Abstract Chinook salmon Oncorhynchus tshawytscha in the Columbia River basin (CRB) comprise three lineages—lower Columbia River and sympatric interior ocean and stream types—each with distinct biological attributes. To evaluate the adaptive and neutral genetic variation of this species in the CRB, we genotyped 54 Chinook salmon populations using a panel of 96 single-nucleotide polymorphism (SNP) loci. All three lineages were represented among the collections and were widely distributed across locations, ranging from the upper Salmon River to near the Columbia River estuary. Our goal was to explore local adaptation as a process shaping the population structure and genetic variation among Chinook salmon beyond the inferences possible with neutral marker data. In our analyses with putatively neutral SNPs, the population structure of Chinook salmon throughout the CRB was generally concordant with that of previous studies using microsatellites. Regression analyses and outlier methods identified 28, 17, and 29 ...

Examining genetic lineages of Chinook salmon in the Columbia River basin.

Abstract We examined 13 microsatellite loci from 51 collections of Chinook salmon Oncorhynchus tshawytscha throughout the Columbia River basin to determine membership in one of three major genetic lineages, introgression of genetic lineages within specific populations, and genetic structure at the subbasin level. Results confirm those of previous studies that three major lineages of Chinook salmon persist in the drainage, representing two interior life histories (ocean and stream types) and one lineage in the lower Columbia River. Novel observations of introgression were noted in specific collections, including three from the lower Columbia River (Sandy River, Kalama Hatchery, and Lewis Hatchery) and one stream-type population (Klickitat River). Estimates of genetic distance were larger in comparisons between ocean- and stream-type populations (G′ST = 0.429) and among stream-type and lower Columbia River populations (G′ST = 0.418) than between ocean-type and lower Columbia River populations (G′ST = 0.271)...

Genetic basis of adult migration timing in anadromous steelhead discovered through multivariate association testing.

Migration traits are presumed to be complex and to involve interaction among multiple genes. We used both univariate analyses and a multivariate random forest (RF) machine learning algorithm to conduct association mapping of 15 239 single nucleotide polymorphisms (SNPs) for adult migration-timing phenotype in steelhead (Oncorhynchus mykiss). Our study focused on a model natural population of steelhead that exhibits two distinct migration-timing life histories with high levels of admixture in nature. Neutral divergence was limited between fish exhibiting summer- and winter-run migration owing to high levels of interbreeding, but a univariate mixed linear model found three SNPs from a major effect gene to be significantly associated with migration timing (p < 0.000005) that explained 46% of trait variation. Alignment to the annotated Salmo salar genome provided evidence that all three SNPs localize within a 46 kb region overlapping GREB1-like (an oestrogen target gene) on chromosome Ssa03. Additionally, multivariate analyses with RF identified that these three SNPs plus 15 additional SNPs explained up to 60% of trait variation. These candidate SNPs may provide the ability to predict adult migration timing of steelhead to facilitate conservation management of this species, and this study demonstrates the benefit of multivariate analyses for association studies.