Developing a high throughput method to genotype coregonines at a standardized panel of loci for genetic monitoring and parentage-based tagging applications

Nicholas Sard (SUNY-Oswego, nicholas.sard@oswego.edu), Amanda Ackiss (USGS), Meredith Bartron (USFWS), Lauren Atkins (USFWS), Chris Wilson (OMNRF), Jared Homola (USGS), Ben Marcy-Quay (University of Vermont)

A central component of coregonine restoration in the Great Lakes is hatchery production, and active supplementation programs are underway in Lakes Ontario and Huron. Importantly, these efforts must consider the decades of work in other salmonids that demonstrate hatchery-reared fish can be morphologically (e.g., Taylor 1986; Busack et al. 2007), behaviorally (e.g., Berejikian et al. 1997; Fleming and Gross 1992) and genetically (e.g., Christie et al. 2012) different than their wild counterparts as these differences can have negative fitness consequences in the wild (see Christie et al. 2014; Koch and Narum 2021 for reviews). As the development of coregonine broodstock lines and methods associated with hatchery rearing and release are investigated in the Great Lakes, it is essential to monitor genetic diversity among hatchery-reared fish and consider the ramifications of releasing these animals into the wild. Yet, coregonine restoration efforts need a critical twenty-first-century tool that could be used to monitor genetic diversity measures, as well as estimate demographic parameters associated with fitness. Genetic monitoring can be used to track changes in measures of genetic diversity through time (Schwartz et al. 2007). Typical measures of diversity monitored are allelic richness, heterozygosity, and effective population size (Allendorf, Luikart, and Aitken 2012, Waples 2022), which are used to monitor the source populations that the hatchery populations are derived from, the hatchery broodstocks, and subsequent offspring. Comparisons of all three groups can identify potential genetic bottlenecks, declines in genetic diversity, and reproductive variance that could result in genetic diversity changes among the groups, resulting in decreased fitness or performance of the hatchery-produced fish in the wild. Genetic monitoring results can inform hatchery management, including broodstock collection, spawning practices, and assessing stocking activities. Previously, genetic monitoring of source populations and resultant offspring for coregonine hatchery programs has been conducted using microsatellites. However, given the scale and proposed magnitude of the stocking program implemented for coregonines, the transition to a new marker type is advantageous for multiple reasons: increased resolution for applications including species identification, genetic monitoring, and genetic parentage analysis, the ability to easily replicate and share consistent data across genetic labs, and decreased cost and processing time, especially for a larger number of samples. Developing high sample throughput, genomic-scale genotyping technologies is necessary to apply genetic monitoring and genetic tagging programs. Genetically “tagging” individuals can be used in several restoration applications, including estimating demographic parameters related to survival (Bravington et al. 2016a), studying aspects of dispersal (Conn et al. 2020), and estimating population size (Marcy-Quay et al. 2020; Ruzzante et al. 2019). While mass physical tagging (i.e., coded-wire tags, fin clips) efforts have been undertaken with other closely related species (e.g., Zerrenner et al. 1997, Elrod and Schneider 1986), such practices have drawbacks. For instance, physical tagging efforts are invasive, require handling fish to be tagged, and may interfere with other behaviors such as navigation (Habicht et al. 1998). Additionally, physical tagging efforts may not tag all offspring produced in a hatchery due to logistical restraints (i.e., time or cost limitations). As a result, agencies are increasingly adopting a genetic-based approach where all adults used in hatchery production are genotyped, thereby enabling the identification of all associated offspring (e.g., Steele et al. 2013; Beacham et al. 2017); hereafter, this process is referred to as parentage-based tagging (PBT). Genomic-scale studies of coregonines have provided information relevant to current restoration efforts. For instance, recent RAD-seq studies detected clear differentiation among coregonine species (e.g., Ackiss et al. 2020, Lachance et al. 2021), and these data were leveraged to develop a cost-effective GT-seq panel capable of species identification. This panel is now in wide use supporting coregonine research and restoration across the basin, including genetic verification of Bloater (Coregonus hoyi) recaptures in Lake Ontario (Weidel et al. 2022). The markers that make the current panel effective (high diversity, high differentiation) for species identification were tested for their use for genetic monitoring and parentage-based tagging but were inadequate to achieve these application goals (see below). We propose to develop an additional GT-seq panel designed explicitly for genetic monitoring and PBT applications. The proposed panel will genotype microhaplotypes, which increase the statistical power for pedigree reconstructions (Baetscher et al., 2018). In particular, half-sibling inferences will be a critical aspect of analyses associated with close-kin mark-recapture (CKMR) applications (Bravington et al. 2016b), as they will allow for the estimation of wild recruitment dynamics. Preliminary data generated by the co-PIs of this proposal suggest that, if funded, a monitoring panel may apply to more than one species. Specifically, two lines of unpublished evidence suggest a monitoring panel could be applied to more than one species. Using the current species-identification panel, we tested if it could accurately reconstruct pedigrees from known crosses of Cisco (Coregonus artedi) and Bloater. We found that parent-offspring and full-sibling relationships could be inferred with high confidence for either species; however, half-siblings could not be inferred with the required confidence level. Given the projected production levels from supplementation efforts, the resolution of family groups with the higher confidence level will maximize applications of the proposed GT-seq panel for genetic monitoring and PBT applications to support coregonine restoration efforts. The lack of confidence in inferring half-sibling relationships using the species identification GT-seq panel is likely related to its use of SNPs, which have only two alleles per locus. Conversely, microhaplotypes can have many more alleles, significantly increasing inferential power. Second, the species identification GT-seq panel has been shown to work in a wide range of related species beyond the ciscoes for which it was designed, such as lake whitefish (Coregonus clupeaformis) and round/pygmy whitefishes (Prosopium cylindraceum/P. coulterii). Thus, developing a multi-species monitoring panel is beneficial because it reduces the need for investments to create similar tools for each species individually, which is vital for Great Lakes coregonine applications, where hatchery programs are currently in place for both Bloater and Cisco. Finally, GT-seq panels are cost-effective, and the lab work involved is simpler than Rapture panels (Meek and Larson 2019).