# Development of a microhaplotype panel for steelhead/rainbow trout (Oncorhynchus mykiss) and application for phylogenetic analysis in California

Ronan Le Gall, Noé Barthelemy, Anthony J Clemento, Cassondra D

Columbus, Ellen Campbell, Elena C A Correa, Jeff A Rodzen, John Carlos

Garza, Devon E Pearse

► To cite this version:

Ronan Le Gall, Noé Barthelemy, Anthony J Clemento, Cassondra D Columbus, Ellen Campbell, et al.. Development of a microhaplotype panel for steelhead/rainbow trout (Oncorhynchus mykiss) and application for phylogenetic analysis in California. Conservation Genetics Resources, 2024, 10.1007/s12686-024-01374-2. hal-04865906

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# Development of a microhaplotype panel for steelhead/rainbow trout (*Oncorhynchus mykiss*) and application for phylogenetic analysis in California

Ronan Le Gall<fnref n="1" /> ID · Noé Barthelemy<fnref n="1" /> ID · Anthony J. Clemento<fnref n="2" /> ID · Cassondra D. Columbus<fnref n="2" /> ID · Ellen Campbell<fnref n="2" /> ID · Elena C. A. Correa<fnref n="2" /> · Jeff A. Rodzen<fnref n="3" /> ID · John Carlos Garza<fnref n="2" />,<fnref n="4" />,<fnref n="5" /> ID · Devon E. Pearse<fnref n="2" />,<fnref n="4" />,<fnref n="6" /> ID

Received: 17 May 2023 / Accepted: 9 November 2024

This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2024

## Abstract

Advances in high-throughput sequencing and bioinformatic data processing have prompted a transition in wildlife and fisheries genetics from the use of allozymes, mtDNA, or microsatellites towards markers that are more amenable to genotyping by sequencing, increasing the amount of data obtained for a lower cost with less time-consuming techniques. Microhaplotypes are novel multi-allelic genetic markers that utilize a high-throughput genomic amplicon sequencing approach to genotype large numbers of individuals for parentage and kinship analysis and population genetic studies, including applications in monitoring and fisheries management. We describe the development of a panel of microhaplotypes for *Oncorhynchus mykiss*, a species of high cultural and economic importance throughout its native range and globally through introductions for aquaculture and due to its reputation as a prized sport fish among recreational fishers. The panel includes 124 loci presumed to be neutral, a marker for the sex determination locus (*SdY*), and 10 loci targeting previously identified adaptive genomic variants associated with important life-history traits in this species. We demonstrate that this panel provides high resolution for phylogeographic and other genetic analysis and on initial standardized reference population genetic baseline of California *O. mykiss*.

**Keywords** Genomics · Microhaplotypes · Genotyping · *Oncorhynchus mykiss* · Phylogeography

<figure><img src="image_2.png" /></figure>

Devon E. Pearse
devon.pearse@noaa.gov

Ronan Le Gall
ronan.legall@univ-brest.fr

Noé Barthelemy
noe.barthelemy@hotmail.fr

Anthony J. Clemento
anthony.clemento@noaa.gov

Cassondra D. Columbus
ccolumbu@ucsc.edu

Ellen Campbell
emcampbe@ucsc.edu

Elena C. A. Correa
elena.correa@noaa.gov

Jeff A. Rodzen
Jeff.rodzen@wildlife.ca.gov

John Carlos Garza
carlos.garza@noaa.gov

<footnote n="1">¹ Université Brest, CNRS, IRD, Ifremer, LEMAR, 29280 Plouzane, France</footnote>

<footnote n="2">² Institute of Marine Sciences, University of California, Santa Cruz, CA, USA</footnote>

<footnote n="3">³ Genetics Research Laboratory, California Department of Fish and Wildlife, Sacramento, CA, USA</footnote>

<footnote n="4">⁴ Southwest Fisheries Science Center, National Marine Fisheries Service, Santa Cruz, CA, USA</footnote>

<footnote n="5">⁵ Department of Ocean Sciences, University of California, Santa Cruz, CA, USA</footnote>

<footnote n="6">⁶ Department of Ecology and Evolutionary Biology, University of California, Santa Cruz, CA, USA</footnote>
**Introduction**

The distribution of genetic variation within and among populations and the patterns of gene flow between populations have been studied with a variety of genetic markers through the years. Historically, the nuclear genetic markers most commonly used in population genetics and parentage analysis were allozymes and microsatellites, and more recently single-nucleotide polymorphism (SNPs), the most abundant form of variation in the genome for most species (Brumfield et al. 2003; Allendorf et al. 2010). When genotyped using single-locus assays, SNPs are characterized by low error rates, easy and fast genotyping with reduced calibration problems between genotyping platforms and laboratories, and simulations demonstrate their utility for parentage analysis (Anderson and Garza 2006; Seeb et al. 2009). Rapid advances in technology have contributed to the growth of a new approach called Genotyping by Sequencing (GBS; Davey et al. 2011) that leverages the high-throughput DNA sequencing that is now dominant in molecular biology. Together with the associated changes in data handling and bioinformatics, GBS has dramatically increased the amount of data obtained for a lower cost with less time-consuming techniques. However, GBS generally surveys SNP variation, and because SNPs are typically bi-allelic, they do not provide the same power per locus as microsatellites (Narum et al. 2008; Hauser et al. 2011).

As addressing most questions in population biology does not require entire genome sequences, but rather a modest number of loci genotyped in a larger number of individuals, population geneticists often focus on a small number of genetic markers that enable more individuals to be genotyped on a limited budget. This has led to an emerging class of markers, microhaplotypes, which are characterized by two or more closely linked SNPs that can be genotyped together in a single marker (Baetscher et al. 2018). Because the multiple linked SNPs can appear in different allelic combinations, their combined sequences produce multiple haplotypes of tightly linked SNPs (Kidd et al. 2014; Oldoni et al. 2019). With their higher per locus statistical power, microhaplotypes are particularly useful for pedigree reconstruction and categorical assignment (McKinney et al. 2017; Baetscher et al. 2018) as well as forensic applications (Pang et al. 2020). Microhaplotypes are amenable to highly reproducible data processing pipelines and provide much greater power per nucleotide of sequence data than approaches that focus on a single bi-allelic SNP. Microhaplotypes are also abundant in the genome, with a low genotyping error rate similar to SNPs, and are becoming a popular molecular marker in genomics (Baetscher et al. 2018; Hendricks et al. 2018).

Rainbow Trout (*Oncorhynchus mykiss*) is a species of salmonid fish whose native distribution ranges from

northern Mexico to the Kamchatka Peninsula, Russia. The species encompasses a wide range of morphological variation, leading early researchers to describe more than 50 species in North America that have now been synthesized into ~14 subspecies (Busby et al. 1996; Pearse et al. 2011). Within the species *O. mykiss* a wide range of migratory behavior can be observed both within and among populations. This includes resident and anadromous life histories, as well as substantial variation in the timing and frequency of juvenile and adult migration. The anadromous form is termed “steelhead” trout, while the freshwater residents, that remain in freshwater throughout their lives, are referred to as “rainbow trout” (Behnke 1992; Bagley and Gall 1998). Steelhead usually spend 1 or 2 years in freshwater before migrating to the sea. After 1 to 3 years of growth, steelhead return to freshwater to spawn, typically returning to their natal stream. Unlike other Pacific salmon, *O. mykiss* can migrate and spawn multiple times, although the rate of iteroparity is typically low (Shapovalov and Taft 1954; Behnke 1992; McPhee et al. 2007; Beulke et al. 2023; Goetz et al. 2024). Adaptive genomic variation associated with migratory life-history traits has been documented in this species, including a chromosomal inversion associated with expression of the resident and anadromous forms as well as variation in disease susceptibility (Pearse et al. 2019; Calboli et al. 2022). Similarly, other genetic loci have been linked to variation in migration timing (Waples et al. 2022) and age-at-return (Waters et al. 2021), further highlighting the need for simple yet efficient genotyping methods to target adaptive variation. In addition, the anadromous form has suffered major declines, especially in the southern part of its range (Swift et al. 1993; Clemento et al. 2009; Abadía-Cardoso et al. 2016), and many populations are listed as threatened or endangered under the US Endangered Species Act (ESA; NOAA 2006). Thus, there is a continuing need for improved methods to understand the genetic diversity and gene flow among *O. mykiss* populations, monitor populations, and guide conservation and management to improve species resilience. Here we present the development of a panel of microhaplotype markers for *O. mykiss*, and explore their utility for elucidation of phylogeographic information among populations in California and beyond.

**Materials and methods**

**Microhaplotype panel development**

**SNP discovery and amplicon design**

To discover suitable genomic targets for development of microhaplotype markers, we used a modified double-digest

<figure><img src="image_3.png" /></figure>
restriction site-associated DNA sequencing approach (ddRAD-seq; Peterson et al. 2012) on 32 individuals from 10 populations of *O. mykiss* and one population sample of coastal cutthroat trout, *O. clarkii clarkii* (Ascertained samples; Supp. Table 1). Following double restriction enzyme digest using EcoR1 and Sph1, a five base pair barcode was ligated to each sample before pooling. Size selection using a Pippin Prep (Sage Science) targeted fragments in the range of 300–400 bp. The samples were then sequenced in one run on a Miseq instrument (Illumina Inc. Shen et al. 2005) using a 600-cycle paired-end sequencing kit. Sequencing yielded 19,324,096 pairs of raw reads. Raw reads obtained from the Illumina sequencing run were filtered based on their average Phred-scaled base quality score (≥ 33) and, as a further quality-control measure, paired-end reads were combined using FLASH v1.2.11 (Fast Length Adjustment of SHort reads; Magoč and Salzberg 2011) with min overlap of 20 and max overlap of 300, retaining 15,499,545 extended reads. Using the *process_radtags* component of Stacks v1.48 (Catchen et al. 2013), reads were truncated to 325 bp and then demultiplexed based on the unique 5 bp individual barcode to assign reads to their corresponding individuals. Raw reads per individual ranged from 74,878 to 651,980 and averaged 449,200. Finally, Stacks was used to assemble reads into loci within and across individuals and populations with a minimum depth of coverage (–m) of four, the distance allowed between stacks of two (M), and distance between catalog loci of two (n). As reported by Stacks, depth of coverage for processed samples ranged from 13.3× to 69.5× and averaged 47.4× across all individuals.

Among the 29,024 potential loci, a total of 5959 had more
than two SNPs that were observed in at least 10 or more
of the 32 individuals sequenced. Since our target ampli-
con insert length in the final panel was 100–105 bases,
we selected only loci that had two or more SNPs within
100–105 bases of each other and had a minimum of 18 non-
variable bases on either side of those variants to attempt
primer design. This left 3049 potential loci. Given the short
lengths of the targeted sequences and the whole genome
duplication event in the common ancestor of salmonid fishes
(Berthelot et al. 2014; Lien et al. 2016), the risk of obtain-
ing amplicons containing paralogous genomic regions was
very high (Pearse et al. 2019). Consensus sequences from
the 3049 candidate loci were mapped to themselves using
BLAT (Kent 2002). BLAT output was processed using R
(R core team 2022), removing 2218 ‘duplicate’ targets that
fully or partially matched another target locus, regardless
of the number of indels or mismatches. These ‘duplicates’
reflect mainly bioinformatics errors (Stacks errantly splitting
reads into separate loci) and/or repetitive elements or par-
alogous regions in the genome. With only unique genomic
regions represented in our filtered dataset, BLAT was used
again to map our 831 remaining targets to the Omyk_1.0

chromosome-scale genome assembly (Pearse et al. 2019) and produce a list of 385 potential targets for the design of microhaplotype markers. Finally, we used the graphical interface of Stacks to assess overall variability (number of SNPs and observed haplotypes), the potential for successful primer design (variation more than 18 bp from either end of the fragment), and the proportion of population- or species-specific alleles in the 32 sequenced individuals, and used these criteria to select loci for primer design.

We designed primers for 192 variable loci with the software Primer 3 (Untergasser et al. 2012) implemented in Geneious v.R11 (Kearse et al. 2012), using the Santa Lucia (1998) melting temperature (Tm) calculation and salt correction method. The length range of primers was 18–27 bp (target length of 20 bp) and contained between 25 and 50% GC bases (optimal content of 50%), allowing a max Tm difference of 2 °C between primers and otherwise using Primer 3 default parameters. We targeted a product size of 130 bp (in the range of 90–143 bp), to optimize sequencing overlap and because short and uniform lengths of target sequences are important factors for uniform PCR amplification among loci. Following initial testing and PCR multiplex optimization, loci that continued to garner the largest proportion of reads (possibly due to a paralogous or repetitive sequence) and the lowest proportion of reads (due to inefficient amplification or variation in the primer sequence) were removed from the panel, as were loci where previously identified SNPs were absent or at much lower frequency than expected, resulting in the final list of 124 presumably neutral loci (Supp. Table 2). In addition to these newly discovered loci from the ddRAD data, markers targeting two previously identified functional gene regions and the Y-chromosome were added to the panel. First, five microhaplotype markers were designed within the chromosomal inversion complex present on chromosome *Omy05*, known to be strongly associated with expression of anadromous or resident migratory life-history phenotypes in some *O. mykiss* populations (Pearse et al. 2014, 2019). Second, we designed primers for micro-haplotype loci targeted on five SNPs in the *Greb1L* gene region on *Omy28* that have been repeatedly and broadly associated with variation run-timing (Waples et al. 2022). Finally, the ‘Omy-Y1-2Sexy’ locus was included by using the primers from Brunelli et al. (2010). This marker amplifies only when the Y chromosome is present (i.e., in males) and has been shown to be highly accurate in identifying males and females in coastal California steelhead (Rundio et al. 2012; Pearse et al. 2019).

Genotyping-by-sequencing

To be able to conduct ‘genotyping in thousands’ (GT-seq), Campbell et al. (2015) developed a genotyping by sequencing (GBS) method to optimize the sequencing capacity of
NGS technologies for population genetics and parentage studies. We used GT-seq to sequence up to 384 individuals with 135 microhaplotype loci in a single Illumina MiSeq® run, using a 150-cycle paired-end approach. All other details of the thermal cycling and library preparation are found in Baetscher et al. (2018). Sequencing reads were de-multiplexed by the MiSeq Analysis Software (Illumina inc. Shen et al. 2005). Paired-end reads were combined using FLASH (min overlap of 4 and max overlap of 50; Magoč and Salzberg 2011); merge rates were generally over 90% although some individuals with lower quality DNA had rates as low as 60%. Merged reads were then mapped to the Stacks consensus sequences for the target loci using the BWA-MEM v0.7.17-r1188 (-M -v 3 -t 10 -R; Li and Durbin 2009). Mapped reads were converted from Sequence Alignment/Map (SAM) files to BAM files with SAMtools v1.13 (view -bhS; Li et al. 2009). We identified variable sites using FreeBayes v1.3.6 (–haplotype-length 0 -kwVa –no-mnps –no-complex; Garrison and Marth 2012); the positions of all SNPs for each locus were recorded in a VCF file. The microhaplot R package (Ng et al., https://doi.org/10.5281/zenodo.820110; R core team 2022) and associated Shiny app (http://shiny.rstudio.com/) were employed to assemble the SNPs for each amplicon into a microhaplotype using the SAM files and the positions specified in the VCF. After filtering loci for a minimum depth of coverage of 10 reads per individual and an allelic balance ratio greater than 0.3, the software was then used to export the microhaplotypes for downstream analyses.

Because the inference of genetic sex with the 'Omy-Y1-2Sexy' locus is based on non-amplification in females, the expected read depth for females is zero. However, due to individual barcode misidentification and other genotyping errors, some reads could possibly be incorrectly assigned to females. Thus, based on the observed distribution of read counts the threshold for the inference of female sex was set to a maximum of 5 reads.

Four sequencing runs were performed, each contain-
ing one to four plates of 96 individuals at the same time.
After each run, variability across loci was assessed, and
loci were filtered according to the following criteria: read
depth, inconsistent allelic balance across individuals,
deviation from Hardy-Weinberg equilibrium (HWE), and
the presence of more than two haplotypes per individual,
likely due to paralog loci or index sequencing errors (Lar-
son et al. 2017). Finally, primers associated with extremely
high read-depth loci were diluted, in order to limit their
over-representation in the sequencing pools. The final
panel was composed of 124 loci for parentage and popu-
lation genetic analysis, 10 adaptive loci in the Omy05 and
greb1L regions, and one locus to identify genetic sex in
our individuals.

Phylogeographic utility

In addition to the individuals genotyped for the panel devel-
opment, samples from 28 additional populations were geno-
typed along with additional samples for some populations
that were already included. Based on previous studies, we
defined several phylogeographic groups; the Cutthroat trout
outgroup, Northern Coastal (Oregon and Klamath system;
Pearse et al. 2011), Southern Coastal (Southern California,
Abadía-Cardoso et al. 2016), the remaining Coastal popula-
tions (Northern California to Central California, Garza et al.
2014), Central Valley Below dams (Pearse and Garza 2015),
Central Valley Above dams (Pearse and Campbell 2018),
Inland (Redband and Eagle Lake subspecies; Nielsen et al.
1999; Moyle 2002), Golden Trout (Subspecies; Cordes et al.
2006), and Hatchery strains. This study is based on 124 loci
genotyped in a total of 1831 tissue samples from 58 popu-
lations (Fig. 1; Table 1) (Pritchard et al. 2012; Garza et al.
2014; Abadía-Cardoso et al. 2019).

<figure><img src="image_4.png" /><figcaption>**Fig. 1** Map of rivers and watersheds of California and Oregon. Each wild population sample used for this study is represented by a point, estimated with GPS coordinates (in Supp. Table 1). The color code is the same as on Table 1, i.e. Black: Cutthroat trout, Blue: Coastal populations, Dark Blue: Northern Coastal, Turquoise: Southern Coastal, Light Blue: Central Valley Below dams, Yellow: Central Valley Above dams, Orange: Inland, Red: Golden Trout. Hatchery strains are not represented on the map</figcaption></figure>
<table><caption>**Table 1** Population samples used for this study</caption><thead><tr><th>#</th><th>Region</th><th>Country, States</th><th>Sample site</th><th>n</th><th>Reference</th></tr></thead><tbody><tr><td>1</td><td>Coastal Cutthroat</td><td>United States, California</td><td>NF Smith R.</td><td>16</td><td>Pritchard et al. 2012</td></tr><tr><td>2</td><td>Coastal</td><td>United States, Oregon</td><td>Willamette R.</td><td>12</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>3</td><td>Coastal</td><td>United States, Oregon</td><td>South Santiam R.</td><td>21</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>4</td><td>Coastal</td><td>United States, Oregon</td><td>Foster Ck.</td><td>9</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>5</td><td>Coastal</td><td>United States, Oregon</td><td>Mckenzie R.</td><td>25</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>6</td><td>Coastal Klamath</td><td>United States, Oregon</td><td>Spring Ck.</td><td>16</td><td>Pearse et al. 2011</td></tr><tr><td>7</td><td>Coastal Klamath</td><td>United States, California</td><td>Shasta R.</td><td>36</td><td>Pearse et al. 2007</td></tr><tr><td>8</td><td>Coastal Klamath</td><td>United States, California</td><td>Horse Linto Ck.</td><td>46</td><td>Garza et al. 2013</td></tr><tr><td>9</td><td>Coastal</td><td>United States, California</td><td>Mad R.</td><td>16</td><td>Pearse & Garza 2015</td></tr><tr><td>10</td><td>Coastal</td><td>United States, California</td><td>Freshwater Ck.</td><td>42</td><td>Garza et al. 2013</td></tr><tr><td>11</td><td>Coastal</td><td>United States, California</td><td>MF Eel R.</td><td>46</td><td>Clemento 2006</td></tr><tr><td>12</td><td>Coastal</td><td>United States, California</td><td>Eel R. (Van Arsdale Fisheries Station)</td><td>46</td><td>This study</td></tr><tr><td>13</td><td>Coastal</td><td>United States, California</td><td>Little NF (Tenmile Ck.)</td><td>44</td><td>Garza et al. 2013</td></tr><tr><td>14</td><td>Coastal</td><td>United States, California</td><td>Dry Ck. (Russian R.)</td><td>78</td><td>Abadia-Cardoso et al. 2013</td></tr><tr><td>15</td><td>Coastal</td><td>United States, California</td><td>Alameda Ck.</td><td>40</td><td>Leitwein et al., 2017</td></tr><tr><td>16</td><td>Coastal</td><td>United States, California</td><td>Big Ck.</td><td>72</td><td>Barthelemy 2018</td></tr><tr><td>17</td><td>Coastal - SoCal</td><td>United States, California</td><td>Hilton Ck. (Santa Ynez R.)</td><td>16</td><td>Clemento et al 2009</td></tr><tr><td>18</td><td>Coastal - SoCal</td><td>United States, California</td><td>Piru Ck. (Santa Clara R.)</td><td>15</td><td>Clemento et al 2009</td></tr><tr><td>19</td><td>Coastal - SoCal</td><td>United States, California</td><td>Coldwater Canyon Ck.</td><td>35</td><td>Abadia-Cardoso et al. 2016</td></tr><tr><td>20</td><td>Coastal - SoCal</td><td>United States, California</td><td>WF San Luis Rey R.</td><td>9</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>21</td><td>Inland</td><td>United States, California</td><td>Buck Ck. (Goose Lake)</td><td>24</td><td>Pearse et al., 2011</td></tr><tr><td>22</td><td>Inland</td><td>United States, California</td><td>NF Deep Ck.</td><td>15</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>23</td><td>Inland</td><td>United States, California</td><td>SF Deep Ck.</td><td>15</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>24</td><td>Inland</td><td>United States, California</td><td>NF Shields Ck. (Pit R.)</td><td>15</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>25</td><td>Inland</td><td>United States, California</td><td>SF Shields Ck. (Pit R.)</td><td>13</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>26</td><td>Central Valley-A</td><td>United States, California</td><td>Sheepheaven Ck. (McCloud R.)</td><td>13</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>27</td><td>Central Valley-A</td><td>United States, California</td><td>Moosehead Ck. (McCloud R.)</td><td>14</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>28</td><td>Inland - Eagle Lake</td><td>United States, California</td><td>Eagle Lake</td><td>123</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>29</td><td>Central Valley-A</td><td>United States, California</td><td>North Arm Rice Ck. (Feather R.)</td><td>29</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>30</td><td>Central Valley-B</td><td>United States, California</td><td>Coleman Hatchery (Battle Ck.)</td><td>76</td><td>Pearse & Garza 2015</td></tr><tr><td>31</td><td>Central Valley-B</td><td>United States, California</td><td>Deer Ck.</td><td>18</td><td>Pearse & Garza 2015</td></tr><tr><td>32</td><td>Central Valley-B</td><td>United States, California</td><td>Thomes Ck.</td><td>6</td><td>Pearse & Garza 2015</td></tr><tr><td>33</td><td>Central Valley-B</td><td>United States, California</td><td>Feather R. Hatchery</td><td>62</td><td>Pearse & Garza 2015</td></tr><tr><td>34</td><td>Central Valley-A</td><td>United States, California</td><td>MF American R.</td><td>42</td><td>Abadia-Cardoso et al. 2019</td></tr><tr><td>35</td><td>Central Valley-B</td><td>United States, California</td><td>Nimbus Hatchery (Amercian R.)</td><td>47</td><td>This study</td></tr><tr><td>36</td><td>Central Valley-B</td><td>United States, California</td><td>Mokelumne R. Hatchery</td><td>95</td><td>Goetz et al. In Prep.</td></tr><tr><td>37</td><td>Central Valley-A</td><td>United States, California</td><td>Frog Ck. (Tuolumne R.)</td><td>20</td><td>Pearse & Campbell 2018</td></tr><tr><td>38</td><td>Inland - Golden Trout</td><td>United States, California</td><td>Chagoopa Ck.</td><td>13</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>39</td><td>Inland - Golden Trout</td><td>United States, California</td><td>Upper Mulkey Ck.</td><td>30</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>40</td><td>Inland - Golden Trout</td><td>United States, California</td><td>Big Whitney (Golden Trout Ck.)</td><td>15</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>41</td><td>Inland - Golden Trout</td><td>United States, California</td><td>Salt Lick Ck. (Golden Trout Ck.)</td><td>26</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>42</td><td>Inland - Golden Trout</td><td>United States, California</td><td>Golden Trout Ck.</td><td>29</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>43</td><td>Inland - Golden Trout</td><td>United States, California</td><td>Tunnel Station (Golden Trout Ck.)</td><td>22</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>44</td><td>Inland - Golden Trout</td><td>United States, California</td><td>Volcanic Ck. (Golden Trout Ck.)</td><td>37</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>45</td><td>Inland - Golden Trout</td><td>United States, California</td><td>Upper Groundhog meadow (Golden Trout Ck.)</td><td>25</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>46</td><td>Inland - Golden Trout</td><td>United States, California</td><td>Groundhog meadow (Golden Trout Ck.)</td><td>26</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>47</td><td>Inland - Golden Trout</td><td>United States, California</td><td>Broders Cabin (Little Kern R.)</td><td>28</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>48</td><td>Inland - Golden Trout</td><td>United States, California</td><td>Kern R.</td><td>14</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>49</td><td>Inland - Golden Trout</td><td>United States, California</td><td>NF Clicks Ck. (Little Kern R.)</td><td>30</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>50</td><td>Hatchery strain</td><td>United States, California</td><td>Coleman Hatchery</td><td>35</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>51</td><td>Hatchery strain</td><td>United States, California</td><td>Hot Ck. Hatchery</td><td>36</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>52</td><td>Hatchery strain</td><td>United States, California</td><td>Pit R. Hatchery</td><td>47</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>53</td><td>Hatchery strain</td><td>United States, California</td><td>Mt. Shasta Hatchery (American R.)</td><td>15</td><td>Pearse & Garza 2015</td></tr><tr><td>54</td><td>Hatchery strain</td><td>United States, California</td><td>Shasta R. Hatchery</td><td>46</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>55</td><td>Hatchery strain</td><td>United States, California</td><td>Wyoming Hatchery</td><td>32</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>56</td><td>Hatchery strain</td><td>United States, California</td><td>Hot Ck. Hatchery (Kamloops Strain)</td><td>16</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>57</td><td>Hatchery strain</td><td>Canada, British Columbia</td><td>Kamloops Hatchery JuncRes</td><td>22</td><td>J. Rodzen, pers. comm.</td></tr><tr><td>58</td><td>Hatchery strain</td><td>Canada, British Columbia</td><td>Kamloops Hatchery June Lake</td><td>20</td><td>J. Rodzen, pers. comm.</td></tr></tbody></table>

Each color refers to a subgroup of populations used in all figures. Black: Cutthroat trout, Blue: Coastal populations, Dark Blue: Northern Coastal, Turquoise: Southern Coastal, Light Blue: Central Valley Below dams, Yellow: Central Valley Above dams, Orange: Inland, Red: Golden Trout, Brown: Hatchery strain. The ID corresponds to the identification name used in our dataset, and n is the number of individuals per population. See Supp. Table 1 for complete sample details and population genetic results
Genetic diversity estimates [i.e., expected heterozygosity (He), observed heterozygosity (Ho), number of alleles (Na), inbreeding coefficient (Fis), and pairwise fixation index (Fst)] were calculated for each population and across all populations using the MStoolkit (Park 2008) as well as the R package 'diveRsity' (R core team 2022; Keenan et al. 2013), and Allelic Richness ($A_R$) was calculated using the software HPrare v1.1 (Kalinowski 2005). Genetic distances between populations were also analyzed with phylogenetic trees from the software PHYLIP (Felsenstein 1989). A neighbour-joining tree (Saitou and Nei 1987), representing genetic distance between populations, was calculated with PHYLIP using pairwise chord distances (Cavalli-Sforza and Edwards 1967). The stability of the tree topology was examined using the Seqboot program, with 1000 bootstrap replicates. Discriminant analysis of principal components (DAPC) was performed with the R package 'adegenet' (Jombart et al. 2010). The number of axes that maximize the results of the DAPC was defined by a "DAPC Cross-Validation" test (Jombart and Collins 2015), and 150 PCA components were kept for the population panel, for each DAPC. In order to observe core patterns more clearly, each of the multivariate analyses had clearly differentiated populations removed for the next DAPC. Finally, individual-based ancestry evaluations were also implemented using the model-based clustering program STRUCTURE (Pritchard et al. 2000). Values of K were evaluated as follows: $K=2-10, 15, 20, 25, \text{and } 30$. STRUCTURE output was then analyzed with CLUMPAK (Kopelman et al. 2015) and DISTRUCT (Rosenberg 2004).

# Results

## Panel validation

Overall, 729 individuals were successfully genotyped for the microhaplotype panel validation, and 96.8% of the 124 presumably neutral loci successfully genotyped for more than 90% of all individuals in each population. No consistent deviations from HWE were observed across populations. Mean global heterozygosity across all loci was high (0.42), with a total of 847 microhaplotype alleles distributed across all populations.

## Population genetics statistics

Estimates of genetic diversity were calculated using the final dataset consisting of 124 loci and 1831 genotyped individuals from 58 populations. No consistent deviations from HWE were observed across populations. Average allelic richness among populations was 1.79, mean values of both expected and observed heterozygosity were 0.26, and the average number of alleles per locus was 2.18. The Sheepheaven Creek

samples displayed the lowest genetic diversity estimates for all metrics (Supp. Table 1; $He=0.06, Ho=0.06, Ar=1.15, Na=1.18$). On the other hand, steelhead from Feather River Hatchery and Mokelumne River Hatchery showed the highest expected heterozygosity (0.40) and observed heterozygosity (0.40) values, and Nimbus Hatchery had a similarly high observed heterozygosity (0.40). Coleman National Fish Hatchery of Battle Creek displayed the highest allelic richness (2.36). The mean values of all genetic diversity estimates were higher for coastal samples ($He=0.33, Ho=0.33, Ar=2.06, N_A=2.60$) than for inland rainbow trout populations ($He=0.18, Ho=0.19, Ar=1.51, N_A=1.76$), but no significant differences were found.

Pairwise Fst estimates (Supp. Table 3) were highest between the outgroup, Cutthroat trout (1), and *O. mykiss* populations, with a mean Fst of 0.660 between those two species. Among the *O. mykiss* samples the mean Fst was 0.354, with Golden Trout complex samples (38–49) showing the greatest differentiation from the rest (mean Fst=0.486; Supp. Table 3). Within the Golden Trout complex, the 'Wyoming' California Golden Trout hatchery strain (55) were similar to Golden Trout Creek populations of wild California Golden Trout, suggesting an absence of strong divergence despite multiple generations in captivity. Finally, the Pit River Hatchery (52), Spring Creek (6) and Coldwater Canyon Creek (19) populations stood out from other populations with consistently high pairwise Fst estimates in comparison to the rest of the populations. In contrast, the coastal and Central Valley populations showed relatively low genetic differentiation.

## Neighbor-joining tree

The neighbor-joining tree highlighted previously known phylogenetic patterns among *O. mykiss* populations, with many nodes having strong bootstrap support (Fig. 2). For example, all Golden Trout complex samples were tightly clustered together, including the Golden Trout hatchery strain of Wyoming, with a bootstrap value of 97% in the Neighbor-Joining tree (Fig. 2). Similarly, the Redband Trout of Deep, Shields, and Buck Creeks strongly clustered together, along with Spring Creek, an upper Klamath tributary (Fig. 2). Central Valley below-dam populations showed mixed ancestry, the majority of them clustered together along with the domesticated rainbow trout strains, with the exception of steelhead at Nimbus Hatchery that grouped with the coastal cluster, as expected given their lineage (Pearse and Garza 2015).

## DAPC: discriminant analysis of principal components

DAPC was used to visualize the differentiation and relationships of all the *O. mykiss* populations (Fig. 3). For
<figure><img src="image_5.png" /><figcaption>**Fig. 2** Neighbor joining tree of *O. mykiss* populations, showing dis-
tance between each nod. Constructed with chord distance, this tree
displays all the population present in this study. The color code is the
same as on Table 1, i.e. Black: Cutthroat trout, Blue: Coastal popu-
lations, Dark Blue: Northern Coastal, Turquoise: Southern Coastal,</figcaption></figure>

Light Blue: Central Valley Below dams, Yellow: Central Valley Above dams, Orange: Inland, Red: Golden Trout, Brown: Hatchery strain. Bootstrap consensus values from 1000 bootstrap replicates shown. Only bootstrap values above 50% are reported

each DAPC, populations that exhibited clear differentia-
tion in the previous DAPC were removed to allow more
detailed relationships among the remaining populations to
resolve. The First DAPC (Fig. 3A) showed the cutthroat
trout outgroup (1) isolated from all *O. mykiss* populations,
as expected. With cutthroat trout removed, DAPC only
including *O. mykiss* populations showed a significant sepa-
ration between some members of the golden trout complex
(38–49), the Wyoming strain of hatchery golden trout (55),
and all other populations (Fig. 3B). Pit River Hatchery (52)

and Coldwater Canyon Creek (19) populations also showed
clear differentiation from the other *O. mykiss* populations.
These results were also shown with golden trout complex
populations removed (Fig. 3C), in which case the redband
trout subspecies (21–25) were differentiated from the rest.
Northern coastal populations (2–8) also displayed a break
from the central group; this split is most visible on the last
DAPC (Fig. 3D), in which Central Valley steelhead popula-
tions below dams (30–33, 35–36) also showed strong links
with coastal populations.
<figure><img src="image_6.png" /><figcaption>**Fig. 3** Four DAPCs showing population relationships on their first two axes that explained most of the variance. **A** all the populations (1–58), **B** *O. mykiss* populations without Cutthroat trout (2–58), **C** *O. mykiss* populations except the golden trout and Coldwater Canyon Creek populations (2–18, 20–37, 50–58), **D** steelhead populations</figcaption></figure>

only (1–18, 20, 30–33, 35–36). The color code correspond to: Black: Cutthroat trout, Blue: Coastal populations, Dark Blue: Northern Coastal, Turquoise: Southern Coastal, Light Blue: Central Valley Below dams, Yellow: Central Valley Above dams, Orange: Inland, Red: Golden Trout, Brown: Hatchery strain

STRUCTURE

STRUCTURE results (Supp. Fig. 1) were clear and consistent among the 10 replicates at all values of K. Since the presence of a highly divergent outgroup would not have yielded informative results on the genetic structure of Californian *O. mykiss*, the Cutthroat trout were excluded from this analysis. As in the DAPC analysis, the first split observed with STRU

CTURE was between the Golden Trout complex and other *O. mykiss* populations. Then at K=10 additional patterns are clear, including separation between coastal and inland populations, and an interior Redband Trout group consisting of Buck Creek, North Fork Shields Creek, South Fork Shields Creek, North Fork Deep Creek, and South Fork Deep Creek. At higher K-values, these patterns remain and most populations are clearly distinct (Supp. Fig. 1).

<figure><img src="image_7.png" /></figure>
## Adaptive genetic variation and sex informative loci

In addition to the microhaplotype loci designed for population genetic analyses, we developed 10 microhaplotype loci specifically to target regions of adaptive genetic variation (*Omy5* and *Greb1L*). The ‘Omy-Y1-2Sexy’ marker and the *Greb1L* and *Omy05* microhaplotypes all amplified successfully in *O. clarkii* as well as *O. mykiss*. However, while this demonstrates that these loci amplify in this species, it does not necessarily indicate that the same associations between these variants and specific life-history traits exist.

The five microhaplotype markers designed within the inversion complex present on chromosome 5 (*Omy05*) produced a total of eight variable SNPs. Similarly, the five microhaplotype loci targeted in the region of the *Greb1L* gene on *Omy28* contained six variable SNPs. Based on previous studies and examination of the genotype data, a single key SNP for each of these regions was used to provide information on the distribution of known adaptive genetic variants in these regions (Supp. Table 1; *Greb1L*: mhap8_71_11667915 *Omy05*: omy5_9_54854574-19). For example, as has been previously shown (Pearse et al. 2019), the Omy05 allele associated with residency is fixed in inland redband trout populations, but the alternate allele appears at high frequencies in populations known to support anadromous migration (Supp. Table 1). Similarly, the population sample from the Middle Fork of the Eel River is fixed for the ‘early’ or ‘premature’ allele at Greb1L loci, as has been previously shown in this summer run steelhead population, while other coastal steelhead populations are fixed for the alternate allele (Waples et al. 2022).

Finally, the ‘Omy-Y1-2Sexy’ marker, which amplifies only when the Y chromosome is present (i.e., in males) provided highly accurate information on sex for the subset of individuals for which morphological sex information was available. All individuals identified as female had 0, 1, or 2 reads except for one individual with 44 reads, likely indicating a misidentified male. Similarly, with the exception of one male that had zero reads, no other known males failed to amplify, suggesting that that individual was likely a female and indicating a possible metadata error. All other known males had a minimum of eight reads, and most had > 50 reads, clearly differentiating males and females.

## Discussion

### Effectiveness of the microhaplotype panel

The novel microhaplotype panel described here provides sufficient variation to resolve population genetic groups previously identified by microsatellite and SNP-based studies in this species in California (e.g., Pearse et al. 2009; Garza

et al. 2014; Pearse and Garza 2015; Pearse and Campbell 2018). However, unlike microsatellites, microhaplotypes benefit from the same low error rates and ease of genotyping as SNPs, making them amenable to use with high-throughput genotype-by-sequencing pipelines and offering a multi-allelic alternative to existing bi-allelic SNP genotyping panels (e.g. Campbell et al. 2015). Thus, this panel of markers provides a valuable new tool for researchers, combining the main advantages of microsatellites and SNPs into a single high-throughput genotyping approach (for an empirical comparison of SNPs and microsatellites see Glaubitz et al. 2003; Hauser et al. 2011). These markers should be especially useful for studies of kinship and parentage, where multi-allelic loci provide significant advantages over bi-allelic markers (Glaubitz et al. 2003; Baetscher et al. 2018).

### Genetic sex determination

As in previous studies (Rundio et al. 2012; Pearse et al. 2019; Kelson et al. 2020), the ‘Omy-Y1-2Sexy’ genetic sex marker used here was very accurate in determining sex of adults from multiple populations, providing high confidence information about the sex of the genotyped individuals. However, the presence/absence detection mechanism of this marker makes it sensitive to technical errors such as failure to amplify in a male (i.e. resulting in a ‘false-negative’ female) or index misidentification. In addition, ‘Omy-Y1-2Sexy’ was designed on the *sdY* gene, which is the master-sex determining gene in *O. mykiss* (Yano et al. 2013). However, the *sdY* gene is often transposed to a different location of the genome in salmonid species (Phillips 2013), so caution should be used in interpreting the information provided by this marker in novel populations.

### Adaptive genetic variation

In the populations studied here, the proportions of anadromous-associated alleles (A) in the Omy05 region and alternative alleles in the *Greb1L* region were concordant with both previous estimations in some of the same populations (e.g. Pearse et al. 2014, 2019; Waples et al. 2022) and with expectations based on the habitats in which they were sampled. In addition to the information provided by single SNPs in both of these regions, further investigations of the linkage disequilibrium and frequency of discordant genotypes among the existing microhaplotypes, as well as the potential to add microhaplotypes targeting additional adaptive variants, will extend our understanding of the structure of adaptive genomic variation in these regions. Furthermore, the flexibility of the amplicon sequencing approach will also allow additional loci to be added to the panel in the future to further assess variation in the associations of specific SNPs, or markers associated with other adaptive phenotypic
variation, such as loci recently identified as important for
additional life-history traits (e.g. Six6 and vgl13; Waters
et al. 2021).

Phylogeography of *O. mykiss*

The relationships recovered using the microhaplotype
panel in this study clearly resolved the expected population
genetic structure of the study populations and concord with
the broad-scale patterns known from previous studies. Cut-
throat trout were strongly differentiated from *O. mykiss* in
all the analyses, and the patterns of differentiation among
the coastal, Central Valley, and inland *O. mykiss* popula-
tions (Buchanan et al. 1994; Pearse et al. 2011; Pearse and
Garza 2015; Leitwein et al. 2017) were clearly visible in
the pairwise Fst, DAPC and STRUCTURE results, as well
as in the phylogeographic trees. Similarly, previous studies
have shown that coastal *O. mykiss* populations above and
below the dams or waterfall barriers are often genetically
more differentiated between basins than populations within
the same basin (Clemento et al. 2009; Pearse et al. 2009),
demonstrating that they still share recent common ancestry.
Furthermore, results from pairwise Fst, STRUCTURE and
phylogeographic trees were consistent with previous studies
that showed a pattern of isolation-by-distance among below-
barrier populations (Pearse et al. 2007; Garza et al. 2014;
Pearse and Garza 2015), as were overall patterns of within-
population variation as reflected in the low estimates of
heterozygosity and allelic richness found in isolated inland
populations and populations above dams, and relatively high
values of these parameters found in coastal steelhead popula-
tions below dams.

Among inland rainbow trout populations, the first group
that showed strong genetic differentiation within all the
results was the Golden Trout complex, a group of inland
populations in the southern Sierra Nevada mountain range.
Golden Trout have long been isolated from the sea and
have diverged from other *O. mykiss* populations. Concord-
ant with previous work, our analyses revealed two major
lineages within the golden trout complex, one represent-
ing the populations of the Kern River (Little Kern Golden
Trout *O. m. whitei* and Kern River Rainbow Trout *O. m.
gilberti*), and another containing California Golden Trout
(*O. m. aguabonita*) from Golden Trout Creek. These two
clusters diverged within the last 5000–10,000 years, since
Golden Trout Creek was isolated above a waterfall that acts
as a complete barrier (Cordes et al. 2006). However, the
Chagoopa Creek population clustered with the Kern River
lineage despite being above the waterfall, a pattern that can
be explained by the fact that Kern River Golden Trout were
heavily introduced from the lower Kern River into Cha-
goopa Creek in the past (Stephens et al. 2004). Like the
Golden Trout, the Sheepheaven Creek and Moosehead Creek

samples of McCloud River Redband Trout (*O. m. stonei*)
were clearly differentiated from other *O. mykiss* and also
displayed low genetic diversity estimates, high pairwise Fst,
likely because they are small isolated populations with low
effective size and limited gene flow (Nielsen et al. 1999;
Simmons et al. 2010). Similarly, some small, isolated, south-
ern coastal populations also had very low genetic diversity
(Garza et al. 2014), as well as introgression or complete
replacement of wild population by hatchery fish (Abadía-
Cardoso et al. 2016).

Conclusions

The suite of novel microhaplotype markers described here
provides a high-throughput amplicon sequencing approach
to genotype large numbers of individuals for applications
in monitoring and fisheries management, as well as having
potential utility for parentage and kinship analysis. These
markers offer high resolution for phylogeographic and other
genetic analysis in a genotyping by sequencing framework
and provide a population genetic baseline of California *O.
mykiss* on which future studies can expand.

**Supplementary Information** The online version contains supplementary material available at https://doi.org/10.1007/s12686-024-01374-2.

**Acknowledgements** This work was supported by the NOAA Southwest Fisheries Science Center, Interagency Agreement (IA) R15PG0006 between the U.S Department of the Interior, Bureau of Reclamation and the National Marine Fisheries Service, ISblue project, Interdisciplinary graduate school for the blue planet (ANR-17-EURE-0015), and by a grant from the French government under the program "Investissements d'Avenir". The authors thank Eric Anderson for help with primer design and for reviewing the draft manuscript.

**Author contributions** LeGall, Barthelemy, Clemento, Rodzen, Garza, and Pearse designed the study; Rodzen, Garza, and Pearse provided biological materials; Columbus, Campbell, Correa, Le Gall, and Barthelemy conducted laboratory work and analyzed the sequence and marker data; Le Gall, Barthelemy, and Clemento conducted the population genetics analyses and made figures; Le Gall, Barthelemy, and Pearse wrote the manuscript. All authors approved the final manuscript.

Declarations

**Conflict of interest** The authors declare no competing interests.

**Open Access** This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

<figure><img src="image_8.png" /></figure>
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