OCS Study

BOEM 2020-062

US Department of the Interior

Bureau of Ocean Energy Management

Environmental Studies Program

Using Advanced Population Genomics 

to Better Understand the Relationship 

Between Offshore and Spawning 

Habitat Use for Atlantic Sturgeon
OCS Study

BOEM 2020-062

US Department of the Interior

Bureau of Ocean Energy Management

Environmental Studies Program

Using Advanced Population Genomics 

to Better Understand the Relationship 

Between Offshore and Spawning 

Habitat Use for Atlantic Sturgeon

January 2020

Authors:

David C. Kazyak, Aaron Aunins, Robin Johnson, Barb Lubinski, Michael Eackles, and Tim L. King

Prepared under IAA No. M15PG00044

By

U.S. Geological Survey Leetown Science Center

11649 Leetown Road

Kearneysville, WV 25430
DISCLAIMER

This study was funded, in part, by the US Department of the Interior, Bureau of Ocean Energy 

Management (BOEM), Environmental Studies Program, Washington, DC, through Interagency 

Agreement Number M15PG00044 with the US Geological Survey (USGS) Leetown Science Center. This 

report has been technically reviewed by BOEM, and it has been approved for publication. The views and 

conclusions contained in this document are those of the authors and should not be interpreted as 

representing the opinions or policies of BOEM. Any use of trade, product, or firm names is for descriptive 

purposes only and does not imply endorsement by the U.S. Government.

REPORT AVAILABILITY

To download a PDF file of this report, go to the US Department of the Interior, Bureau of Ocean Energy 

Management Data and Information Systems webpage (http://www.boem.gov/Environmental-StudiesEnvData/), click on the link for the Environmental Studies Program Information System (ESPIS), and 

search on 2020-062. The report is also available at the National Technical Reports Library at 

https://ntrl.ntis.gov/NTRL/.

CITATION

Kazyak DC, Aunins A, Johnson R, Lubinski B, Eackles M, King TL. 2019. Using advanced population 

genomics to better understand the relationship between offshore and spawning habitat use for Atlantic 

sturgeon. Sterling (VA): US Department of the Interior, Bureau of Ocean Energy Management. OCS 

Study BOEM 2020-062. 70 p.

ABOUT THE COVER

The cover image is an ordination plot showing the relationships among nine populations of Atlantic 

sturgeon as resolved from 118 nuclear single nucleotide polymorphism (SNP) markers using discriminant 

analysis of principal components. 

ACKNOWLEDGMENTS

Our foray into sturgeon genomics owes its existence to Dr. Tim King, who had the vision to propose this 

initiative and led work on the project during its early stages. Unfortunately, he passed away unexpectedly 

before the research was completed. We also thank the numerous sturgeon researchers who submitted 

tissue samples to the Atlantic Coast Sturgeon Tissue Research Repository, including Jason Kahn, 

Amanda Higgs, Mark Fritts, Matt Fisher, Gayle Zydlewski, and Bill Post, without whom this work would 

have been intractable. In addition, we specifically thank Evan Ingram at Stony Brook University for 

providing tissues samples collected in a separate BOEM-funded project (cooperative agreement 

M16AC00003) so that assignment testing could be conducted on Atlantic sturgeon from the continental 

shelf. Many other researchers were responsible for collecting samples that were used to develop our 

genetic and genomic baselines, and we thank them for their contributions. This study was funded by the 

U.S. Department of the Interior, Bureau of Ocean Energy Management through Interagency Agreement 

M15PG00044 with the U.S. Geological Survey.
i

Contents

List of Tables.................................................................................................................................................iii

List of Abbreviations and Acronyms............................................................................................................. iv

Executive Summary ...................................................................................................................................... v

1 Background and Overview of Study Approach.....................................................................................1

2 Marker Discovery ..................................................................................................................................3

2.1 Shotgun Sequencing for De Novo Reference Assembly...................................................................3

2.2 Double Digest RAD-seq.....................................................................................................................3

2.3 SNP Identification ..............................................................................................................................6

3 Development of a Cost-effective High Throughput Genomics Approach and Creation of a Population 

Genomic Baseline.........................................................................................................................................8

3.1 GT-seq Methods ................................................................................................................................9

3.1.1 Primer Design for GT-seq........................................................................................................9

3.1.2 Optimization Run ...................................................................................................................26

3.1.3 Production Runs ....................................................................................................................26

3.1.4 Bioinformatic Analyses...........................................................................................................31

3.1.5 Assessment of the Utility of Mitogenomic SNPs for MSA......................................................31

3.2 Results.............................................................................................................................................32

3.2.1 GT-seq Optimization Run ......................................................................................................32

3.2.2 Production Runs ....................................................................................................................32

3.2.3 GT-seq Run Summary ...........................................................................................................45

4 Population Genomics and Assessment of Marker Utility....................................................................47

4.1 Within-population Diversity ..............................................................................................................49

4.2 Among-population Diversity.............................................................................................................51

4.4 Assignment Testing .........................................................................................................................56

5 Application of Molecular Markers to Sturgeon of Unknown Origin .....................................................59

5.1 Use of Microsatellites.......................................................................................................................59

5.2 DNA Extractions and Microsatellite Genotyping..............................................................................59

5.3 Assignment Testing .........................................................................................................................60

6 General Conclusions...........................................................................................................................65

7 References..........................................................................................................................................67
ii

List of Figures

Figure 1. Number of reads partitioned by nuclear SNP containing amplicons, mtDNA SNP containing 

amplicons, and microsatellites for an initial GT-seq optimization run.........................................................41

Figure 2. Number of reads partitioned by nuclear SNP amplicons, mtDNA SNP amplicons, and 

microsatellites for the first GT-seq run of n = 96 individuals.......................................................................42

Figure 3. Number of reads partitioned by nuclear SNP amplicons, mtDNA SNP amplicons, and 

microsatellites for the second GT-seq run of n = 96 individuals. ................................................................43

Figure 4. Number of reads partitioned by nuclear SNP amplicons, mtDNA SNP amplicons, and 

microsatellites for the third GT-seq run of n = 96 individuals......................................................................44

Figure 5. DAPC plot illustrating the relationships among n = 65 Atlantic sturgeon from among 10 Atlantic 

coastal rivers genotyped at n = 40 mitochondrial SNP loci. .......................................................................46

Figure 6. Natal rivers of nine populations of Atlantic sturgeon represented in our genomic baseline........48

Figure 7. Histogram showing the frequency of deviations (P < 0.000262) from HWE expectations for 118 

nuclear SNP loci surveyed across nine populations of Atlantic sturgeon...................................................50

Figure 8. Scatterplot showing the results of discriminant analysis of principle components (axis 1 and axis 

2). ............................................................................................................................................................53

Figure 9. Scatterplot showing the results of discriminant analysis of principle components (axis 1 and axis 

3). ............................................................................................................................................................54

Figure 10. Population clusters of Atlantic sturgeon inferred using STRUCTURE (based on an assumed K 

= 2 through K = 9). ......................................................................................................................................55

Figure 11. Results of an ad hoc sensitivity analysis comparing the performance of varying numbers of 

nuclear SNP loci and microsatellite loci for assigning Atlantic sturgeon their natal river using GeneClass2.

............................................................................................................................................................58

Figure 12. Visualization of stock composition of 186 Atlantic sturgeon sampled in coastal waters off the 

Rockaway Peninsula, New York. ................................................................................................................64
iii

List of Tables

Table 1. Atlantic sturgeon samples used for SNP discovery........................................................................4

Table 2. Samples of 16 Atlantic sturgeon DNA subjected to double digest RAD-sequencing for the 

identification of nuclear SNPs. ......................................................................................................................6

Table 3. Primers used in the development of a GT-seq assay for Atlantic sturgeon..................................10

Table 4. Primer sequences of 12 microsatellite loci currently used by the Leetown Science Center King 

Conservation Genetics Laboratory (LSC-KCGL) for genotyping of Atlantic sturgeon................................21

Table 5. List of 49 single nucleotide polymorphisms (SNPs) identified from shotgun genomic sequences 

mapped to the complete mitochondrial DNA genome of the Atlantic sturgeon (GenBank Accession 

KP997217) by TL King................................................................................................................................21

Table 6. PCR primers used to incorporate unique 8 bp indices and Illumina flow cell adapters into Atlantic 

sturgeon amplicons.....................................................................................................................................25

Table 7. Individual Atlantic sturgeon samples and their corresponding source river run to develop a 

genomic baseline for Atlantic sturgeon.......................................................................................................27

Table 8. Summary of sequencing results for each locus that was considered for inclusion within the GTseq panel.....................................................................................................................................................34

Table 9. Collections of Atlantic sturgeon used to develop a genomic baseline..........................................47

Table 10. Within-population diversity metrics for nine populations of Atlantic sturgeon based on 118 

nuclear SNP markers..................................................................................................................................49

Table 11. Results of tests for linkage disequilibrium between 118 pairs of nuclear SNP loci conducted in 

GENEPOP. A critical test statistic of α = 0.05 was applied to each test.....................................................51

Table 12. Pairwise differentiation scores (FST) for nine populations of Atlantic sturgeon based on 118 

nuclear SNP markers..................................................................................................................................51

Table 13. Pairwise FST scores for nine populations of Atlantic sturgeon based on 12 microsatellite 

markers. ......................................................................................................................................................52

Table 14. Classification confusion matrix for genomic population assignments to river based on 118 

nuclear SNPs using the Atlantic sturgeon baseline. ...................................................................................57

Table 15. Classification confusion matrix for genomic population assignments to river based on 118 

nuclear SNPs using the Atlantic sturgeon baseline. ...................................................................................57

Table 16. Classification confusion matrix for genetic population assignments to river using the Atlantic 

sturgeon baseline........................................................................................................................................61

Table 17. Classification confusion matrix for genetic population assignments to DPS using the Atlantic 

sturgeon baseline........................................................................................................................................62

Table 18. Stock composition of 186 Atlantic sturgeon sampled in coastal waters off the Rockaway 

Peninsula, New York as part of the BOEM-funded project titled “Monitoring Endangered Atlantic Sturgeon 

and Commercial Finfish Habitat Use Offshore New York.”.........................................................................63
iv

List of Abbreviations and Acronyms

BOEM Bureau of Ocean Energy Management

bp base pair(s)

DAPC discriminant analysis of principal components

DPS Distinct Population Segment

ESA Endangered Species Act

GT-seq Genotyping-in-Thousands by sequencing

HWE Hardy-Weinberg equilibrium

LSC Leetown Science Center

LSC-KCGL Leetown Science Center – King Conservation Genetics Laboratory

mtDNA Mitochondrial DNA

PCR polymerase chain reaction

RAD-seq restriction site associated DNA sequencing

RRJ river-resident juvenile

SNP single nucleotide polymorphism

TL total length

USGS U.S. Geological Survey

VCF vCard file
v

Executive Summary

Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) are a large-bodied anadromous fish that historically 

supported important fisheries along the east coast of the United States. Following years of overharvest 

and habitat degradation, populations experienced severe declines. In 2012, the National Marine Fisheries 

Service listed Atlantic sturgeon under the Endangered Species Act (ESA; 61 FR 4722). Their listing 

named five Distinct Population Segments (DPSs), predicated on genetic groups composed of 

geographically proximate populations. 

Federal management of Atlantic sturgeon presents challenges, as sturgeon from each of the five DPSs mix 

extensively in coastal and marine habitats, and take and recovery progress must be evaluated separately 

for each unit. Genetic assignment testing based on mitochondrial and microsatellite markers allows 

individuals to be assigned back to their natal river and DPS. However, this approach is not perfect, and 

some individuals may be incorrectly assigned. Recent advances in genomics offer the potential of a higher 

resolution approach to genetic assignment testing, and thus may reduce uncertainty associated with 

assignment testing. In addition, genomics allows a greater number of markers to be examined from across 

a broader portion of the sturgeon genome, thus may provide an enhanced perspective of population 

structure for the species and potentially allow other previously intractable questions to be addressed 

(Bernatchez et al. 2017, Supple and Shapiro 2018).

We used next-generation sequencing to develop a draft genome for Atlantic sturgeon and identify single 

nucleotide polymorphisms (SNPs) that could be used to resolve the natal river and DPS of individual 

Atlantic sturgeon. We identified 1,210 candidate SNPs within the nuclear genome as well as 49 SNPs 

within the mitochondrial genome. After filtering and review, we selected 161 nuclear SNPs and 39

mitochondrial SNPs for further testing and evaluation. We used genotyping-in-thousands by sequencing 

(GT-seq) to simultaneously sequence nuclear SNP loci, mitochondrial SNP loci, and the existing panel of 

12 microsatellite loci. This effort required a pilot sequencing run on a single sturgeon sample to test 

marker amplification and refine primer strengths, followed by a series of sequencing runs to generate 

baseline data for 288 individuals representing nine populations of Atlantic sturgeon in four DPSs.

Using baseline data from the nine populations, we ran a series of genomic analyses to characterize 

diversity within and among populations, providing a benchmark for this species using the new SNP 

markers. Allelic richness was similar for all populations, although there was a general trend of more 

northern population containing greater levels of allelic richness. Interestingly, we observed linkage 

disequilibrium among many pairs of loci within many populations. This might be the result of physical 

linkage but could also suggest these populations are recovering from genetic bottlenecks and/or are 

effectively small, leading to specific haplotypes to be favored by chance. Pairwise differentiation among 

populations varied among the populations (FST range: 0.010–0.098) and was significantly correlated (r = 

0.771; P < 0.001) to pairwise FST observed using microsatellite markers). Population clustering and 

ordination techniques using the new genomic data both support an overall population structure that is 

similar to the current DPS management units (which were developed primarily based on microsatellite 

genetic data). Overall, this suggests that existing microsatellite markers and the panel of SNP markers 

developed in this study provide similar information about the populations structure and ecology of 

Atlantic sturgeon. Given the observed differences in allele frequencies among populations, our genomic 

baseline supports previous assertations that Atlantic sturgeon show natal homing, despite mixing 

extensively in marine waters during non-breeding periods. Lower levels of differentiation between 

populations in the South Atlantic DPS suggest that populations in this region may have greater levels of 

gene flow relative to their more northerly conspecifics, which has also previously been suggested based 
vi

on microsatellite data. The observed differentiation among populations provides the necessary foundation

for determining the natal river and DPS of Atlantic sturgeon using assignment testing.

We tested the utility of our new genomic baseline for resolving the population and DPS of Atlantic 

sturgeon. Our nuclear SNP markers showed utility for identifying the origin of unknown Atlantic 

sturgeon samples, as 86.5% were assigned to the correct DPS, and 66.3% were assigned to the correct 

natal river. However, since this study was funded, the U.S. Geological Survey (USGS) Leetown Science 

Center – King Conservation Genetics Laboratory has made significant improvements to their 

microsatellite genetic baseline, which now performs more effectively than our new genomic approach

(the genetic baseline includes 12 populations and 5 DPSs, and correctly assigns 95.8% of individuals to 

DPS and 84.9% of individuals to their natal population using 12 microsatellite loci). We conducted an ad 

hoc exploration of how additional microsatellite or nuclear SNP loci may further improve the accuracy of 

assignment testing. We found that additional microsatellite markers are likely to result in greater 

improvements in assignment efficiency than additional nuclear SNPs. However, a much larger number of 

SNP loci (which if identified could be sequenced using other methods that are now available; e.g., the 

RAD-Capture approach published by Ali et al. 2016) could produce assignment efficiencies that are 

greater than what is currently feasible using microsatellites. In the absence of further research and 

development of additional SNP markers for Atlantic sturgeon (possibly using an approach other than GTseq), the existing microsatellite loci are the most effective means available to determine the natal river 

and DPS of Atlantic sturgeon encountered in offshore waters.

Because our new genomic markers were less effective than the existing panel of 12 microsatellite 

markers, we chose to use the existing microsatellite markers to assign Atlantic sturgeon captured in 

another study funded by the Bureau of Ocean Energy Management (BOEM) (cooperative agreement 

M16AC00003; “Monitoring Endangered Atlantic Sturgeon and Commercial Finfish Habitat Use Offshore 

New York”) following consultation with our project officer at BOEM. Using this approach, we genotyped 

and assigned 186 Atlantic sturgeon captured in coastal waters off the Rockaway Peninsula, New York. 

The vast majority of these sturgeon were assigned to the New York Bight DPS (94.62%), and most 

appear to belong to the Hudson River population (87.10%) with smaller contributions from the Delaware 

River population (7.53%). Smaller contributions (≤ 2.15%) were observed from six other populations, 

including those from the James, York, Kennebec, Ogeechee, and Edisto rivers. Although most of the fish 

we assigned were assigned to the nearest spawning rivers (Hudson and Delaware), the contributions from 

distant rivers is consistent with the propensity of this species to move long distances and form mixed 

stock aggregations along the continental shelf. This finding indicates that spawning populations (and their 

corresponding DPS) from distant locations may potentially be impacted by offshore activities. In fact, 

activities in this region of the New York Bight could negatively impact Atlantic sturgeon population from 

at least four different DPSs. Genetic or genomic assignment testing remains an essential tool to 

characterize potential impacts to Atlantic sturgeon populations and could be applied more broadly to 

better characterize potential impacts of activities in other locations. 
1

1 Background and Overview of Study Approach

Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) are a large, ancient fish native to the Atlantic coast 

of North America. As an anadromous species, Atlantic sturgeon spawn in freshwater over rocky substrate 

in rivers from the southeastern U.S. to maritime Canada (Hilton et al. 2016). Although juvenile sturgeon 

are confined to their natal rivers and estuaries, sub-adult and adult Atlantic sturgeon spend the majority of 

their lives in nearshore coastal areas and the continental shelf, where they are known to exhibit long 

distance migrations and occupy habitats far from their natal rivers. The species is long-lived and late to 

mature, and does not spawn annually. The life history of Atlantic sturgeon makes them vulnerable to 

numerous anthropogenic impacts.

Historically, Atlantic sturgeon supported important fisheries for meat and caviar. Archaeological evidence 

suggests that sturgeon were important to early European settlers as a food source (Bowen and Andrews 

2000) and an early commercial export (Balazik et al. 2010). Harvest of Atlantic sturgeon peaked in the 

late 1800s, when 3,200 metric tons were reported to have been harvested in a single year (Secor 2002). 

By 1901, Atlantic sturgeon populations had crashed coastwide (US Commission of Fish and Fisheries 

1884–1905). Landings continued at greatly reduced levels through most of the 20th century and

populations remained severely depleted. By the mid-1990s, many populations were thought to be at 

historic lows.

As the result of dramatic population declines and continued harvest pressure, many states enacted and

implemented harvest restrictions or moratoria in their jurisdictions. In 1998, the Atlantic States Marine 

Fisheries Commission established a coastwide moratorium on the sturgeon fishery in U.S. waters. In 

2012, the National Marine Fisheries Service enacted further legal protections when it listed Atlantic 

sturgeon under the Endangered Species Act (ESA). The final rules established five Distinct Population 

Segments (DPSs; USFWS and NMFS 1996), based on distinct genetic and physical characteristics that 

had been described in a previous status review (ASSRT 2007). The New York Bight, Chesapeake Bay, 

Carolina, and South Atlantic DPSs were each listed as endangered, whereas the Gulf of Maine DPS was 

listed as threatened. 

Under the ESA, DPSs are essentially managed as different species. This presents a significant challenge 

for Atlantic sturgeon (75 FR 61904), as sub-adult and adult Atlantic sturgeon frequently form mixed

stock aggregations in marine and estuarine waters (Waldman et al. 1996, Dunton et al. 2012, Wirgin et al. 

2015). Consequently, non-breeding sub-adult and adult individuals can only be assigned to their DPS 

using molecular genetic techniques.

To date, most molecular work on Atlantic sturgeon has employed a modest number of microsatellite 

markers (described in May et al. 1997, King et al. 2001, Henderson-Arzapalo and King 2002) to assess 

population structure and run individual-based assignment tests. This approach provided the underlying 

information that supported the establishment of DPSs and has proven fairly useful in addressing applied 

conservation questions. Notably, the USGS Leetown Science Center (LSC) – King Conservation Genetics 

Laboratory (LSC-KCGL) has developed a microsatellite baseline comprised of over 1,600 individuals 

from 12 rivers. This genetic baseline can be used to assign Atlantic sturgeon to their natal area (DPS or 

river) and provides much needed information to help assess the impacts of specific permitted activities on

each management unit. This genetic baseline (as well as a similar baseline produced by New York 

University) has provided the basis for many studies characterizing the composition of mixed stock 

aggregations and describing patterns of population structure and genetic diversity (Grunwald et al. 2008, 

Waldman et al. 2013, O’Leary et al 2014, Wirgin et al. 2015).

Despite considerable research and management attention, Atlantic sturgeon continue to face a broad suite 

of threats to their recovery, and these most likely vary by DPS. Sturgeon are taken as incidental catch in 
2

commercial fisheries and may be killed by ship strikes (NMFS 2010). Dredging activities and marine 

construction can also result in sturgeon mortality (NMFS 2010). Recent interest in the development of 

offshore energy resources on the continental shelf raises the importance of understanding potential 

impacts of development activities on this federally listed species. Until recently, Atlantic sturgeon were 

thought to primarily use shallow inshore habitats while in marine environments (Stein et al. 2004). 

However, recent telemetry work funding by the Bureau of Ocean Energy Management (BOEM)

demonstrates that Atlantic sturgeon use offshore wind energy areas extensively during certain periods

(Ingram et al. 2019), and the stock composition of these fish is largely unknown. An improved 

understanding of the makeup of Atlantic sturgeon using offshore areas will help managers understand the 

impacts of development activities and enhance recovery and monitoring efforts (NMFS 2010).

Rapid advances in conservation genomics offer new opportunities to provide conservation relevant 

information to support the management of fishery resources. In particular, new technologies allow 

sequencing vast amounts of DNA sequence at reasonable costs, offering unprecedented insight into the 

molecular underpinnings of living organisms. For conservation practitioners, these new approaches offer 

the prospect of much larger numbers of loci from throughout the genome, including not only neutral 

markers but also those that may be under selective pressures. The current study was designed to leverage 

recent advances in genomics to develop new molecular tools to support the management of Atlantic 

sturgeon. A genomic approach could provide a higher resolution tool to characterize mixed stock 

aggregations, reveal undetected population structure, and provide insight into patterns of natural selection.

Our approach to develop genomic tools for Atlantic sturgeon was based on four key components:

1. Marker discovery

2. Refinement of a cost-effective higher throughput genomics approach

3. Generation of a genomic baseline

4. Application to select set of BOEM-funded samples to demonstrate proof of concept

For the marker discovery component, we used two approaches. First, we performed shotgun genomic 

sequencing to generate a draft reference genome to assist with identifying the locations of the single 

nucleotide polymorphism (SNP) loci to be ascertained and to provide flanking sequence to the SNPs for 

the design of primers for polymerase chain reaction (PCR) amplification. Next, we sequenced a small 

number of individuals (collected by cooperating scientists from other agencies) from select populations 

across the native range of the species using double digest RAD-seq for mapping against the new draft 

reference genome to identify a suite of candidate SNP loci that were variable among populations and 

functionally diploid. To develop these SNPs into a cost-effective approach for genotyping large numbers 

of individuals for population scale work, we operationalized a genotyping-in-thousands by sequencing 

(GT-seq; Campbell et al. 2014) technique based on these markers. With this new approach, we genotyped 

hundreds of Atlantic sturgeon at the new loci we identified to create a representative baseline for 

populations across the eastern U.S. We conducted basic population genetic analyses using this new class 

of markers and evaluated the effectiveness of our genomic approach for assigning individuals to their 

population of origin. Although we initially intended to apply our new genomic approach to provide 

assignments for samples collected in another BOEM-funded study, our preliminary results suggested that 

we were unable to attain comparable levels of assignment success using the new GT-seq panel, and many 

more SNP loci would likely be necessary to achieve the assignment power of the current microsatellitebased approach. As a result, we consulted with BOEM and instead applied the existing microsatellite 

markers to Atlantic sturgeon collected by Stony Brook University in marine waters near Long Island, 

New York. 
3

2 Marker Discovery

As part of our research plan to identify candidate SNP markers for studying Atlantic sturgeon, we first 

generated a draft genome using individuals from multiple populations across their native range. The 

purpose of this draft assembly was to provide a genomic reference for mapping short reads from RAD-seq 

libraries against to identify candidate SNP markers. In addition, by having long contig sequences to which 

the RAD-seq reads could be mapped, we would potentially have flanking sequence that could be used for 

primer design to amplify the SNP. Following completion of the draft genome, we performed double 

digest RAD-seq on a sample of sturgeon from throughout their native range to ascertain their SNP. The 

following sections describe the construction of the reference genome, double digest RAD-seq, and 

bioinformatic processing of these data to obtain candidate SNP markers for further investigation.

2.1 Shotgun Sequencing for De Novo Reference Assembly

Several shotgun genomic libraries of Atlantic sturgeon from multiple populations were 150 base pairs 

(bp) single end sequenced on a NextSeq 500 at LSC utilizing high output cartridges (Table 1). Briefly, 1 

nanogram of DNA was used to make dual indexed libraries with the Illumina Nextera XT library 

preparation kit following the manufacturer’s instructions. Reads from each demultiplexed library were 

downloaded from Illumina’s Basespace and imported into CLC Genomics Workbench (Qiagen). CLC 

parameters for length and quality trimming included a quality limit of 0.025, ambiguous nucleotide limit 

of 1, and minimum read length of 30 bp. After trimming, all reads from among the libraries were 

combined (n = 1,014,479,716) and used for de novo assembly (automatic bubble size = yes, minimum 

contig length = 200, word size = 25, perform scaffolding = yes). This assembly yielded 1,317,463 contigs 

with an average length of 753 bp (min = 200 bp, max = 13,704 bp), and N50 = 1,104 (deposited at 

GenBank under the accession JABEPO000000000). 

When developing a draft reference genome assembly, the initial assembly may yield multiple highly 

similar contigs as a consequence of sequencing repetitive genomic regions, especially within a tetraploid 

genome like the Atlantic sturgeon. To reduce the over-representation of these repetitive regions in the 

final draft assembly, we clustered the contigs at 95% similarity using cd-hit-est (Li and Godzik 2006). 

This reduced the number of contigs to 1,215,580. 

2.2 Double Digest RAD-seq

DNA of 16 adult Atlantic sturgeon from among six rivers sampled during their respective spawning 

migrations was identified for double digest RAD-seq (Table 2). These samples were chosen because they 

span a large geographic range of Atlantic sturgeon natal rivers and should therefore represent genetic 

variation from throughout this range. 

Double digest RAD-seq library preparation and sequencing was performed by the sequencing core facility 

Admera Health, South Plainfield, NJ. Briefly, two 4 base-cutting restriction enzymes NlaIII and MluCI 

were used to digest 200 ng DNA of each sample in separate reactions at 37 oC for 2 hours, followed by 

heat inactivation of the enzyme at 65 oC for 20 minutes. Five base pair unique indexed sequencing 

adapters were ligated to the NlaIII cut site to enable unambiguous demultiplexing of each sample after 

sequencing. The size selection of adapter-ligated fragments was 410 base pairs (this includes primers and 

barcode lengths), which would result in sequenced fragments of approximately 284 bp after 

demultiplexing. Samples were 150 bp paired-end sequenced on an Illumina HiSeq. 
4

Table 1. Atlantic sturgeon samples used for SNP discovery.

Individual Population Run

Number of quality trimmed 

single end reads

AoxyDe-5097 Delaware Aoxy-GBS-3-05-07-16 14940892

AoxyDe-5099 Delaware Aoxy-GBS-3-05-07-16 393514

AoxyDe-5102 Delaware Aoxy-GBS-3-05-07-16 9957104

AoxyDe-5101 Delaware Aoxy-GBS-3-05-07-16 9315814

AoxyDe-5100 Delaware Aoxy-GBS-3-05-07-16 10088973

AoxyDe-5103 Delaware Aoxy-GBS-3-05-07-16 10844866

AoxyDe-5091 Delaware Aoxy-GBS-3-05-07-16 8435277

AoxyDe-5098 Delaware Aoxy-GBS-3-05-07-16 12143633

AoxySav-11263 Savannah Aoxy-GBS-3-05-07-16 22491929

AoxySav-11286 Savannah Aoxy-GBS-3-05-07-16 21426709

AoxySav-11275 Savannah Aoxy-GBS-3-05-07-16 13285966

AoxySav-11278 Savannah Aoxy-GBS-3-05-07-16 8404217

AoxySav-11258 Savannah Aoxy-GBS-3-05-07-16 28589038

AoxySav-11279 Savannah Aoxy-GBS-3-05-07-16 14082252

AoxySav-11251 Savannah Aoxy-GBS-3-05-07-16 13146957

AoxySav-11280 Savannah Aoxy-GBS-3-05-07-16 15987005

AOS-95 Edisto AoxyGBS-04-11-16 7861517

AOS-94 Edisto AoxyGBS-04-11-16 8760466

AOS-98 Edisto AoxyGBS-04-11-16 15741224

AOS-92 Edisto AoxyGBS-04-11-16 22144652

AOS-96 Edisto AoxyGBS-04-11-16 4387607

AOS-97 Edisto AoxyGBS-04-11-16 17904667

AOS-93 Edisto AoxyGBS-04-11-16 17746879

AOS-99 Edisto AoxyGBS-04-11-16 11661017

AOK-62 Kennebec AoxyGBS-04-11-16 36143734

AOK-59 Kennebec AoxyGBS-04-11-16 5028440

AOK-61 Kennebec AoxyGBS-04-11-16 4682542

AOK-63 Kennebec AoxyGBS-04-11-16 6093374

AOK-58 Kennebec AoxyGBS-04-11-16 1673641

AOK-60 Kennebec AoxyGBS-04-11-16 4363991

AOK-57 Kennebec AoxyGBS-04-11-16 19627774

AOK-56 Kennebec AoxyGBS-04-11-16 15332004

AOOG-102 Ogeechee AoxyGBS-04-11-16 8347448

AOOG-111 Ogeechee AoxyGBS-04-11-16 11769619

AOOG-107 Ogeechee AoxyGBS-04-11-16 9400498

AOOG-104 Ogeechee AoxyGBS-04-11-16 12288583

AOOG-112 Ogeechee AoxyGBS-04-11-16 18721556

AOOG-116 Ogeechee AoxyGBS-04-11-16 26657280

AOOG-113 Ogeechee AoxyGBS-04-11-16 6701612

AOOG-115 Ogeechee AoxyGBS-04-11-16 6996640
5

Individual Population Run

Number of quality trimmed 

single end reads

AOSL-01 Saint Lawrence AoxyGBS-04-11-16 8796548

AOSL-08 Saint Lawrence AoxyGBS-04-11-16 13705049

AOSL-06 Saint Lawrence AoxyGBS-04-11-16 9700928

AOSL-07 Saint Lawrence AoxyGBS-04-11-16 10915608

AOSL-05 Saint Lawrence AoxyGBS-04-11-16 16163180

AOSL-02 Saint Lawrence AoxyGBS-04-11-16 8874970

AOSL-03 Saint Lawrence AoxyGBS-04-11-16 9095166

AOSL-04 Saint Lawrence AoxyGBS-04-11-16 9409230

AOY-15-35 York Aox-GBS-12-02-15 6191806

AOY-15-65 York Aox-GBS-12-02-15 4327142

AOY-15-23 York Aox-GBS-12-02-15 4993244

AOY-15-48 York Aox-GBS-12-02-15 4283733

AOY-15-20 York Aox-GBS-12-02-15 3902816

AOY-15-42 York Aox-GBS-12-02-15 7039248

AOY-15-29 York Aox-GBS-12-02-15 6546380

Aoxy-6556 Altamaha Aox-GBS-12-02-15 27706376

Aoxy-6548 Altamaha Aox-GBS-12-02-15 35882449

Aoxy-6550 Altamaha Aox-GBS-12-02-15 49593457

Aoxy-6555 Altamaha Aox-GBS-12-02-15 82440135

Aoxy-6553 Altamaha Aox-GBS-12-02-15 54294381

Aoxy-6554 Altamaha Aox-GBS-12-02-15 45526251

James_Spring-5 James Aox-GBS-12-02-15 3359337

James_Fall-5 James Aox-GBS-12-02-15 5178678

AOH-96-13 Hudson Aox-GBS-12-02-15 20096662

AOH-96-11 Hudson Aox-GBS-12-02-15 5982964

AOH-96-04 Hudson Aox-GBS-12-02-15 7503405

AOH-96-15 Hudson Aox-GBS-12-02-15 10368553

AOH-97-06 Hudson Aox-GBS-12-02-15 8897969

AOH-97-09 Hudson Aox-GBS-12-02-15 4351801

AOH-97-01 Hudson Aox-GBS-12-02-15 8487941

AOH-97-05 Hudson Aox-GBS-12-02-15 7293398
6

Table 2. Samples of 16 Atlantic sturgeon DNA subjected to double digest RAD-sequencing for the 

identification of nuclear SNPs.

Natal River Collection Year Leetown Science 

Center ID

Admera ID Concentration 

(ng/μl)

St. Lawrence 2013 AOSL-13-012 AOX_01 56.0

St. Lawrence 2013 AOSL-13-027 AOX_02 78.2

Kennebec 2010 AOK-051 AOX_03 21.1

Kennebec 2011 AOK-064 AOX_04 26.1

Hudson 2015 AoxyH15-9167 AOX_05 62.3

Hudson 2015 AoxyH15-9168 AOX_06 44.0

Hudson 2015 AoxyH15-9171 AOX_07 56.1

Hudson 2015 AoxyH15-9173 AOX_08 108.0

James 2013 AoxyJ13-9633 AOX_09 88.2

James 2012 AoxyJ12-9639 AOX_10 74.0

James 2012 AoxyJ12-9640 AOX_11 83.5

James 2012 AoxyJ12-9643 AOX_12 95.4

Edisto 1998 AOE-108 AOX_13 20.6

Edisto 1998 AOE-109 AOX_14 22.8

Altamaha 2011 AoxyALT11-6789 AOX_15 68.8

Altamaha 2011 AoxyALT11-6824 AOX_16 157.0

Notes: Samples were sequenced by the provider Admera Health, South Plainfield, NJ. Please refer to the text for 

details of library construction.

Two sequencing runs of the same libraries were ultimately performed to reach the minimum read number 

threshold set by Admera Health. The first run produced 133,891,708 read pairs with 49,988,713 forward 

reads demultiplexed among the 16 samples. The average number of reads per sample was 3.1 million, and 

the standard deviation was 1.5 million. For the second run, the total number of read pairs was 94,097,618 

with 92,177,684 reads demultiplexed among the 16 samples. The average number of reads in the second 

run was 5.8 million, and the standard deviation was 0.6 million. When the reads for each individual were 

combined across runs, the average number of reads per sample increased to 8.9 million, and the standard 

deviation was 1.4 million. 

2.3 SNP Identification

The result of the double digest RAD-seq libraries was large numbers of genomic fragments beginning 

with the NlaIII cut site on the forward read of each pair. To identify SNPs within these tags, we first 

sought to reduce the tags to only a set of those that could be mapped to a contig in our draft sturgeon 

genome assembly. The main reason for this was to place each tag within the larger contig sequence from 

which they were derived, allowing more opportunity for primer design in a high throughput SNP 

genotyping assay. For example, if an SNP within a tag is close to the 5` or 3` end of the 150 bp RAD-seq 

fragment, there is limited opportunity for primer development, whereas if the contig sequence the tag 

maps to is available, potentially a few hundred bases of contig sequence may be used for primer design. 

To accomplish this, all tag reads from the 16 sturgeon across both sequencing runs were clustered into 

provisional loci using a pairwise-identity threshold of 85% in the program Vsearch (Rognes et al. 2016). 

Representatives of each cluster were then aligned to the draft sturgeon genome contigs using BLASTN, 

resulting in 157x103 tags. Only these tags that mapped to the draft genome were retained for subsequent 

SNP identification.

Variable sites within tags were identified by mapping all reads to the cluster representatives identified by 

Vsearch using bowtie2 (Langmead and Salzberg 2012) with the fast and local parameter switches and a 

minimum mapping quality of 30 (phred scaled). The samtools mpileup function (Li et al. 2009) was then 

used to tabulate the base counts occurring at each position and their statistical distributions, with base 
7

alignment qualities recalibrated using the ‘-E’ flag in mpileup. Variant sites were then discriminated from 

sequence error using the bcftools call function and multiallelic calling model (Li et al. 2009). However, 

given the functional diploidization of the Atlantic sturgeon genome (Ludwig et al. 2001), ultimately only 

bi-allelic variants were retained. Additional quality filters included the exclusion of sites within three 

bases of an indel (due to potentially reduced alignment accuracy near indels), exclusion of sites within 

five bases of either end of a tag (also due to potentially reduced alignment accuracy), and exclusion of 

tags with more than three variant sites (due to potential mapping bias against the most diverged 

haplotypes). Finally, only loci with a minor allele frequency of ≥ 5% within the read pool were retained, 

as low frequency alleles are more difficult to discriminate from error, and the autosomal sample size 

limited the theoretical minimum allele frequency to 1/32 (~3%). Application of these additional quality 

filters resulted in a set of 1,210 candidate SNP loci. 

A histogram of minor allele frequencies among these 1,210 loci indicated an over-representation of SNPs 

with intermediate allele frequencies, suggesting undetected paralogy. Paralogous alignments, in which 

similar but non-allelic sequences are mistakenly treated as orthologous, is a significant source of error for 

SNP detection in polyploid organisms, even if the organism is functionally diploid (Clevenger et al. 

2015). We therefore performed additional analyses of locus coverage, as well as comparisons with known 

population structure from existing microsatellite-based population genetic analyses, to further restrict the 

number of candidate SNPs. The goal of this additional filtering was to identify loci that had similar 

coverage ranges in test populations and gave estimates of genetic distances between populations 

comparable to existing microsatellite data. 

We first used a subset of the sequence data previously generated by shotgun sequencing for our de novo

reference assembly to estimate allele frequencies at the 1,210 candidate loci after grouping the sequenced 

samples into eight populations (Kennebec, Hudson, Ogeechee, Satilla, Saint Lawrence, Savannah, 

Delaware, and York). These shotgun genomic reads were mapped against the contigs of the reference 

assembly containing the 1,210 candidate SNPs with bowtie2 using the sensitive and local parameter 

switches and a minimum phred-scaled mapping quality of 20. SAMtools mpileup was performed as 

before to generate allele counts for each variant. Based on these results, we removed 90 SNPs with 

excessive or low coverage within each population. Next, we added sequence data from the RAD-seq runs 

of four populations for which there were comparable shotgun genomic data from the same populations 

(Hudson, Kennebec, Saint Lawrence, Savannah). Balanced coverage in all four populations was found for 

625 SNPs (i.e., within the range 15–30X, which was chosen subjectively based inflection points as well 

as the number of samples from each population). We then calculated Nei’s pairwise genetic distance (Nei 

1972, 1978) for these 625 SNPs and compared them to the same metric for previously estimated 

microsatellite allele frequencies for those populations. Because the most variable 161 SNPs (i.e., with 

allele frequency coefficient of variation ≥ 0.5) showed somewhat greater concordance with microsatellite 

patterns than did the entire set of 625, these were chosen for development of a high throughput SNP 

genotyping assay. 
8

3 Development of a Cost-effective High Throughput Genomics 

Approach and Creation of a Population Genomic Baseline

The next step in our study required the genotyping of large numbers of Atlantic sturgeon at the SNP loci 

we identified and selected for further analysis. Although double digest RAD-seq can be used for 

repeatable genotyping of specific loci if enzyme digestion and size selection of fragments are precisely 

controlled in each sequencing run, these conditions are not always feasible in practice. Consequently, 

some loci may have varying levels of recovery across runs leading to missing data and analytical 

challenges. Therefore, RAD-seq is usually employed for initial identification of a large number of loci, 

which are then ported over to a different sequencing methodology for genotyping a large number of 

individuals for population genomic analyses, especially when sequencing needs to be conducted 

repeatedly over longer periods of time and conducted at multiple laboratories.

In order to develop a tractable method for repeatably genotyping large numbers of Atlantic sturgeon at the 

SNP loci we identified, we reviewed the scientific literature and evaluated a suite of different published 

methods. We considered chip-based assays, capture-based approaches, methods that focus on restriction

site associated loci, and techniques that use multiplexed amplicon sequencing. Key factors in our review 

included the number of loci that could be assayed, repeatability of sequencing specific loci, cost per 

sample, and flexibility in running batches containing different numbers of samples. A viable approach 

needed to be reproducible, scalable, and cost-effective. SNP “chips” using microarray technology (e.g., 

the ‘ssalar01’ Affymetrix array for Atlantic salmon SNP genotyping; Houston et al. 2014) and

hybridization probe based sequencing technologies (e.g., RAD-Capture; Ali et al. 2016) are efficient 

means of genotyping thousands of SNP loci in hundreds of individuals but require a substantial up-front 

investment in array or probe design. Because we had identified 161 SNP loci for additional investigation 

and needed to first generate more data to assess their utility before application to thousands of individuals, 

we chose to develop a genotyping method called genotyping-in-thousands by sequencing (GT-seq; 

Campbell et al. 2014) for Atlantic sturgeon that would be readily repeatable and cost less than developing 

a custom microarray or designing hybridization. 

GT-seq is an SNP genotyping method developed by Campbell et al. (2014) that leverages highly 

multiplexed PCR and massively parallel sequencing to genotype a modest number (~50 to ~500) SNP 

markers at a reasonable cost per sample. Briefly, primers for all loci that flank either side of each SNP 

locus are designed and combined into a primer cocktail. These primers contain adapters that enable 

incorporation of unique barcode indices and sequencing adapters in subsequent PCR steps to allow 

multiple samples to be pooled and sequenced on a massively parallel sequencer (Illumina platform, Ion 

Torrent, etc.). We developed our GT-seq approach using an Illumina MiSeq instrument for sequencing, as 

this platform is in widespread use and is the most cost-effective sequencing option for the number of 

samples anticipated. Barcode design and indexing strategies can be tailored to fit the level of 

multiplexing, throughput, and coverage desired. We decided to employ GT-seq for the genotyping of 288 

Atlantic sturgeon representing nine populations at 161 loci to generate a genomic baseline for individual 

assignment to DPS and river of origin. These individuals were selected from samples that were already 

included as part of the microsatellite baseline at LSC, and they represent individuals captured in spawning 

rivers as either river-resident juveniles (< 500 mm total length [TL]) or adults (≥ 1,500 mm TL).

As a novel adaptation of the GT-seq method, we sequenced three classes of markers simultaneously: 

nuclear SNPs, mitochondrial SNPs, and microsatellite loci. If tractable, this approach would allow 

inferences to be made across all three classes of markers, thus providing a comprehensive picture of 

population structure and maximizing the amount of information available for assignment testing.
9

3.1 GT-seq Methods

3.1.1 Primer Design for GT-seq

Contigs containing the 161 selected nuclear SNPs were input into the online primer design program 

BatchPrimer3 (You et al. 2008), which designs large numbers of PCR primers based on user defined 

parameters. Although this program designs primers that can be used in a multiplex, it does not explicitly 

evaluate interactions among all possible primer pairs in the multiplex (e.g., heterodimers). As a result, 

amplification success of each locus can only be assessed through actual PCR and sequencing. We sought 

to design primers that flanked either side of the candidate SNP marker, creating PCR amplicons of 200 bp 

or less. A maximum of up to 200 bp or less on each side of the SNP was excised from each contig for 

input into BatchPrimer (in some cases, the SNP was less than 200 bp from the one of the contig ends, in 

which case all of the flanking sequence was excised on that side). Settings for BatchPrimer3 were as 

follows: max amplicon size = 200 bp, min amplicon size = 50, min primer Tm = 55, max primer Tm = 65, 

max self-complementarity = 4, max 3` self-complementarity = 2, max poly X = 3. Using these settings, 

153 primer pairs were successfully designed that met our design parameters (Table 3). Primers for the 

remaining eight loci were unable to be designed within the parameters we set within BatchPrimer and 

therefore were not included in the GT-seq panel. 

In addition to the new nuclear SNP markers, we also decided to include the existing full panel of 12 

microsatellite loci used for Atlantic sturgeon assignment testing (Table 4), as well as newly designed 

primers for amplifying several of the mitochondrial SNPs previously identified by T.L. King (Table 5) to 

increase the potential utility of the GT-seq panel. Because all allele sizes for these 12 microsatellite loci 

were already 350 bp or less, the primers for these loci were not redesigned as paired-end 250 bp or 300 bp 

sequencing should recover the full microsatellite alleles. Also, it should be noted that the annealing 

temperatures of these microsatellite primers were largely different than those specified for the primers 

designed in BatchPrimer, and therefore their amplification success in the multiplex was not optimized. 

Seven mtDNA primer pairs were designed in BatchPrimer3 using the same settings as for the nuclear 

SNPs to amplify most of the SNPs identified by T.L. King within nad1, tRNAIle, tRNA-Gln, cox1, atp6, 

nad4L, nad5, and the control region (Table 5). 

In order to sequence multiple samples in one high throughput sequencing run and be able to subsequently 

demultiplex these reads into individual samples, each sample needs to have a unique index (or barcode) 

attached to its reads. We employed a two-step approach for dual indexing of each sample for multiplex 

sequencing on an Illumina MiSeq (Glenn et al. 2016). First, each primer pair (all nuclear SNPs, 

microsatellites, and mtDNA SNP primers) was synthesized with the sequence 

‘CGACAGGTTCAGAGTTCTACAGTCCGACGATC’ on the 5` end of the forward primer and the 

sequence ‘GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT’ on the 5’ end of the reverse primer. 

After an initial PCR amplification step consisting of all primers in the multiplex, all amplicons had these 

adapter sequences incorporated into them. Next, the actual dual indices and flow cell adapters are added 

in another PCR cycle with the primer 

‘AATGATACGGCGACCACCGAGATCTACACXXXXXXXXCGACAGGTTCAGAG*T’ as the 

forward primer and ‘CAAGCAGAAGACGGCATACGAGATXXXXXXXXGTGACTGGAGTTCA*G’ 

as the reverse primer, where ‘XXXXXXXX’ denotes a unique 8 bp barcode sequence and the ‘*” denotes 

a phosphorothioate linkage added during primer synthesis. We used 8 unique forward indexed primers 

and 12 unique reverse indexed primers (Table 6), which results in the ability to dual index and sequence 

96 samples in one sequencing run. 
10

Table 3. Primers used in the development of a GT-seq assay for Atlantic sturgeon.

Contig

SNP 

position Primers

Amplicon 

size 

(without 

adapters)

contig_767 1527 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGCCATTACAGAACATCAGTCACA 129

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTGCAGATGGAGATGCTACCT

contig_2585 1869 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCGCTGTGATGTCAGTGTTGG 158

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAGCAGGTGGGAGCTAACTGA

contig_6443 339 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAGATGTGCCAATTAAAGCAA 200

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAGAGAATGGGTATTGCACAGC

contig_8889 1236 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAAGGTTTGCTTGGGACAGC 153

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCAGAACCCTGTGTATGTGCTG

contig_17037 1619 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCACCTGGCATGGCTGAAAG 200

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCGGATGAAGCGCCAATC

contig_24799 775 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGCGAAAGCAGAAGCAAGAAA 142

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGACTCCTATTGCATCGCTGTT

contig_31935 606 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAAAGGTATAGCCCATTTGCTT 181

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAAATAAAGTGAGTGGAAAGTATAGGT

contig_39444 2584 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCACAGAAAGTGAAGATTGCATGA 190

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCCTTCATCAGTCGGGAAAGT

contig_44181 571 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTGTTCCCAAAGGCAAGTCTC 195

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCTCCTGAATTGTATATGGCACAA

contig_48956 96 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTCAATGCTGTGTTGGTTGCT 160

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGGTCCAGATCAGCGTGAAAC

contig_61910 325 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCACCACAATTCTCAGCCAAGC 175

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGTACCTCTTGCTGCGATGC

contig_74051 490 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTGTTTCATTCCTTGACATTTCTG 178

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCTCAGGCCAGGATAATTGCT

contig_75258 616 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGGTATAACCTCTGGCGTCACT 200

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTCATTTCCATCCCTGTTGGT
11

Contig

SNP 

position Primers

Amplicon 

size 

(without 

adapters)

contig_80054 1773 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCGGTTCCACTTTAGTTGCTTG 134

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCCGGGACATTATTCTCTTGA

contig_86226 859 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTCAACTTCTATGTTCCAGGCATT 183

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTGAAATGCAGATGAGGTTGG

contig_88389 951 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTGGCAGTGAGACAGCACTTT 191

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTGCCTCAAATACCGAACCTT

contig_91914 6072 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTGAATGGCTATCCCAGAGAA 81

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAAGCGTCCGTGCTTACTGAT

contig_93145 774 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCTAGGGATACTGCAATCAATAAA 195

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCCACACAAAGACAGCGATCA

contig_93920 151 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGTTGGCCTGCCTTGATGA 174

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCTTCTCCCTGTACCCGTCAA

contig_98816 288 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGCTCTCTGGTTTCCCATGTA 170

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAAATCCAGAATCCAACACCAGT

contig_102467 514 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTATGTTTCAATGCGGGAAGC 199

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGCCAGAACTAAAGGACAGTAATCAG

contig_119516 2249 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTTTGCTGACATGCTGAGGTG 159

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAGAGAGTGGCAGAGGAGAGC

contig_124134 2224 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTCAGGGTCGTTACTGGCTTC 158

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTGCAGTCGGGATTAGCTCTAT

contig_124581 2348 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTTGTTATTAGCTCACCATGCAA 168

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCTCAATGTATGCTCCGCTGT

contig_128545 659 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTTCCCTGCTGAAGAAGTGTG 175

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGGTGCATGACTTTGCAGATTT

contig_145014 469 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCCTCCACCACAAGGGTACAA 195

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGAGCAGGCATGAAAGTGAAA

contig_146123 3698 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGATCCCAACGATTCAGCAGT 190

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGCCAATCTGAAACTCCAACC
12

Contig

SNP 

position Primers

Amplicon 

size 

(without 

adapters)

contig_154779 984 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAACGGTAAGGGTCATGCAAA 183

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCCACCGAGATGATGAAGGAG

contig_155051 532 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTCTGATTAAAGTGTCCGATAGCA 173

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGCTGCTGTGTGGAGTCTTGT

contig_159128 3981 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGGGTATCATCCATTCCCATC 199

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTGGTACAAAGTCTCGGCAAT

contig_167621 382 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTGGCTGACTCCTCAAGCAAT 187

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCCAGCTCATAGCCATTCACA

contig_168922 1110 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCTGAATCTGGCCTCTTACCC 168

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAGAGGGAGCCAAGATGCTTT

contig_176276 1833 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTGCTGTCAAGATGCAGAAAGA 144

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTGCTTTGAGAAACATGCCTTT

contig_182703 529 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAGTGGGTGAAATGGATGGAG 187

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAGCAGCCCTTGTGCTACATT

contig_195684 2875 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGTTCAGAGCAATCCAACAAT 197

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCACACAGCACAGCGAGCAG

contig_197214 688 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGCACGCTCACTGCTGATAAA 111

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTCCGATAACGTCAAACAGGA

contig_198579 649 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTTTCTTGCGTCTGGATTACAA 164

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAAACACGCCCAGGTGAGTAA

contig_198706 212 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAAATCTGAAGCTCTTTGTGCAG 196

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTTTACCACAAACAATCGAAGGA

contig_204708 190 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGTGGGATCTACGTCCAGCAG 199

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAAATATCAAACCTTAACAGAGTACCA

contig_205045 683 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCTGAGACGGGACAGAGCAAC 160

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTTCAGGAAGTGGTTGGTGTG

contig_210463 240 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCATGAACGAGTCACGCCACAG 188

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCCCAATCAACACCATTGACC
13

Contig

SNP 

position Primers

Amplicon 

size 

(without 

adapters)

contig_211497 481 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAGGAGATTTGGACAGGCAGA 200

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCACAAGAGACGCACGTAGGA

contig_212828 664 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCTGTGAAGAGTGCAGCCAGT 195

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTTTGCACACTTTGGCACAT

contig_213642 1202 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTGCCTATTTGTGTTCTTTATGC 197

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTTCTGTCTGTATGTATGGTAGTTTGC

contig_215639 432 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGGTCTTGTAGGCTTGCATGG 198

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGCGGTTAGTGTAGCGGTTAGG

contig_223774 1536 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCCACTCTGTTCATCGGCTCT 155

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCTGCCTCCATCAACAGGATT

contig_242683 197 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTGCAATCCTTATGAGATGGTCTT 200

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGCAGTGACAAGATCCCACAG

contig_251136 85 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAAATGGTTCCACCTCGTCAG 180

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCATCGCTGGGTACTGGATGTC

contig_252163 990 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGTTGGCATCAGTCCACCAAG 156

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGCCACACACAAACGAAACAC

contig_252793 913 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCCCTGGTACTCGTTTCACTT 121

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGCCAGGGTAGAAATAACTGCAT

contig_253460 737 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCACCGTGGGCATGGATATAAA 183

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGGAAGTATGAAGGAAGGACGAA

contig_254281 573 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAGTGAAAGCCGTTGCTTACC 192

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCCCGTGTAGAACCATCATTCA

contig_256737 1907 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCTGCATCCTTCCCACCTCAT 161

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAAAGGCTCCGTCTAATTGTTGA

contig_260617 915 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGAGCACCACAACAGGCTTTA 153

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCCATGAACCCTGAAACATCC

contig_264486 310 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGTGAGAAATGCCGCCACAC 163

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGAATCGCACAGCATCCTTTC
14

Contig

SNP 

position Primers

Amplicon 

size 

(without 

adapters)

contig_266918 1436 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTCGAAATCTGCAACAACAAA 183

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCCTCAGCCCATTTGCAC

contig_270080 782 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAGCGTATTCCAGTTCATAGGG 197

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTGAGCTGAACAAACATCTGCT

contig_270624 353 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTTGTCCCATGACTTGTGTCTTT 198

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAGGGCACTTCACGAAAGACA

contig_273784 467 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGGAACCACAAACCAACAGGT 163

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTGTCGAAACTTGGGATTGTG

contig_279175 234 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTTTGTTCCTTTGGGTCATATC 196

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTCCGTTTAGGTCAATCAGCA

contig_292868 2246 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGCCATTCTTCTAGCATCGACA 200

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTGTGGTGAATGGAAGTGGAA

contig_297854 170 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTGAAACGCTGCTTCCTAGTG 199

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTGTGTGCTTGAGGTATTCAAA

contig_298604 707 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCCACCAAGCAAATGAAGCTA 199

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTCATTGCATTTGAACATAACTG

contig_299947 2425 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCATCAGCAAGGATTGATTCG 197

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTTGTGGTGAGTGTAGCATCCA

contig_300700 130 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTTTGCGCGTAGTGGATTT 200

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCGACTCAAACAGCGCAAATA

contig_304245 271 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCTTTCAGTTCCCAGGCTTCA 199

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGTGCCAATCATCCCTTACCT

contig_305031 243 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCAGACCAGCACGGTATTTGTT 169

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCATCAGTCAATAAACCCACCA

contig_309381 286 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCCCTTTGGATTTGCTAAGTGA 170

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGGAACCACTGCAAGATTTGA

contig_323589 1334 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCAAGTAAGCAACAAACTTAGAAAGG 185

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCACTGGCTGTACCGGCTATT
15

Contig

SNP 

position Primers

Amplicon 

size 

(without 

adapters)

contig_327571 242 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAACGTGTCTGTCTTTGCCAGT 165

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTGTTCCGGTGTGAACCACT

contig_335132 559 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGGCAGCATGTCCATCACTAA 177

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCACAGGTAGTCGAGGGACAGG

contig_338725 877 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTGCGGAGAGAAGAACATGAA 200

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCGGGTTCAAAGTTGTCGAAGG

contig_341935 824 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTCAGCCCTACCTAATAACAAGCA 195

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCACCACGAACTCCACTGCT

contig_343910 817 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTTGATCGGCAGGAATTAAGG 178

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCTATCCGGGCTGTCCATATT

contig_345578 752 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAAAGAACAAGCAACACAAGC 183

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCTTGTTGCTCATCTTGCACCT

contig_347490 1012 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCCCTACCCGATAACGACATGC 175

REV: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAAAGGAACGATGCGAACTTG

contig_349978 217 FWD: CGACAGGTTCAGAGTTCTACAGTCCGACGATCAACAGCAACCCACAAGATCA 188

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTAGACAGCACGGCAGAACAC

contig_351321 1199 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTGTGGTTAAAGCTGGCTGTG 154

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTACAAGGGTGAGGCGTGACT

contig_353457 745 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGGCCCATTTCTTCTTTCAGAC 185

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTATTCCCTTTGCAGGATCACA

contig_361665 430 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCATGGAGCAGGGACACC 154

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTATGCACCATGCTTTGGATT

contig_363259 920 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCAGTGATTGCTTCCCTCTGTC 182

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCAAATGAACAAGCAAAGACACA

contig_367859 97 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTGAAACACTTTAGGCAACTTATT 199

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAGATGCAGGTGTTGAAATGG

contig_369024 1194 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGCAACTTTGGTTGAGTGTGC 164

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCCAATCTGAAACATCCCAAT
16

Contig

SNP 

position Primers

Amplicon 

size 

(without 

adapters)

contig_369587 1273 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAAATAGGGAGTGCAATATGGTT 179

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCAGGGAAACAACTCAAGTCCA

contig_383472 225 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCGCCACTGCCTGTTATAGT 196

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGGCAGACAAACGCCTGATTA

contig_390428 864 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGGCTTTCACTGCCTTTGATT 192

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAAATCGCTAGGTGCCTGCT

contig_392395 284 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTCTGTAAAGTGGTGATAAAGACTGTG 199

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCCACCAGAGGAAAGGAAAT

contig_407775 155 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCAGCCCTTCTTTGCAGTTATG 159

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCAGGTGTTGGTTATGGTTG

contig_408529 378 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTTCACCAAGGAGCAGTGTTG 184

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAATTGAGCCTGCTTGCGATA

contig_411127 3955 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCCAGTGTAGCTGGTAGGTTG 123

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCACTATGCAGGGACAGGTTTC

contig_429827 739 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCGGGTAAAGGCTGCTAACAA 191

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTTAAAGGTGGCATGGGTCTT

contig_433349 1601 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCATGCAGAGAGAAGGTTCA 199

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGTTATTGGCGGGCGACCT

contig_437923 191 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTCAAATACCGTGGGACAACA 198

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCTGCACCTCAGCACCAATAG

contig_456933 540 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCAGATGATGGATGAGGCAGA 182

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAGAGAACTGGAGAGGGACAGC

contig_463504 2834 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTACCACAGGATTCTCCCATCA 200

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTACTGCCTGGTTTGTGGTGT

contig_471552 1074 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCATGCAAGAGCAGGACTCATT 199

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCTCGTGCCTTCAGCAGTGT

contig_471638 1688 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCGTCTGAGTGCATGTGTGTG 145

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTGCATCATCACAAGCTGTTC
17

Contig

SNP 

position Primers

Amplicon 

size 

(without 

adapters)

contig_479703 1283 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAGCAGAGCTTGGGAAGATGA 195

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAGCCTTCTCGTTCGCTACTG

contig_487120 1790 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGAAGCTCCATCAGGTGTTCC 199

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAACGCATGGCTTGTTAAATTG

contig_502728 1169 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGGTCAAACGAAAGTCAAACGA 200

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTACACCAGAGTACCAAATCAGG

contig_509349 450 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTCAGGGTCAATTAGTCAAGGTG 172

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAAACCTGTCTTGTGGCATTT

contig_512906 1610 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTGCTGCTGACTATGGGTTCC 190

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTGTGCATTCAAATCTCTCCA

contig_520290 1100 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAGGAAGACAGGGTGCAAAGA 193

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTACAGAAACCTGGAGCAGAGG

contig_520553 4280 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAGGACAGCCTTGAACGAGAG 200

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAACTCAACACGGCACACAAG

contig_531481 538 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAGCATCCCACGCAAACTAAC 190

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTCTGCTGAAGCCAGTTTGTT

contig_536471 444 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCTGAAGTCGCGTGGCATA 197

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAAAGAAAGCCGACATATTCCAA

contig_538841 676 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGCTGTTGTTTCAGATTAGTGCAT 156

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCAGTGGATCTGTCCCAGCTT

contig_559002 252 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGCTGCTGATTCGTGCATTT 197

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGCATGAAGTACAAAGAAACTGATTG

contig_591595 343 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGGACGATGAGGAAGATGTGG 196

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCTAAAGGAGGACCCGAAGG

contig_592686 221 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCAGTCGTGCCCTATTCAGTTT 197

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGGAGGACAACAACACTGCAA

contig_593190 224 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTATTCAACACCTGGGTCCAC 200

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAATCCATACATTTCCATTCAGT
18

Contig

SNP 

position Primers

Amplicon 

size 

(without 

adapters)

contig_596653 817 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTGCTACACCGACACACAGAA 159

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGGGCTGCTTCTCTCTGGTAA

contig_597294 908 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGGTGTGCCATCGTGAACTTT 197

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAGTCAGCTCTCCGTGTCTCC

contig_618918 1670 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGATCAGGGTGAAATCCAAACA 196

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCAAGGTTTGCGAAGGGAAT

contig_625315 831 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGATCACCCTCGCCTTAATCA 134

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGGCACTGATTAAACGGGAGT

contig_629792 1174 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTACCGTCCGCGTATCTATCAC 199

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCGTTAGGGTTCCTGGCTA

contig_630189 273 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCGACGTTGTGGAATATGAG 97

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGCTGTTTGAGATAAATACTGCACA

contig_664819 1473 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGGAGAACAAAGCGTTTCTGAC 195

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCCTTTCTGGATGCTGTGTT

contig_672725 213 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAAAGAGAAGCGACAGCCAAA 183

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTATCACCTCCGACATCAGACC

contig_675727 486 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCAGGACCAGGATTGCCTACC 134

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCAGGGAAGTATGGTCAGTCA

contig_682427 799 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCTGAGGTTGCATTGTTATTTGTG 171

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTGAACGAACCTCCTGTTTGA

contig_713657 688 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCGAGACATGAAAGCTGCAAA 138

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAAATTGCGTTGTTGGAATCA

contig_716352 70 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCAGTCTACACAGGCCAGAGC 199

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAGGCTGGAACTGGGCTAACT

contig_718337 1120 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAAAGGCCGATTATGAGTGTAAA 192

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTCATCCACACTGCTTCTCCA

contig_778708 635 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCATGCCTGACTTTGTGAAGTTT 168

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGGAGCCATGTGGGTGTATTC
19

Contig

SNP 

position Primers

Amplicon 

size 

(without 

adapters)

contig_801222 119 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTTAATTGACGTGCAGCCTTCT 142

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCAACACCTCCAGACAGACGTT

contig_805784 333 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCACAGAGCATAGCATGACCTT 170

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCTTGCTTTGCTGTTGCAGTT

contig_843891 798 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTGGAGTCATCTCTGATCTGCTG 194

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTGTGCAGACAGTGAAATGAGG

contig_845093 112 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTATTCAGGCCCGTCTGTGTC 160

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGGTTAAGCAGTGTAGGGCTGA

contig_847347 356 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAACACAAAGCCGCCACA 196

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCTGAGCAGGGCATTGTTCC

contig_847878 183 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAACTGCACACAGACCCTCGT 175

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTACCCAGAGTCCCACATGGT

contig_850177 68 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGGATTTAGCATTCCCAAGGTC 108

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCAATAATAATAACAACAGCAATAACAA

contig_852468 214 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGGGCCACCAGAGGAACC 180

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCACAGTATTTGTTCTGCATTTCTG

contig_863648 1069 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAAACTCCGTGGTGTGTGGA 156

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCACGAAGCTGCAATAAGTGG

contig_871803 747 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAAACCTCACAAAGCATCCAAG 169

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCACCATGCAGTCATTAAGCA

contig_895412 689 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTGTGAGAAACTCGTCAAAGTCG 163

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGGAGATTCAGAGAGCAGTGATG

contig_902317 288 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGCAACTGCAACATGAACAACT 200

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTGCAATCCATATTGTACCCAAG

contig_913003 698 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGGCTTTGTTTAGCCTGTTGA 164

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAGAGAAGTGAGGGCGTCGTA

contig_930173 363 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGGGACTCGGTATGGCAAAT 160

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAATGCTCTGTTTCCATTAGGC
20

Contig

SNP 

position Primers

Amplicon 

size 

(without 

adapters)

contig_932915 559 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGCAGCACACAAGACATTATAGAAG 164

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGAGACGGAGAACTGCATTGA

contig_947914 972 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCTGGAGGTGAATGGAGATG 150

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAACAGGACCACCATCTGTAGG

contig_959459 651 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCATGCCAATCGAGCTAATAA 197

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCCATATCCAGTGTCCATGC

contig_999448 97 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGGGTCAGCACTCGGTTTATG 191

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCCAGACCACGAAGTTGACAC

contig_1001037 77 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTTGAGCCCTCGCCTGTC 140

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCTTTCCTCTTGCTGGCTGAT

contig_1018361 307 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCGACGAGAAGTGAAGGTACAG 150

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTGTACCATAGGAGACATTCTGTATT

contig_1032892 120 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAAACTGCATAGTACCAAACTTGAAA 156

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTACTGATTGACCGCATCCTCT

contig_1049854 640 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGCCGAAGACAGCTACTGGAC 187

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGGGAGCTGGAAAGGAGAGAG

contig_1056163 186 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGCTGCTTCTTCTCACGAGGT 171

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCTTTACCAGATGCGCCACTT

contig_1112399 367 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCAGTGACGTGCTTGCTGTTC 147

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGCTCTAACCAGAAGCCCACA

contig_1122966 470 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGTCACCACCCTGCGTCA 187

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCGCCTGATGCTCTCTTTGAC

contig_1143132 625 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTCCATCCTTTGATTGCTTCC 159

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTAAAGGTTTAGTGGAACACAATGA

contig_1215906 151 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCACGGCAGCTACTTCCAAAC 198

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCTCGACTTGCGTGAAATAACC

contig_1299478 95 FWD: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTATGGAAAGGGTCATCTCAGC 171

REV: GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGCGAGCGAGAGAGAGAGAGA
21

Table 4. Primer sequences of 12 microsatellite loci currently used by the Leetown Science Center King Conservation Genetics 

Laboratory (LSC-KCGL) for genotyping of Atlantic sturgeon.

Locus name Forward primer Reverse primer Allelic size range

(min, median, max)

Aox12 CAGAGTCTTCCTGCAGCACTT GTGCAAAAAGAGAGTGTGTGTGTC 163, 187, 209

Aox23 GTTAGCTTAACCATGAATTGTG CAGTGTGCTAGCTTTCTCAATA 91, 100, 133

Aox45 TTGTCCAATAGTTTCCAACGC TGTGCTCCTGCTTTTACTGTC 106, 127, 166

LS-19 CATCTTAGCCGTCTGTGGTAC CAGGTCCCTAATACAATGGC 125, 146, 164

LS-39 TTCTGAAGTTCACACATTG ATGGAGCATTATTGGAAGG 113, 125, 131

LS-54 CTCTAGTCTTTGTTGATTACAG CAAAGGACTTGAAACTAGG 165, 185, 189

LS-68 TTATTGCATGGTGTAGCTAAAC AGCCCAACACAGACAATATC 115, 151, 171

AoxD44 ACCGAGTTTCAAATCAAATAGC TGAAACTGCTGTGCAATAAGAG 124, 156, 198

AoxD165 TTTGACAGCTCCTAAGTGATACC AAAGCCCTACAACAAATGTCAC 186, 202, 234

AoxD170 GAACCATTTTATTGACATTCGAC CCCTGTCTCACGTACATTTTATG 132, 148, 168

AoxD188 TGAAGTCATTGGTGATGTGTATG ATGGAAATGTTTTATGGTAATGTG 261, 309, 353

AoxD241 TGTTCACAATATAGTCTTCCAGGTC CACAACAAATCAAAACAGAAGC 178, 218, 298

Notes: The allelic size range includes the minimum, median, and maximum allele sizes observed among n = 1,658 Atlantic sturgeon genotyped from 12 

populations. 

Table 5. List of 49 single nucleotide polymorphisms (SNPs) identified from shotgun genomic sequences mapped to the complete 

mitochondrial DNA genome of the Atlantic sturgeon (GenBank Accession KP997217) by TL King.

Region Reference 

Position Type Length Reference Allele Zygosity Count Coverage Forward/Reverse Balance

Average 

Quality

12S 

rRNA 300 SNV 1 C T Hetero 83 312 0.47 34.7

ND1 3085 SNV 1 A G Hetero 863 2,685 0.47 35.0

Sturg_nd1F- CCCTGTCCAGTCTAGCTGTATACTC

Sturg_nd1R-GGGCTAGCAGTCAAATTGC 

Amplifies bp 3212–3421

Region Reference 

Position Type Length Reference Allele Zygosity Count Coverage Forward/Reverse Balance

Average 

Quality

ND1 3250 SNV 1 G A Hetero 102 322 0.49 35.6

ND1 3252 SNV 1 C T Hetero 98 315 0.42 35.4

ND1 3279 SNV 1 T C Hetero 115 320 0.46 35.4
22

Region Reference 

Position Type Length Reference Allele Zygosity Count Coverage Forward/Reverse Balance

Average 

Quality

ND1 3291 SNV 1 C T Hetero 108 312 0.47 35.5

ND1 3312 SNV 1 T C Hetero 107 298 0.48 35.2

ND1 3318 SNV 1 C T Hetero 101 286 0.43 35.0

ND1 3354 SNV 1 T C Hetero 71 186 0.38 35.5

ND1 3364 SNV 1 G T Hetero 66 163 0.42 35.2

ND1 3369 SNV 1 C T Hetero 74 174 0.42 35.3

ND1 3372 SNV 1 C T Hetero 65 164 0.42 34.6

ND1 3381 SNV 1 T C Hetero 62 248 0.37 35.4

ND1 3393 SNV 1 T C Hetero 224 303 0.40 35.3

Sturg_tRNAF CAATATAGGAATCGTGCCTGAA

Sturg_tRNAR TGGTGTAGTGGAAGCACCAA 

Amplifies bp 3852–3992

Region Reference 

Position Type Length Reference Allele Zygosity Count Coverage Forward/Reverse Balance

Average 

Quality

tRNA-Ile 3876 SNV 1 C T Homo 1,056 1,280 0.27 35.2

tRNAGln 3945 SNV 1 T C Homo 2,513 2,517 0.34 35.1

ND2 5070 SNV 1 A G Hetero 1,056 3,771 0.48 35.3

Sturg_CO1F CCGAAATTTAAACACCACCTTC

Sturg_CO1R GCTCATACTATTCCCATGTAGCC

Amplifies bp 6137–6328

Region Reference 

Position Type Length Reference Allele Zygosity Count Coverage Forward/Reverse 

Balance

Average 

Quality

COI 6186 SNV 1 A G Hetero 64 181 0.48 35.4

COI 6233 SNV 1 C T Hetero 27 93 0.22 36.0

COI 6236 SNV 1 C T Hetero 25 79 0.28 35.5

COI 6239 SNV 1 G A Hetero 27 75 0.30 35.4

COI 6248 SNV 1 G A Hetero 27 75 0.33 35.7
23

Region Reference 

Position Type Length Reference Allele Zygosity Count Coverage Forward/Reverse Balance

Average 

Quality

COI 6275 SNV 1 G A Hetero 25 97 0.36 35.5

COI 6674 SNV 1 G A Hetero 1,264 3,289 0.47 35.0

ATP6 8567 SNV 1 G A Hetero 43 118 0.21 35.0

Sturg_atp6F TAGCGTTAGGCGTTCGACTT

Sturg_atp6R GCTTGAATTATTGCTACGGCTA

Amplifies bp 8611–8782

Region Reference 

Position Type Length Reference Allele Zygosity Count Coverage Forward/Reverse Balance

Average 

Quality

ATP6 8708 SNV 1 G A Hetero 40 156 0.40 35.6

ATP6 8714 SNV 1 A G Hetero 45 168 0.42 35.0

ATP6 8717 SNV 1 C T Hetero 50 189 0.48 35.3

COIII 9266 SNV 1 T C Hetero 104 381 0.22 35.0

ND3 9698 SNV 1 G A Hetero 1,094 3,863 0.38 35.3

Sturg_nd4LF CCTCCTCTCTGCCCTTCTCT

Sturg_nd4LR GTGGTCTGTGCCGTGTGTT

Amplifies bp 10182–10574

Region Reference 

Position Type Length Reference Allele Zygosity Count Coverage Forward/Reverse Balance

Average 

Quality

ND4L 10221 SNV 1 G A Hetero 954 1,767 0.50 35.2

ND4L 10322 SNV 1 G A Hetero 77 222 0.42 35.3

ND4L 10326 SNV 1 G A Hetero 71 178 0.39 35.2

ND4L 10329 SNV 1 T C Hetero 73 170 0.49 35.3

ND4L 10332 SNV 1 A G Hetero 79 207 0.47 35.5
24

Sturg_nd5F GCCATCATTGAAGCCCTAAA

Sturg_nd5R TCAGGCAAGTCGTTTGATTG

Amplifies bp 13186–13383

Region Reference 

Position Type Length Reference Allele Zygosity Count Coverage Forward/Reverse Balance

Average 

Quality

ND5 13233 SNV 1 G A Hetero 69 176 0.39 35.1

ND5 13236 SNV 1 C T Hetero 61 174 0.46 35.9

ND5 13246 SNV 1 A G Hetero 62 149 0.39 35.3

ND5 13248 SNV 1 C T Hetero 52 147 0.42 35.3

ND5 13269 SNV 1 G A Hetero 56 142 0.36 34.7

ND5 13279 SNV 1 C T Hetero 44 125 0.36 35.4

ND5 13299 SNV 1 A T Hetero 36 99 0.39 35.6

CR 15708 SNV 1 G A Hetero 1,032 1,892 0.49 35.0

CR 15750 SNV 1 A G Homo 2,262 2,591 0.43 35.4

CR 16029 SNV 1 G A Hetero 709 2,065 0.39 34.9

Sturg_CRF ATCTGTGTCCGGCATTTGAT

Sturg_CRR TGCCATTCACTGTTTGTCCT

Amplifies bp 16105–16284

Region Reference 

Position Type Length Reference Allele Zygosity Count Coverage Forward/Reverse Balance

Average 

Quality

CR 16126 SNV 1 A G Hetero 141 321 0.36 34.7

CR 16151 SNV 1 G A Homo 865 968 0.44 35.0

CR 16206 SNV 1 A G Hetero 786 1,586 0.47 35.2

CR 16236 SNV 1 T C Hetero 1,561 2,140 0.41 35.3

Notes: Primers designed in this study to amplify certain mtDNA SNPs are inserted into the table around the SNPs they amplify, which are shaded grey. Nonshaded SNPs are not amplified.
25

Table 5. PCR primers used to incorporate unique 8 bp indices and Illumina flow cell adapters into Atlantic sturgeon amplicons.

Name Sequence (5`-3`)

D501-F AATGATACGGCGACCACCGAGATCTACACTATAGCCTCGACAGGTTCAGAG*T

D502-F AATGATACGGCGACCACCGAGATCTACACATAGAGGCCGACAGGTTCAGAG*T

D503-F AATGATACGGCGACCACCGAGATCTACACCCTATCCTCGACAGGTTCAGAG*T

D504-F AATGATACGGCGACCACCGAGATCTACACGGCTCTGACGACAGGTTCAGAG*T

D505-F AATGATACGGCGACCACCGAGATCTACACAGGCGAAGCGACAGGTTCAGAG*T

D506-F AATGATACGGCGACCACCGAGATCTACACTAATCTTACGACAGGTTCAGAG*T

D507-F AATGATACGGCGACCACCGAGATCTACACCAGGACGTCGACAGGTTCAGAG*T

D508-F AATGATACGGCGACCACCGAGATCTACACGTACTGACCGACAGGTTCAGAG*T

D701-R CAAGCAGAAGACGGCATACGAGATCGAGTAATGTGACTGGAGTTCA*G

D702-R CAAGCAGAAGACGGCATACGAGATTCTCCGGAGTGACTGGAGTTCA*G

D703-R CAAGCAGAAGACGGCATACGAGATAATGAGCGGTGACTGGAGTTCA*G

D704-R CAAGCAGAAGACGGCATACGAGATGGAATCTCGTGACTGGAGTTCA*G

D705-R CAAGCAGAAGACGGCATACGAGATTTCTGAATGTGACTGGAGTTCA*G

D706-R CAAGCAGAAGACGGCATACGAGATACGAATTCGTGACTGGAGTTCA*G

D707-R CAAGCAGAAGACGGCATACGAGATAGCTTCAGGTGACTGGAGTTCA*G

D708-R CAAGCAGAAGACGGCATACGAGATGCGCATTAGTGACTGGAGTTCA*G

D709-R CAAGCAGAAGACGGCATACGAGATCATAGCCGGTGACTGGAGTTCA*G

D710-R CAAGCAGAAGACGGCATACGAGATTTCGCGGAGTGACTGGAGTTCA*G

D711-R CAAGCAGAAGACGGCATACGAGATGCGCGAGAGTGACTGGAGTTCA*G

D712-R CAAGCAGAAGACGGCATACGAGATCTATCGCTGTGACTGGAGTTCA*G

Notes: All amplicons were previously PCR amplified with primers containing the adapter CGACAGGTTCAGAGTTCTACAGTCCGACGATC on the 

forward primer and GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT on the reverse primer. Unique indices are denoted in green, ‘F’ and ‘R’ in the primer 

name denotes forward and reverse, and a ‘*’ indicates a phosphorothioate bond.
26

3.1.2 Optimization Run

An initial trial sequence run of one sturgeon individual and the entire multiplex panel was performed to 

gauge amplification success and evenness of representation among loci. Aliquots of all 172 loci primer 

pairs (153 nuclear SNPs + 7 mtDNA SNPs + 12 microsatellite loci) were combined into a stock solution 

of 5 μM each primer. A 10 μl first step PCR reaction was set up following the protocol of Aykanat et al. 

(2016) consisting of 1X Qiagen multiplex master mix, equal amounts of forward and reverse primers at a 

final concentration of 0.054 μM, and 50 ng of genomic DNA. Thermal cycling conditions were as 

follows: initial denaturation at 95 oC for 15 min; seven cycles of 95-58-72 for 30-60-45 s, respectively;

and 15 cycles of 95-62-72 oC for 30-60-45 s, respectively. The multiplex PCR was cleaned with an equal 

volume of AmPure beads (Agencourt) and washed twice with 200 μl of 80% ethanol. The cleaned PCR 

products were eluted from the beads with 12.5 μl 10 mM Tris-HCl ph 8.5, and 10 μl was used for the 

subsequent PCR. For the second step PCR to incorporate the dual indices and Illumina sequencing flow

cell adapters, a 10.8-μl reaction was set up with 1 μl of 1:10 diluted PCR product from the first PCR, 

forward and reverse indices at a final concentration of 0.3 μM, and 1X Kapa Hifi PCR ready-mix. 

Thermal cycling conditions were as follows: initial denaturation at 95 oC for 4 min; 15 cycles of 98-60-72 

oC for 20-15-20 s, respectively; followed by a final extension step at 72 oC for 3 minutes. The library was 

subjected to a final clean up with an equal volume of AmPure XP beads, washed twice with 80% ethanol,

and eluted with 32.5ul 10mM Tris-HCl pH 8.5. The cleaned PCR product was quantified on a Qubit 

fluorometer using the high sensitivity DS DNA assay and diluted to a concentration of 6 nM for MiSeq 

sequencing. The sample was 300 bp paired-end sequenced alongside several other unrelated libraries on a 

600 cycle V2 cartridge. 

3.1.3 Production Runs

After the initial optimization run to assess amplification of loci within the multiplex PCR, we identified 

288 Atlantic sturgeon for construction of an SNP genotype baseline, with the plan that 96 individuals 

would be sequenced among three separate MiSeq runs (Table 7). Samples were chosen such that nine 

populations of n = 32 each were represented. All samples were collected from river-resident juveniles

(< 500 mm TL) or adults (≥ 1,500 mm TL) captured in their natal river by permitted researchers from 

other agencies. DNA of each set of 96 samples was aliquoted into a 96-well plate at a standardized 

concentration of 20 ng/μl. The first and second PCRs followed the protocols for the optimization run, 

with the exception that some primer concentrations were adjusted between runs to try and influence overor under-representation of certain loci (see results). Each run utilized the same set of dual indexed 

primers, allowing all 96 individuals within a single run to be demultiplexed.

To have even sequence representation of each individual within a MiSeq run, the amount of PCR product 

loaded on the flow cell needs to be standardized among individuals. To streamline this quantification and 

reduce the hands-on time, the indexed PCR product was processed through a 96 well SequalPrep 

normalization plate following the manufacturer’s instructions. This step resulted in all samples being 

standardized to a similar concentration and volume. The final dilution of the pooled libraries was to a 

concentration of 6 nM. Each set of 96 samples was paired-end sequenced (250 bp x 2) on an Illumina 

MiSeq utilizing a 500 cycle V2 kit (the 600 cycle sequencing kit was unnecessary for these amplicons, 

most of which had sizes of approximately 200 bp or less).
27

Table 6. Individual Atlantic sturgeon samples and their corresponding source river run to develop 

a genomic baseline for Atlantic sturgeon.

Sample ID Source 

River

AOK-051 Kennebec

AOK-052 Kennebec

AOK-053 Kennebec

AOK-054 Kennebec

AOK-055 Kennebec

AOK-056 Kennebec

AOK-057 Kennebec

AOK-058 Kennebec

AOK-059 Kennebec

AOK-061 Kennebec

AOK-062 Kennebec

AOK-063 Kennebec

AOK-065 Kennebec

AOK-066 Kennebec

AOK-067 Kennebec

AOK-068 Kennebec

AOK-069 Kennebec

AOK-070 Kennebec

AOK-071 Kennebec

AOK-072 Kennebec

AOK-073 Kennebec

AOK-074 Kennebec

AOK-075 Kennebec

AOK-076 Kennebec

AOK-077 Kennebec

AOK-078 Kennebec

AOK-079 Kennebec

AOK-080 Kennebec

AOK-081 Kennebec

AOK-082 Kennebec

AOK-083 Kennebec

AOK-084 Kennebec

Aoxy-03951 Delaware

Aoxy-03953 Delaware

Aoxy-03955 Delaware

Aoxy-03957 Delaware

Aoxy-03962 Delaware

Sample ID Source 

River

Aoxy-03963 Delaware

Aoxy-03983 Delaware

Aoxy-03994 Delaware

Aoxy-04831 Delaware

Aoxy-04832 Delaware

Aoxy-04834 Delaware

Aoxy-04835 Delaware

Aoxy-04836 Delaware

Aoxy-09638 James

Aoxy-09639 James

Aoxy-09640 James

Aoxy-09641 James

Aoxy-09642 James

Aoxy-09643 James

Aoxy-09644 James

Aoxy-09645 James

Aoxy-09666 James

Aoxy-09726 James

Aoxy-09727 James

Aoxy-09728 James

Aoxy-09729 James

Aoxy-09730 James

Aoxy-09731 James

Aoxy-09914 James

Aoxy-09915 James

Aoxy-09916 James

Aoxy-09917 James

Aoxy-09918 James

Aoxy-09919 James

Aoxy-09921 James

Aoxy-09927 James

Aoxy-09928 James

Aoxy-09931 James

Aoxy-09935 James

Aoxy-09939 James

Aoxy-09945 James

Aoxy-09946 James
28

Sample ID Source 

River

Aoxy-09947 James

Aoxy-09948 James

Aoxy02915 Edisto

Aoxy02916 Edisto

Aoxy02917 Edisto

Aoxy02923 Edisto

Aoxy02924 Edisto

Aoxy02927 Edisto

Aoxy02932 Edisto

Aoxy02933 Edisto

Aoxy02934 Edisto

Aoxy02940 Edisto

Aoxy02944 Edisto

Aoxy02947 Edisto

Aoxy02949 Edisto

Aoxy02950 Edisto

Aoxy02960 Edisto

Aoxy02961 Edisto

Aoxy02964 Edisto

Aoxy02968 Edisto

Aoxy02969 Edisto

Aoxy02973 Edisto

Aoxy02974 Edisto

Aoxy02975 Edisto

Aoxy02976 Edisto

Aoxy02979 Edisto

Aoxy02982 Edisto

Aoxy02984 Edisto

Aoxy02986 Edisto

Aoxy02989 Edisto

Aoxy02990 Edisto

Aoxy03003 Edisto

Aoxy03004 Edisto

Aoxy03005 Edisto

Aoxy06575 Altamaha

Aoxy06576 Altamaha

Aoxy06577 Altamaha

Aoxy06578 Altamaha

Aoxy06579 Altamaha

Aoxy06580 Altamaha

Sample ID Source 

River

Aoxy06581 Altamaha

Aoxy06582 Altamaha

Aoxy06583 Altamaha

Aoxy06584 Altamaha

Aoxy06585 Altamaha

Aoxy06586 Altamaha

Aoxy06587 Altamaha

Aoxy06588 Altamaha

Aoxy06589 Altamaha

Aoxy06590 Altamaha

Aoxy06591 Altamaha

Aoxy06592 Altamaha

Aoxy06593 Altamaha

Aoxy06594 Altamaha

Aoxy06595 Altamaha

Aoxy06596 Altamaha

Aoxy06597 Altamaha

Aoxy06598 Altamaha

Aoxy06599 Altamaha

Aoxy06600 Altamaha

Aoxy06609 Altamaha

Aoxy06610 Altamaha

Aoxy06611 Altamaha

Aoxy06612 Altamaha

Aoxy06613 Altamaha

Aoxy06614 Altamaha

Aoxy09163 Hudson

Aoxy09164 Hudson

Aoxy09165 Hudson

Aoxy09166 Hudson

Aoxy09167 Hudson

Aoxy09168 Hudson

Aoxy09169 Hudson

Aoxy09170 Hudson

Aoxy09177 Hudson

Aoxy09179 Hudson

Aoxy09180 Hudson

Aoxy09181 Hudson

Aoxy09182 Hudson

Aoxy09183 Hudson
29

Sample ID Source 

River

Aoxy09184 Hudson

Aoxy09185 Hudson

Aoxy09186 Hudson

Aoxy09187 Hudson

Aoxy09189 Hudson

Aoxy09192 Hudson

Aoxy09195 Hudson

Aoxy09196 Hudson

Aoxy09197 Hudson

Aoxy09198 Hudson

Aoxy09200 Hudson

Aoxy09201 Hudson

Aoxy09202 Hudson

Aoxy09203 Hudson

Aoxy09204 Hudson

Aoxy09206 Hudson

Aoxy09299 Hudson

Aoxy09632 James

Aoxy09716 Hudson

AoxyDe08-04881 Delaware

AoxyDe08-04902 Delaware

AoxyDe09-05066 Delaware

AoxyDe09-05067 Delaware

AoxyDe09-05079 Delaware

AoxyDe09-05081 Delaware

AoxyDe09-05082 Delaware

AoxyDe09-05088 Delaware

AoxyDe09-05089 Delaware

AoxyDe09-05095 Delaware

AoxyDe09-05096 Delaware

AoxyDe09-05097 Delaware

AoxyDe09-05098 Delaware

AoxyDe09-05099 Delaware

AoxyDe09-05100 Delaware

AoxyDe09-05101 Delaware

AoxyDe09-05105 Delaware

AoxyDe09-05106 Delaware

AoxyDe09-05107 Delaware

AoxyOOG14-09969 Ogeechee

AoxyOOG14-09970 Ogeechee

Sample ID Source 

River

AoxyOOG14-09971 Ogeechee

AoxyOOG14-09972 Ogeechee

AoxyOOG14-09973 Ogeechee

AoxyOOG14-09975 Ogeechee

AoxyOOG14-09976 Ogeechee

AoxyOOG14-09977 Ogeechee

AoxyOOG14-09978 Ogeechee

AoxyOOG14-09979 Ogeechee

AoxyOOG14-09980 Ogeechee

AoxyOOG14-09981 Ogeechee

AoxyOOG14-09983 Ogeechee

AoxyOOG14-09984 Ogeechee

AoxyOOG14-09985 Ogeechee

AoxyOOG14-09986 Ogeechee

AoxyOOG15-10004 Ogeechee

AoxyOOG15-10006 Ogeechee

AoxyOOG15-10007 Ogeechee

AoxyOOG15-10008 Ogeechee

AoxyOOG15-10010 Ogeechee

AoxyOOG15-10011 Ogeechee

AoxyOOG15-10013 Ogeechee

AoxyOOG15-10014 Ogeechee

AoxyOOG15-10016 Ogeechee

AoxyOOG15-10017 Ogeechee

AoxyOOG15-10018 Ogeechee

AoxyOOG15-10019 Ogeechee

AoxyOOG15-10020 Ogeechee

AoxyOOG15-10021 Ogeechee

AoxyOOG15-10022 Ogeechee

AoxyOOG15-10023 Ogeechee

AoxySav08-05386 Savannah

AoxySav08-05387 Savannah

AoxySav08-05390 Savannah

AoxySav08-05391 Savannah

AoxySav08-05392 Savannah

AoxySav08-05403 Savannah

AoxySav08-05426 Savannah

AoxySav08-05427 Savannah

AoxySav08-05440 Savannah

AoxySav08-05461 Savannah
30

Sample ID Source 

River

AoxySav13-11210 Savannah

AoxySav13-11214 Savannah

AoxySav13-11227 Savannah

AoxySav13-11246 Savannah

AoxySav13-11248 Savannah

AoxySav13-11249 Savannah

AoxySav13-11251 Savannah

AoxySav13-11258 Savannah

AoxySav13-11263 Savannah

AoxySav13-11275 Savannah

AoxySav13-11278 Savannah

AoxySav13-11279 Savannah

AoxySav13-11280 Savannah

AoxySav13-11286 Savannah

AoxySav13-11296 Savannah

AoxySav13-11298 Savannah

AoxySav13-11299 Savannah

AoxySav13-11301 Savannah

AoxySav13-11320 Savannah

AoxySav13-11347 Savannah

AoxySav13-11349 Savannah

AoxySav13-11361 Savannah

AOY-13-03 York

AOY-13-04 York

AOY-13-05 York

AOY-13-06 York

AOY-13-07 York

AOY-13-08 York

AOY-13-09 York

AOY-13-10 York

AOY-14-19 York

AOY-14-20 York

AOY-14-21 York

AOY-14-22 York

AOY-14-44 York

AOY-14-45 York

AOY-14-46 York

AOY-14-47 York

AOY-15-57 York

AOY-15-58 York

Sample ID Source 

River

AOY-15-59 York

AOY-15-60 York

AOY-15-61 York

AOY-15-62 York

AOY-15-63 York

AOY-15-64 York

AOY-17-01 York

AOY-17-02 York

AOY-17-03 York

AOY-17-04 York

AOY-17-05 York

AOY-17-06 York

AOY-17-07 York

AOY-17-08 York
31

3.1.4 Bioinformatic Analyses

Each set of sequences obtained for an individual represent multiple SNP loci. In order to identify the 

genotypes of each SNP, these sequences need to be mapped against the reference dataset of contigs 

containing the SNPs (i.e., the 153 nuclear SNP loci reads are mapped against the corresponding 153 

contigs that contain them from the de novo reference assembly). Then, the genotype at each locus for each 

individual can be determined by looking at the nucleotide distribution at the pre-defined SNP location 

within the contig. Due to inefficient amplification of the microsatellite loci in production runs (see 

results), we only concentrate here on the bioinformatic steps used to obtain genotypes of the SNP loci.

To evaluate the optimization run, the demultiplexed sequences were trimmed with a quality threshold of 

Q10 using bbduk.sh, overlapping read pairs were merged using bbmerge.sh (default parameters) of the 

bbmap package (B. Bushnell, https://sourceforge.net/projects/bbmap). To assess amplification success, a 

custom perl script was used to search for and enumerate the number of sequences matching each expected 

amplicon. The script required the forward and reverse primers to be exact matches to be counted. These 

counts were input into a spreadsheet and visualized to see which loci amplified and in what proportion. 

Loci with exceedingly high or low coverage were identified, so that their concentration in subsequent 

production runs could be changed to result in more even amplification. 

For production runs, bioinformatic processing was adjusted to improve speed and retain fastq quality 

information, as map quality was no longer relevant (due to the much reduced search space, and high 

distinctiveness of the final targets), but base quality scores were more relevant for genotyping individuals 

at the low end of coverage. Processing of each production run followed the same basic pipeline. Sequence 

reads of each sample were demultiplexed based on the assigned dual index and placed into individual 

folders. All reads were screened for the presence of residual Illumina Truseq adapters on both ends using 

bbduk.sh (option flags included k = 29, mink = 11, hdist = 1, tpe, tbo). Read pairs were then merged using 

the default settings of bbmerge.sh, with no quality trimming performed. A fasta formatted file of the 153 

contigs was indexed for the mapping program bbmap, using the bbmap.sh script (option flags included 

build = 1, k = 13). In addition, a list of targeted SNP positions to genotype was created in the form of a 

tab delimited text file, with two columns corresponding to the contig name and SNP base position within 

the contig respectively. Merged read pairs from each sample were then mapped against the reference file 

using bbmap.sh to create a *.bam file (option flags with bbmap included build = 1, minid = 0.90). The 

resulting *.bam file was indexed and sorted using ‘samtools sort’ and ‘samtools view’. The indexed 

*.bam file used ‘samtools mpileup’ to provide a summary of the coverage of mapped reads and the data in 

the right format for bcftools (option flags included -d 100, -C 50, -I, ‘-t DP,INFO/ADF,INFO/ADR’, 

-Rvf). Finally, ‘bcftools call’ was used to output the genomic variants into a vCard file (VCF file). For the 

mitogenomic amplicons within the GT-seq panel, SNPs were processed identically as for the nuclear 

SNPs, with the exception of changing the ploidy flag to ‘--ploidy 1’ for the bcftools call portion of the 

script. The bash shell scripts used to process these data can be provided by the authors upon request.

For each individual sturgeon, we generated a VCF file that contains the SNP genotypes. All VCF files 

were placed in one directory, compressed with bgzip, and indexed using bcftols index. All indexed VCF

files were then merged into one large combined VCF file of all samples and variants using bcftools 

merge. This final VCF file, and though it contains the number of samples and genotypes of variants, is not 

initially filtered to require any set depth of coverage, number of genotyped loci per population, etc.

Filtering of the VCF file was accomplished with vcftools (Danecek et al. 2011). 

3.1.5 Assessment of the Utility of Mitogenomic SNPs for MSA

When primers were designed for the mitogenomic SNPs identified earlier by T.L. King (Table 5), it was 

assumed that the SNPs would be phylogeographically informative. However, examination of the SNPs we 
32

were able to genotype in our production runs through GT-seq revealed that these SNPs identified by T.L. 

King were largely invariant (see results). Due to the sudden loss of T.L. King and the inability to fully 

reproduce his earlier analyses, we performed a reanalysis of the mitogenomic data from among the 

Atlantic sturgeon used for shotgun genomic sequencing and construction of a reference genome. Because 

this reanalysis was performed after testing of the GT-seq panel, none of the SNPs identified by our 

reanalysis had amplicons designed or sequenced. Our inclusion of these data here is to assess the potential 

utility of a mitogenomic variation for future mixed stock analyses and assignment testing efforts.

Quality trimmed reads from the same 71 Atlantic sturgeon used for the de novo reference assembly were 

used for mapping against an Atlantic sturgeon mitogenomic reference from GenBank with the accession 

number KP997217. Reference indexing, read mapping, and variant calling were performed identically to 

the GT-seq dataset, with the exception that the bcftools call ‘--ploidy’ flag was set to 1. 

All VCF files were placed in the same directory, indexed, and merged. The resulting merged VCF file 

was filtered using vcftools and the following parameters: minDP = 5, maf = 0.05, minQ = 30, maxmissing 0.1, remove any individual with > 50% missing data. The filtered VCF file was imported into R 

as a genind object using the adegenet package (Jombart 2008, Jombart 2011), and the genind file ploidy 

was set to 1. A Discriminant Analysis of Principal Components plot (DAPC; Jombart et al. 2010) was 

constructed from this genind file in the adegenet package to visualize population structure. The number of 

principal components retained was 12, and the number of discriminant functions retained was 9. 

3.2 Results

3.2.1 GT-seq Optimization Run

The initial GT-seq optimization run, which consisted of just one individual amplified with each of the n = 

151 nuclear SNP, n = 12 microsatellites, and n = 7 mtDNA SNP primer pairs at 0.54 μM final 

concentration resulted in 610,572 merged read pairs, of which 577,097 reads (~95%) could be assigned to 

a locus (Table 8; Figure 1). Eight of the 151 nuclear SNP loci did not amplify, whereas the remainder 

had coverage ranging from 1–24,196 reads. Among the seven mtDNA SNP loci, all loci were represented, 

though one locus had only two reads, while the next least abundant mtDNA amplicon had 5,053 reads. 

Overall, the mtDNA loci had the highest average number of reads, with one locus containing 188,075 

reads accounting for 33% of the total reads in the entire run. Amplification of the microsatellite loci was 

the least successful among the marker types, with four loci having no amplification, and four loci with 

one to nine reads. The remaining four loci had 28–656 reads. 

3.2.2 Production Runs

We sought to target a minimum coverage level of 10X per locus for SNP calling, and approximately 

100,000 reads per sample, which should be attainable with 96 individuals in one paired-end sequencing 

MiSeq run. It was evident from the optimization run that when the number of reads obtained was scaled 

down to estimate what level of coverage would be expected per locus at 100,000 total reads with the 

current primer cocktail, 28 out of the 143 amplifying nuclear and one of the mtDNA loci would not meet 

this 10X threshold. Therefore, we adjusted the concentrations of primers in the multiplex mastermix in an 

attempt to make coverage more even for the production runs. First, any locus with zero reads was 

removed from the multiplex under the assumption that they were never going to amplify in the master 

mix (n = 8 nuclear SNPs, n = 4 microsatellites; Table 8). Next, we examined the actual sequences of any 

locus with less than 10 reads and omitted any locus with off target reads not matching the expected 

amplicon (n = 3 nuclear SNPs, n = 1 microsatellites, n = 1 mtDNA SNP). After omission of these loci, the 

remaining loci were grouped into three somewhat arbitrary categories: loci with greater than 5,000 reads, 

loci with less than 500 reads, and loci between 501 and 4,999 reads. Loci with more than 5,000 reads 
33

were adjusted to have a multiplex concentration of 0.025 nM, loci with less than 500 reads were increased 

to 0.1 nM, and loci with reads between 501 and 4,999 remained at a concentration of 0.054. Locus 

‘contig_618918’ was accidentally omitted from the primer cocktail in the first production run.

The first production run of n = 96 Atlantic sturgeon with the adjusted primer concentrations resulted in an 

average of 143,822 reads per fish across all marker types (range 51,617–184,768, standard deviation 

22,230). The adjustment of primer concentrations had a pronounced effect on the number of reads 

obtained for each locus (Table 8; Figure 2). The loci in Figure 2 are in the same order as within 

Figure 1, and each bar in Figure 2 represents the average number of reads at that locus across the 96 

sequenced individuals. Although Figure 1 represents actual counts from a single individual at each locus 

and Figure 2 represents the average number of reads across 96 individuals at each locus, the general 

pattern of amplification success can still be assessed. Overall, there was a general flattening of the read 

distribution, indicating that adjustment of the primer concentrations did have the anticipated impact of 

changing locus representation. However, some nuclear loci completely ceased to be represented 

(contig_273784, contig_270080, contig_266918, contig_264486; Table 8), and this could not be 

explained by the lower number of reads per individual versus the optimization run (i.e., adjusting the 

number of reads expected per locus in the original optimization run of 577,097 total reads to the relative 

abundance expected at ~100,000 should not result in these loci falling below zero reads). Four of the 

mtDNA loci no longer amplified in the adjusted primer cocktail (sturg_CR, sturg_nd5, sturg_nd1, 

sturg_nd4L), and the two that still amplified were among the most highly represented loci (sturg_co1, 

sturg_atp6). One microsatellite locus did not amplify in the adjusted primer cocktail (Aox19). 

The primer concentrations in second production run of the next n = 96 Atlantic sturgeon were again 

slightly adjusted to try and further even out representation of a few loci. Three nuclear SNP loci 

(contig_155051, contig_1056163, contig_863648), and five mtDNA SNP loci were adjusted (sturg_CR, 

sturg_nd5, sturg_nd1, sturg_atp6, sturg_nd4l; Table 8; Figure 3). These libraries were run twice due to 

inefficient clustering in the first library, resulting in a low number of reads. Consequently, the numbers 

presented in Table 8 and Figure 3 represent the read counts across the two runs combined. Adjustment of 

these three nuclear loci did not appreciably change their representation. For the adjusted mtDNA loci, 

four of them continued to not amplify, while one representation of one locus did decrease as intended 

(sturg_atp6). Sequencing of the third production run utilized the same primer concentrations as for the 

second run (Table 8; Figure 4). Overall, the results were very similar to the second run. 
34

Table 7. Summary of sequencing results for each locus that was considered for inclusion within the GT-seq panel.

Optimization Run Production Run 1 Production Run 2 Production Run 3

Locus Class

Primer 

Conc. 

[μM] Count

Primer 

Conc. 

[μM]

Average Count 

(SD)

Primer 

Conc. 

[μM]

Average Count 

(SD)

Primer 

Conc. 

[μM]

Average Count 

(SD)

contig_252163 Nuc 0.054 0 0 - 0 - 0 -

contig_252793 Nuc 0.054 0 0 - 0 - 0 -

contig_253460 Nuc 0.054 0 0 - 0 - 0 -

contig_254281 Nuc 0.054 0 0 - 0 - 0 -

contig_256737 Nuc 0.054 0 0 - 0 - 0 -

contig_260617 Nuc 0.054 0 0 - 0 - 0 -

contig_592686 Nuc 0.054 0 0 - 0 - 0 -

contig_593190 Nuc 0.054 0 0 - 0 - 0 -

contig_305031 Nuc 0.054 1 0 - 0 - 0 -

contig_801222 Nuc 0.054 3 0.1 11.1 (5.1) 0.1 11.8 (7.3) 0.1 6.7 (5.1)

contig_323589 Nuc 0.054 4 0.1 41.3 (16.5) 0.1 3.7 (3.2) 0.1 32.3 (21.3)

contig_902317 Nuc 0.054 4 0 - 0 - 0 -

contig_349978 Nuc 0.054 6 0.1 19.5 (10) 0.1 5.1 (4.5) 0.1 11.1 (6.6)

contig_270624 Nuc 0.054 8 0 - 0 - 0 -

contig_512906 Nuc 0.054 9 0.1 78.8 (21.6) 0.1 21.2 (9.6) 0.1 74.2 (22.8)

contig_61910 Nuc 0.054 12 0.1 28.8 (10.8) 0.1 181.1 (72.6) 0.1 22 (11.3)

contig_154779 Nuc 0.054 14 0.1 40.2 (10.4) 0.1 161.4 (51.9) 0.1 36.5 (15.7)

contig_1112399 Nuc 0.054 14 0.1 232.8 (68.3) 0.1 78.3 (34.3) 0.1 159 (91)

contig_17037 Nuc 0.054 17 0.1 13.4 (5.7) 0.1 338.4 (151.3) 0.1 10.9 (6.4)

contig_80054 Nuc 0.054 18 0.1 102.6 (40.4) 0.1 509.2 (244.1) 0.1 71.1 (58)

contig_597294 Nuc 0.054 19 0.1 71.6 (38.4) 0.1 113.9 (66.4) 0.1 49.8 (33.9)

contig_1032892 Nuc 0.054 22 0.1 2.7 (3) 0.1 6.6 (9.2) 0.1 0.5 (0.8)

contig_204708 Nuc 0.054 24 0.1 2,564.1 (806.3) 0.1 4,102.3 (1510.2) 0.1 3,119.5 (1,638.7)

contig_273784 Nuc 0.054 26 0.1 0 (0) 0.1 0 (0) 0.1 0 (0)
35

Optimization Run Production Run 1 Production Run 2 Production Run 3

Locus Class

Primer 

Conc. 

[μM] Count

Primer 

Conc. 

[μM]

Average Count 

(SD)

Primer 

Conc. 

[μM]

Average Count 

(SD)

Primer 

Conc. 

[μM]

Average Count 

(SD)

contig_223774 Nuc 0.054 28 0.1 1,266.7 (359.8) 0.1 211.5 (91.1) 0.1 808.7 (273)

contig_75258 Nuc 0.054 29 0.1 36.8 (13.3) 0.1 936.4 (354.2) 0.1 28.3 (17.3)

contig_124134 Nuc 0.054 29 0.1 9.1 (4.2) 0.1 10.4 (6.2) 0.1 2.8 (2.5)

contig_1299478 Nuc 0.054 29 0.1 900.8 (1,383.1) 0.1 770.7 (1,026.2) 0.1 832.1 (1,436.8)

contig_48956 Nuc 0.054 38 0.1 30.6 (10.4) 0.1 182.6 (65.7) 0.1 23.6 (15)

contig_119516 Nuc 0.054 40 0.1 84.6 (20.8) 0.1 1,028.5 (437.3) 0.1 83.1 (32.6)

contig_672725 Nuc 0.054 40 0.1 226.9 (68.9) 0.1 0.1 (0.2) 0.1 1.1 (1.1)

contig_852468 Nuc 0.054 44 0.1 143.6 (36.4) 0.1 232.5 (82.2) 0.1 92.8 (28)

contig_102467 Nuc 0.054 46 0.1 160 (36.9) 0.1 1,395.5 (388.8) 0.1 102.6 (35.1)

contig_383472 Nuc 0.054 52 0.1 14.1 (6.1) 0.1 124.2 (59.3) 0.1 63.8 (36.6)

contig_176276 Nuc 0.054 55 0.1 59.1 (20) 0.1 711.3 (271) 0.1 32.9 (18.4)

contig_347490 Nuc 0.054 56 0.1 191.9 (282.6) 0.1 40.4 (45.9) 0.1 45.7 (77.3)

contig_351321 Nuc 0.054 78 0.1 791.7 (660.2) 0.1 325.6 (230.8) 0.1 738 (636.4)

contig_98816 Nuc 0.054 81 0.1 302.6 (88) 0.1 2,875.2 (996.5) 0.1 256.3 (98.5)

contig_1049854 Nuc 0.054 83 0.1 413.2 (94.7) 0.1 40.2 (16) 0.1 220 (62.9)

contig_300700 Nuc 0.054 87 0.1 491 (115.1) 0.1 97.1 (36.2) 0.1 331.6 (100.3)

contig_625315 Nuc 0.054 91 0.1 827.9 (222.1) 0.1 72.7 (36.2) 0.1 357 (149.8)

contig_215639 Nuc 0.054 102 0.1 906.1 (206) 0.1 744.8 (219.6) 0.1 457.6 (133.1)

contig_86226 Nuc 0.054 104 0.1 799.3 (196) 0.1 1,085.8 (353.1) 0.1 688.6 (224.6)

contig_805784 Nuc 0.054 112 0.1 166.4 (40.4) 0.1 392.3 (121.7) 0.1 141.4 (47.2)

contig_195684 Nuc 0.054 121 0.1 464.9 (96.5) 0.1 811.2 (244.5) 0.1 328.7 (120.2)

contig_31935 Nuc 0.054 126 0.1 179.6 (63.5) 0.1 1,078.7 (422.9) 0.1 129.6 (58.4)

contig_211497 Nuc 0.054 127 0.1 553 (110.8) 0.1 261.6 (71.2) 0.1 355.6 (96.8)

contig_999448 Nuc 0.054 129 0.1 1,169.4 (264.6) 0.1 387 (114.8) 0.1 689.7 (213.6)

contig_895412 Nuc 0.054 142 0.1 83.6 (21.2) 0.1 388.7 (134) 0.1 72.6 (26.3)

contig_88389 Nuc 0.054 144 0.1 3,837.8 (773.3) 0.1 5,165.1 (1,345.3) 0.1 3,759.2 (1,020.4)
36

Optimization Run Production Run 1 Production Run 2 Production Run 3

Locus Class

Primer 

Conc. 

[μM] Count

Primer 

Conc. 

[μM]

Average Count 

(SD)

Primer 

Conc. 

[μM]

Average Count 

(SD)

Primer 

Conc. 

[μM]

Average Count 

(SD)

contig_675727 Nuc 0.054 152 0.1 424.9 (151.1) 0.1 159.9 (53.5) 0.1 198.3 (76.9)

contig_44181 Nuc 0.054 153 0.1 164.5 (51.5) 0.1 3,875.3 (1,216.9) 0.1 139.5 (45)

contig_407775 Nuc 0.054 157 0.1 881.9 (499.5) 0.1 41.5 (31.2) 0.1 215.6 (128.5)

contig_8889 Nuc 0.054 173 0.1 257.3 (113.4) 0.1 2,776.3 (1,263.2) 0.1 236.5 (105.2)

contig_682427 Nuc 0.054 176 0.1 2,418.8 (550.1) 0.1 1,497.6 (464.1) 0.1 2,007.7 (628.5)

contig_91914 Nuc 0.054 177 0.1 651.9 (336.5) 0.1 151.2 (84.5) 0.1 310 (173.4)

contig_168922 Nuc 0.054 191 0.1 5,351.5 (2,232) 0.1 5,061.2 (1,915.7) 0.1 4,033.7 (1,192.6)

contig_279175 Nuc 0.054 201 0.1 474.4 (107.6) 0.1 473.1 (165) 0.1 383.6 (113.5)

contig_270080 Nuc 0.054 203 0.1 0 (0) 0.1 0 (0) 0.1 0 (0)

contig_664819 Nuc 0.054 212 0.1 3,160.3 (829.5) 0.1 170.8 (176.7) 0.1 456.7 (410.1)

contig_713657 Nuc 0.054 213 0.1 425.1 (134.5) 0.1 149.7 (60.4) 0.1 190.3 (97.2)

contig_767 Nuc 0.054 219 0.1 5,013.2 (1,370.2) 0.1 5,909.2 (1,922.7) 0.1 3,948 (1,210.6)

contig_128545 Nuc 0.054 222 0.1 1,840.8 (532.8) 0.1 1,958.8 (647.1) 0.1 1,501.3 (501)

contig_845093 Nuc 0.054 238 0.1 2,219.6 (520.4) 0.1 133.3 (53.4) 0.1 607.6 (240.7)

contig_630189 Nuc 0.054 243 0.1 2,124.3 (1,078.7) 0.1 340.3 (164.2) 0.1 1,482.2 (756.4)

contig_335132 Nuc 0.054 264 0.1 721.4 (144) 0.1 422.8 (167.1) 0.1 623.5 (214.1)

contig_298604 Nuc 0.054 281 0.1 897.6 (596.5) 0.1 126.9 (81) 0.1 635.9 (405)

contig_146123 Nuc 0.054 354 0.1 886 (183.8) 0.1 6,609.7 (1,936.9) 0.1 827.3 (213)

contig_718337 Nuc 0.054 355 0.1 131.3 (35.8) 0.1 41.1 (19.6) 0.1 56.6 (28.6)

contig_629792 Nuc 0.054 360 0.1 279.8 (67.3) 0.1 720.4 (284.2) 0.1 269.9 (86.4)

contig_338725 Nuc 0.054 376 0.1 596.8 (206.4) 0.1 124.5 (58.7) 0.1 267.8 (116)

contig_408529 Nuc 0.054 396 0.1 197.4 (51) 0.1 237.9 (127.2) 0.1 153.8 (70.4)

contig_145014 Nuc 0.054 406 0.1 2,503.3 (501) 0.1 5,028.4 (1,586.8) 0.1 1,528.6 (455.7)

contig_198579 Nuc 0.054 409 0.1 4,563.3 (1,006.1) 0.1 2,644.5 (1,049.5) 0.1 3,444.2 (1,103.8)

contig_242683 Nuc 0.054 423 0.1 1,981 (511.5) 0.1 303.3 (128.3) 0.1 453.7 (217.5)

contig_392395 Nuc 0.054 470 0.1 928.3 (338) 0.1 60.9 (29.5) 0.1 328.7 (185.9)
37

Optimization Run Production Run 1 Production Run 2 Production Run 3

Locus Class

Primer 

Conc. 

[μM] Count

Primer 

Conc. 

[μM]

Average Count 

(SD)

Primer 

Conc. 

[μM]

Average Count 

(SD)

Primer 

Conc. 

[μM]

Average Count 

(SD)

contig_850177 Nuc 0.054 470 0.1 1,721.9 (711.6) 0.1 482.5 (273.6) 0.1 1,141 (515.8)

contig_341935 Nuc 0.054 481 0.1 2,017.8 (415.4) 0.1 43.9 (17.1) 0.1 378 (110.4)

contig_198706 Nuc 0.054 509 0.054 127.7 (27.1) 0.054 831.9 (267.3) 0.054 77.1 (26.1)

contig_369587 Nuc 0.054 560 0.054 235.6 (58.4) 0.054 347.5 (122.3) 0.054 134.3 (46.7)

contig_266918 Nuc 0.054 566 0.054 0 (0) 0.054 0 (0) 0.054 0 (0)

contig_1001037 Nuc 0.054 579 0.054 64.3 (18.7) 0.054 61.9 (26.8) 0.054 35.6 (16.2)

contig_93145 Nuc 0.054 618 0.054 122.6 (30.5) 0.054 198.5 (64.9) 0.054 68.4 (31.5)

contig_1122966 Nuc 0.054 675 0.054 504.5 (99.6) 0.054 43.6 (22) 0.054 87.5 (31.7)

contig_124581 Nuc 0.054 683 0.054 88 (21.6) 0.054 509.1 (152.4) 0.054 46.7 (19.5)

contig_778708 Nuc 0.054 684 0.054 336.1 (141.6) 0.054 599.6 (219.4) 0.054 231.8 (99.8)

contig_509349 Nuc 0.054 702 0.054 49.9 (12.1) 0.054 61 (25.2) 0.054 28.6 (10.4)

contig_343910 Nuc 0.054 723 0.054 42.4 (11.4) 0.054 81.4 (26.9) 0.054 22.5 (9.7)

contig_361665 Nuc 0.054 782 0.054 289.4 (91.7) 0.054 164.6 (73.8) 0.054 114 (55.4)

contig_847347 Nuc 0.054 785 0.054 8.3 (3.2) 0.054 26.6 (11.4) 0.054 5.1 (3.1)

contig_251136 Nuc 0.054 787 0.054 412 (216.6) 0.054 935.7 (462.8) 0.054 290.7 (158.2)

contig_159128 Nuc 0.054 788 0.054 129.9 (33.4) 0.054 178.7 (66.3) 0.054 57.3 (27.8)

contig_213642 Nuc 0.054 838 0.054 1,507.5 (346.6) 0.054 1,256.2 (566.5) 0.054 941.4 (349.5)

contig_913003 Nuc 0.054 858 0.054 23.3 (8.3) 0.054 33.7 (10) 0.054 11.8 (5.9)

contig_471552 Nuc 0.054 924 0.054 152.6 (31.6) 0.054 178.1 (65.1) 0.054 87.7 (26.7)

contig_429827 Nuc 0.054 1047 0.054 160.1 (41.1) 0.054 284.9 (98) 0.054 88.9 (36.6)

contig_463504 Nuc 0.054 1049 0.054 393.2 (128.8) 0.054 514.6 (216.1) 0.054 235.6 (81.5)

contig_182703 Nuc 0.054 1118 0.054 1,683.4 (371.4) 0.054 3,653.8 (1,203.3) 0.054 1,112 (338.9)

contig_591595 Nuc 0.054 1141 0.054 147.7 (36.2) 0.054 253.4 (94.8) 0.054 101.2 (40)

contig_456933 Nuc 0.054 1168 0.054 511.1 (127) 0.054 556.5 (190.6) 0.054 307.3 (113.5)

contig_2585 Nuc 0.054 1174 0.054 189.4 (43.2) 0.054 382 (148.1) 0.054 100.7 (35.9)

contig_304245 Nuc 0.054 1266 0.054 263.6 (55.4) 0.054 223.8 (80.2) 0.054 124.4 (35.1)
38

Optimization Run Production Run 1 Production Run 2 Production Run 3

Locus Class

Primer 

Conc. 

[μM] Count

Primer 

Conc. 

[μM]

Average Count 

(SD)

Primer 

Conc. 

[μM]

Average Count 

(SD)

Primer 

Conc. 

[μM]

Average Count 

(SD)

contig_39444 Nuc 0.054 1275 0.054 111.8 (31.5) 0.054 1,985.5 (566) 0.054 90.8 (27.6)

contig_536471 Nuc 0.054 1287 0.054 1,030.8 (244.8) 0.054 1,461.4 (539.7) 0.054 636.3 (204.8)

contig_843891 Nuc 0.054 1308 0.054 199.7 (48.2) 0.054 48.9 (24.2) 0.054 42.3 (18.9)

contig_871803 Nuc 0.054 1356 0.054 58.9 (16.3) 0.054 100.6 (36.4) 0.054 33.3 (13.5)

contig_369024 Nuc 0.054 1513 0.054 107.2 (57.1) 0.054 170.1 (91.4) 0.054 86.8 (34.7)

contig_197214 Nuc 0.054 1518 0.054 577.4 (235.5) 0.054 1,292.4 (552.2) 0.054 328.8 (175.6)

contig_367859 Nuc 0.054 1539 0.054 1,704 (647.7) 0.054 2,184.7 (915.2) 0.054 796.9 (356.8)

contig_299947 Nuc 0.054 1584 0.054 1,518 (388.6) 0.054 1,413.1 (638) 0.054 1,175.9 (428.7)

contig_947914 Nuc 0.054 1664 0.054 145.1 (37.3) 0.054 183.8 (94.5) 0.054 90.9 (38.8)

contig_264486 Nuc 0.054 1737 0.054 0 (0) 0.054 0 (0) 0.054 0 (0)

contig_345578 Nuc 0.054 1799 0.054 1,127.4 (227.7) 0.054 1,167.9 (378.5) 0.054 640.7 (161.8)

contig_297854 Nuc 0.054 1816 0.054 636.8 (134.7) 0.054 1,162.6 (341.3) 0.054 392.5 (130.4)

contig_1018361 Nuc 0.054 1937 0.054 2,304.1 (601.8) 0.054 1,809.5 (704.8) 0.054 1,107 (376.5)

contig_363259 Nuc 0.054 1992 0.054 1,240.1 (312.1) 0.054 1,096.8 (439.1) 0.054 683.1 (215.9)

contig_167621 Nuc 0.054 2020 0.054 901.4 (184.8) 0.054 1,315.5 (499.4) 0.054 72 (39.7)

contig_74051 Nuc 0.054 2050 0.054 72.3 (17.5) 0.054 339.1 (117.4) 0.054 47.4 (14.9)

contig_520290 Nuc 0.054 2072 0.054 987.9 (187.4) 0.054 952 (378.8) 0.054 684.2 (191.8)

contig_210463 Nuc 0.054 2186 0.054 1,847.4 (476.8) 0.054 529.4 (223.8) 0.054 344.4 (129)

contig_502728 Nuc 0.054 2189 0.054 1,142.9 (296.7) 0.054 1,570.6 (656) 0.054 563 (240.8)

contig_411127 Nuc 0.054 2233 0.054 1,807 (548.2) 0.054 1,363.8 (685.4) 0.054 1,115 (405.7)

contig_390428 Nuc 0.054 2302 0.054 453.6 (192.8) 0.054 47.5 (23.2) 0.054 44.4 (17.7)

contig_520553 Nuc 0.054 2314 0.054 1,325.4 (256.8) 0.054 1,506.1 (559.5) 0.054 893.2 (283.7)

contig_471638 Nuc 0.054 2325 0.054 2,062.2 (421.3) 0.054 989.9 (520.5) 0.054 1,319.2 (423.9)

contig_212828 Nuc 0.054 2392 0.054 3,096 (878.6) 0.054 521.7 (194.5) 0.054 684.5 (209.3)

contig_559002 Nuc 0.054 2410 0.054 853.2 (205.4) 0.054 1,076.8 (386.8) 0.054 579.6 (169.2)

contig_932915 Nuc 0.054 2608 0.054 2,267.6 (397.4) 0.054 854.6 (329.7) 0.054 1,086.1 (377)
39

Optimization Run Production Run 1 Production Run 2 Production Run 3

Locus Class

Primer 

Conc. 

[μM] Count

Primer 

Conc. 

[μM]

Average Count 

(SD)

Primer 

Conc. 

[μM]

Average Count 

(SD)

Primer 

Conc. 

[μM]

Average Count 

(SD)

contig_433349 Nuc 0.054 2650 0.054 1,844.6 (392.9) 0.054 2,192.5 (893.5) 0.054 1,406.8 (498.6)

contig_205045 Nuc 0.054 2664 0.054 631 (153.9) 0.054 3,964.4 (1,020.5) 0.054 371.4 (122.4)

contig_6443 Nuc 0.054 2706 0.054 1,018.2 (210.2) 0.054 5,691.5 (1,555.2) 0.054 668.1 (193.2)

contig_596653 Nuc 0.054 2966 0.054 2,745.7 (596.2) 0.054 3,525.5 (1,060.8) 0.054 1,877.1 (503.6)

contig_479703 Nuc 0.054 3038 0.054 1,147 (262.3) 0.054 1,347.1 (360.9) 0.054 607.3 (174.3)

contig_487120 Nuc 0.054 3093 0.054 1,302.8 (356.4) 0.054 1,630.6 (556) 0.054 887.5 (324.1)

contig_93920 Nuc 0.054 3333 0.054 1,507.2 (323.2) 0.054 3,629.8 (1,070.1) 0.054 779.5 (236.2)

contig_538841 Nuc 0.054 3394 0.054 1,281.7 (303.2) 0.054 1,365.6 (588.9) 0.054 906 (256.2)

contig_716352 Nuc 0.054 4290 0.054 1,298.1 (333.6) 0.054 1,102.9 (466.2) 0.054 681.5 (239.1)

contig_959459 Nuc 0.054 4321 0.054 2,142.9 (663.3) 0.054 25,36.2 (1,030.4) 0.054 1,227.3 (556)

contig_24799 Nuc 0.054 4553 0.054 854.4 (191.6) 0.054 2,173.9 (779.9) 0.054 475.2 (143.1)

contig_292868 Nuc 0.054 4556 0.054 2,860.5 (592.1) 0.054 1,162.9 (676.2) 0.054 1,634.4 (623.4)

contig_1143132 Nuc 0.054 4702 0.054 3,414.6 (853) 0.054 3,394.9 (1,320.2) 0.054 2,474.3 (750.3)

contig_155051 Nuc 0.054 5047 0.025 3.7 (2.1) 0.035 3.1 (1.9) 0.035 0.6 (0.8)

contig_327571 Nuc 0.054 5198 0.025 791.7 (186) 0.025 4.5 (2.9) 0.025 2.1 (1.5)

contig_618918 Nuc 0.054 5683 0* - 0.054 187.1 (71.6) 0.054 83.3 (26.8)

contig_437923 Nuc 0.054 6065 0.025 91.8 (25.3) 0.025 0.5 (0.7) 0.025 0.4 (0.6)

contig_309381 Nuc 0.054 6084 0.025 1,241.9 (226) 0.025 2.4 (1.9) 0.025 1.5 (1.6)

contig_847878 Nuc 0.054 6872 0.025 146.6 (38) 0.025 4.4 (2.8) 0.025 2.2 (1.7)

contig_1215906 Nuc 0.054 6925 0.025 495.5 (116) 0.025 2.5 (1.9) 0.025 1.1 (1.2)

contig_531481 Nuc 0.054 9208 0.025 781 (272.5) 0.025 4.5 (3) 0.025 2.2 (1.6)

contig_1056163 Nuc 0.054 9929 0.025 1 (0.8) 0.035 0.8 (0.9) 0.035 0.2 (0.6)

contig_353457 Nuc 0.054 15713 0.025 271.6 (82.8) 0.025 7.3 (3.4) 0.025 2.5 (1.8)

contig_930173 Nuc 0.054 17437 0.025 6.7 (3.1) 0.025 7.1 (4.4) 0.025 3.4 (2.4)

contig_863648 Nuc 0.054 24196 0.025 12.2 (6.4) 0.035 19.5 (10.5) 0.035 4.3 (4.4)

sturg_tRNA mt 0.054 2 0 - 0 - 0 -
40

Optimization Run Production Run 1 Production Run 2 Production Run 3

Locus Class

Primer 

Conc. 

[μM] Count

Primer 

Conc. 

[μM]

Average Count 

(SD)

Primer 

Conc. 

[μM]

Average Count 

(SD)

Primer 

Conc. 

[μM]

Average Count 

(SD)

sturg_CR mt 0.054 5053 0.025 0 (0) 0.035 0 (0) 0.035 0 (0)

sturg_nd5 mt 0.054 6669 0.025 0 (0) 0.035 0 (0) 0.035 0 (0)

sturg_nd1 mt 0.054 9883 0.025 0 (0) 0.035 0 (0) 0.035 0 (0)

sturg_co1 mt 0.054 31545 0.025 2,703.3 (3,743) 0.025 116.8 (63.5) 0.025 10.3 (8.8)

sturg_atp6 mt 0.054 90253 0.025 1,8475.3 (1,0631.1) 0.005 246.8 (141.1) 0.005 957.7 (494.1)

sturg_nd4L mt 0.054 188075 0.025 0 (0.1) 0.035 0 (0) 0.035 0 (0)

AOX23 Msat 0.054 0 0 - 0 - 0 -

AOXLS39 Msat 0.054 0 0 - 0 - 0 -

AOXLS54 Msat 0.054 0 0 - 0 - 0 -

AOXD170 Msat 0.054 0 0 - 0 - 0 -

AOXLS68 Msat 0.054 1 0.1 0.2 (0.4) 0.1 0.1 (0.4) 0.1 2.4 (15.4)

AOXD188 Msat 0.054 1 0 - 0 - 0 -

AOXD44 Msat 0.054 6 0.1 380.3 (98.9) 0.1 108.4 (36.6) 0.1 256.2 (121.9)

AOX12 Msat 0.054 9 0.1 5.6 (3.4) 0.1 0 (0) 0.1 0 (0.1)

AOXD241 Msat 0.054 28 0.1 448.8 (435.6) 0.1 79.2 (75.2) 0.1 262.4 (240.5)

AOXLS19 Msat 0.054 84 0.1 0 (0) 0.1 0 (0) 0.1 0 (0)

AOXD165 Msat 0.054 570 0.054 561.2 (159.8) 0.054 45.7 (22) 0.054 75.1 (41.6)

AOX45 Msat 0.054 656 0.054 180.5 (97.8) 0.054 0.3 (0.5) 0.054 1.4 (1.5)

Notes: The marker classes are abbreviated as follows: “Nuc” = nuclear, “mt” = mitochondrial, and “Msat” = microsatellite. The optimization run was based on a 

single individual, whereas each of the three production runs used 96 unique individuals.
41

Figure 1. Number of reads partitioned by nuclear SNP containing amplicons, mtDNA SNP containing amplicons, and microsatellites for 

an initial GT-seq optimization run.

The total number of reads is 577,097, which were amplified from a single individual and sequenced on a MiSeq. See text for details and Table 8 for a summary of 

data used to make the plot. Red triangles represent loci that had zero reads, and blue circles represent loci with 1 read. Note the log scale on the y-axis. 
42

Figure 2. Number of reads partitioned by nuclear SNP amplicons, mtDNA SNP amplicons, and microsatellites for the first GT-seq run of 

n = 96 individuals.

The order of loci is the same as within Figure 1. The y-axis represents the average number of reads across all 96 individuals at each locus. See text for details and 

Table 8 for data used to make the plot. Red triangles represent loci that had zero reads, blue circles represent loci with an average of one read or less, and black 

“X”s represent primer pairs that were removed from the multiplex prior to amplification. Note the log scale on the y-axis. 
43

Figure 3. Number of reads partitioned by nuclear SNP amplicons, mtDNA SNP amplicons, and microsatellites for the second GT-seq run 

of n = 96 individuals.

The order of loci is the same as within Figure 1. The y-axis represents the average number of reads across all 96 individuals at each locus. See text for details and 

Table 8 for data used to make the plot. Red triangles represent loci that had zero reads, blue circles represent loci with an average of one read or less, and black 

“X”s represent primer pairs that were removed from the multiplex prior to amplification. Note the log scale on the y-axis.
44

Figure 4. Number of reads partitioned by nuclear SNP amplicons, mtDNA SNP amplicons, and microsatellites for the third GT-seq run of 

n = 96 individuals.

The order of loci is the same as within Figure 1. The y-axis represents the average number of reads across all 96 individuals at each locus. See text for details and 

Table 8 for data used to make the plot. Red triangles represent loci that had zero reads, blue circles represent loci with an average of one read or less, and black 

“X”s represent primer pairs that were removed from the multiplex prior to amplification. Note the log scale on the y-axis.
45

3.2.3 GT-seq Run Summary

3.2.3.1 Nuclear SNPs

The final merged VCF file combining data from all the nuclear SNP loci across n = 288 individuals was 

filtered to retain genotypes with a minimum coverage depth of 10 and loci with a maximum of 70% 

missing data; any loci with indels were removed. Application of these filters resulted in a final VCF file 

containing all n = 288 individuals and n = 118 loci.

3.2.3.2 mtDNA SNPs

Sequencing of the candidate mitogenomic SNPs identified previously by TL King within the GT-seq 

panel was largely unsuccessful. Only two of the primer pairs (Sturg_cox1, and Sturg_atp6) ultimately 

amplified consistently among most individuals with greater than 10X coverage. However, the putative 

SNPs identified by TL King within these two amplicons were invariant.

3.2.3.3 Microsatellites

Only two of the microsatellite loci (AoxD44 and AoxD165) amplified consistently among the three GTseq runs. Their inclusion was largely exploratory, and they were not examined further. 

3.2.3.4 Assessment of the Utility of Mitogenomic SNPs for MSA

Assessment of the utility of mitogenomic SNPs for MSA – A reanalysis of the shotgun genomic data for 

mitogenomic variation yielded a VCF file that was filtered to retain loci with a minor allele frequency of 

0.05 or above, minimum coverage depth of 5, and minimum mapping quality of 30. This final VCF file 

contained 65 individuals and 45 sites. Comparison of these 45 sites against those in Table 5 revealed the 

following 10 shared variant sites with the earlier analyses of T.L. King: 3085, 3393, 6674, 9698, 10221, 

15750, 16029, 16206, 16126, and 16236. 

Twenty-one of the SNPs we identified were in the control region (within bp 15708–16565 of reference 

KP997217). An earlier assessment of variation within a 203 bp portion of the control region among 322 

Atlantic sturgeon corresponding to bp 15680–15883 of the reference KP997217 by Wirgin et al. (2000) 

and Grunwald et al. (2008) were in agreement with our results. Within this 203 bp region, Wirgin et al. 

(2000) and Grunwald et al. (2008) identified 27 polymorphic nucleotide sites, and within our sample of 

65 Atlantic sturgeon, we identified seven of the same variant sites. This result makes sense given that our 

sample size was almost five times smaller than that of Wirgin et al. (2000), and therefore we failed to 

characterize all the same sites that Wirgin et al. (2000) did. However, our sequencing of the remainder of 

the control region identified 14 SNPs previously not identified by Wirgin et al. (2000), as well as multiple 

SNPs within other genes. With a larger sample size, it is probable that multiple additional SNP loci may 

be present in the mitogenome. 

DAPC analysis of the mitochondrial variation largely supported the population structure observed with 

the microsatellite and SNP-based analyses (Figure 5). Three primary groupings were evident, including a 

northern group (Kennebec, James, Saint Lawrence, Hudson, and Delaware), a southern group (Altamaha, 

Edisto, Savannah, Ogeechee), and the York River as a separate cluster. 
46

Figure 5. DAPC plot illustrating the relationships among n = 65 Atlantic sturgeon from among 10 Atlantic coastal rivers genotyped at n = 

40 mitochondrial SNP loci.

Rivers are abbreviated as YK = York, OG = Ogeechee, SAV = Savannah, ALT = Altamaha, ED = Edisto, DE = Delaware, HU = Hudson, KEN = Kennebec, JM = 

James, SL = Saint Lawrence. The plot was constructed from in the adegenet package (Jombart 2008, Jombart 2011) within R. The number of principal 

components retained was 12, and the number of discriminant functions retained was 9.
47

4 Population Genomics and Assessment of Marker Utility

Our GT-seq panel provided genotype data of Atlantic sturgeon river-resident juveniles (< 500 mm TL) or 

adults (≥ 1,500 mm TL) captured in their natal river from representative populations from across their 

range (Table 9; Figure 6). Following bioinformatic analyses, our genomic baseline for Atlantic sturgeon

included 288 individuals genotyped at 118 nuclear SNP loci. On average, 99.1% of loci were able to be 

genotyped for each individual across the entire data set. In addition, the majority of these individuals had 

previously been genotyped using 12 microsatellite loci by the USGS LSC as part of their genetic baseline, 

which enabled a comprehensive comparison between the marker types.

Table 8. Collections of Atlantic sturgeon used to develop a genomic baseline.

River Samples Collection Year(s) Age Class

Kennebec 32 2010–2011 Adult

Hudson 32 2015 Adult

Delaware 32 2008–2009 RRJ

York 32 2013–2015, 2017 Adult

James 32 2013–2015 Adult

Edisto 32 2005 RRJ

Savannah 32 2008, 2013 RRJ

Ogeechee 32 2014–2015 RRJ

Altamaha 32 2011 Adult
48

Figure 6. Natal rivers of nine populations of Atlantic sturgeon represented in our genomic 

baseline.

Thirty-two individuals were chosen to represent each population, consisting of river-resident juveniles (< 500 mm TL) 

and/or adults (≥ 1,500 mm TL).
49

4.1 Within-population Diversity

Diversity metrics were calculated for each population using the divBasic function from the diveRsity 

package in R (Keenan et al. 2013). Allelic richness averaged 1.77 alleles per locus across all populations 

(range: 1.71–1.82; Table 10). Although allelic richness was similar for all populations, there was a 

general trend of more northern populations containing greater levels of allelic richness. Observed 

heterozygosity averaged 0.32 (range: 0.31–0.34) across populations and was always greater than expected 

heterozygosity (mean: 0.25, range: 0.23–0.27). A chi-square test for goodness of fit to Hardy-Weinberg 

equilibrium expectations (HWE) was run at each locus within each population. Using a simple 

Bonferroni-adjustment for multiple comparisons (0.05/118) within each population, a critical test statistic 

of α = 0.000424 was applied to identify significant deviations from HWE. Within each population, an 

average of 17.1 loci (range = 15–19 loci) exhibited significant deviations from HWE. Upon closer 

examination, seven loci were found to deviate from HWE in all nine populations (Figure 7). We suspect 

these deviations reflect relatively small sample sizes, as well as small effective population sizes, and may 

be further confounded by and ongoing shift from tetraploidy to diploidy in this taxa. In particular, the 

overall pattern of heterozygote excess suggests that some loci may be paralogous and fixed 

heterozygotes—an inherent challenge when working with species that have a history of polyploidy. 

Future work may wish to consider filtering these loci.

Table 9. Within-population diversity metrics for nine populations of Atlantic sturgeon based on 

118 nuclear SNP markers.

Population

Average 

Number of 

Individuals 

Genotyped 

per Locus

Total 

Alleles 

Observed

Proportion 

of Total 

Alleles 

Observed 

in 

Population

Allelic 

Richness

Observed 

Heterozygosity 

(HO)

Expected 

Heterozygosity 

(HE)

Kennebec 31.83 219 95.76 1.82 0.33 0.26

Hudson 31.76 217 95.20 1.82 0.34 0.27

Delaware 31.57 214 93.79 1.78 0.32 0.25

York 31.99 210 92.09 1.75 0.31 0.23

James 31.90 214 94.07 1.78 0.33 0.25

Edisto 30.81 204 89.69 1.71 0.31 0.23

Savannah 31.73 210 92.37 1.74 0.31 0.23

Ogeechee 31.83 213 93.22 1.77 0.32 0.25

Altamaha 31.85 211 92.66 1.75 0.31 0.24
50

Figure 7. Histogram showing the frequency of deviations (P < 0.000262) from HWE expectations 

for 118 nuclear SNP loci surveyed across nine populations of Atlantic sturgeon.

We used GENEPOP version 4.2.1 (Raymond and Rousset 1995) to evaluate if there was linkage 

disequilibrium between loci within each population. Using a critical test statistic of α = 0.05, we found 

fewer instances of linkage disequilibrium between pairs of loci in each of the nine populations examined 

than would be expected by random chance (Table 11), so no further adjustments for multiple 

comparisons were implemented. In the future, a more complete and thoroughly annotated sturgeon 

genome would be helpful to ascertain the physical proximity of our SNP loci. Sampling of a larger 

number of individuals from a larger number of populations would also provide greater statistical power to 

detect linkage between loci. Based on the available data, we assumed our markers were independent. 
51

Table 10. Results of tests for linkage disequilibrium between 118 pairs of nuclear SNP loci 

conducted in GENEPOP. A critical test statistic of α = 0.05 was applied to each test.

Population Number of Loci Pairs Tested Number of Significant Comparisons

Kennebec 4,362 124

Edisto 3,121 63

Altamaha 3,652 91

Hudson 4,265 105

James 4,093 88

Delaware 3,881 112

Ogeechee 3,821 146

Savannah 3,474 93

York 3,570 100

4.2 Among-population Diversity

Pairwise differentiation (FST) scores were calculated using the diffCalc function from the diveRsity

package (Kennan et al. 2013) in R and are presented in Table 12. Mean differentiation between 

populations was 0.0421 (range: 0.010–0.0975). There was a general pattern of isolation by distance, as 

populations with natal rivers that were in closer proximity typically exhibited less differentiation. The 

four populations in the South Atlantic DPS were particularly similar to one another (FST range: 0.0146–

0.0327). In contrast, pairwise comparisons between the Kennebec River and Hudson River populations to 

populations in the South Atlantic DPS showed the greatest levels of differentiation (range: 0.0442–

0.0975).

Table 11. Pairwise differentiation scores (FST) for nine populations of Atlantic sturgeon based on 

118 nuclear SNP markers.

Kennebec Hudson Delaware York James Edisto Savannah Ogeechee Altamaha

Kennebec 0.0000 - - - - - - - -

Hudson 0.0201 0.0000 - - - - - - -

Delaware 0.0223 0.0125 0.0000 - - - - - -

York 0.0568 0.0604 0.0321 0.0000 - - - - -

James 0.0381 0.0304 0.0114 0.0376 0.0000 - - - -

Edisto 0.0975 0.0854 0.0594 0.0378 0.0454 0.0000 - - -

Savannah 0.0830 0.0704 0.0424 0.0281 0.0375 0.0182 0.0000 - -

Ogeechee 0.0643 0.0442 0.0260 0.0353 0.0264 0.0327 0.0146 0.0000 -

Altamaha 0.0890 0.0768 0.0529 0.0353 0.0475 0.0171 0.0100 0.0159 0.0000

These overall patterns of genetic differentiation among populations were very similar to what has been 

previously reported among the same nine populations using microsatellite markers (Table 13). A mantel 

test comparing pairwise FST values based on SNPs and microsatellites for these populations showed a 

strong (r = 0.771) and statistically significant (P < 0.001) correlation. This result suggests that the 

microsatellite panel is a reasonable proxy for genome-wide patterns of differentiation. However, the 

pairwise values were generally smaller for SNPs than those reported with microsatellite markers. This is 

an expected result as microsatellites exhibit high mutation rates and diverge much more rapidly among 

populations with restricted gene flow when compared to nuclear SNPs (Fischer et al 2017). 
52

Table 12. Pairwise FST scores for nine populations of Atlantic sturgeon based on 12 microsatellite 

markers.

Kennebec Hudson Delaware York James Edisto Savannah Ogeechee Altamaha

Kennebec 0.0000 - - - - - - - -

Hudson 0.0443 0.0000 - - - - - - -

Delaware 0.0566 0.0211 0.0000 - - - - - -

York 0.1094 0.1176 0.1096 0.0000 - - - - -

James 0.0687 0.0539 0.0488 0.0955 0.0000 - - - -

Edisto 0.1192 0.1097 0.1139 0.1061 0.0788 0.0000 - - -

Savannah 0.1028 0.1004 0.1041 0.0900 0.0591 0.0248 0.0000 - -

Ogeechee 0.0837 0.0833 0.0943 0.1079 0.0531 0.0391 0.0286 0.0000 -

Altamaha 0.1206 0.1201 0.1180 0.1015 0.0769 0.0308 0.0120 0.0453 0.0000

We used a DAPC (Jombart et al. 2010) implemented in the adegenet package (Jombart 2008, Jombart

2011) in R to visualize the relationships among populations (Figures 8–9). In our analysis, we retained 60 

principal components and eight discriminant functions, based on the guidance provided in an online 

tutorial (http://adegenet.r-forge.r-project.org/files/tutorial-dapc.pdf) written by the developer of the 

adegenet R package. This analysis found results that were largely concordant with past inferences based 

on microsatellite markers and the existing DPS management system, predicated on genetic variation 

across the coast with geographic clusters of genetically similar populations. The Kennebec River 

population formed a cluster that was nearly discrete from other populations. The Hudson and Delaware 

River populations were located in close proximity to one another, with some overlap with the James River 

population. In contrast, the York River population formed another nearly discrete cluster. The uniqueness 

of the York River populations could be the result of recent genetic bottlenecks and genetic drift, or

perhaps might reflect a unique evolutionary lineage that has persisted for longer periods. The four 

populations we examined from the South Atlantic DPS clustered together with substantial overlap among 

the groups. This is consistent with inferences drawn from microsatellite markers and may reflect greater 

connectivity among these geographically proximate populations.

We ran STRUCTURE 2.3.4 (Falush et al. 2003), a Bayesian clustering program, to provide an additional 

insight into Atlantic sturgeon population structure (Figure 10). The number of clusters tested ranged from 

K = 1 through K = 10. The model we used allowed admixture to occur among clusters, used correlated 

allele frequencies, and did not include informative priors based on collection location. Each run consisted 

of a burn-in period of 100,000 steps followed by 100,000 steps that were used for data collection. Five 

iterations of each level of K were run, and each was started using a sequential random seed. We evaluated 

the model outputs using STRUCTURE HARVESTER (Earl and vonHoldt 2012) and Structure Selector 

(Li and Liu 2018). We used the default settings of CLUMPAK (Kopelman et al. 2015) implemented 

using StructureSelector (Li and Liu 2018) to visualize the consensus solution for each candidate K. 

The most supported number of clusters varied among the different diagnostic tools considered. Evanno’s 

delta K most strongly supported two genetic clusters (Evanno et al. 2005). The maximum likelihood 

approach LnP(K) found the highest level of support was associated with four genetic clusters. The 

estimators published by Puechmaille (2016) supported either four (MedMedK) or five (MedMeaK, 

MaxMedK, and MaxMeaK) genetic clusters within the data set.

At K = 2, two genetic clusters were identified that largely reflected northern and southern populations 

with a break around the Chesapeake Bay. Interestingly, the James River showed stronger affinity for the 

northern cluster than the York River, despite their relative locations on the coast. The Edisto, Savannah, 

and Altamaha River populations showed strong affinity to the same cluster, but the Ogeechee River was 
53

not as clearly resolved. At K = 3, an additional cluster was identified that was represented primarily by 

fish in the James and Ogeechee River populations, with a smaller component in the Delaware River. For 

the STRUCTURE runs that assumed K = 4, the York River population clearly formed a distinct cluster 

relative to the other populations we considered. This is consistent with our DAPC analysis, which also 

found the York River population to be unusual relative to the other populations we evaluated. At K = 5 

through K = 10, all additional clusters appeared to be spurious (Puechmaille 2016).

Figure 8. Scatterplot showing the results of discriminant analysis of principle components (axis 1 

and axis 2).

Populations are denoted with the following abbreviations: KEN – Kennebec River, HUD – Hudson River, DEL –

Delaware River, YOR – York River, JAM – James River, EDI – Edisto River, SAV – Savannah River, OGE –

Ogeechee River, ALT – Altamaha River. Each DPS is shown in a different color: Gulf of Maine – blue, New York 

Bight – green, Chesapeake – yellow, South Atlantic – red.
54

Figure 9. Scatterplot showing the results of discriminant analysis of principle components (axis 1 

and axis 3).

Populations are denoted with the following abbreviations: KEN – Kennebec River, HUD – Hudson River, DEL –

Delaware River, YOR – York River, JAM – James River, EDI – Edisto River, SAV – Savannah River, OGE –

Ogeechee River, ALT – Altamaha River. Each DPS is shown in a different color: Gulf of Maine – blue, New York 

Bight – green, Chesapeake – yellow, South Atlantic – red.
55

Figure 10. Population clusters of Atlantic sturgeon inferred using STRUCTURE (based on an 

assumed K = 2 through K = 9).

We ran five iterations of each potential K using 100,000 burn-in steps followed by 100,000 steps that were recorded 

for data collection. We used the default settings of CLUMPAK (Kopelman et al. 2015) implemented using 

StructureSelector (Li and Liu 2018) to visualize the consensus solution for each candidate K. Each individual is 

represented as a vertical bar, and the proportion of its genome assigned to each of the inferred clusters is 

represented by separate colors. 
56

4.4 Assignment Testing

Genetic assignment testing was conducted using GeneClass2 (Piry et al. 2004) using the criterion of 

Rannala and Mountain (1997) with the newly established genomic baseline as a reference. We tested the 

efficiency of this approach by assigning known samples to rivers within the baseline using a leave-oneout approach. Overall, 66.3% of individuals were correctly assigned to their river of origin, (Table 14). 

Individuals from the Kennebec and James rivers were most clearly resolved (81.3%). In contrast, fewer 

than half of the individuals collected in the Delaware and York rivers were correctly assigned (40.6% and 

46.9%, respectively). Assignment efficiencies in the South Atlantic averaged 63.2% accuracy to river but 

were much stronger when aggregated at the DPS level. Many of the individuals that were assigned to the 

wrong natal river were at least assigned to a river within the same DPS. The Chesapeake DPS was a 

notable exception, as misclassified individuals from the James and York rivers were often incorrectly 

assigned to the New York Bight or South Atlantic DPS. 

Overall, 86.5% of individuals were correctly assigned to their DPS of origin (Table 15). Because Federal 

management of Atlantic sturgeon is based on the five DPSs, this performance metric warrants discussion. 

Prior to the current study, assignment testing for Atlantic sturgeon was primarily conducted with 

microsatellite markers and there was a desire to improve assignment efficiency to reduce misassignments. 

However, the microsatellite genetic baseline maintained by LSC-KCGL was substantially improved 

through the addition of many more individuals within reference populations in recent years. The current 

microsatellite genetic baseline maintained by LSC-KCGL correctly assigns 84.9% of individuals to their 

natal river and 95.8% of individuals to their DPS, based on 12 reference populations. Consequently, the 

current genomic baseline is several times more likely to incorrectly assign the origin of an Atlantic 

sturgeon.

The performance of our genomic baseline may be inhibited by several factors. First, we used a relatively 

modest number of SNP markers for assignment testing (n = 118). There is a recent trend in genomics of 

moving towards a much larger number of loci for stock assignments (thousands of loci or more). This 

trend has been driven by rapidly decreasing sequencing costs and the development of new methods (e.g., 

Ali et al. 2016). Our genomic baseline was developed from a modest (n = 32) number of individuals per 

population, and as a result our characterization of baseline allele frequencies is imperfect. This is 

especially important when biallelic SNPs with subtle differences in allele frequencies are considered. 

Although we were not able to genotype every individual at every loci, our high overall coverage (99.1%) 

suggests that this was not a major factor influencing our assignment efficiencies. 

To explore how the number of markers we used may have influenced our assignment success, we 

conducted an ad hoc sensitivity analysis using varying numbers of microsatellite and SNP markers from 

our existing assays. We reran GeneClass2 and omitted varying numbers of loci from the baseline 

assignment tests based on the order they were included in the input file, which was assumed to be 

random. There results were plotted to show the rate of improvement as additional loci were considered 

with each class of marker (Figure 11). Our ad hoc sensitivity suggested that greater increases in 

assignment performance could be realized by adding a few additional microsatellites relative to larger 

numbers of SNPs. Still, a much larger SNP panel (which may have to be genotyped using a different 

method than GT-seq) could offer enhanced accuracy over the current microsatellite markers.
57

Table 13. Classification confusion matrix for genomic population assignments to river based on 118 nuclear SNPs using the Atlantic 

sturgeon baseline.

Assigned River

Collection Location Kennebec Hudson Delaware York James Edisto Savannah Ogeechee Altamaha

Kennebec 26 1 4 - - - - 1 -

Hudson 1 21 9 - 1 - - - -

Delaware 1 9 13 2 4 1 2 - -

York - - 1 24 3 - 2 - 2

James - - 4 - 26 - - 2 -

Edisto - - - - 1 25 3 - 3

Savannah - - 1 1 - 2 20 3 5

Ogeechee - - 3 - 2 6 2 15 4

Altamaha - - - 1 1 5 2 2 21

Grand Total 28 31 35 28 38 39 31 23 35

Notes: Overall, 66.3% of individuals within the baseline were correctly assigned to their river of origin using GeneClass2. Correct assignments are denoted in bold 

text.

Table 14. Classification confusion matrix for genomic population assignments to river based on 118 nuclear SNPs using the Atlantic 

sturgeon baseline.

Assigned DPS

Collection Location Gulf of Maine New York Bight Chesapeake Bay South Atlantic

Gulf of Maine 26 5 0 1

New York Bight 2 52 7 3

Chesapeake Bay 0 5 53 6

South Atlantic 0 4 6 118

Notes: Overall, 86.5% of individuals within the baseline were correctly assigned to their river of origin using GeneClass2. Correct assignments are denoted in bold 

text.
58

Figure 11. Results of an ad hoc sensitivity analysis comparing the performance of varying 

numbers of nuclear SNP loci and microsatellite loci for assigning Atlantic sturgeon their natal 

river using GeneClass2.

A maximum of 118 nuclear SNP loci and 12 microsatellite loci were available for assignments. Across the range of 

loci numbers considered, it required approximately an order of magnitude more nuclear SNP loci to achieve 

comparable assignment testing performance relative to microsatellite loci.
59

5 Application of Molecular Markers to Sturgeon of Unknown Origin

The funded proposal indicated the USGS LSC would perform population genomics techniques on 

samples collected by the U.S. Navy under a separate BOEM-funded project. However, the U.S. Navy 

project did not capture Atlantic sturgeon in offshore habitats. As a result, we coordinated with researchers 

working under a different BOEM contract (cooperative agreement M16AC00003, titled “Monitoring 

Endangered Atlantic Sturgeon and Commercial Finfish Habitat Use Offshore New York”; co-PIs Michael 

Frisk and Brian Dunton) to obtain samples from Atlantic sturgeon collected in coastal waters.

Sample collections were authorized by the National Marine Fisheries Services (Endangered Species 

Permits 16422 and 20351), New York State Department of Environmental Conservation 

(Endangered/Threatened Species Scientific License 336), and Stony Brook University’s Institutional 

Animal Care and Use Committee (IRB-1022451-4). The Atlantic sturgeon considered herein were 

collected during targeted research trawls aboard the RV Seawolf. Tows occurred in known marine 

aggregation sites located off the Rockaway Peninsula, New York, and sampling gear used was similar to 

that described by Dunton et al. (2015). The Atlantic sturgeon we genotyped and assigned were on average 

998 mm TL (range 715–2,320 mm TL) and weighed 6.70 kg (range 1.72–77.00 kg).

5.1 Use of Microsatellites

Because assignment efficiency was lower using the genomic approach developed in the current study 

when compared to existing microsatellite markers, we consulted with our project officer at BOEM and 

chose to use our microsatellite markers to provide genetic assignments for these fish to maximize the 

value of the information generated to BOEM, our colleagues that collected the samples, and the 

management community.

5.2 DNA Extractions and Microsatellite Genotyping

Molecular analyses were performed by the USGS LSC, Kearneysville, WV. Genomic DNA was isolated 

from fish tissue using the Puregene Tissue Kit (Gentra Systems, Minneapolis, MN). DNA concentrations 

were evaluated using the Qubit 2.0 Fluorometer (Thermo Fisher Scientific) and stock DNA was diluted 

prior to PCR.

All samples were screened for 12 Atlantic sturgeon microsatellite loci (LS19, LS39, LS54, LS68, Aox12, 

Aox23, Aox45, AoxD170, AoxD188, AoxD165, AoxD44, AoxD241; described in May et al. 1997, King et 

al. 2001, and Henderson-Arzapalo and King 2002). Seven multiplexed or single PCR reactions were 

generated to genotype the 12 microsatellite DNA markers. Amplifications were carried out on either a 

PTC-200 thermal cycler (MJ Research) or T100 thermal cycler (BioRad) using the following procedure: 

initial denaturing at 94 °C for 2 min; 35 cycles of 94 °C for 30 s, 56 °C for 30 s, 72 °C for 2 min; and a 

final extension at 72 °C for 10 min. The annealing temperature for loci LS19, LS54, LS68, Aox23, and 

Aox45 was lower (53 °C), and the annealing temperature for locus LS39 was even lower (48 °C).

PCR products were combined, diluted, and run in six separate reactions on an Applied Biosystems (Foster 

City, CA, USA) 3130XL Genetic Analyzer using an internal size standard (LIZ-500 or ROX-500, 

Applied Biosystems). Genemapper 4.0 fragment analysis software was used to bin and output allelic data 

for each sample. All microsatellite scoring was automated and then checked by eye. 

A positive control sample (of known multi-locus genotype) was included on each PCR plate for checking 

success of PCR amplifications and for correct binning success in the Genemapper analysis software. A 

negative control sample (containing all the ingredients for PCR amplification except DNA) was included 

on each PCR plate to make sure there was no contamination in the PCR. Redo PCR was performed on all 
60

samples with missing data due to weak or unamplified alleles. Redo PCR amplifications were done with 

single loci and not multiplexed PCR as the original samples were done. All Genemapper files were 

double-checked for scoring errors. Duplicate samples were checked for using the excel add-in 

Microsatellite Toolkit. 

Two matching samples were found within the dataset (Aoxy21255 and Aoxy21258), which is highly 

unlikely to happen by chance. These two samples were re-extracted using new tissue received and 

amplified at all loci. After re-checking for matches, Aoxy21255 was found to match with its re-extracted 

tissue sample, but the new Aoxy21258 now matched another sample Aoxy21259. After re-extracting and 

amplifying a new sample of Aoxy21259, the new sample did not match the old sample but matched

Aoxy21257. Results for all three of these individuals have been omitted from the results presented herein.

It is recommended that new tissue samples be extracted, amplified, and genotyped for these individuals. 

5.3 Assignment Testing

Genetic assignment testing was conducted using GeneClass2 (Piry et al. 2004) using the criterion of 

Rannala and Mountain (1997) with the previously described genetic baseline as a reference. We tested the 

efficiency of this approach by assigning known samples to rivers within the baseline. Overall, 84.9% of 

individuals were correctly assigned to their river of origin, and 95.8% of individuals were correctly

assigned to their DPS of origin. (Tables 16–17). Most of the individuals that were assigned to an 

incorrect river were collected within the South Atlantic DPS and misclassified to another river within the 

South Atlantic DPS, which primarily reflects lower levels of differentiation in this area (and potentially 

more recent and/or ongoing gene flow) relative to other areas. No clear patterns were apparent in 

misclassifications at the DPS level, other than that misclassified individuals were typically assigned to a 

neighboring DPS. This reflects that neighboring DPSs are generally more similar than those that are 

further apart. 

Next, we used GeneClass2 to assess the origin of genotyped individuals collected in the Atlantic Ocean 

off of the Rockaway Peninsula in New York. The river with the highest assignment probability was 

assumed to be the river of origin. Most assignment probabilities were strong (mean: 90.6% and range: 

30.7–100.0%). However, results should be interpreted with appropriate caution as the assignments are 

based solely on allele frequencies, and it is possible that by chance an individual has a genotype that is 

more common in another population.

The majority of Atlantic sturgeon were assigned to the Hudson River population (87.1%, Table 18 and 

Figure 12). This result is generally congruent with previous mixed stock analyses in the New York Bight, 

which have also found that most Atlantic sturgeon sampled in this area are from the Hudson River 

population (Dunton et al. 2012, Waldman et al. 1996, O’Leary et al. 2014, Wirgin et al. 2015). A smaller 

proportion of the individuals were assigned to the Delaware River population (7.53%). In total, 94.6% of 

the Atlantic sturgeon we analyzed were assigned to the New York Bight DPS. Also consistent with 

previous studies, a small proportion of the sturgeon represented other populations from across the coast 

(Table 18 and Figure 12), including individuals assigned to the Kennebec, York, James, Edisto, and 

Ogeechee rivers. Four of the five ESA-listed DPSs were represented in the Stony Brook samples. This 

highlights the propensity of Atlantic sturgeon to roam widely from their natal rivers and form mixed stock 

aggregations and is also supported by telemetry data (see review by Hilton et al. 2016). Furthermore, it

suggests that development activities in this region may impact Atlantic sturgeon stocks across the coast.

These assignment testing results were provided to Stony Brook University so that they can be integrated 

with tagging and telemetry data to provide a more comprehensive perspective of Atlantic sturgeon

ecology. Future genetic assignment testing is necessary to evaluate the impacts of proposed and permitted 

anthropogenic activities on endangered Atlantic sturgeon populations, and to track their recovery.
61

Table 1615. Classification confusion matrix for genetic population assignments to river using the Atlantic sturgeon baseline.

Assigned River

Collection Location St Lawrence St John Kennebec Hudson Delaware York James Albemarle Edisto Savannah Ogeechee Altamaha Grand Total

St Lawrence 30 - - - - - - - - - - - 30

St John - 30 1 - - - - - - - - - 31

Kennebec 1 1 49 - - - 1 - - - - - 52

Hudson - - 7 305 23 - 2 - - - - - 337

Delaware - 1 1 22 149 - 8 - - - - - 181

York - - - 1 - 125 8 1 - 1 - - 136

James 1 - - 9 9 - 312 - 1 4 5 5 346

Albemarle - - - - - - - 30 - 5 1 1 37

Edisto - - - - - - - - 85 9 7 8 109

Savannah - - - - - - 1 1 5 64 9 18 98

Ogeechee - - - - - - - - 12 11 80 12 115

Altamaha - - - - - - - - 7 29 2 148 186

Grand Total 32 32 58 337 181 125 332 32 110 123 104 192 1,658

Notes: Overall, 84.9% of individuals within the baseline were correctly assigned to their river of origin using GeneClass2. Correct assignments are denoted in bold 

text.
62

Table 16. Classification confusion matrix for genetic population assignments to DPS using the Atlantic sturgeon baseline.

Assigned DPS

Collection Location Canadian Rivers Gulf of Maine New York Bight Chesapeake Carolina South Atlantic Grand Total

Canadian Rivers 60 1 0 0 0 0 61

Gulf of Maine DPS 2 49 0 1 0 0 52

New York Bight DPS 1 8 499 10 0 0 518

Chesapeake DPS 1 0 19 445 1 16 482

Carolina DPS 0 0 0 0 30 7 37

South Atlantic DPS 0 0 0 1 1 506 508

Grand Total 64 58 518 457 32 529 1,658

Notes: Overall, 95.8% of individuals within the baseline were correctly assigned to their DPS of origin using GeneClass2. Correct assignments are denoted in bold 

text.
63

Table 1817. Stock composition of 186 Atlantic sturgeon sampled in coastal waters off the 

Rockaway Peninsula, New York as part of the BOEM-funded project titled “Monitoring Endangered 

Atlantic Sturgeon and Commercial Finfish Habitat Use Offshore New York.”

Distinct Population 

Segment

Population Number of Assigned 

Individuals

Percentage of Assigned 

Individuals

Canadian Rivers St Lawrence 0 0.00%

Canadian Rivers St John 1 0.54%

Gulf of Maine Kennebec 1 0.54%

New York Bight Hudson 162 87.10%

New York Bight Delaware 14 7.53%

Chesapeake York 2 1.08%

Chesapeake James 4 2.15%

Carolina Albemarle 0 0.00%

South Atlantic Edisto 1 0.54%

South Atlantic Savannah 0 0.00%

South Atlantic Ogeechee 1 0.54%

South Atlantic Altamaha 0 0.00%

Notes: Assignments are based on genotypes at 12 microsatellite markers and were run in GeneClass2 using a 

genetic baseline developed by USGS-LSC. The most probable stock of origin is reported for each individual sample.
64

Figure 12. Visualization of stock composition of 186 Atlantic sturgeon sampled in coastal waters 

off the Rockaway Peninsula, New York.

Sturgeon were collected as part of the BOEM-funded project titled, “Monitoring Endangered Atlantic Sturgeon and 

Commercial Finfish Habitat Use Offshore New York.” Assignments are based on genotypes at 12 microsatellite 

markers and were run in GeneClass2 using a genetic baseline developed by USGS-LSC. The most probable stock of 

origin is reported for each individual sample. The inner circle shows assignments grouped by DPS, whereas the outer 

ring shows assignments to specific spawning rivers within each DPS.
65

6 General Conclusions

Our results from SNP genotyping were largely concordant with previous insights provided by 

microsatellite markers. Based on the observed differences in allele frequencies among populations, our 

genomic baseline supports previous assertations that Atlantic sturgeon show homing to natal rivers, 

despite mixing extensively in marine waters during non-breeding periods. These differences in allele 

frequencies provide the foundation for genetic assignment testing to determine the natal river and DPS of 

Atlantic sturgeon captured in offshore habitats.

Overall, there was a general pattern of genetic isolation by distance, as populations that spawn in rivers 

that were further apart tended to be more differentiated than those that were geographically proximate. 

The observed levels of differentiation among populations were somewhat lower using nuclear SNPs 

relative to those derived from microsatellites, but this is an expected result as microsatellites are known to 

mutate much more rapidly than other regions of the genome. Lower levels of differentiation (based on 

SNPs) between populations in the South Atlantic DPS suggest that populations in this region may have 

greater levels of gene flow relative to their more northerly conspecifics, which has also previously been 

suggested based on microsatellite data. Population clustering and ordination techniques using the new 

genomic data both support an overall population structure that is similar to the current DPS management 

units that were developed primarily based on using microsatellite genetic data.

The GT-seq panel we developed represents an advance in our ability to study the population genomics of 

Atlantic sturgeon. We were able to efficiently genotype 118 nuclear SNP loci with a cost-effective assay 

using tools that are available to most genetic laboratories. Our approach allows multiple classes of 

markers to be sequenced simultaneously (mitochondrial and nuclear SNPs, as well as microsatellites). In 

the future, our GT-seq approach could be modified to include markers for specific traits (as they become 

available), such as sex or disease resistance. If implemented, this would provide a truly comprehensive 

genomics approach—allowing genotypic data from microsatellites, nuclear SNPs, mitochondrial SNPs, 

and specific coding regions to be generated simultaneously and at reasonable cost.

Although GT-seq is an attractive approach for high throughput genotyping, it can be challenging to get 

balanced representation of loci. Adjustments of primer concentrations, both among the nuclear loci as 

well as the mtDNA and microsatellite loci, had somewhat unpredictable effects. For example, some loci 

ceased to amplify after adjustment of the concentration of unrelated locus primers. This suggests that the 

interactions of primers in the multiplex is unpredictable, and that small changes of the concentration of 

some loci can have unanticipated effects on others. Considering just the nuclear loci, we were ultimately 

able to reliably genotype 118/151 loci for a success rate of ~78%, whereas Campbell et al. (2014) 

reported a genotyping success rate of 187/192 loci (~97%). However, our success rate is aligned well with 

that of Aykanat et al. (2016), who successfully amplified 217 of 282 loci (~75%). Our results suggest that 

when designing a GT-seq assay, it is prudent to over-estimate the number of loci needed for the research 

problem at hand, with the knowledge that ~3–25% will likely fail to amplify or be represented at a 

coverage threshold insufficient for high confidence genotyping.

Although we chose the Illumina MiSeq for sequencing, we are aware that other higher throughput 

platforms exist such as the Illumina HiSeq that can allow for more cost-effective sequencing of a larger 

number of individuals (Campbell et al. 2014). For our study, given the exploratory nature of a relatively 

small number of individuals and loci, as well as the potential need in the future to quickly sequence 

modest numbers of individuals, the MiSeq was an appropriate choice for sequencing. Nevertheless, 

studies seeking to genotype many hundreds or thousands of individuals would be best suited to follow the 

approach of Campbell et al. (2014) and incorporate more barcoding indices and sequence libraries on a 

HiSeq.
66

In order to improve assignment success, we recommend developing a genomic assay that includes a much 

larger number of informative SNP markers and/or successfully genotypes a larger number of 

microsatellite markers. One molecular marker class that deserves additional investigation is the 

mitochondrial genome, as it harbors a number of SNPs that were previously identified by Wirgin et al. 

(2000) as phylogeographically informative as well as additional SNP loci identified by this study. Our 

assessment of variation in the mitogenome located most of the variability within the control region (n =

21 SNPs), which is not surprising because it should be under less selective pressure than other protein 

coding genes within the mtDNA. Nevertheless, we did find an additional n = 24 SNPs distributed among 

protein coding genes such as nad1, nad3, and cox1. This suggests additional sequencing of more 

individuals should be performed to see if any of these nucleotides are population specific and if they 

provide increased resolution over previous population structure estimates using just a 203 bp section of 

the control region. Results of Wirgin et al. (2012) suggest that inclusion of the 203 bp region of the 

control region combined with microsatellite results from 11 loci only had a small effect on the results of a 

mixed stock analysis, but no results were presented of the analysis of the mtDNA SNPs alone. Our study

found additional SNPs within part of the control region not sequenced by Wirgin et al. (2000) and 

Grunwald et al. (2008). We hypothesize a large full mtDNA dataset of ~700 fish as in Grunwald et al. 

(2008) could recover additional loci with phylogeographic utility. Given that mtDNA is circular and more 

resistant to degradation than nuclear DNA, and at a higher copy number in genomic DNA samples, a high 

throughput amplicon sequencing panel targeting mtDNA SNPs could be a valuable tool. Such an 

approach would likely work on degraded DNA samples and potentially offer good stock resolution. In the 

absence of further research and development of additional SNP markers for Atlantic sturgeon (possibly 

using an approach other than GT-seq), the existing microsatellite loci are the most effective means 

available to determine the natal river and DPS of Atlantic sturgeon encountered in offshore waters.

Although our study provides a foundation for additional genomic work on Atlantic sturgeon, we 

encountered several challenges that are likely to impact future studies. First, Atlantic sturgeon are 

undergoing functional diploidization from tetraploidy (Rajkov et al. 2014). This process has likely 

produced many paralogous loci, which if undetected could skew inferences made from SNP markers. 

Although we attempted to screen out paralogous loci bioinformatically, these loci could present a source 

of error if treated as orthologous. At the present, there is not a published genome for Atlantic sturgeon or 

any closely related taxa that can be leveraged to help resolve this issue. In addition, our laboratory has 

found that quality of DNA extracted from Atlantic sturgeon samples tends to be lower than for other 

species we have studied, which may present challenges if genomics approaches that rely on long sections 

of high-quality DNA are to be implemented.

In recent years, there has been increasing interest in the development of offshore energy sources to meet 

the needs of an expanding U.S. economy. Our genetic assignments of Atlantic sturgeon captured off the 

coast of New York indicates that spawning populations (and their corresponding DPS) from distant 

locations may potentially be impacted by offshore activities. In fact, activities in this region of the New 

York Bight could negatively impact Atlantic sturgeon population from at least four different DPSs. 

Consequently, there continues to be a need to understand the impacts of offshore activities on Atlantic 

sturgeon, so as to help support the management and conservation of this federally listed species across the 

vast coastal areas where it occurs. In particular, genetic and genomic assignment testing can link sturgeon 

encountered in marine aggregations to their natal habitats and help partition the impacts of permitted 

activities on specific management units (DPS or population). To develop effective conservation 

strateogies, we recommend that genetic and genomic insights be integrated with the vast amounts of 

acoustic telemetry data that have been collected over the last decade to improve our understanding of the 

life history of Atlantic sturgeon and the risks of human activities.
67

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