# Presentation: 487619

## Interlaboratory Standardization of Coast-wide Chinook Salmon Genetic Data for International Harvest ManagementYear 2 Progress Report

- Paul Moran
- Conservation Biology Division
- Northwest Fisheries Science Center

- Portland, Oregon30 November 2005

**Notes:**
I appreciate this opportunity to describe the recent completion of a coast-wide standardized mixed fishery baseline for international harvest management of Chinook salmon.  I list myself as author in this slide only because of the long list of coauthors on this report and several current draft manuscripts.

## GAPS

- Bill Ardren, US Fish and Wildlife Service, Abernathy
- Melissa Baird, Northwest Fisheries Science Center
- Michael Banks, Oregon State University
- Terry Beacham, Canadian Department of Fisheries and Oceans
- Reneé Bellinger, Oregon State University
- Scott Blankenship, Southwest Fisheries Science Center
- Craig Busak, Washington Department of Fish and Wildlife
- Matt Campbell, Idaho Department of Fish and Game
- Cheryl Dean, Southwest Fisheries Science Center
- Nick Decovich, Alaska Department of Fish and Game
- Carlos Garza, Southwest Fisheries Science Center
- Chuck Guthrie, Alaska Fisheries Science Center, Auk Bay
- Eric Kretschmer, US Fish and Wildlife Service, Anchorage
- David Kuligowski, Northwest Fisheries Science Center
- Tricia Lundrigan, University of Washington
- Paul Moran, Northwest Fisheries Science Center
- Shawn Narum, Columbia River Intertribal Fish Commission
- Devon Perce, Southwest Fisheries Science Center
- Madison Powell, University of Idaho
- Lisa Seeb, Alaska Department of Fish and Game
- Jim Shaklee, Washington Department of Fish and Wildlife
- Christian Smith, Alaska Department of Fish and Game
- Ora Stefanowski, US Fish and Wildlife Service
- Jeff Stephenson, Columbia River Intertribal Fish Commission
- Janine Supernault, Canadian Department of Fisheries and Oceans
- David Teel, Northwest Fisheries Science Center
- Bill Templin, Alaska Department of Fish and Game
- Don VanDoornik, Northwest Fisheries Science Center
- Ken Warheit, Washington Department of Fish and Wildlife
- John Wenburg, US Fish and Wildlife Service
- Richard Wilmot, Alaska Fisheries Science Center
- Ruth Withler, Canadian Department of Fisheries and Oceans
- Sewall Young, Washington Department of Fish and Wildlife

- Collaborators
- 2005

**Notes:**
GAPS is the Genetic Analysis of Pacific Salmon Consortium, an ad hoc group of West Coast laboratories assembled in response to a request for proposals from the Chinook Technical Committee of the Pacific Salmon Commission. The broad representation of states, tribes, university, and federal laboratories indicates substantial support and commitment by the research community.  

## Chinook microsatellite standardization objectives

- Develop a standardized coast-wide DNA baseline that will resolve lineages and stocks within the region for which the CTC is responsible
- Facilitate future expansion of the DNA baseline and addition of novel genetic markers through exchange of DNA samples
- Develop a data base application to support the dissemination and growth of the baseline

**Notes:**
The ultimate goal of this project was 3-fold...

## Challenges to microsatellite standardization

- Few loci in common

- Precise but inaccurate sizing

**Notes:**
Microsatellite development in salmonids in the mid-1990s occurred rapidly and independently.  Also, the focus of individual lab efforts was at a local and regional level so there was little need and little or no financial support for standardization.  Consequently, there was little overlap in marker sets among labs.  Moreover, laboratories were using different electrophoretic instruments with different performance characteristics.  These performance differences in the sizing of the microsatellite alleles complicated standardizing allele names.

At the beginning of this exercise, a total of 62 loci were in active use in 7 labs coast-wide.  The upper plot tells a compelling story--most markers were being assayed routinely in only a single laboratory.  Only 3 loci were used in more than half of the labs.  Without a common set of markers there can be no standardized baseline, and typically no meaningful comparison of data generated in different labs.

The “electropherograms” in the lower panel represent genotypes for the exact same Chinook salmon individual assayed on two different instruments.  The same size standard and sizing algorithm were used, yet dramatic differences in size are seen that relate to fundamentally different physics between platforms (slab gel versus capillary).  The figure illustrates a significant difference in the estimated size of the alleles, in addition to a different relative size of  the repeat unit (i.e., 4.14 versus 4.25 bp). Both of these phenomena complicate standardization.  Modern genotyping instruments are extremely precise, but not necessarily very accurate. 

## 2-Year GAPS study design

- Year 1:  Standardization
- Select a common set of markers
- Standardize allele designations
- Select baseline populations for year 2
- Year 2:  Baseline development
- Verification via blind samples
- Sample exchange
- Data collection and storage
- Power analysis

**Notes:**
The project has two phases.  The first year is dedicated to selecting and standardizing a very powerful and robust common set of markers.  In the second year, the collaborators built a coast-wide baseline that captures all the major genetic lineages of Chinook and focuses on the primary contributors to fisheries of interest to the Chinook Technical Committee.  Quality control and power analyses are built in to the experimental design.  

## “Marker (=locus) sets” and “Sample sets”

**Markers***
- 62 sponsored loci
- 25 candidate loci
- 13 finalists for baseline construction

**Samples**
- 500-sample survey set
- (10 individuals from each of 47 populations, range-wide)
- 96-sample reference set
- (8 individuals from each of 12 regions)
- 96-sample blind tests
- 	  (diverse mixture samples)
- 15,000-sample baseline set
- (144 individuals from each of 105 populations )

**Notes:**
In a hierarchical process of marker elimination and data accumulation, various sets of population samples were examined for various sets of microsatellite loci.  A carefully considered decision was made to implement microsatellite markers rather than single nucleotide polymorphisms (SNPs).  The consensus was that the SNP technology was not sufficiently mature to obtain the power required by the CTC.  This will undoubtedly change in the coming years as more SNPs become available.

## Blind test of standardization

| A) Initial submission |  |  |  |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Locus | Lab 1 | Lab 2 | Lab 3 | Lab 4 | Lab 5 | Lab 6 | Lab 7 | Lab 8 | Lab 9 | Average |
| Ogo2 | 0.987 | 1.000 | 1.000 | 1.000 | 0.993 | 0.000 | 1.000 | 0.993 | 1.000 | 0.886 |
| Ogo4 | 0.439 | 1.000 | 1.000 | 0.995 | 1.000 | 0.000 | 0.995 | 0.994 | 0.990 | 0.824 |
| Oki100 | 0.978 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.970 | 1.000 | 0.994 |
| OMM1080 | 1.000 | 1.000 | 0.995 | 1.000 | 1.000 | 0.000 | 1.000 | 0.994 | 1.000 | 0.995 |
| Ots201b | 0.984 | 1.000 | 1.000 | 1.000 | 1.000 | 0.993 | 0.995 | 0.985 | 1.000 | 0.990 |
| Ots208b | 0.994 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.995 | 0.970 | 0.995 | 0.996 |
| Ots211 | 1.000 | 1.000 | 0.994 | 1.000 | 0.993 | 0.955 | 0.994 | 0.985 | 0.994 | 0.884 |
| Ots212 | 0.989 | 1.000 | 1.000 | 1.000 | 1.000 | 0.989 | 0.995 | 0.994 | 1.000 | 0.886 |
| Ots213 | 0.987 | 1.000 | 0.982 | 1.000 | 0.985 | 0.000 | 1.000 | 1.000 | 1.000 | 0.999 |
| Ots3M | 1.000 | 1.000 | 0.988 | 0.994 | 1.000 | 0.000 | 1.000 | 1.000 | 0.995 | 0.980 |
| Ots9 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.823 | 1.000 | 1.000 | 1.000 | 0.991 |
| OtsG474 | 0.995 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.999 |
| Ssa408 | 0.987 | 0.929 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.991 |
| Average | 0.949 | 0.995 | 0.997 | 0.999 | 0.998 | 0.597 | 0.998 | 0.991 | 0.998 | 0.955 |

**Notes:**
Blind tests demonstrated excellent scoring consistency among the laboratories (concordance with the modal genotype).  However, this exercise caught a number of record-keeping errors that resulted in low concordance scores.  We focused attention on the cells that were below the .95 threshold that was established by the group as a minimum.  In every case the errors were due to incorrect binning algorithms or data transcription errors (errant lookup tables).  

## Partially corrected concordance

| B) Partially corrected |  |  |  |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Locus | Lab 1 | Lab 2 | Lab 3 | Lab 4 | Lab 5 | Lab 6 | Lab 7 | Lab 8 | Lab 9 | Average |
| Ogo2 | 0.987 | 1.000 | 1.000 | 1.000 | 0.993 | 0.988 | 1.000 | 0.993 | 1.000 | 0.996 |
| Ogo4 | 0.994 | 1.000 | 1.000 | 0.995 | 1.000 | 0.968 | 0.995 | 0.994 | 0.990 | 0.993 |
| Oki100 | 0.978 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.970 | 1.000 | 0.994 |
| OMM1080 | 1.000 | 1.000 | 0.995 | 1.000 | 1.000 | 0.938 | 1.000 | 0.994 | 1.000 | 0.995 |
| Ots201b | 0.984 | 1.000 | 1.000 | 1.000 | 1.000 | 0.993 | 0.995 | 0.985 | 1.000 | 0.990 |
| Ots208b | 0.994 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.995 | 0.970 | 0.995 | 0.996 |
| Ots211 | 1.000 | 1.000 | 0.994 | 1.000 | 0.993 | 0.955 | 0.994 | 0.985 | 0.994 | 0.994 |
| Ots212 | 0.989 | 1.000 | 1.000 | 1.000 | 1.000 | 0.989 | 0.995 | 0.994 | 1.000 | 0.992 |
| Ots213 | 0.987 | 1.000 | 0.982 | 1.000 | 0.985 | 0.994 | 1.000 | 1.000 | 1.000 | 0.999 |
| Ots3M | 1.000 | 1.000 | 0.988 | 0.994 | 1.000 | 0.949 | 1.000 | 1.000 | 0.995 | 0.980 |
| Ots9 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.979 | 1.000 | 1.000 | 1.000 | 0.991 |
| OtsG474 | 0.995 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.999 |
| Ssa408 | 0.987 | 0.929 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 1.000 | 0.991 |
| Average | 0.992 | 0.995 | 0.997 | 0.999 | 0.998 | 0.981 | 0.998 | 0.991 | 0.998 | 0.994 |

**Notes:**
Those errors were corrected in this table if we were convinced that the corrected values reflected the genotypes that were submitted to the baseline.  It’s actually surprising that the errors were so few, even in the initial uncorrected table.  All laboratories had to modify their normal genotyping procedures and incorporate new, unfamiliar loci.  Two labs completely changed genotyping platforms midway through the project.  To appreciate the significance of changing platform, recall the electropherogram figure I presented that showed such divergent size estimates.

## Example blind test errors from one of the laboratories

**No. ****	****Source of Error**
- 6	Faint or large allele dropout - DNA quality
- 4	Conversion typo
- 4	Sample order?
- 1	Artifact?

**Notes:**
This summary of the kinds of errors observed in one lab is typical.  Upper allele drop-out is a pervasive problem, especially with poor-quality DNA and microsatellite loci with large amplification products.  Several labs reported conversion errors.  Sometimes these were typographic if data were converted by hand to CTC allele designations.  Other labs used look-up tables or other automated conversion utilities.  The latter approach was generally less error-prone, however, when logic errors were made in look-up tables, the results could be extreme.  Neither typos nor logic errors are likely to persist in the future.  Only about 1/3 of errors are unexplained.

## Current Chinook Coast-wide baseline

- ~16,000 individuals
- 110 populations (>230 samples)
- 37 regions, Gulf of AK to Central Valley CA
- Preliminary analyses complete
- Descriptive pop gen
- Power analysis

**Notes:**
DFO is accustomed to dealing with data sets of this size, but this is one of the larger projects I’ve worked on.  Routine analyses tax even powerful computers, and some commonly used software is not capable of these numbers.

## Slide 11

**Notes:**
110 population samples from SE Alaska to Central Valley California were selected to represent all the major lineages of Chinook in areas of interest to the CTC.

## Standardized GAPS loci

- Alleles per locus:  37.6 (7 – 71)
- Heterozygosity:  0.85 (0.52 – 0.95)
*F*ST:  0.071 (0.03 – 0.22)

**Notes:**
Nearly 500 alleles were identified, and high levels of variability were revealed within and among population samples.

## CTC Coast-wide microsatellite baseline 2005

**Notes:**
Multivariate analysis was used to evaluate the integrity of the regional reporting groups that were derived from a combination of previous genetic and ecological data, life-history information, and geography.  This dendrogram based on chord distance shows a close concordance with geography largely consistent with isolation by distance.  Some distinct exceptions are evident that were already well-known, e.g., different life-history forms in the Columbia River show highly divergent allele frequencies with allozymes, mitochondrial DNA and microsatellite loci.  A couple of populations showed anomalous relationships relative to expectation or to geographically proximate populations thought to represent the same evolutionary lineage.

## Simulated 100% “mixture” analysis

**Notes:**
Continuity in the reporting groups was reflected in excellent (stunning!) proportional estimates.  Nearly all reporting groups had point estimates above 90%.  Plotted are point estimates with 90% confidence limits.  Although the 100% mixture analyses are known to be overly optimistic, comparison with similar studies indicates this is a very powerful mixed fishery baseline.

## Simulated mixed fishery sample

**Notes:**
Simulated mixed fishery samples showed estimates within half a percent in nearly every cases.

## CTC Coast-wide microsatellite baseline 2005

- California CV

- Columbia sp

- Alaska/BC Transboundary

- Central BC Coast

- Deschutes fa

- E Vancouver Is

- Klamath Basin

- California Coast

- L Columbia fa

- L Columbia sp

- L Skeena

- L Thompson

- Columbia tule

- Mid Fraser

- Mid Oregon Coast

- Nass

- N California, S Oregon Coast

- N Gulf Coast; Alsek

- N Puget Sound

- N Thompson

- NSE Alaska; King Salmon

- NSE Alaska; Chilkat

- Rogue

- Snake R fa

- S Puget Sound

- S Mainland

- SSE Alaska

- SSE Alaska; Stikine

- U Columbia su/fa

- U Fraser

- U Skeena

- Washington Coast

- Washington Coast

- W Vancouver Is

- Willamette

- N Gulf Coast; Situk

- Snake sp/su

**Notes:**
Some reporting groups are not distinct from one another, reducing accuracy, e.g., Deschutes fall-run populations are very similar to upper Columbia summer-/fall-run and Snake R falls. 

## Results summary

- Concordant genotyping among laboratories
- High heterozygosity (~500 alleles)
- No obvious HW departures or linkage disequilibrium
- High concordance with previous genetic studies
- Powerful mixture analyses, both proportional estimates and individual assignment

**Notes:**
Genotyping consistency among labs rivals genotyping accuracy within labs.  The standardized baseline is completed and will clearly represent a very powerful management tool for both harvest and general population genetic descriptive analysis.

## Ahead...

- Expand baseline
- Comprehensive power analysis
- Safe and secure repository for data
- QC and certification of new labs

**Notes:**
In the future we look forward to expansion of the baseline and refinement of the reporting groups to improve accuracy.  We also work toward development of a web application that would serve spatially-referenced standardized genetic data to the research community.  Finally, we work to develop allele ladders, synthetic allele standards that will facilitate the certification of new labs to produce standardized genetic data and will provide on-going quality control for participating labs.