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

Anton Antonovich, Alaska Department of Fish and GameBill Ardren, US Fish and Wildlife Service, AbernathyMelissa 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 GameCheryl Dean, Southwest Fisheries Science Center

Nick Decovich, Alaska Department of Fish and GameCarlos Garza, Southwest Fisheries Science Center

Chuck Guthrie, Alaska Fisheries Science Center, Auk BayEric Kretschmer, US Fish and Wildlife Service, AnchorageDavid 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 WildlifeChristian 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 OceansDavid Teel, Northwest Fisheries Science Center

Bill Templin, Alaska Department of Fish and GameDon VanDoornik, Northwest Fisheries Science Center

Ken Warheit, Washington Department of Fish and WildlifeJohn Wenburg, US Fish and Wildlife Service

Richard Wilmot, Alaska Fisheries Science Center Ruth Withler, Canadian Department of Fisheries and OceansSewall Young, Washington Department of Fish and Wildlife

GAPSCollaborators2005

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

0

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Microsatellite marker (N = 62)

Num

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of

lab

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s

L

85.32

500

1000

1500

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89.57

100.0075.00

4.25 bp

500100015002000

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89.07 93.21

75.00 100.00

4.14 bp

70 75 80 85 90 95 100

Fragment size (bp)

Rel

ati v

ef lu

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cen t

unit s

Few loci in common

Precise but inaccurate sizing

Challenges to microsatellite standardization

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

“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 )

*Current focus on microsatellites, future interest in SNPs

Blind test of standardizationA) 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

Partially corrected concordanceB) 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

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?

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

Standardized GAPS loci 0

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Ots9

Ots3M

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OtsG474

Ogo4Ogo2

Ots21

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Ssa40

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Ots21

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Oki100

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Ots20

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Locus

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•Alleles per locus: 37.6 (7 – 71)

•Heterozygosity: 0.85 (0.52 – 0.95)

•FST: 0.071 (0.03 – 0.22)

CTC Coast-wide microsatellite baseline 2005

Microsoft PowerPoint Presentation

California CVU Columbia sp

Alaska/BC Transboundary

Central BC Coast

Deschutes fall

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Klamath Basin

California Coast

L Columbia fa

L Columbia sp

L Skeena

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Columbia tule Mid Fraser

Mid Oregon CoastNass

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N Gulf Coast; Alsek

N Puget Sound

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SSE AlaskaSSE Alaska; Stikine

U Columbia su/fa

U Fraser

U SkeenaWashington Coast

Washington Coast

W Vancouver IsWillamette

N Gulf Coast; Situk

Snake sp/su

L Fraser

Simulated 100% “mixture” analysis

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California Coast

L Columbia fa

L Columbia sp

L Skeena

L Thompson

Columbia tule Mid Fraser

Mid Oregon CoastNass

N California, S Oregon Coast

N Gulf Coast; Alsek

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SSE AlaskaSSE Alaska; Stikine

U Columbia su/fa

U Fraser

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Washington Coast

W Vancouver IsWillamette

N Gulf Coast; Situk

CTC Coast-wide microsatellite baseline 2005

Snake sp/su

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

Ahead…

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