Evaluating the Performance of SNPs for individual assignment in a non-model organism

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2011 American Fisheries Society National Meeting presentation. Comparison of different techniques for identifying informative SNPs. The published manuscript "Rank and Order: Evaluating the Performance of SNPs for individual assignment in a non-model organism" is available open access through PLoS One.

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  • Genetic information greatly contributes to the management of Alaska's fishery resources. Along with other kinds of information, genetic markers are used to identify appropriate population units (discrete stocks) for management. These markers can also be used to identify individuals of particular stocks in mixed-stock fisheries to allow escapement of spawners to declining populations. The ability to identify stock origins can also assist the enforcement of conservation closures. In addition to providing population tags, genetic variability itself is important for the survival of a population. The State's genetic policy attempts to project the level and integrity of genetic variability within populations, by limiting stock transfers between distinct stocks and by limiting the effects of hatchery fish on wild stocks.
  • Genetic information greatly contributes to the management of Alaska's fishery resources. Along with other kinds of information, genetic markers are used to identify appropriate population units (discrete stocks) for management. These markers can also be used to identify individuals of particular stocks in mixed-stock fisheries to allow escapement of spawners to declining populations. The ability to identify stock origins can also assist the enforcement of conservation closures. In addition to providing population tags, genetic variability itself is important for the survival of a population. The State's genetic policy attempts to project the level and integrity of genetic variability within populations, by limiting stock transfers between distinct stocks and by limiting the effects of hatchery fish on wild stocks.
  • Genetic information greatly contributes to the management of Alaska's fishery resources. Along with other kinds of information, genetic markers are used to identify appropriate population units (discrete stocks) for management. These markers can also be used to identify individuals of particular stocks in mixed-stock fisheries to allow escapement of spawners to declining populations. The ability to identify stock origins can also assist the enforcement of conservation closures. In addition to providing population tags, genetic variability itself is important for the survival of a population. The State's genetic policy attempts to project the level and integrity of genetic variability within populations, by limiting stock transfers between distinct stocks and by limiting the effects of hatchery fish on wild stocks.
  • FST Scaled among population variance in allele frequencyIn Estimates potential for an allele to be assigned to one population in comparison to an average populationBELS Ranks a locus’ performance for maximizing mixture estimation accuracy during individual assignmentWL Determines locus efficiency for correct population assignment and propensity to cause false assignment
  • The greatest differences in rank were observed for loci with small heterozygosities. Often these loci received a low rank (large number) from BELS and high rank (small number) from WHICHLOCI (e.gOne_U1010-81 and One_sys1-230).
  • The greatest differences in rank were observed for loci with small heterozygosities. Often these loci received a low rank (large number) from BELS and high rank (small number) from WHICHLOCI (e.gOne_U1010-81 and One_sys1-230).
  • The greatest differences in rank were observed for loci with small heterozygosities. Often these loci received a low rank (large number) from BELS and high rank (small number) from WHICHLOCI (e.gOne_U1010-81 and One_sys1-230).
  • The greatest differences in rank were observed for loci with small heterozygosities. Often these loci received a low rank (large number) from BELS and high rank (small number) from WHICHLOCI (e.gOne_U1010-81 and One_sys1-230).
  • Evaluating the Performance of SNPs for individual assignment in a non-model organism

    1. 1. RANK AND ORDER:EVALUATING THE PERFORMANCEOF SNP ASSAYS FOR SOCKEYESALMONAmerican Fisheries Society Annual Meeting 2011Caroline G. Storer1, Carita E. Pascal1, Steven B. Roberts1, William D. Templin2, Lisa W.Seeb1, and James E. Seeb11School of Aquatic and Fishery Sciences, University of Washington, Seattle, Washington 98195, USA2Gene Conservation Laboratory, Division of Commercial Fisheries, Alaska Department of Fish and Game, Anchorage, Alaska99518, USA
    2. 2. http://grizzlybearblog.wordpress.com/category/grizzly-bears-fishing/
    3. 3. MICHAEL MELFORD/National Geographic Stock
    4. 4. MICHAEL MELFORD/National Geographic Stock
    5. 5. Applying genetics to fisheriesmanagement
    6. 6. Applying genetics to fisheriesmanagement  Stock identification  Inferring population history  Parentage analysis  Fisheries forensics  Estimating mixed stock composition
    7. 7. Applying genetics to fisheriesmanagement  Stock identification  Inferring population history  Parentage analysis  Fisheries forensics  Estimating mixed stock compositionAckerman et al. 2011Genetically similar spawning populations
    8. 8. Molecular Markers, Today
    9. 9. Molecular Markers, TodaySingle nucleotide polymorphisims (SNPs)
    10. 10. Molecular Markers, TodaySingle nucleotide polymorphisims (SNPs)Population 1 ACTC GPopulation 2 ACAC G SNP locus
    11. 11. Molecular Markers, TodaySingle nucleotide polymorphisims (SNPs)Population 1  Abundant ACTC G  The number of available markers is growingPopulation 2  Not all SNPs are equal ACAC G SNP locus
    12. 12. Objectives1)Rank existing SNPs in sockeye salmon for performance1)Evaluate ranking methods for SNP panel selection
    13. 13. Approach1)Measure genetic variation at 114 SNP loci1)Rank loci according to five different measures of performance1)Evaluate ranking measures for developing 48- and 96-SNP panels
    14. 14. The populations South-central Bristol Bay Alaska Russia British Alaska Peninsula Columbia Washington Genotyped 12 populations, 61- 93 fish per population, representing 6 regions
    15. 15. Range wide genetic variation Principal Coordinate 1 (44.5%) Bristol Bay Russia Alaska Peninsula South-central Alaska British Columbia Washington Principal Coordinate 2 (15.5%)
    16. 16. Bristol Bay Alaska Peninsula South-central AlaskaRussia Washington British Columbia
    17. 17. Ranking approaches FST (Weir & Cockerham 1984) Average contribution of each locus to principal components (LC) Informativeness of assignment (In; Rosenberg et al. 2003) BELS (Bromaghin 2008) WHICHLOCI (Banks et al. 2003)
    18. 18. Locus ranksLocus rank Loci ordered by average rank
    19. 19. Locus ranksLocus rank top ranked loci Loci ordered by average rank
    20. 20. Testing panel performance Created 48- and 96-SNP panels containing top ranked loci for each approach
    21. 21. 96-SNP panelLocus rank Loci ordered by average rank
    22. 22. 48-SNP panelLocus rank Loci ordered by average rank
    23. 23. Testing panel performance Created 48- and 96-SNP panels containing top ranked loci for each approach fORCA simulation of individual assignment (Rosenberg 2005) - Uses the allele frequencies for user described populations to assign a simulated individual back to the correct population - Reports the probability that this assignment is correct
    24. 24. 96-SNP panel performance FST In LC B S I R W fORCA score E H a L I n C d H o L m O C
    25. 25. 48-SNP panel performance W L H O FST In LC I C B LC I R d E SH a o n m fORCA score
    26. 26. SNP panel performance 96 SNPs 48 SNPs fORCA score FST In LC B LW LR d E SH Oa o I Cn m C I H
    27. 27. Results  Most loci were ranked differently using each method  96-SNP panels contained many of the same loci with only 3-7 loci differing between panels  96-SNP panels performed similarly better then 48- SNP panels  FST, In, and LC 48-SNP panels performed similarly and significantly better than the BELLS and WHICHLOCI panels
    28. 28. Lessons learned  More loci is often better  The more loci to choose from the more important the ranking approach  Be wary of upward bias
    29. 29. Next StepsDetermining the value of a locus for its resolving powerversus its value due to uniqueness of information High resolving power Correlation of loci with first two principal componants
    30. 30. Next StepsDetermining the value of a locus for its resolving powerversus its value due to uniqueness of information Unique information High resolving power Correlation of loci with first two principal components
    31. 31. Acknowledgements Alaska Sustainable Salmon Fund Bristol Bay Regional Seafood Development Group The Gordon and Betty Moore Foundation The Seeb Lab The School of Aquatic and Fishery Sciences AFS Genetics Section

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