1. Molecular marker data and their impact on gene bank management Chris Richards NCGRP, Fort Collins, CO Curator Workshop, Atlanta Georgia

2. Outline Review of molecular data tables What the future holds Data flow and opportunities for genebanks

3. Malli Aradhya Nahla Bassil Ed Buckler Clare Coyne Candice Gardner L. J. Grauke Ted Kisha Laura Marek Mark Millard, Ray Schnell Chuck Simon Angela Baldo Gayle Volk Molecular Marker Committee

4. Evaluation (eval) Molecular Marker (mrk) Genotypic Assay (ga) Genotypic Observation (gob) Molecular marker data tables

5. Evaluation name (ename) Location (geono) Methods (methods) Site (site) Study type (studytype) Evaluation Table

6. Marker Crop Synonyms Repeat Motif Primers Assay Conditions Range Products Known standards Genbank No. Map location Position Polymorphic type Molecular Marker Table

7. Gen assay no. Marker Evaluation Method Scoring method Control values No. of observed alleles Max observed alleles Size of alleles Unusual alleles Comment Genotypic Assay Table

8. Genetic observation Genotypic assay no. Inventory Individual Genbank link Image link Genetic Observation Table

9. Design of GRIN molecular table schema Four new molecular tables in GRIN that Accommodate multiple marker types Provide raw allelic data for individuals Accept polyploid data Record methods and standards Impact Apple, Cacao, Grape, Hazelnut, Hops, Pea, Pear, Rhubarb and Vaccinium data are available NCBI is increasing interoperability with GRIN MARKER CROP GENETIC OBSERVATION GENOTYPIC ASSAY LIT MARKER CITATION EVALUATION INVENTORY ACCESSION

10. Sample representatively. Maintain viability and longevity. Provide annotation that authenticates, calibrates and characterizes samples. Key Metrics for genebanking

11. Data integration for genebanking Core functions involve the acquisition and maintenance of genetic variation Sample accessions are the priority. GENEBANK Plant materials Data

12. Data integration for genebanking Genomic approaches and new stakeholders. New expectations and new services that are distinctly data centered. GENEBANK Plant materials Data

13. Wild Genetic Resources GENEBANK Plant materials Data BreedingGenomics Genomic databases Ecological information

14. Wild Genetic Resources GENEBANK Plant materials Data BreedingGenomics Genomic databases Ecological information

15. Wild Genetic Resources GENEBANK Plant materials Data BreedingGenomics Ecological information Genomic databases Marker assisted breeding

16. GENEBANK Plant materials Data Genomics Gene genealogies of functional loci Genetic integrity estimates Lineage identification of allele mining Spatial and environmental correlates of diversity and structure Trait specific subsets

17. Improved data integration provides tool to maintain and validate genetic diversity, provide more efficient subsets for further characterization. Integration with geo-reference and ecological data increases efficiency of locating collection sites for new accessions Impact for genebanking

18. Allele mining will benefit from collections that have information about history and structure –Partitioning of genetic clusters allows better estimation of lineages within a collection –Requires methods for SNP genotyping or targeted re-sequencing for large sets of individual samples (breadth not depth)

19. Interactions with Data Standards community –Standardized phenotyping methods-trait ontology. –Adoption of Darwin Core standards. –Development of revised habitat fields for GRIN Global.

20. Capacity building –Standardized phenotyping methods –High throughput genotyping (centralized) –Computational biology