Importance of data sharing and germplasm movement

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Susan McCouch, Cornell University

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Importance of data sharing and germplasm movement

  1. 1. Obregón,  Mexico   March  24,  2014   Importance  of  data  sharing     and  germplasm  movement     Susan  McCouch   Cornell  University  
  2. 2. Why  share?   •  Humans  have  always  shared;  it  is  in  our  nature  to   share.   •  Sharing  creates  and  sustains  relaDonships,   enhances  knowledge,  protects  from  ignorance     •  Sharing  is  powerful,  helps  reach  goals,  increases   competence,  brings  rewards  
  3. 3. Knowledge  Sharing   •  Knowledge  sharing  is  a  fundamental  process  of   civilizaDon,  it  is  central  to  learning,  it  creates   community   •  Making  specific  knowledge  available  to  the  right   people  at  the  right  Dme  is  key  to  achieving  goals   •  People  who  have  knowledge  and  relevant  data   gain  respect  when  they  share  what  they  know   •  Today,  informaDon  Technology  (IT)  codifies  &   helps  manage  knowledge  and  data  sharing  
  4. 4. Explicit  knowledge  and  data  sharing  is  greatly   facilitated  by  InformaDon  technology  (IT)   In  the  informaDon  age,  we  share  more  content,  from   more  sources,  with  more  people  than  ever  before   We  also  have  created  an  elaborate  legal  system  designed  to  allow   “ownership”  of  knowledge  through  Intellectual  Property  laws  
  5. 5. Germplasm  sharing   •  Historically  Plant  GeneDc  Resources  and  breeding   materials  were  shared  openly  and  moved  rapidly             around  the  globe   •  Germplasm  was  conceived  of  and  treated  as  a                 “public  good”  -­‐  available  to  all  without  restricDon;             its  value  was  enhanced  (not  diminished)  by  use   •  Over  last  50  years,  free  and  open  germplasm  exchange   has  been  slowly  restricted     –  InterpretaDons  of  the  Treaty  on  Biological  Diversity   –  Expanded  legal  (IP)  protecDon  of  varieDes   –  PrivaDzaDon  of  plant  breeding  and  plant  breeding  research   –  Significant  spill-­‐over  effects  of  biotechnology  &  genomics    
  6. 6. Biotechnology  &  Genomics:     powerful  partners  in  plant  breeding   The  Bourlag  Global  Rust  IniDaDve  (BGRI)  aims  to  uDlize   biotechnology,  genomics  and  classical  breeding  to  develop   &  deploy  rust  resistant  wheat  varieDes     •  IdenDfy  host  resistance  genes   •  Provide  markers  for  selecDon  of  mulD-­‐genic  rust  resistance   •  Create  novel  forms  of  resistance  or  immunity   •  Enable  modeling  and  genomic  predicDon  (GS)   •  Increase  breeding  efficiency   •  Basis  for  intellectual  property  (IP)  protecDon     BGRI  advocates  the  sharing  of  data  &  germplasm  among   parEcipants  to  hasten  the  development  of  resistance  
  7. 7. Rules  of  exchange   GERMPLASM   –  Landraces  and  wild  species  (Gene  Banks)   –  Advanced  breeding  lines  (Breeding  Programs)   •  Governed  by  different  rules  of  exchange   –  Gene  Banks  =>  InternaDonal  Treaty  on  Biological  Diversity   •  InternaDonal  vs  NaDonal  Gene  Banks   –  Breeding  Lines  =>  Bi-­‐lateral  agreements   •  Freely  distribute  if  no  IP,  formal  agreements  if  protected     DATA   •  Bi-­‐  or  mulD-­‐lateral  agreements   •  Centralized  data  &  informaDon  resource  (open  or  restricted  access)   –  Genomic  info  &  R-­‐gene  haplotypes  -­‐  accessions  &  advanced  public  lines   –  Pedigree  info,  IBD  blocks,  geneDc  relaDonships,  pop  structure   –  Phenotype,  Environment  (use  of  ontologies  &  controlled  vocabulary)  
  8. 8. Big  Data   •  High  throughput  data  collecDon  (sequencing,  “omics”)   generates  so  much  data  so  quickly  that  it  outpaces  our   ability  to  analyze  &  make  sense  of  them   •  Standards  urgently  needed  to  guide  data  collecDon,   management,  annotaDon/  curaDon,  sharing,  &  integraDon   •  Improved  experimental  designs  needed  to  help  opDmize   value  of  field  evaluaDon  &  take  advantage  of  “big  data”   •  CriDcally  important  to  link  genotypic,  phenotypic  and   environmental  informaDon  with  seed  stocks  and  geneDc   resources  being  evaluated  =>  enormous  tracking  problem  
  9. 9. Genotype   •   captures  wide  range  of   polymorphisms   •   supports  full  range  of  ploidy   •   connects  to  genomic  map   Field/Plant   ObservaEon   •   integrates  planDng,   treatment,  locality   data     • links  to  individual   plant  sample     Germplasm     •   pedigree   descripDon   •   seed  stock   informaDon   Phenotype   •   quanDtaDve  or  qualitaDve   traits   •   supports  ontology  integraDon   Track  seed  stocks,  experiments,  reps,  genotypes,  phenotypes,  environments;     provide  data  download  opDons,  querying  tools,  pipeline  for  GWAS,     mulD-­‐variate  analysis  and  data  mining.   Diversity  Database  
  10. 10. Data  informaDon  resources  
  11. 11. Can  we  connect  the  dots?   •  Everything  is  moving  very  quickly-­‐     –  Data  and  informaDon   –  UG99   •  Many  people  have  informaDon  that  could  help   others  but  it  is  not  always  available  in  Dme  or  in   ways  that  are  useful   •  Germplasm  movement  more  restricted  than   informaDon  sharing  =>  informaDon  alone  can  be   criDcal  –  many  sources  of  the  same  alleles,  many   forms  of  local  adaptaDon  
  12. 12. Introgression  lines,  mapping  populaEons   5,000  lines   300  lines  80  lines   RUF48 RUF47 RUF46 RUF45 RUF44 RUF43 RUF42 RUF41 RUF40 RUF39 RUF38 RUF37 RUF36 RUF35 RUF34 RUF33 RUF32 RUF31 RUF30 RUF29 RUF28 RUF27 RUF26 RUF25 RUF24 RUF23 RUF22 RUF21 RUF20 RUF19 RUF18 RUF17 RUF16 RUF15 RUF14 RUF13 RUF10 RUF09 RUF08 RUF07 RUF06 RUF05 RUF04 RUF03 RUF02 RUF01 Chromosome 1 2 3 4 5 6 7 8 9 10 11 12 0 50 100 150 200 Number of markers CSSL   Bi-­‐parental   MAGIC  
  13. 13. •  Small  public  or  private  sector  groups  band  together  as   “research  consorDa”  &  agree  to  share  data  and  create   “economy  of  scale”  for  problem-­‐solving  (BGRI)   •  Large,  private  sector  enterprises  already  at  “economy  of   scale”,  benefit  by  greater  access  to  info  &  materials,  may   contribute  financially  but  do  not  share  data  or  germplasm   •  Large,  coordinated  public  sector  enterprises  (China,  India,   Brazil)  may  represent  “economies  of  scale”  but  lack  strong   breeding  &  informaDon  infrastructure,  want  to  parDcipate   in  research  consorDa,  but  lack  moDvaDon  to  share   ConflicEng  moEvaEons  
  14. 14. Concept  of  “public  good”   –  A  public  good  is  a  resource  that  is  accessible  to  all,  individuals   cannot  be  excluded  from  using  it,  and  its  value  and  availability   does  not  diminish  from  use.    Generally  paid  for  by  taxaDon.   –  Free  Rider  Problem:  occurs  when  people  are  allowed  to  use  a   resource  without  paying  for  it   –  If  enough  people  can  use  the  resource  without  paying,  there  is  a   danger  that  it  breaks  the  system,  or  in  a  free  market,  that  the   resource  will  be  under-­‐provided  or  not  provided  at  all   –  In  case  of  germplasm,  disseminaDon  of  gene  bank  material  key  to   gene  bank  mission;  development  &  deployment  of  improved   varieDes  key  to  breeding;  capturing  value  of  varieDes  is   moDvaDon  to  “own  &  exclude”  (private  &  public  sector  players)    
  15. 15. SoluEons  to  Free  Rider  problems   •  Tax:  Divide  the  cost  equally  among  beneficiaries;   ensure  that  everyone  who  benefits  contributes   financially  to  maintain  &  support  the  resource.     •  Altruism:  Ask  for  donaDons.  Some  ‘free  riders’  will   not  donate,  but  enough  people  willing  to  do  so  if   cost  of  public  good  low  and  value  high.   •  PrivaEze:  Restrict  access  to  those  willing  to  pay.   ~  Mix  –N-­‐  Match  ~  
  16. 16. IRTP   India   Mexico   Iraql   Ethiopia   Uganda   Turkey   Kenya   Spain   Data  sharing  among  partners  to  improve  rate   of  geneEc  gain  in  breeding     Germplasm   Genotype   Phenotype   MulE-­‐trait  modeling   Genome  Wide  AssociaEon  Studies   Database  needed  to  manage  data  for  consorEum  partners     Define  IDB  blocks  =>  impute  genotypes,  impute  phenotypes,  characterize  germplasm  
  17. 17. DisrupEve  change   •  Big  data  (geno,  pheno,  environment)  and  computaDonal  power   provide  basis  for  mulD-­‐variate  modeling  of  plant  performance.     •  More  &  more  emphasis  on  using  genotype  to  predict   phenotypic  potenDal  of  individuals  &  populaDons  =>  driver  is   low  cost  of  genotyping  and  high  performance  compuDng.   •  Data  sharing  and  germplasm  exchange  allows  consorDum   members  to  assemble  info  about  pathogen,  R-­‐genes,  iteraDvely   develop  and  test  predicDons  about  performance  x  environment   •  Requires  new  data  management  pracDces  among  partners  to   ensure  Dmely  &  systemaDc  sharing  of  data  and  germplasm  
  18. 18. And  most  importantly,  requires  real  people  on  the   ground  observing,  monitoring,  selecDng,  and   reporDng  about  their  work  with  wheat  rust  
  19. 19. Thanks  for  listening!   And  thank  you  all  for  inviDng  me  to   your  meeDng  and  for  being  such  a   wonderful,  communicaDve  and   dedicated  scienDfic  community...  

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