Software for population genetics

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Software for population genetics
Structure J. K. Pritchard et al. Geneclass 2
S. Piry et al.
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Structure

• Identification of genetic clusters
• Identification of subclustering within breeds or
  relationships between breeds
• Breed assignment of unkown samples to reference
  set

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StructureIdentification of genetic clusters

• Baysian likelihood method of identifying K
  clusters
• K number of clusters/populations provided by
  user or inferred by Structure

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StructureAncestry models

• No admixture model Each individual originates
  from one of the K populations
• Admixture models Each individual has genomic
  fractions of more than one of the K populations
• Linkage model admixture model, but linked loci
  are more likely to originate from the same
  population.
• Prior information model user pre-defines (some
  of) the clusters
• NB the model is also determined by the type of
  data one has!!

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StructureAnchestry models and input data

• Dominant markers noadmixture model.
• AA and Aa cannot be distinguished so only a
  present or absent genotype is available.
• AFLP, RFLP etc
• Sequence data, Y chrom or mtDNA haplotypes
  linkage model. Consider this as a single locus
  with many alleles.

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Structureallele frequency models

• Correlated allele frequencies frequencies in
  different populations are likely to be similar
  (due to migrations or shared ancestry).
• Independent allele frequencies allele freqencies
  are independent draws from a distribution
  specified by a factor ?

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StructureDetermining the K

• How to estimate the number of populations /
  clusters in your dataset?
• Fully resolving all the groups in your data (high
  K) testing all K values until highest likelihood
  values are reached.
• Determining the rough relations (low K)
• Trail and error

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Structurerunning parameters

• Likelihood method the program optimizes its own
  internal parameters.
• Startup configuration can have a very low
  probability, so Structure needs a learning run
  the burnin (10.000-100.000 replicates)
• Actual run enough replicates to obtain
  statistically sound results (depending on your
  dataset) 50.000 (?)

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Geneclass 2breed assignment

• Software for Genetic assignment and
  first-generation Migrant Detection
• S. Piry, A. Alpetite, J.-M. Cornuet, D. Paetkau,
  L. Baudouin, A. Estoup
• INRA, Fr.
• Journal of Heredity 200495(6) 536-9

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Geneclass 2breed assignment

• Infers the probability of assignment of reference
  populations as origin of sampled individuals on
  the basis of multilocus genotypic data.
• Haploid or diploid or mix.
• Likelihood criteria
• Genetic distances
• Allele frequencies
• Bayesian algorithm
• Monte Carlo resampling

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Two examples

• Products of protected geographical origin (PGI)
• Vitellone dellAppennino Centrale
• Allowed breeds Chianina,Romagnola, Marchicana
• Not allowed Piedmontese, Maremmana, Pezzata
  Rossa Italiana, Italian Brown, Italian Friesian,
  Charolais, Limousin, Belgian Blue
• Veau du Limousin
• Allowed breeds Limousin, Blonde
  d'Aquitaine,Limousin, Bazadaise
• Not allowed Holstein, Friesian, Fries-Hollands,
  Belgian Blue, Main-Anjou, Normand,
  Bretonne-pied-noire, Charolais, Hereford,
  Aberdeen Angus, Gasconne, Aubrac, Salers,
  Montbélliard, Simmental, Piedmontese, Swiss
  Brown, Pirinaica

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Objective?

• Identify a representative sample from a batch
• Traceability
• Fraud?
• Protection of the (cultural, economic) integrity
  of the product

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How?

• Typing with microsatellites.
• Compare patterns / allele frequencies with
  reference set.
• Reference library product of EU diversity
  project Resgen
• 45 breeds (still adding)
• 20 animals per breed
• 30 microsatellite markers

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Title
Markerorder
Genotypes (allele1allele2)
Populations
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Optimization

• No need to type all 30 microsatellites
• Product specific level of marker information
• Geneclass 2 option selfidentification
• Isolate breeds involved in the product (allowed
  or not allowed)
• Infer the level of successful selfidentification
  per maker
• Rank the markers in order of level of information

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Conclusions

• Breed assignment of unknown samples to a (large)
  reference set is quite successful
• Optimizing markerorder for each question greatly
  decreases the amount of typing necessary.
• For a more detailed picture of relationships,
  data can be analyzed in structure

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Exercise

• 37 unknown samples (file exercise.txt)
• Use the reference set (file reference.txt) to
  assign breednames to the samples
• Play with the loci to see the effect of different
  markers on the solution

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Solution