1. Are my haplotypes sensitive enough?
To validate power of tool used, one needs to be able to differentiate among closely related individual
Generate progeny
Make sure each meiospore has different haplotype
Calculate P

2. RAPD combination

3. Conclusions
Only one RAPD combo is sensitive enough to differentiate 4 half-sibs (in white)
Mendelian inheritance?
By analysis of all haplotypes it is apparent that two markers are always cosegregating, one of the two should be removed

4. If we have codominant markers how many do I need
IDENTITY tests = probability calculation based on allele frequency… Multiplication of frequencies of alleles
10 alleles at locus 1 P1=0.1
5 alleles at locus 2 P2=0,2
Total P= P1*P2=0.02

5. Do the data make sense, based on the known biology?
Fungus that disperses through basidiospores
If we find the same genotype in different locations…..
Markers may not be sensitive enough

6. Have we sampled enough? Resampling approaches Saturation curves
A total of 30 polymorphic alleles
Our sample is either 10 or 20
Calculate whether each new sample is characterized by new alleles

7. Saturation (rarefaction) curvesNo Of
New
alleles

8. Dealing with dominant anonymous multilocus markers
Need to use large numbers (linkage)
Repeatability
Graph distribution of distances
Calculate distance using Jaccard’s similarity index

9. Jaccard’s Only 1-1 and 1-0 count, 0-0 do not count 1010011 1001011

10. Jaccard’s Only 1-1 and 1-0 count, 0-0 do not count
A: AB= (1-AB)
B: BC=
C: AC=
Eliminate markers that are cosegregating (probable duplication, from same locus)

11. Now that we have distances….
Plot their distribution (clonal vs. sexual)

12. Now that we have distances….
Plot their distribution (clonal vs. sexual)
Analysis:
Similarity (cluster analysis); a variety of algorithms. Most common are NJ and UPGMA

13. Now that we have distances….
Plot their distribution (clonal vs. sexual)
Analysis:
Similarity (cluster analysis); a variety of algorithms. Most common are NJ and UPGMA
AMOVA; requires a priori grouping

14. AMOVA groupings Individual Population Region
AMOVA: partitions molecular variance amongst a priori defined groupings

15. Example
SPECIES X: 50%blue, 50% yellow

16. AMOVA: example
Scenario 1
Scenario 2 v
POP 1
POP 2 v

17. Expectations for fungi
Sexually reproducing fungi characterized by high percentage of variance explained by individual populations
Amount of variance between populations and regions will depend on ability of organism to move, availability of host, and
NOTE: if genotypes are not sensitive enough so you are calling “the same” things that are different you may get unreliable results like 100 variance within pops, none among pops

18. The “scale” of disease
Dispersal gradients dependent on propagule size, resilience, ability to dessicate, NOTE: not linear
Important interaction with environment, habitat, and niche availability. Examples: Heterobasidion in Western Alps, Matsutake mushrooms that offer example of habitat tracking
Scale of dispersal (implicitely correlated to metapopulation structure)---

20. RAPDS> not used often now

21. RAPD DATA W/O COSEGREGATING MARKERS

23. PCA

24. AFLP Amplified Fragment Length Polymorphisms Dominant marker
Scans the entire genome like RAPDs
More reliable because it uses longer PCR primers less likely to mismatch
Priming sites are a construct of the sequence in the organism and a piece of synthesized DNA

25. How are AFLPs generated?
AGGTCGCTAAAATTTT (restriction site in red)
AGGTCG CTAAATTT
Synthetic DNA piece ligated
NNNNNNNNNNNNNNCTAAATTTTT
Created a new PCR priming site
Every time two PCR priming sitea are within bp you obtain amplification

28. Distances between study sites
White mangroves:
Corioloposis caperata

29. AFLP study on single spores
Forest fragmentation can lead to loss of gene flow among previously contiguous populations. The negative repercussions of such genetic isolation should most severely affect highly specialized organisms such as some plant-parasitic fungi.
AFLP study on single spores
Coriolopsis caperata on
Laguncularia racemosa

32. Using DNA sequences Obtain sequence
Align sequences, number of parsimony informative sites
Gap handling
Picking sequences (order)
Analyze sequences (similarity/parsimony/exhaustive/bayesian
Analyze output; CI, HI Bootstrap/decay indices

33. Good chromatogram! Bad chromatogram…
Reverse reaction suffers same problems in opposite direction
Pull-up (too much signal)
Loss of fidelity leads to slips, skips and mixed signals

34. Alignments (Se-Al)

35. Using DNA sequences Testing alternative trees: kashino hasegawa
Molecular clock
Outgroup
Spatial correlation (Mantel)
Networks and coalescence approaches

36. Using DNA sequences
Bootstrap: the presence of a branch separating two groups of microbial strains could be real or simply one of the possible ways we could visualize microbial populations. Bootstrap tests whether the branch is real. It does so by trying to see through iterations if a similar branch can come out by chance for a given dataset
BS value over 65 ok over 80 good, under 60 bad

37. Using DNA sequences Testing alternative trees: kashino hasegawa
Molecular clock
Outgroup
Spatial correlation (Mantel)
Networks and coalescence approaches

40. From Garbelotto and Chapela, Evolution and biogeography of matsutakes
Biodiversity within species
as significant as between
species