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

RAPD combination 1 2 1010101010 1010000000 1011101010 1010111010 1010001010 1011001010 1011110101

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

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

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

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

Saturation (rarefaction) curves No Of New alleles 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

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

Jaccard’s Only 1-1 and 1-0 count, 0-0 do not count 1010011 1001011 1001000

Jaccard’s Only 1-1 and 1-0 count, 0-0 do not count A: 1010011 AB= 0.6 0.4 (1-AB) B: 1001011 BC=0.5 0.5 C: 1001000 AC=0.2 0.8 Eliminate markers that are cosegregating (probable duplication, from same locus)

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

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

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

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

Example SPECIES X: 50%blue, 50% yellow

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

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

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)---

RAPDS> not used often now

RAPD DATA W/O COSEGREGATING MARKERS

PCA

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

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 400-1600 bp you obtain amplification

Distances between study sites White mangroves: Corioloposis caperata

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

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

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

Alignments (Se-Al)

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

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

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

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