summary aflp, rapds, rflps, microsatellites repeatability test for power (pid and test progeny) have...

Summary

• AFLP, RAPDs, RFLPs, microsatellites

• Repeatability

• Test for power (PID and test progeny)

• Have we sampled enough? Rarefaction curves, resampling, need to be ob flat portion of curve

Microsatellites or SSRs

• AGTTTCATGCGTAGGT CG CG CG CG CG AAAATTTTAGGTAAATTT

• Number of CG is variable• Design primers on FLANKING region, amplify DNA• Electrophoresis on gel, or capillary• Size the allele (different by one or more repeats; if number

does not match there may be polimorphisms in flanking region)

• Stepwise mutational process (2 to 3 to 4 to 3 to2 repeats)

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

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

Results: Jaccard similarity coefficients

0.3

0.90 0.92 0.94 0.96 0.98 1.00

00.10.2

0.40.50.60.7

Coefficient

Fre

quen

cy

P. nemorosa

P. pseudosyringae: U.S. and E.U.

0.3

Coefficient0.90 0.92 0.94 0.96 0.98 1.00

00.10.2

0.40.50.60.7

Fre

quen

cy

Fre

quen

cy

0.9 0.91 0.92 0.93 0.94 0.95 0.96 0.97 0.98 0.99

Pp U.S.

Pp E.U.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Jaccard coefficient of similarity

0.7

P. pseudosyringae genetic similarity patterns are different in U.S. and E.U.

0.1

4175A

p72

p39

p91

1050

p7

2502

p51

2055.2

2146.1

5104

4083.1

2512

2510

2501

2500

2204

2201

2162.1

2155.3

2140.2

2140.1

2134.1

2059.2

2052.2

HCT4

MWT5

p114

p113

p61

p59

p52

p44

p38

p37

p13

p16

2059.4

p115

2156.1

HCT7

p106

P. nemorosa

P. ilicisP. pseudosyringae

Results: Results: P. nemorosaP. nemorosa

Results: Results: P. pseudosyringaeP. pseudosyringae

0.1

4175A2055.2p44

FC2DFC2E

GEROR4 FC1B

FCHHDFCHHCFC1A

p80FAGGIO 2FAGGIO 1FCHHBFCHHAFC2FFC2CFC1FFC1DFC1Cp83p40

BU9715 p50

p94p92

p88p90

p56Bp45

p41p72p84p85p86p87p93p96p39p118p97p81p76p73p70p69p62p55p54

HELA2HELA 1

P. nemorosaP. ilicis

P. pseudosyringae

= E.U. isolate

AMOVA groupings

• Individual (within populations)

• Population (among populations)

• Region (between or among groups of populations)

AMOVA: partitions molecular variance amongst a priori defined groupings

AMOVA

• Percentage of variance by grouping (%)

• Its statistical significance (P<0.05)

• PHIst: ranges between 0 and 1 (1= populations are completely different; >0.2 significantly different, 0.1-02 moderately different, <0.1=not different. Remember PHI st can only be calculated among populations, not within. It is a proxy for Fst

How to interpret AMOVA results

• Significant amount of genetic variance within populations= populations are constituted by genetically distinct individuals. Normally indication of sexual reproduction ongoing in population

• Significant amount of genetic variance between populations= populations are genetically different, suggesting limited gene flow between them

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)---

The scale of disease

• Curves of spore dispersal (rapid dilution effect, e.g most spores fall near source, but a long low tail, a few spores will travel long distances

• Genetic structure of species: the more structure the more fragmented the less dispersal

• Mantel tests, spatial autocorrelation: plot the genetic distance against the geographic distance

y = 0.2452x + 0.5655

r 2 = 0.0266

0

1

2

3

4

5

6

7

8

1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5

Ln Geographic Distance (m)

ΦS

T/(

1- Φ

ST)

1

2

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

1 10 100 1000 10000 100000 1000000

Mean Geographical Distance (m)

Mo

ran

's I

White mangroves:Corioloposis caperata

Coco Solo Mananti Ponsok DavidCoco Solo 0Mananti 237 0Ponsok 273 60 0David 307 89 113 0

Distances between study sites

Coriolopsis caperataCoriolopsis caperata on on Laguncularia racemosaLaguncularia racemosa

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

Site # of isolates # of loci % fixed alleles

Coco Solo 11 113 2.6

David 14 104 3.7

Bocas 18 92 15.04

Distances =PhiST between pairs ofpopulations. Above diagonal is the ProbabilityRandom distance > Observed distance (1000iterations).

Coco Solo Bocas David

Coco Solo 0.000 0.000 0.000

Bocas 0.2083 0.000 0.000

David 0.1109 0.2533 0.000

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…

Pull-up (too much signal) Loss of fidelity leads to slips, skips and mixed signals

Reverse reaction suffers same problems in opposite direction

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

From Garbelotto and Chapela, From Garbelotto and Chapela, Evolution and biogeography of matsutakesEvolution and biogeography of matsutakes

Biodiversity within speciesBiodiversity within speciesas significant as betweenas significant as betweenspeciesspecies