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