Transcript DISEASES AND TREES - UC Berkeley College of Natural Resources

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
• 1011101010
• 1010101010
• 1010111010
• 1010101010
• 1010001010
• 1010101010
• 1010000000
• 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
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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
B: 1001011 BC=0.5
C: 1001000 AC=0.2
0.4 (1-AB)
0.5
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
v
v
Scenario 2
POP 1
POP 2
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)---
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
RAPDS> not used often now
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
RAPD DATA W/O COSEGREGATING MARKERS
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
PCA
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
AFLP
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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
– NNNNNNNNNNNNNNCTAAATTTTT
• Every time two PCR priming sitea are within 4001600 bp you obtain amplification
Coco Solo
Mananti
Ponsok
David
Coco Solo
0
237
273
307
Mananti
Ponsok
David
0
60
89
0
113
0
Distances between study sites
White mangroves:
Corioloposis caperata
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 plantparasitic fungi.
AFLP study on single spores
Coriolopsis caperata on
Laguncularia racemosa
Site
# of isolates
# of loci
% fixed alleles
Coco Solo
11
113
2.6
David
14
104
3.7
Bocas
18
92
15.04
Coco Solo
Coco Solo
Bocas
David
0.000
0.000
0.000
Bocas
0.2083
0.000
0.000
David
0.1109
0.2533
0.000
Distances =PhiST between pairs of
populations. Above diagonal is the Probability
Random d istance > Observed distance (1000
iterations).
Using DNA sequences
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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
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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
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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