Download Summary of sixth lesson

Summary of sixth lesson
• Janzen-Connol hypothesis; explanation of why diseases lead
to spatial heterogeneity
• Diseases also lead to heterogeneity or changes through time
– Driving succession
– The Red Queen Hypothesis: selection pressure will increase number of
resistant plant genotypes
• Co-evolution: pathogen increase virulence in short term, but
in long term balance between host and pathogen
• Complexity of forest diseases: primary vs. secondaruy, modes
of dispersal etc
Summary of seventh lesson
• SEX; the great homogenizing force, and also ability to create new alleles
• INTERSTERILITY/ MATING> SOMATIC COMPATIBILITY
• NEED TO USE MULTIPLE MARKERS; SC does that, otherwise go to
molecular markers
•
PCR/ RAPDS
How to get multiple loci?
• Random genomic markers:
– RAPDS
– Total genome RFLPS (mostly dominant)
– AFLPS
• Microsatellites
• SNPs
• Multiple specific loci
– SSCP
– RFLP
– Sequence information
Watch out for linked alleles (basically you are looking at the same thing!)
Sequence information
• Codominant
• Molecules have different rates of mutation, different
molecules may be more appropriate for different questions
• 3rd base mutation
• Intron vs. exon
• Secondary tertiary structure limits
• Homoplasy
Sequence information
• Multiple gene genealogies=definitive phylogeny
• Need to ensure gene histories are comparable” partition of
homogeneity test
• Need to use unlinked loci
DNA template
QuickTime™ and a
TIFF (Uncompressed) decompressor
are needed to see this picture.
Forward primer
Thermalcycler
Reverse primer
Gel electrophoresis to visualize
PCR product
Ladder (to size
DNA product)
From DNA to genetic information
(alleles are distinct DNA sequences)
• Presence or absence of a specific PCR
amplicon (size based/ specificity of primers)
• Differerentiate through:
– Sequencing
– Restriction endonuclease
– Single strand conformation polymorphism
Presence absence of amplicon
• AAAGGGTTTCCCNNNNNNNNN
• CCCGGGTTTAAANNNNNNNNN
AAAGGGTTTCCC (primer)
Presence absence of amplicon
• AAAGGGTTTCCCNNNNNNNNN
• CCCGGGTTTAAANNNNNNNNN
AAAGGGTTTCCC (primer)
Result: series of bands that are
present or absent (1/0)
Root disease center in true fir caused by H. annosum
Ponderosa pine
Incense cedar
Yosemite Lodge 1975 Root disease centers outlined
Yosemite Lodge 1997 Root disease centers outlined
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
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
– NNNNNNNNNNNNNNCTAAATTTTT
• Every time two PCR priming sitea are within 4001600 bp you obtain amplification
AFLPs are read like RAPDs (0/1)
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
B: 1001011 BC=0.5
C: 1001000 AC=0.2
0.4 (1-AB)
0.5
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
AMOVA groupings
• Individual
• Population
• Region
AMOVA: partitions molecular variance
amongst a priori defined groupings
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
– Discriminant, canonical analysis
Results: Jaccard similarity coefficients
Frequency
P. nemorosa
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0.90
0.92
0.94
0.96
Coefficient
1.00
0.98
Frequency
P. pseudosyringae: U.S. and E.U.
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0.90
0.92
0.94
0.96
Coefficient
0.98
1.00
P. pseudosyringae genetic similarity
patterns are different in U.S. and E.U.
0.7
Frequency
0.6
0.5
Pp U.S.
0.4
Pp E.U.
0.3
0.2
0.1
0.0
0.9
0.91
0.92
0.93
0.94
0.95
0.96
0.97
Jaccard coefficient of similarity
0.98
0.99
Results: P. nemorosa
4175A
p72
p39
p91
1050
P. ilicis
P. pseudosyringae
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
0.1
P. nemorosa
Results: P. pseudosyringae
P. ilicis
P. nemorosa
4175A
2055.2
p44
= E.U. isolate
0.1
FC2D
FC2E
GEROR4
FC1B
FCHHD
FCHHC
FC1A
p80
FAGGIO 2
FAGGIO 1
FCHHB
FCHHA
FC2F
FC2C
FC1F
FC1D
FC1C
p83
p40
BU9715
p50
p94
p92
p88
p90
p56B
p45
p41
p72
p84
p85
p86
p87
p93
p96
p39
p118
p97
p81
p76
p73
p70
p69
p62
p55
p54
HELA2
HELA 1
P. pseudosyringae
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
– Discriminant, canonical analysis
– Frequency: does allele frequency match expected (hardy
weinberg), F or Wright’s statistsis
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)---
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 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
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
White mangroves:
Corioloposis caperata
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
•
•
•
•
•
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
Using DNA sequences
•
•
•
•
Testing alternative trees: kashino hasegawa
Molecular clock
Outgroup
Spatial correlation (Mantel)
• Networks and coalescence approaches
Pacifico
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
Caribe
From Garbelotto and Chapela,
Evolution and biogeography of matsutakes
Biodiversity within species
as significant as between
species