Download plant pathology basics - College of Natural Resources

Summary
•
•
•
•
•
DNA evolves leading to unique sequences that may be used to identify
species, biological species, provenences of strains, genotypes, genetic or
allelic richness and genetic structure
Mutations and recombinations drive evolution of DNA sequences. Isolation,
drift, and selection lead to unique sequences associated with different
species or isolated populations
Isolation: allopatric vs. sympatric. In both cases there is no gene flow
between species
DNA sequences can be used to identify species. They need to be aligned
and compared. If each species is unequivocally found within a statistically
supported clade, then that sequence can be used to identify species and
provenance for that group of organisms
Diagnostic sequence,narrower concept need to be from a locus that is less
variable within species and more variable in between species. Alternatively
fixed alleles may be the most powerful. Rare alleles or private alleles are
also important in defining populations (individuals that are freely mating):
allele frequencies used by assignment tests such as structure
Summary
• Sequences used to identify species either by comparison of actual
sequence or by use of taxon specific PCR primers that will only
amplify target organism. Need for control. I.e. primers that will
amplify any organism to make sure reaction is working.
• If sequences are obtained and compared they can
– Aligned with sequences of similar organisms to determine presence of
statistically significant clades
– Compared with sequences present in public databases such as
GenBank. BLAST engine
– Beware that a single locus may be deceiving, because history of locus
(gene geneaology is not necessarily history of organism)
Summary
•
If more than just species identification is needed, multiple genetic markers
will be needed. These should be as much as possible unlinked. These
multiple markers can be used to identify genotypes and study their
distribution to understand epidemiology of a disease or perform paternity
tests; determine allelic richness: this is considered an important issue in
conservation biology (normally small or isolated populations tend to loose
alleles); study the genetic structure of a species, I.e. Are populations
genetically different (are their alleleic frequencies significantly different) and
if so at what scale does the difference become significant; finally multiple
genetic markers can be used to understand if species is reproducing
sexually or not. This is important to understand epidemiology
•
Genetic information can be supported by other types of information. For
fungi for instance the use of somatic compatibility and of mating allele
richness can be used to make inferences on genotypic composition, and
relatedness of genotypes.
Mitochondrial analysis can also be used to make inferences on genetic
relatedness
•
Recognition of self vs. non self
• Intersterility genes: maintain species gene
pool. Homogenic system
• Mating genes: recognition of “other” to
allow for recombination. Heterogenic
system
• Somatic compatibility: protection of the
individual.
Recognition of self vs. non self
• It is possible to have different genotypes
with the same vc alleles
• VC grouping and genotyping is not the
same
• It allows for genotyping without genetic
tests
• Reasons behing VC system: protection of
resources/avoidance of viral contagion
Somatic incompatibility
Quic kTime™ and a
TIFF (Unc ompres sed) decompress or
are needed to see this picture.
Quic kTime™ and a
TIFF (Unc ompres sed) decompress or
are needed to see this picture.
More on somatic compatibility
• Perform calculation on power of approach
• Temporary compatibility allows for
cytoplasmic contact that then is
interrupted: this temporary contact may be
enough for viral contagion
SOMATIC COMPATIBILITY
• Fungi are territorial for two reasons
– Selfish
– Do not want to become infected
• If haploids it is a benefit to mate with other, but
then the n+n wants to keep all other genotypes
out
• Only if all alleles are the same there will be
fusion of hyphae
• If most alleles are the same, but not all, fusion
only temporary
SOMATIC COMPATIBILITY
• SC can be used to identify genotypes
• SC is regulated by multiple loci
• Individual that are compatible (recognize one
another as self, are within the same SC group)
• SC group is used as a proxy for genotype, but in
reality, you may have some different genotypes
that by chance fall in the same SC group
• Happens often among sibs, but can happen by
chance too among unrelated individuals
Recognition of self vs. non self
• What are the chances two different
individuals will have the same set of VC
alleles?
• Probability calculation (multiply frequency
of each allele)
• More powerful the larger the number of
loci
• …and the larger the number of alleles per
locus
Recognition of self vs. non
self:
probability of identity (PID)
• 4 loci
• 3 biallelelic
• 1 penta-allelic
• P= 0.5x0.5x0.5x0.2=0.025
• In humans 99.9%, 1000, 1 in one million
INTERSTERILITY
• If a species has arisen, it must have some
adaptive advantages that should not be
watered down by mixing with other
species
• Will allow mating to happen only if
individuals recognized as belonging to the
same species
• Plus alleles at one of 5 loci (S P V1 V2 V3)
INTERSTERILITY
• Basis for speciation
• These alleles are selected for more
strongly in sympatry
• You can have different species in allopatry
that have not been selected for different IS
alleles
MATING
• Two haploids need to fuse to form n+n
• Sex needs to increase diversity: need
different alleles for mating to occur
• Selection for equal representation of many
different mating alleles
MATING
• If one individuals is source of inoculum,
then the same 2 mating alleles will be
found in local population
• If inoculum is of broad provenance then
multiple mating alleles should be found
MATING
• How do you test for mating?
• Place two homokaryons in same plate and
check for formation of dikaryon
(microscopic clamp connections at septa)
Clamp connections
QuickTi me™ and a
TIFF ( Uncompressed) decompr essor
are needed to see thi s p icture.
QuickTi me™ and a
T IFF (Uncom pressed) decom pressor
are needed to see t his pict ure.
QuickTime™ and a
TIFF (U ncompressed) decompressor
are needed to see t his picture.
MATING ALLELES
• All heterokaryons will have two mating allelels,
for instance a, b
• There is an advantage in having more mating
alleles (easier mating, higher chances of finding
a mate)
• Mating allele that is rare, may be of migrant just
arrived
• If a parent is important source, genotypes should
all be of one or two mating types
Two scenarios:
• A, A, B, C, D, D, E, H,
I, L
• A, A, A,B, B, A, A
Two scenarios:
• A, A, B, C, D, D, E, H,
I, L
• A, A, A,B, B, A, A
• Multiple source of
infections (at least 4
genotypes)
• Siblings as source of
infection (1 genotype)
SEX
• Ability to recombine and adapt
• Definition of population and
metapopulation
• Different evolutionary model
• Why sex? Clonal reproductive approach
can be very effective among pathogens
Long branches in
between groups
suggests no sex is
occurring in between
groups
NJ
Het INSULARE
Fir-Spruce
True Fir EUROPE
Spruce EUROPE
True Fir NAMERICA
Pine Europe
Pine EUROPE
Pine N.Am.
Pine NAMERICA
0.05 substitutions/site
NJ
11.10 SISG CA
Small branches within a clade
indicate sexual reproduction is
ongoing within that group of
individuals
2.42 SISG CA
BBd SISG WA
F2 SISG MEX
NA S
BBg SISG WA
14a2y SISG CA
15a5y M6 SISG CA
6.11 SISG CA
9.4 SISG CA
AWR400 SPISG CA
9b4y SISG CA
15a1x M6 PISG CA
1M PISG MEX
9b2x PISG CA
A152R FISG EU
A62R SISG EU
A90R SISG EU
890 bp
CI>0.9
EU S
A93R SISG EU
J113 FISG EU
J14 SISG EU
J27 SISG EU
J29 SISG EU
0.0005 substitutions/site
EU F
NA P
Index of association
Ia= if same alleles are associated
too much as opposed to random,
it means sex is not occurring
Association among alleles
calculated and compared to
simulated random distribution
Evolution and Population
genetics
• Positively selected genes:……
• Negatively selected genes……
• Neutral genes: normally population genetics
demands loci used are neutral
• Loci under balancing selection…..
Evolution and Population
genetics
• Positively selected genes:……
• Negatively selected genes……
• Neutral genes: normally population genetics
demands loci used are neutral
• Loci under balancing selection…..
Evolutionary history
• Darwininan vertical evolutionary models
• Horizontal, reticulated models..
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
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
B: 1001011
C: 1001000
AB= 0.6
BC=0.5
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
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
Plotting distances
• Pairwise genetic distances can be plotted:
the distribution of distances can be
informative of biology of organism
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
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
• 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
400-1600 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
•
•
•
•
•
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
From Garbelotto and Chapela,
Evolution and biogeography of matsutakes
Biodiversity within species
as significant as between
species
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)
ACACACACACACACACAC
MS18
(AC)38
(AC)39
(AC)40
218 bp
220 bp
222 bp
MS43a
MS43a
MS43a
(CAGA)70
(CAGA)71
(CAGA)72
373 bp
377 bp
381 bp
(220-218)2
(222-218)2
22
42
(377-373)2
(381-373)2
42
82
(39-38)2
(40-38)2
12
22
(71-70)2
(72-70)2
12
22
AMOVA Analysis of Molecular Variance
75
Example 1: Origins of the Sudden Oak Death
Epidemic in California
(Mascheretti et al., Molecular Ecology (2008) 17: 2755-2768)
Photo: UC Davis
Photo: www.membranetransport.org
76
Photo: Northeast Plant Diagnostic Network
Spatial autocorrelation
Moran’s I
Within approx. 100
meters the genetic
structure
correlates
with the geographical
distance
0
10
100
1000
Geographical distance (m)
77
Spatial autocorrelation
0.6
0.5
0.4
Moran's I
0.3
0.2
0.1
0
-0.1
-0.2
1
10
100
1000
10000
100000
1000000
Mean Geographical Distance (m)
Moran’s I (coefficient of departure from spatial randomness) correlates
with distance up to Distribution of genotypes (6 microsatellite markers)
in different populations of P.ramorum in California
78
NJ tree of P. ramorum populations in
California
HU-1
MA-1
HU-2
MA-2
MA-3
SC-2
MO-1
MO-2
SO-1
SO-2
MA-5
SC-3
SC-1
MA-4
NURSERY
79
Example:
microsatellites genotyping of P. ramorum isolates
• Phytophthora ramorum (Oomycete)
– causal agent of Sudden Oak Death (SOD) first reported in
California in 1994
– SOD affects tanoak (Lithocarpus densiflora), coast live oak
(Quercus agrifolia), Californian black oak (Quercus kelloggii),
and Canyon live oak (Quercus chrysolepis)
– P.ramorum also cause a disease characterized mostly by leaf
blight and/or branch dieback in over 100 species of both wild
and ornamental plants, including California bay laurel
(Umbellularia cailfornica), California redwood (Sequoia
sempervirens), Camellia and Rhododrendron species
Collection of infected bay leaves from several
forests in Sonoma, Monterey, Marin, Napa,
Alameda, San Mateo
80
Microsatellites (I)
mating type A1 (EU) and mating type A2 (US)
A2 (US)
A1 (EU)
Locus 29
325/ -/337
325/337
Locus 33
315/337
325/337
Locus 65
234/252
220/222
236/244
81
Ind.
MS39a
MS39b
MS43a
MS43b
MS45
MS18
MS64
Mating type
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
129-129
129-129
129-129
129-129
129-129
129-129
129-129
129-129
129-129
129-129
129-129
129-129
129-129
129-129
129-129
129-129
129-129
129-129
129-129
129-129
246-246
246-246
246-246
246-246
246-246
246-246
246-246
246-246
250-250
250-250
250-250
250-250
250-250
250-250
250-250
246-246
246-246
246-246
246-246
246-246
369-369
369-369
373-373
373-373
373-373
373-373
373-373
373-373
369-369
369-369
369-369
377-377
377-377
377-377
377-377
377-377
377-377
369-369
381-381
381-381
486-486
486-486
486-486
486-486
486-486
486-486
486-486
486-486
486-486
486-486
486-486
490-490
490-490
490-490
490-490
490-490
486-486
486-486
486-486
494-494
167-187
167-187
167-187
167-187
167-187
167-187
167-187
167-187
167-187
167-187
167-187
167-187
167-187
167-187
167-187
167-187
167-187
167-187
167-187
167-187
220-278
220-278
220-274
220-274
220-274
220-274
220-278
220-278
220-278
220-278
220-278
220-278
220-278
220-278
220-278
220-278
220-278
220-278
222-null
222-null
342-374
342-374
342-374
342-378
342-378
342-378
342-378
342-374
342-374
342-374
342-374
342-374
342-381
342-381
342-381
342-374
342-374
342-374
342-374
342-374
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A1
A2
A2
82
Genetic analysis requires
variation at loci, variation of
markers (polymorphisms)
• How the variation is structured will tell us
– Does the microbe reproduce sexually or clonally
– Is infection primary or secondary
– Is contagion caused by local infectious spreaders or by a longdisance moving spreaders
– How far can individuals move: how large are populations
– Is there inbreeding or are individuals freely outcrossing
CASE STUDY
A stand of adjacent trees is infected by a disease:
How can we determine the way trees are infected?
CASE STUDY
A stand of adjacent trees is infected by a disease:
How can we determine the way trees are infected?
BY ANALYSING THE GENOTYPE OF THE MICROBES: if the
genotype is the same then we have local secondary
tree-to-tree contagion. If all genotypes are different then primary
infection caused by airborne spores is the likely cause of
Contagion.
CASE STUDY
WE HAVE DETERMINED AIRBORNE SPORES (PRIMARY
INFECTION ) IS THE MOST COMMON FORM OF INFECTI
QUESTION: Are the infectious spores produced by a
spreader, or is there a general airborne population of spores
may come from far away ?
HOW CAN WE ANSWER THIS QUESTION?
If spores are produced by a
local spreader..
• Even if each tree is infected by different
genotypes (each representing the result of
meiosis like us here in this class)….these
genotypes will be related
• HOW CAN WE DETERMINE IF THEY
ARE RELATED?
HOW CAN WE DETERMINE IF
THEY ARE RELATED?
• By using random genetic markers we find
out the genetic similarity among these
genotypes infecting adjacent trees is high
• If all spores are generated by one
individual
– They should have the same mitochondrial
genome
– They should have one of two mating alleles
WE DETERMINE INFECTIOUS
SPORES ARE NOT RELATED
• QUESTION: HOW FAR ARE THEY COMING FROM?
….or……
• HOW LARGE IS A POPULATION?
Very important question: if we decide we want to wipe out
an infectious disease we need to wipe out at least the
areas corresponding to the population size, otherwise
we will achieve no result.
HOW TO DETERMINE
WHETHER DIFFERENT SITES
BELONG TO THE SAME POP
OR NOT?
• Sample the sites and run the genetic markers
• If sites are very different:
– All individuals from each site will be in their own exclusive clade, if two
sites are in the same clade maybe those two populations actually are
linked (within reach)
– In AMOVA analysis, amount of genetic variance among populations will
be significant (if organism is sexual portion of variance among
individuals will also be significant)
– F statistics: Fst will be over ) 0.10 (suggesting sttong structuring)
– There will be isolation by distance
Levels of Analyses

Individual
•

identifying parents & offspring– very important in
zoological circles – identify patterns of mating between
individuals (polyandry, etc.)
In fungi, it is important to identify the "individual" -determining clonal individuals from unique individuals that
resulted from a single mating event.
Levels of Analyses cont…
• Families – looking at relatedness within colonies
(ants, bees, etc.)
• Population – level of variation within a
population.
– Dispersal = indirectly estimate by calculating
migration
– Conservation & Management = looking for
founder effects (little allelic variation),
bottlenecks (reduction in population size leads
to little allelic variation)
• Species – variation among species = what are
the relationship between species.
• Family, Order, ETC. = higher level phylogenies
What is Population
Genetics?
 About microevolution (evolution of species)
 The study of the change of allele frequencies,
genotype frequencies, and phenotype
frequencies
Goals of population genetics
• Natural selection (adaptation)
• Chance (random events)
• Mutations
• Climatic changes (population expansions and contractions)
•…
To provide an explanatory framework to describe the evolution
of species, organisms, and their genome, due to:
Assumes that:
• the same evolutionary forces acting within species
(populations) should enable us to explain the differences we see
between species
• evolution leads to change in gene frequencies within
populations
Pathogen Population Genetics
• must constantly adapt to changing environmental
conditions to survive
– High genetic diversity = easily adapted
– Low genetic diversity = difficult to adapt to changing
environmental conditions
– important for determining evolutionary potential of a pathogen
• If we are to control a disease, must target a population
rather than individual
• Exhibit a diverse array of reproductive strategies that
impact population biology
Analytical Techniques
– Hardy-Weinberg Equilibrium
• p2 + 2pq + q2 = 1
• Departures from non-random mating
– F-Statistics
• measures of genetic differentiation in populations
– Genetic Distances – degree of similarity between
OTUs
•
•
•
•
Nei’s
Reynolds
Jaccards
Cavalli-Sforza
– Tree Algorithms – visualization of similarity
• UPGMA
• Neighbor Joining
Allele Frequencies
• Allele frequencies (gene frequencies) =
proportion of all alleles in an all individuals
in the group in question which are a
particular type
• Allele frequencies:
p + q = 1
• Expected genotype frequencies:
p2 + 2pq + q2
Evolutionary principles: Factors
causing changes in genotype
frequency
• Selection = variation in fitness; heritable
• Mutation = change in DNA of genes
• Migration = movement of genes across populations
– Vectors = Pollen, Spores
• Recombination = exchange of gene segments
• Non-random Mating = mating between neighbors rather
than by chance
• Random Genetic Drift = if populations are small
enough, by chance, sampling will result in a different
allele frequency from one generation to the next.
The smaller the sample, the
greater the chance of deviation
from an ideal population.
Genetic drift at small population
sizes often occurs as a result of
two situations: the bottleneck
effect or the founder effect.
Founder Effects; typical of
exotic diseases
• Establishment of a population by a few individuals can
profoundly affect genetic variation
– Consequences of Founder effects
•
•
•
•
Fewer alleles
Fixed alleles
Modified allele frequencies compared to source pop
GREATER THAN EXPECTED DIFFERENCES AMONG
POPULATIONS BECAUSE POPULATIONS NOT IN EQUILIBRIUM
(IF A BLONDE FOUNDS TOWN A AND A BRUNETTE FOUND
TOWN B ANDF THERE IS NO MOVEMENT BETWEEN TOWNS,
WE WILL ISTANTANEOUSLY OBSERVE POPULATION
DIFFERENTIATION)
Bottleneck Effect
• The bottleneck effect occurs when the numbers of
individuals in a larger population are drastically reduced
• By chance, some alleles may be overrepresented
and others underrepresented among the survivors
• Some alleles may be eliminated altogether
• Genetic drift will continue to impact the gene pool
until the population is large enough
Founder vs Bottleneck
Northern Elephant Seal:
Example of Bottleneck
Hunted down to 20 individuals in
1890’s
Population has recovered to over
30,000
No genetic diversity at 20 loci
Hardy Weinberg Equilibrium
and F-Stats
• In general, requires co-dominant marker
system
• Codominant = expression of heterozygote
phenotypes that differ from either
homozygote phenotype.
• AA, Aa, aa
Hardy-Weinberg Equilibrium
• Null Model = population is in HW
Equilibrium
– Useful
– Often predicts genotype frequencies well
Hardy-Weinberg Theorem
if only random mating occurs, then allele frequencies
remain unchanged over time.
After one generation of random-mating, genotype
frequencies are given by
AA
Aa
aa
p2
2pq
q2
p = freq (A)
q = freq (a)
Expected Genotype
Frequencies
• The possible range for an allele frequency or
genotype frequency therefore lies between ( 0 –
1)
• with 0 meaning complete absence of that allele
or genotype from the population (no individual in
the population carries that allele or genotype)
• 1 means complete fixation of the allele or
genotype (fixation means that every individual in
the population is homozygous for the allele -i.e., has the same genotype at that locus).
ASSUMPTIONS
1) diploid organism
2) sexual reproduction
3) Discrete generations (no overlap)
4) mating occurs at random
5) large population size (infinite)
6) No migration (closed population)
7) Mutations can be ignored
8) No selection on alleles
IMPORTANCE OF HW
THEOREM
If the only force acting on the population is random
mating, allele frequencies remain unchanged and
genotypic frequencies are constant.
Mendelian genetics implies that genetic variability
can persist indefinitely, unless other evolutionary
forces act to remove it
Departures from HW Equilibrium
• Check Gene Diversity = Heterozygosity
– If high gene diversity = different genetic sources due
to high levels of migration
• Inbreeding - mating system “leaky” or breaks
down allowing mating between siblings
• Asexual reproduction = check for clones
– Risk of over emphasizing particular individuals
• Restricted dispersal = local differentiation leads
to non-random mating
Pop 3
Pop 4
FST = 0.30
Pop 2
Pop 1
FST = 0.02
Pop1
Pop2
Pop3
Sample
size
AA
20
20
20
10
5
0
Aa
4
10
8
aa
6
5
12
Pop1
Pop2
Pop3
Freq
p
(20 + 1/2*8)/40 (10+1/2*20)/40 (0+1/2*16)/40
= 0.60
= .50
= 0.20
q
(12 + 1/2*8)/40 (10+1/2*20)/40 (24+1/2*16)/40
= 0.40
= .50
= 0.80
Local Inbreeding Coefficient
• Calculate HOBS
– Pop1: 4/20 = 0.20
– Pop2: 10/20 = 0.50
– Pop3: 8/20 = 0.40
• Calculate HEXP (2pq)
– Pop1: 2*0.60*0.40 = 0.48
– Pop2: 2*0.50*0.50 = 0.50
– Pop3: 2*0.20*0.80 = 0.32
• Calculate F = (HEXP – HOBS)/ HEXP
• Pop1 = (0.48 – 0.20)/(0.48) = 0.583
• Pop2 = (0.50 – 0.50)/(0.50) = 0.000
• Pop3 = (0.32 – 0.40)/(0.32) = -0.250
F Stats
Proportions of Variance
• FIS = (HS – HI)/(HS)
• FST = (HT – HS)/(HT)
• FIT = (HT – HI)/(HT)
Pop Hs
HI
p
q
1
0.48 0.20 0.60 0.40
2
0.50 0.50 0.50 0.50
3
0.32 0.40 0.20 0.80
HT
FIS
FST
FIT
Mea 0.43 0.37 0.43 0.57 0.49 0.12 0.24
n
0.14
Important point
• Fst values are significant or not
depending on the organism you are
studying or reading about:
– Fst =0.10 would be outrageous for humans,
for fungi means modest substructuring
RESEARCHARTICLE
Isolation by landscape in populations of a prized edible
mushroom Tricholoma matsutake
Anthony Amend Æ Matteo Garbelotto Æ
Zhendong Fang Æ Sterling Keeley
Conserv Genet
DOI 10.1007/s10592-009-9894-0
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
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)
Host islands within the
California Northern Channel
Islands create fine-scale
genetic structure in two
sympatric
species of the symbiotic
ectomycorrhizal fungus
Rhizopogon
Rhizopogon occidentalis
Rhizopogon vulgaris
Rhizopogon sampling & study
area
• Santa Rosa, Santa
Cruz
– R. occidentalis
– R. vulgaris
• Overlapping ranges
– Sympatric
– Independent
evolutionary histories
Sampling
Bioassay – Mycorrhizal pine
roots
Local Scale Population
Structure
Rhizopogon occidentalis
FST = 0.26
N
5 km
T
B
FST = 0.24
Populations are similar
Grubisha LC, Bergemann SE, Bruns TD
Molecular Ecology in press.
FST
W
E
8-19 km
FST = 0.33
= 0.17
Populations are different
Local Scale Population
Structure
Rhizopogon vulgaris
FST = 0.21
N
FST = 0.20
W
E
FST = 0.25
Populations are different
Grubisha LC, Bergemann SE, Bruns TD
Molecular Ecology in press
B.
Locus
Rvu24.9
Rvu20.80
Allele
234
237
240
Santa Cruz Island (SCI)
SCI East
SCI No rth
SCI West
0.267
0.458
0.576
0.467
0.479
0.424
0.267
0.063
144
153
156
159
162
165
168
0.033
0.383
0.133
0.400
195
198
201
204
207
210
0.050
Rvu20.46
Rvu21.83
Rvu19.80
Rvu21.13
0.033
0.017
0.156
0.323
0.281
0.104
0.135
0.033
0.076
0.065
0.739
0.087
Santa Rosa
Island (SRI)
SRI
1.000
0.833
0.167
0.100
0.017
0.817
0.017
0.167
0.042
0.125
0.010
0.615
0.042
0.054
0.033
0.663
0.228
0.022
1.000
144
147
0.017
0.983
0.042
0.958
0.478
0.522
0.417
0.583
291
294
297
300
303
306
309
0.433
0.300
0.050
0.200
0.017
0.021
0.646
0.125
0.010
0.115
0.073
0.010
0.587
0.043
0.370
1.000
261
264
0.983
0.017
0.865
0.135
0.989
0.01 1
1.000
How do we know that we are
sampling a population?
• We actually do not know
• Mostly we tend to identify samples from a
discrete location as a population,
obviously that’s tautological
• Assignment tests will use the data to
define population, that is what Grubisha et
al. did using the program STRUCTURE
Four phases of INVASION
• TRANSPORT
• SURVIVAL AND ESTABLISHMENT (LAG
PHASE)
• INVASION
• POST-INVASION
TRANSPORT
• Biology will determine how
• Normally very few organisms will make it
• Use phylogeographic approach to determine
origin ( Armillaria, Heterobasidion)
• Use population genetic approach
(Cryphonectria, Certocystis fimbriata)
TRANSPORT-2
• Need to sample source pop or a pop that is
close enough
• Need markers that are polymorphic and will
differentiate genotypes haplotypes
• Need analysis that will discriminate amongst
individuals and identify relationships ( similarity
clusterying, parsimony, Fst & N, coalescent)
ESTABLISHMENT
• LAG PHASE; normally effects not noticed
because mortality are masked by background
normal mortality
• By the time the introduction is discovered,
normally too late to eradicate
• Short lag phase= aggressive pathogen
• Long lag phase= less aggressive pathogen
ESTABLISHMENT
• NORMALLY REDUCED GENETIC
VARIABILITY
INVASION
• Because of lack of equilibrium, high Fst values, I.e.
strong genetic structuring among populations
• Normally dominance of a few genotypes
• Spatial autocorrelation analyses to tell us exten of
spread
INVASION-2
• Later phase: genetic differentiation
• Higher genetic difference in areas of older establishment