Summary DNA evolves leading to unique sequences that may be used to identify species, biological...
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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
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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)
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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
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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.
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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
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Somatic incompatibility
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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
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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
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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
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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
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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
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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)
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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
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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
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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
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MATING
• How do you test for mating?
• Place two homokaryons in same plate and check for formation of dikaryon (microscopic clamp connections at septa)
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Clamp connections
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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
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Two scenarios:
• A, A, B, C, D, D, E, H, I, L
• A, A, A,B, B, A, A
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Two scenarios:
• A, A, B, C, D, D, E, H, I, L
• Multiple source of infections (at least 4 genotypes)
• A, A, A,B, B, A, A
• Siblings as source of infection (1 genotype)
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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
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Long branches in Long branches in between groups between groups suggests no sex is suggests no sex is occurring in between occurring in between groupsgroups
Het INSULARE
True Fir EUROPE
Spruce EUROPE
True Fir NAMERICA
Pine EUROPE
Pine NAMERICA
0.05 substitutions/site
NJ
Fir-SpruceFir-Spruce
Pine EuropePine Europe
Pine N.Am.Pine N.Am.
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Small branches within a clade Small branches within a clade indicate sexual reproduction is indicate sexual reproduction is
ongoing within that group of ongoing within that group of individualsindividuals
11.10 SISG CA
2.42 SISG CA
BBd SISG WA
F2 SISG MEX
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
A93R SISG EU
J113 FISG EU
J14 SISG EU
J27 SISG EU
J29 SISG EU
0.0005 substitutions/site
NJ
890 bpCI>0.9
NA S
NA P
EU S
EU F
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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
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Evolution and Population genetics
• Positively selected genes:……• Negatively selected genes……• Neutral genes: normally population genetics
demands loci used are neutral• Loci under balancing selection…..
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Evolution and Population genetics
• Positively selected genes:……• Negatively selected genes……• Neutral genes: normally population genetics
demands loci used are neutral• Loci under balancing selection…..
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Evolutionary history
• Darwininan vertical evolutionary models
• Horizontal, reticulated models..
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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
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RAPD combination1 2
• 1010101010
• 1010101010
• 1010101010
• 1010101010• 1010000000
• 1011101010
• 1010111010
• 1010001010
• 1011001010• 1011110101
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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
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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
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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
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Saturation (rarefaction) curves
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
NoOf Newalleles
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Dealing with dominant anonymous multilocus markers
• Need to use large numbers (linkage)
• Repeatability
• Graph distribution of distances
• Calculate distance using Jaccard’s similarity index
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Jaccard’s
• Only 1-1 and 1-0 count, 0-0 do not count
1010011
1001011
1001000
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Jaccard’s
• Only 1-1 and 1-0 count, 0-0 do not count
A: 1010011 AB= 0.60.4 (1-AB)
B: 1001011 BC=0.5 0.5
C: 1001000 AC=0.2 0.8
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Now that we have distances….
• Plot their distribution (clonal vs. sexual)
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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
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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
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AMOVA groupings
• Individual
• Population
• Region
AMOVA: partitions molecular variance amongst a priori defined groupings
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Example
• SPECIES X: 50%blue, 50% yellow
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AMOVA: example
v
Scenario 1 Scenario 2
POP 1
POP 2v
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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
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Plotting distances
• Pairwise genetic distances can be plotted: the distribution of distances can be informative of biology of organism
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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
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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.
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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
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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
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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)---
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RAPDS> not used often now
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RAPD DATA W/O COSEGREGATING MARKERS
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PCA
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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
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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
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White mangroves:Corioloposis caperata
Coco Solo Mananti Ponsok DavidCoco Solo 0Mananti 237 0Ponsok 273 60 0David 307 89 113 0
Distances between study sites
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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
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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
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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
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Using DNA sequences
• Testing alternative trees: kashino hasegawa • Molecular clock• Outgroup• Spatial correlation (Mantel)
• Networks and coalescence approaches
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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
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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)
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MS18 (AC)38 218 bp(AC)39 220 bp(AC)40 222 bp
MS43a (CAGA)70 373 bpMS43a (CAGA)71 377 bpMS43a (CAGA)72 381 bp
(220-218)2 22
(222-218)2 42
(377-373)2 42
(381-373)2 82
(39-38)2 12
(40-38)2 22
(71-70)2 12
(72-70)2 22
ACACACACACACACACAC
AMOVA Analysis of Molecular Variance
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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
Photo: Northeast Plant Diagnostic Network
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Spatial autocorrelation
Geographical distance (m)
10 100 1000
Mor
an’s
I
0
Within approx. 100 meters the genetic structure correlates with the geographical distance
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-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
Spatial autocorrelation
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
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NJ tree of P. ramorum populations in California
SC-1MA-4
NURSERY
SC-3
MA-3
SO-1SO-2MA-5
SC-2MO-1MO-2
MA-2
MA-1
HU-1
HU-2
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• 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
Example: microsatellites genotyping of P. ramorum isolates
Collection of infected bay leaves from several forests in Sonoma, Monterey, Marin, Napa, Alameda, San Mateo
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Microsatellites (I)mating type A1 (EU) and mating type A2 (US)
A2 (US) A1 (EU)
Locus 29 325/ - 325/337
-/337
Locus 33 315/337 325/337
Locus 65 234/252 236/244
220/222
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Ind. MS39a MS39b MS43a MS43b MS45 MS18 MS64 Mating type
1 129-129 246-246 369-369 486-486 167-187 220-278 342-374 A1
2 129-129 246-246 369-369 486-486 167-187 220-278 342-374 A1
3 129-129 246-246 373-373 486-486 167-187 220-274 342-374 A1
4 129-129 246-246 373-373 486-486 167-187 220-274 342-378 A1
5 129-129 246-246 373-373 486-486 167-187 220-274 342-378 A1
6 129-129 246-246 373-373 486-486 167-187 220-274 342-378 A1
7 129-129 246-246 373-373 486-486 167-187 220-278 342-378 A1
8 129-129 246-246 373-373 486-486 167-187 220-278 342-374 A1
9 129-129 250-250 369-369 486-486 167-187 220-278 342-374 A1
10 129-129 250-250 369-369 486-486 167-187 220-278 342-374 A1
11 129-129 250-250 369-369 486-486 167-187 220-278 342-374 A1
12 129-129 250-250 377-377 490-490 167-187 220-278 342-374 A1
13 129-129 250-250 377-377 490-490 167-187 220-278 342-381 A1
14 129-129 250-250 377-377 490-490 167-187 220-278 342-381 A1
15 129-129 250-250 377-377 490-490 167-187 220-278 342-381 A1
16 129-129 246-246 377-377 490-490 167-187 220-278 342-374 A1
17 129-129 246-246 377-377 486-486 167-187 220-278 342-374 A1
18 129-129 246-246 369-369 486-486 167-187 220-278 342-374 A1
19 129-129 246-246 381-381 486-486 167-187 222-null 342-374 A2
20 129-129 246-246 381-381 494-494 167-187 222-null 342-374 A2
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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 long-
disance moving spreaders– How far can individuals move: how large are populations– Is there inbreeding or are individuals freely outcrossing
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CASE STUDY
A stand of adjacent trees is infected by a disease:
How can we determine the way trees are infected?
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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.
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CASE STUDY
WE HAVE DETERMINED AIRBORNE SPORES (PRIMARY INFECTION ) IS THE MOST COMMON FORM OF INFECTION
QUESTION: Are the infectious spores produced by a localspreader, or is there a general airborne population of spores thatmay come from far away ?
HOW CAN WE ANSWER THIS QUESTION?
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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?
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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
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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.
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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
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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.
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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
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What is Population Genetics?
About microevolution (evolution of species)
The study of the change of allele frequencies,
genotype frequencies, and phenotype
frequencies
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• Natural selection (adaptation)• Chance (random events)• Mutations• Climatic changes (population expansions and contractions)• …To provide an explanatory framework to describe the evolutionof 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 seebetween species• evolution leads to change in gene frequencies within populations
Goals of population genetics
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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
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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
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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
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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.
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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.
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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)
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• 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
Bottleneck Effect
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Founder vs Bottleneck
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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
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Hardy Weinberg Equilibriumand F-Stats
• In general, requires co-dominant marker system• Codominant = expression of heterozygote
phenotypes that differ from either homozygote phenotype.
• AA, Aa, aa
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Hardy-Weinberg Equilibrium
• Null Model = population is in HW Equilibrium– Useful– Often predicts genotype frequencies well
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if only random mating occurs, then allele frequenciesremain unchanged over time.
After one generation of random-mating, genotype frequencies are given by
AA Aa aap2 2pq q2
p = freq (A)q = freq (a)
Hardy-Weinberg Theorem
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• 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).
Expected Genotype Frequencies
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1) diploid organism2) sexual reproduction3) Discrete generations (no overlap)4) mating occurs at random5) large population size (infinite)6) No migration (closed population)7) Mutations can be ignored8) No selection on alleles
ASSUMPTIONS
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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
IMPORTANCE OF HW THEOREM
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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
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Pop 1
Pop 2Pop 3
Pop 4
FST = 0.02FST = 0.30
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Pop1 Pop2 Pop3
Sample size
20 20 20
AA 10 5 0
Aa 4 10 8
aa 6 5 12
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Pop1 Pop2 Pop3
Freq
p (20 + 1/2*8)/40 = 0.60
(10+1/2*20)/40 = .50
(0+1/2*16)/40 = 0.20
q (12 + 1/2*8)/40 = 0.40
(10+1/2*20)/40 = .50
(24+1/2*16)/40 = 0.80
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• 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
Local Inbreeding Coefficient
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F StatsProportions of Variance
• FIS = (HS – HI)/(HS)
• FST = (HT – HS)/(HT)
• FIT = (HT – HI)/(HT)
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Pop Hs HI p q HT FIS FST FIT
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
Mean
0.43 0.37 0.43 0.57 0.49 -0.14
0.12 0.24
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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
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R E S E A R C H A R T I C L E
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 aTIFF (LZW) decompressor
are needed to see this picture.
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
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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)
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Rhizopogon vulgaris
Rhizopogon occidentalisHost islands within the California Northern ChannelIslands create fine-scale genetic structure in two sympatricspecies of the symbiotic ectomycorrhizal fungusRhizopogon
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Rhizopogon sampling & study area
• Santa Rosa, Santa Cruz– R. occidentalis– R. vulgaris
• Overlapping ranges– Sympatric– Independent
evolutionary histories
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Sampling
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Bioassay – Mycorrhizal pine roots
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BT
N E
W
Local Scale Population Structure
Rhizopogon occidentalis
FST = 0.26
FST = 0.33FST = 0.24
Grubisha LC, Bergemann SE, Bruns TDMolecular Ecology in press.
FST = 0.17
Populations are differentPopulations are similar
8-19 km
5 km
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N E
W
Local Scale Population Structure
Rhizopogon vulgaris
FST = 0.21
FST = 0.25FST = 0.20
Grubisha LC, Bergemann SE, Bruns TDMolecular Ecology in press
Populations are different
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B.
Santa Cruz Island (SCI) Santa Rosa Island (SRI)
Locus Allele SCI East SCI North SCI West SRI Rvu24.9 234 0.267 0.458 0.576
237 0.467 0.479 0.424 1.000 240 0.267 0.063
Rvu20.80 144 0.033 0.033 153 0.383 0.156 0.076 0.833 156 0.133 0.323 0.065 159 0.400 0.281 0.739 0.167 162 0.104 0.087 165 0.033 0.135 168 0.017
Rvu19.80 195 0.050 0.167 0.054 198 0.042 0.033 201 0.100 0.125 0.663 204 0.017 0.010 207 0.817 0.615 0.228 1.000 210 0.017 0.042 0.022
Rvu20.46 144 0.017 0.042 0.478 0.417 147 0.983 0.958 0.522 0.583
Rvu21.83 291 0.021 294 0.433 0.646 0.587 1.000 297 0.300 0.125 0.043 300 0.050 0.010 0.370 303 0.200 0.115 306 0.017 0.073 309 0.010
Rvu21.13 261 0.983 0.865 0.989 1.000 264 0.017 0.135 0.011
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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
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Four phases of INVASION
• TRANSPORT
• SURVIVAL AND ESTABLISHMENT (LAG PHASE)
• INVASION
• POST-INVASION
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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)
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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)
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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
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ESTABLISHMENT
• NORMALLY REDUCED GENETIC VARIABILITY
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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
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INVASION-2
• Later phase: genetic differentiation
• Higher genetic difference in areas of older establishment