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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

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Summary

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


Summary1

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)


Summary2

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

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 self1

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

Somatic incompatibility


More on somatic compatibility

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

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 compatibility1

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 self2

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

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

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)


Intersterility1

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

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


Mating1

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


Mating2

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

Clamp connections


Mating alleles

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:


Two scenarios1

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)

Two scenarios:


Summary

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

Long branches in between groups suggests no sex is occurring in between groups

Fir-Spruce

Pine Europe

Pine N.Am.


Summary

Small branches within a clade indicate sexual reproduction is ongoing within that group of individuals

NA S

NA P

EU S

890 bp

CI>0.9

EU F


Index of association

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

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 genetics1

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

Evolutionary history

  • Darwininan vertical evolutionary models

  • Horizontal, reticulated models..


Are my haplotypes sensitive enough

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

1010101010

1010101010

1010101010

1010000000

1011101010

1010111010

1010001010

1011001010

1011110101

RAPD combination1 2


Conclusions

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

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

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

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

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

Jaccard’s

  • Only 1-1 and 1-0 count, 0-0 do not count

    1010011

    1001011

    1001000


Jaccard s1

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=0.50.5

    C: 1001000 AC=0.20.8


Now that we have distances

Now that we have distances….

  • Plot their distribution (clonal vs. sexual)


Now that we have distances1

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 distances2

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

AMOVA groupings

  • Individual

  • Population

  • Region

    AMOVA: partitions molecular variance amongst a priori defined groupings


Example

Example

  • SPECIES X: 50%blue, 50% yellow


Amova example

AMOVA: example

Scenario 1

Scenario 2

v

POP 1

POP 2

v


Expectations for fungi

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

Plotting distances

  • Pairwise genetic distances can be plotted: the distribution of distances can be informative of biology of organism


Summary

0.7

0.6

0.5

0.4

Frequency

0.3

0.2

0.1

0

0.90

0.92

0.94

1.00

0.96

0.98

Coefficient

0.7

0.6

0.5

0.4

0.3

Frequency

0.2

0.1

0

0.90

0.92

0.94

0.96

0.98

1.00

Coefficient

Results: Jaccard similarity coefficients

P. nemorosa

P. pseudosyringae: U.S. and E.U.


P pseudosyringae genetic similarity patterns are different in u s and e u

0.7

0.6

Pp U.S.

0.5

Pp E.U.

0.4

Frequency

0.3

0.2

0.1

0.0

0.9

0.91

0.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99

Jaccard coefficient of similarity

P. pseudosyringae genetic similarity patterns are different in U.S. and E.U.


Summary

P. ilicis

P. pseudosyringae

P. nemorosa

Results: P. nemorosa


Summary

Results: P. pseudosyringae

P. ilicis

P. nemorosa

P. pseudosyringae

= E.U. isolate


The scale of disease

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)---


Rapds not used often now

RAPDS> not used often now


Summary

RAPD DATA W/O COSEGREGATING MARKERS


Summary

PCA


Summary

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

How are AFLPs generated?

  • AGGTCGCTAAAATTTT (restriction site in red)

  • AGGTCGCTAAATTT

  • 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


Summary

Distances between study sites

White mangroves:

Corioloposis caperata


Summary

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

Coriolopsis caperata on

Laguncularia racemosa


Using dna sequences

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 sequences1

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

From Garbelotto and Chapela, Evolution and biogeography of matsutakes

Biodiversity within species

as significant as between

species


Microsatellites or ssrs

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)


Summary

MS18(AC)38218 bp

(AC)39220 bp

(AC)40222 bp

MS43a(CAGA)70373 bp

MS43a(CAGA)71377 bp

MS43a(CAGA)72381 bp

(220-218)222

(222-218)242

(377-373)242

(381-373)282

(39-38)212

(40-38)222

(71-70)212

(72-70)222

ACACACACACACACACAC

AMOVA Analysis of Molecular Variance

75


Summary

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

Spatial autocorrelation

Moran’s I

0

Within approx. 100 meters the genetic structure correlates with the geographical distance

10 100 1000

Geographical distance (m)

77


Spatial autocorrelation1

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

78


Nj tree of p ramorum populations in california

NJ tree of P. ramorum populations in California

MA-3

SO-1

SO-2

MA-5

SC-1

MA-4

NURSERY

SC-3

HU-1

MA-1

HU-2

MA-2

SC-2

MO-1

MO-2

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

Example:microsatellites genotyping of P. ramorum isolates

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

Microsatellites (I)mating type A1 (EU) and mating type A2 (US)

A2 (US)A1 (EU)

Locus 29325/ - 325/337

-/337

Locus 33315/337325/337

Locus 65234/252 236/244

220/222

81


Summary

Ind.MS39aMS39bMS43aMS43bMS45MS18MS64Mating type

1129-129246-246369-369486-486167-187220-278342-374A1

2129-129246-246369-369486-486167-187220-278342-374A1

3129-129246-246373-373486-486167-187220-274342-374A1

4129-129246-246373-373 486-486167-187220-274342-378A1

5129-129246-246373-373 486-486167-187220-274342-378A1

6129-129246-246373-373 486-486167-187220-274342-378A1

7129-129246-246373-373 486-486167-187220-278342-378A1

8129-129246-246373-373 486-486167-187220-278342-374A1

9129-129250-250369-369486-486167-187220-278342-374A1

10129-129250-250 369-369486-486167-187220-278342-374A1

11129-129250-250369-369486-486167-187220-278342-374A1

12129-129250-250 377-377490-490167-187220-278342-374A1

13129-129250-250 377-377 490-490 167-187220-278342-381A1

14129-129250-250 377-377 490-490 167-187220-278342-381A1

15129-129250-250 377-377 490-490 167-187220-278342-381A1

16129-129246-246377-377 490-490 167-187220-278342-374A1

17129-129246-246377-377 486-486167-187220-278342-374A1

18129-129246-246369-369486-486167-187220-278342-374A1

19129-129246-246381-381486-486167-187222-null342-374A2

20129-129246-246381-381494-494167-187222-null342-374A2

82


Genetic analysis requires variation at loci variation of markers polymorphisms

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


Case study

CASE STUDY

A stand of adjacent trees is infected by a disease:

How can we determine the way trees are infected?


Case study1

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 study2

CASE STUDY

WE HAVE DETERMINED AIRBORNE SPORES (PRIMARY INFECTION ) IS THE MOST COMMON FORM OF INFECTION

QUESTION: Are the infectious spores produced by a local

spreader, or is there a general airborne population of spores that

may come from far away ?

HOW CAN WE ANSWER THIS QUESTION?


If spores are produced by a local spreader

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

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

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

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

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

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

What is Population Genetics?

  • About microevolution (evolution of species)

  • The study of the change of allele frequencies, genotype frequencies, and phenotype frequencies


Summary

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

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

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

  • 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

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.


Summary

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

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

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

Founder vs Bottleneck


Summary

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

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


Summary

Hardy-Weinberg Equilibrium

  • Null Model = population is in HW Equilibrium

    • Useful

    • Often predicts genotype frequencies well


Summary

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

AAAaaa

p22pqq2

p = freq (A)

q = freq (a)


Summary

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).


Summary

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


Summary

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

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


Summary

Pop 3

Pop 2

Pop 1

Pop 4

FST = 0.30

FST = 0.02


Local inbreeding coefficient

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


Summary

F StatsProportions of Variance

  • FIS = (HS – HI)/(HS)

  • FST = (HT – HS)/(HT)

  • FIT = (HT – HI)/(HT)


Important point

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


Summary

R E S E A R C H A R T I C L E

Isolation by landscape in populations of a prized edible

mushroomTricholoma matsutake

Anthony AmendÆMatteo GarbelottoÆ

Zhendong FangÆSterling Keeley

Conserv Genet

DOI 10.1007/s10592-009-9894-0


Microsatellites or ssrs1

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)


Summary

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


Summary

Rhizopogon sampling & study area

  • Santa Rosa, Santa Cruz

    • R. occidentalis

    • R. vulgaris

  • Overlapping ranges

    • Sympatric

    • Independent evolutionary histories


Sampling

Sampling


Bioassay mycorrhizal pine roots

Bioassay – Mycorrhizal pine roots


Summary

Local Scale Population StructureRhizopogon occidentalis

FST = 0.26

8-19 km

N

E

5 km

FST = 0.33

FST = 0.24

W

B

T

FST = 0.17

Populations are similar

Populations are different

Grubisha LC, Bergemann SE, Bruns TD

Molecular Ecology in press.


Summary

Local Scale Population StructureRhizopogon vulgaris

FST = 0.21

N

E

FST = 0.25

FST = 0.20

W

Populations are different

Grubisha LC, Bergemann SE, Bruns TD

Molecular Ecology in press


How do we know that we are sampling a population

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

Four phases of INVASION

  • TRANSPORT

  • SURVIVAL AND ESTABLISHMENT (LAG PHASE)

  • INVASION

  • POST-INVASION


Transport

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

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

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


Establishment1

ESTABLISHMENT

  • NORMALLY REDUCED GENETIC VARIABILITY


Invasion

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

INVASION-2

  • Later phase: genetic differentiation

  • Higher genetic difference in areas of older establishment


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