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

Good chromatogram!

Bad chromatogram…

Pull-up (too much signal) Loss of fidelity leads to slips, skips and mixed signals

Reverse reaction suffers same problems in opposite direction

Alignments (Se-Al)

Using DNA sequences

• Testing alternative trees: kashino hasegawa • Molecular clock• Outgroup• Spatial correlation (Mantel)

• Networks and coalescence approaches

Using DNA sequences

• Bootstrap: the presence of a branch separating two groups of microbial strains could be real or simply one of the possible ways we could visualize microbial populations. Bootstrap tests whether the branch is real. It does so by trying to see through iterations if a similar branch can come out by chance for a given dataset

• BS value over 65 ok over 80 good, under 60 bad

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

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

• A grouA stand of adjacent trees is infected by a disease:

How can we determine the way trees are infected?

CASE STUDY

• A grouA 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

• A grouWE 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?

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

• 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

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)

• 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

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 Equilibriumand 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

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

• 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

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

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

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 1

Pop 2Pop 3

Pop 4

FST = 0.02FST = 0.30

Pop1 Pop2 Pop3

Sample size

20 20 20

AA 10 5 0

Aa 4 10 8

aa 6 5 12

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

• 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

F StatsProportions of Variance

• FIS = (HS – HI)/(HS)

• FST = (HT – HS)/(HT)

• FIT = (HT – HI)/(HT)

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

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

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)

Rhizopogon vulgaris

Rhizopogon occidentalisHost islands within the California Northern ChannelIslands create fine-scale genetic structure in two sympatricspecies of the symbiotic ectomycorrhizal fungusRhizopogon

Rhizopogon sampling & study area

• Santa Rosa, Santa Cruz– R. occidentalis– R. vulgaris

• Overlapping ranges– Sympatric– Independent evolutionary

histories

Sampling

Bioassay – Mycorrhizal pine roots

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

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

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

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