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Quiz • What were the two most significant consequences of geographic isolation of some mangrove stand in Panama? • In the Hogberg et al paper on Fomitopsis what were the two most significant findings? • ------• Why is there a Somatic Compatibility system in fungi and whay it is a good proxy for genotyping? • Why do we talk of balancing selection with regards to mating alleles and how would you use mating allele analysis to prove the relatedness of fungal genotypes 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 • Raraefaction 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 AB= 0.6 B: 1001011 BC=0.5 C: 1001000 AC=0.2 0.4 (1-AB) 0.5 0.8 Now that we have distances…. • Plot their distribution (clonal vs. sexual) Now that we have distances…. • Plot their distribution (clonal vs. sexual) • Analysis: – Similarity (cluster analysis); a variety of algorithms. Most common are NJ and UPGMA Now that we have distances…. • Plot their distribution (clonal vs. sexual) • Analysis: – Similarity (cluster analysis); a variety of algorithms. Most common are NJ and UPGMA – AMOVA; requires a priori grouping 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 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 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 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. 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). 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 32 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 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 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