Test #2 Review Flashcards
Dominance in selection
Most of the time its not purley dominant or recessive –> can quantofy the degree of dominannce
h
Dominance coefficient for an allele –> describes the degree of intermediate state***Always a value between 0 and 1
Meaning of selection coefficants 0, 0.25, 0.5
0 –> no selection against that0.25 – 25% cost to have that genotype 0.5 –> 50% decrease in fitness relative to other fitness % Cost/decrease in fitness
Modifying for dominance
Quantify s for allle (rather than genotypes) and the modify with a dominance coeffciants
Meaning of h = 0 for AA
h = 0 –> there is no dominance in a –> A is complteley dominant to a - There is no affact of a in Aa Selection against Aa is 0 ***Add inmage slide 19 (postclass)
Modifying s to account for degree of dominance
Use 1 selection coefficient AA = h Aa = hs (selection coefcinat X SC) aa = s Example h = 0 s of AA = 0 s of Aa = hss of aa = s HERE – A is completely dominant to a
Meaning of h = 1 for AA
a is completely dominant to A - Affect of SC s – s of aa is domiant Selection against Aa is 1 X s
Example of Quantofying the degree of dominance WAA = 1WAa = 0.75Waa = 0.5s AA = 0s Aa = hss aa = s
SAA = 1-1 = 0 (1 - RF) SAa = 1 - 0.75 = 0.25 Saa = 1 - 0.5 = 0.5 –> means s = 0.5 1 - WAa = hs1 - WAa = h X 0.51 - 0.75 = h X 0.5h = 0.5
Example – Quantify the degree of dominance s AA = 0s Aa = hss aa = sWAA = 1WAa = 0.55Waa = 0.5
TO FIND S: s aa = 1 - RF aa –> 1 - 0.5 = 0.5S Aa = 1- RF Aa = 1 - 0.55 = 0.45 0.45 = hs0.45 = 0.5hh = 0.9 LOGIC: Saa = 1-RFS aa = 1 - 0.5S aa = 0.5hs = 1 - WAa1 - 0.55 = hs1 - 0.55 = 0.5 X h h = 0.9
Meaning of h = 0.9
Dominance coefficient is closer to 1 = phenotypic affect of a is bigger than A
- Bigger affect if a; less affect of A
Meaning of all h values
h tells us about the degree of similarity between heterozygote and homozygote fitnessIf h = 1 –> Aa is exactly like the deleterious homozygotes If h = 0 –> Aa is exactly like the selectively favored homozygote If h = 0.5 –> The fitness of Aa is exactly intermediate
Graphing the dominance
REMEMBER – we looking at h from the persecutive of a (is Aa like AA or aa) –> get straight line –> Aa falls on the line at h = 0 –> A is dominant and a is recessive(Because is at WAA) - At h = 0 Aa has the same fitness as AA –> therefore a is completely recessive h = 1 – Aa is like aa –> aa is dominant (Because is at Waa) - Aa has the same fitness as aa –> therefore aa is completely dominant ***Image on lide 31
Quantifying mutation rate
u (Mu)
Mutation rate for one BP at one locus
All of the rates are very low (often order of magnitudes less than 1) for rate at a single BP –> probability of a given BP chnaging from one generation to the next is low –> BUT when scale up over entire genome = get 30-40 mutations occuring - Chance at any one mutation is small mu BUT the chance of Mu across genome is large Looking at change of one allele at one locus from one generation to the next = use very small numbers***Those mutation rate values on the previous slide werevery small numbers: ~10-8 for humans
How does mutation affect H-WExample
Start: 90% A – P = 0.9 10% a – q = 0.1 Mutation occurs in germline producing gametes – Parental genotype –> gamete – gamete now has mutation) Mutation rate = 10^-4 (relatuvley high rate of mutation) Convert 1 A –> a out of every 10,000 gametes in gamete pool Change in allele frequncey – p = 0.8991; q = 0.10009 –. get very small chnage in popultion with a relativley high mutation rate = small chnage in one gneration 1 out if every 10,000 gamete
Overall: Get very small chnage in popultion with a relativley high mutation rate = small change in one generation
Mutation rate over time (scaling up)
Mutationrate can add up through time Rate of chnage in one generation: dP = -up - Because p –> q = use negitive mutation rate dP = chnage in one generation We can scale this up over any number of generations: Pn = P0e^-un P0 = starting allele frequcey n = number of generations HERE = use exponetial (in one generation we just multiplied) Pn = p after n generstions from a starting point of P0
Allele frequcney chnage from mutation after 100 generation
Allele frequncey change is still very small (even after 100 generations) - Still not much chnage but adds up over very long amounts of time
Mutation as a force of evolutionary change
Mutation is necessary for change BUT it is not sufficient
- Mutation by itself does almost nothing – mutation is not what causes evolutionary change
- Mutation is needed (need variation) – necessary for evolution but not a good force by itself – works with other forces
- Other forces act on mutation and drive change in allele frequency
Mutation is necessary for long-term evolutionary change, but it is no where near sufficient to explain genetic and phenotypic change in nature
What does selection need
Selection requires genetic variation – some prvious mutation events are a prerequisite for evolution by NS
What does mutation produce
In the case of favorable alleles –> mutation occasionally produces fodder for adaptations shaped by NS - Have mutation –> THEN selection can act
Mutation working with selection
Experiment = tells us that mutations does make beneficial mutations + might have beneficial effect
- IF impose stress = new variation matters – when you have selection = you can see much more rapid change - New variation = NS can act on it = evolve more rapidly with weak variation + Strong selection
Mutation Vs. Mutation + selection
Mutation + Selection – The selection lines show that selection acting on that new variation leads to much larger changes
Mutation – The control populations evolveddegree of improve salt tolerance through mutation alone
Selection vs. Deletrious alleles
Evolutionary force of selection and force of mutations work against each other Selection = wants to get rid of deletrious alleles Have two forces 1. Mutation adding in new mutants (u) 2. Selection taking out mutants from popultion (s)u = weak –> se;ection pushes together – push against each other S = strong QUESTION – if u is weak and s is strong why doesn’t selection just push out deletrious alleles
If u is weak and s is strong why doesn’t selection just push out deletrious alleles
ANSWER – dominance
Selection vs. Deletrious alleles equillirbium
You can have a stable equillirbirum point where forceses are balanced – we can define an equillirbium point where these forces are balanced - Can calculate teh expected allele frequncey of deletrious mutations at this point Where equillirbium point occurs = affected by dominance vs. recessive
Where is equillirboum beteween selection vs. deletrious alleles
Where that point occurs is affected by dominance vs. recessive
Calculating mutation selection balance for anything more than purley recessive (if have effect in Aa)
q> = u/hs Used if h is anything other than 0 Exampleh = 0.05 (have a small amount of dominance – get some deleterious affect in Aa –> 5% deleterious affect) u = 10^-4 2 = 0.98 (very deletrious) NOW q> = 0.002Used for anything other than pure recessive (if h is anytjing other than 0)h = 0.05 = have 5% deleterious affect
Why is CF maintained at 2%
The answer is likely an overdominant effect of heterozygote resistance to Typhoid (which interacts with the same proteins when it infects lung tissue)
Constaints on NS’s ability to optimize popultions
- The outcome of natural selection doesn’t just depend on the overall fitness of an allele, it depends on the population context that theallele exists in
- Mutation can interact with the weak effects of selection on deleterious mutations at low frequencies to maintain low fitness genotypes in populations
When does Genetic Drift occur
Occurs if you violate infinate popultion size MEANS it is possible it is going on in all popultions
How important is genetic Drift
Very important – tied with Natural selection (almost if not just as important as natural selection)
Where is drift important
Important in small popultions BUT it is not absent in large popultions - Can still have drift in large popultions Important for variations within/between species
What does H-W mean by infinate popultion size
Means that the ganete pool is sampled exuhstivley - Every single copy of allele in gamete pool gets represented exactley once in the next generation
What happens when we subset popultion
If we subset a popultion = we open ourselves uo to errorIf sample 50 alleles out of total popultion–> we might get the actual allelle frequncey but we might now = driftIf we are not sampling fully = open to drift
How do we get drift
Has to do with sampleing error –> if we do not sample the popultion fully then open to sampling error + open to drift that has to do with sample errior - Sampling error in gamete pool = open to chnage in allele frequncey = drift –> continous mechanism in popultion
Parts of genetic drift (thing that influences it)
- Random mortality2. Sampleing errors in zygote formation
Larger sample size + dirft
Larger smaple size = more sure we are to get the same intial underlying frequency
What happens if lose infinate popultion size
As soon as we ease off this assumption – chance events can start to influence allele frequenciesTrue evern in very large popultions
Genetic Drift
Random chnages in allele frequcneues in popultion
Selection vs. Drift
BIG difference = in predictability Selection = determanistic (if you know start then know how it ends) Drift = Stochastic (probablistic) – don’t know exatcley what will happen because it is based on random sampling - NOW using a different perspective (because proabbilistic) – don’t know exactley what will hapen because based on random sampling - Can look at allele frequncies that are more likley BOTH chnage allele frequencies but in different ways
Drift is…
Stichastic – we can’t predict the outcome - If we know the starting point of the system we can’t know where it will end up
At a given point we might be able to calculate the proability of ending up at a particular state but we won’t know what will actually happen in that instance - We can look at the most likley outcome from one generation to the next
Some aspects of dirft are…
Some general aspects of the outcome of drift are inevitable BUT we can’t know the end state (nor even the state of the next generation) for a particular popultion - Has some inevitable features
Example #2 – how drift leads to chnage in allele frequncey (sampleing gametes with red and blue)
IF start with 50% A and 50% a (50% red and 50% blue) – have 20 alelles in gamete pool (have 10 red and 10 blue) IF we sample a fixed amount of times (if we pick 20 gametes) –> we can get p = 0.5 and q = 0.5 - Each round the alleles have a 1 in 20 chnace of getting chosen BUT some might get chosen X2 abnd some might not get pciked at all Here = sampleing 20 times with replacment = some might get chosen twice and some might not get chosen at all - Means that you can start with 10 and 10 BUT end with 7 and 13 = have evolutiion BUT – what if we did this many many time (Sampled 20 in many rounds each woth replacemnt) Reuslt = stabilize around 50:L50
What happens when have repeated sampleing (if do samples of 20 many times)
If start with 50:50 –> End up stabilizing around 50:50 - If kept sampleing 20 again and again –> end up stabilizing around 50:50 - The highest proabbility outcome = 50:50 – end up stabilizing around 50:50 Avergae end outcome = 50:50 BUT you do not get 50:50 in each generation If end at 50:50 –> why do populations chnage?
If our highest probabilityoutcome is to get thesame allele frequency,why does this result inevolution?(If we end up stabilizing around 50:50 – why do we get evolution)
- In a single generation,the ”on average” partdoesn’t matter – there’sonly one round of zygoteselection* Any sampling errors(deviation of a subsetfrom the true populationvalues) stick* Even if started at p = 0.5 –> THEN The population isrestocked at the newvalue of p = 0.35 TEHN you sample another 20 gametes BUT you are sampleing from p = 0.35 and then might get p = 0.4
Experimnet – Most likley outcome from each sample
If start at p = 0.6 – when sampling gametes – most often get back to p = 0.6 - Getting back to p = 0.6 is the most likley outcome BUT in experiment it only occured 18% if the time MEANS that 82% of the time get something other that p = 0.6 –> 82% of the time get chnage – the liklihood of statying at 0.6 is low even though it is the most common outcome - 82% of the time = get chnage in allele frequencey - Standrad error = can avergae how far you are for subset sample size
How to we talk about genetic drift
You can look at the probability of chnage even though you can’t know if P will increase or decrease or the magnitude of chnage BUT you can get probability that P will chnage ***There is no dP equation for drift
Inevitable aspects of drift
When sampling pools a finite number of times = eventually get fixation for one allele - Means sampling errors are important – critical affect = when lose sometuing = lose it for good - Each time one is missed from sampling error, it’s gone for good Means that dirft will tend towards fixation in the long one but don’t know which allele that will be Affect of drift = genetcually decrease – every time misample something and soemthing is not representaed = that copy is lost = lose variation
Liklihood of picking certain alleles + liklihood of fixing for alleles
Every copy of each allele has equal probability of going to fixation (Probability of any one copy of A or any one copy of A is equally likley to reach fixation) - In a given population, each individual allele segregating in the population has an equal probability of being the one that goes to fixation eventually (Doesn’t mean that p and q alelles are equally likley to go to fixation – means individual copies of alleles in the gamete pool are equally likley to go to fixation) - If have 20 copies of an allele in a population then porbvility of any one copy if allele going to fixation is 1/20 IF P = 0.6 and q = 0.4 –> NOt equally likeley for p or q ro be fixed BUT each individuakl copy has the sameprobability Each copy in the gamate pool has the same probability Example – if have 20 copies of an allele – each copy has a 1/20 chance of going to fication IF p = 0.6 and q = 0.4 –> Probaboliyu pf P OR q going to fixation is Mutaually exclusive - One copy pf p or one copy of q Since ME (one copy of P will go to fixation or a different copy of P will go to fixation) - P(One copy of P going to fixation) OR P(Andifferent copy of P going tp fixation) = 1/20 + 1/20 – since have 12 copis of P (the probability of any one copy going to fixation is mutation exulsive) – add all 1/20 = get 12/20 = 0.6
Example of probability of P or Q going to fixation
P = 0.6 and q = 0.4 –> porbaility of P going to fixation = 60% and q going to fixation is 40% (because probability of any one copy gping to fixation is 1/20 and ME = can add individual orobabilities together)— The probability being 0.4 is ONLY for the starting generation –> As soon as the allele frequncey chnages due to drift that porbabiloity resets THEN if P = 0.45 and q = 0.55NOW probability of P going tp fixation is 45% and Probability of q going to fixation is 55%
Probability of P or Q going to fixation
Probability of P or Q going to fixation = equal to the frequncey of P or Q in the popultion BUT only in that starting point – IF chnage frequncey then that chnage sticks around and change probability
Probability of P or Q going to fixation
Probability of P or Q going to fixation = equal to the frequncey of P or Q in the popultion BUT only in that starting point – IF chnage frequncey then that chnage sticks around and change probability
What happens to probability of allele going to fixation once allele frequencey chnages
As soon as the allele frequency changes due to drift, that probability resets
Example – if start with p = 0.4 IF the sampling error going to the next generation causes the frequcncey to chnage to p = 0.55 - NOW the proabbility of blue going tp fixation has gone from 0.4 to 0.55NOTE: There was nothing intrinsic about blue that made it less likely to go to fixation in the last generation
Drift in small populations
Drift is a stronger evolutionary force in small populations (harder for NS to recon with) - True for small popultions that stay small for many generations (like rare or endagered species) AND it is also true for events in large popultions that cause temperary popultion reductions
Affect of drift after larger popultion that contracted increases back in size
Affect of drift stays around even after gets larger again
2 terms describing evens that lead to strong drift in otherwise large popultions
- Founder events
- Bottlenecks
Cause otherwise large popultion to go through contraction BOTH have short lived stage of small popultion
Founder events
A new popultion is derived from a small number of individuals drawn from a large ancestral population - Start popultion derived from small amount of individuals Ex. A small numver of people go to colonize islands - The allele frequncey of the new popultion has lots oif drift = very different allele frequncey (frequcney is very different than ancestral population)
Bottleneck effect
A population’s history is marked by one or more generation of very small population size before regrowth
Founding events in DR
Because the foundingpopulation was small, eachperson had a large chance ofcontributing disproportionatelyto future generations –> One of these people, AltagraciaCarrasco, did have adisproportionate effect and ithad consequences later on – Carrasco had children with atleast four women –and his genesrose in frequency in thepopulationCarrasco – He was also a carrier for amutation in the gene for 5-alpha-reductase-2
Potential affect of drift
Drift can lead to increased frequencies of deleterious allles – in small popultions this effect can be dramatic
Use of Founder events
Founder effects can be useful in studying human genetics –> The effects of rare allles are hard to study in larger popultions even if they have large effects BUT rare alles can be studies in smaller popultions with higher frequncey due to found effects
Time to build varaition
It takes a lot of time to build up varaition –> If that is wpied out (like from bottle neck or founder events) = wipe out million of years of varaitionand will take millions of years to get that varaition again***Strong drift in historical events can instantly undue the work of natural selection and thousands of generation’s worth of mutation accumulation
Effect of bottle neck or founder event
Low genetic diversity – that effect stays even after the popultion increases in size - effect of drift when small popultion stciks around *** Strong drift in historical events can instantly undue the work of natural selection and thousands of generation’s worth of mutation accumulation - when in low popultion size sticks around loss of varaition = low varaition because population crash –> low varaition + effect of drift when small popultion stciks around - Only the varaition from the small popultion gets passed down = ONLY get varaition from 28 inidviuals when small popultion size
Subpopultions
Many (most) species do not exist as a single continous gene pool – they are subdivided into seperate pools across space - There are cases of species NOT in subdivsion popultions BUT most are - They are vaiarble across space = doesn’t amke sense to treat as one gamete pool Ex. Balck baers in upstate NY do not form a cohesive reproductive popultion with the black bears in florida - Doesn’t it make sense to make one gamete pool across all North America because there are baers in florida that are not likley to mate with baers in adirondacls == have subdivisions populations of species
Differences in Subdived popultions
Subdivided = different = important to treat them seperatley 1. Subdivided population can differ due to differences in selection pressures and the mutations that occur in one but not the others - Difference in selection pressures - Difference in mutations –> have different amounts of genetic varaition – it would be hard to get mutations from one subpopultion to another 2. But they are also bound to differ from each other due to drift - In subdivided populations, random changes are inevitably going to accumulate between them - They are bound to have differences due to drift – error in one popultion = bound to be different than error in the other popultion = change the subdivided popultions to be difefrent even if they started at the same frequncey ***They are vaiarble across space = doesn’t amke sense to treat as one gamete poolEx. Balck baers in upstate NY do not form a cohesive reproductive popultion with the black bears in florida - Doesn’t it make sense to make one gamete pool across all North America because there are baers in florida that are not likley to mate with baers in adirondacls – have subdivisions populations of species
Subpopulations + Drift
But they are also bound to differ from each other due to drift - In subdivided populations, random changes are inevitably going to accumulate between them - They are bound to have differences due to drift – error in one popultion = bound to be different than error in the other popultion = change the subdivided popultions to be difefrent even if they started at the same frequncey
Varaition within subpopultion vs across all subpopultions
Variation within popultin is lost BUT varaition across popultions is maintained – By producvt of probability based drift starting out - Within popultion varaition decreases BUT maintain varaition across subdivide popultion - Because stochastic = mainatin across popultions Allele frequncey across all popultions = still around 0.5 – dirft decreased varaition within popultions but moantained varaition across popultionsBut the probabilities of which allele goes to fixation work out such that geneticvariation in maintained among populations at the starting allele frequency
Drift + Subpopultion example – blue and red
Start with p = 0.4 –> and subdivide into 10 smaller popultions - Porbability in each popultions = 60% blue and 40% red – same for all populations (smae proabbility for each one) MEANS 40% on average end red and 60% end blue fixation = IN THE END the avergae varaition will stay the same - Varaition across popultions is mainatained BUT the varaition within popultions is miantained - Axross subdivided popultions = have varaition - Within = lose varaition (Fix for one allele) - When divide into sub-popultions – each sub popultions strats as the same as sancestrla popultion – each sub popultion starts with 40% red and 60% blue –> Each subpopultion has 40% chance of fixing for red and 60% chance for fixing for blue –> Each will lose varaition because each will fix for an allele BUT each will fix for different alelle (Because stochastic) so across population mainatins varaition - Since each have the same 0.4 chnace and 0.6 chance –> the most likley outcome is that 0.4 fix for red and 0.6 will fix for blue = maintain varaition across popultions BUT within popultions fiox for one allele (p is either 1 or 0) = lose varaition within popultsions BUT among popultions you still have p = 0.4 and 1 = 0.6 (Still have 60% red and 40% blue– becaus eexpect 60% of subpopultions to fix for red and 40% of subpopultions to fix for blue) They will all fix for one allele = lose varaition BUT they will fix for different alles = across popultions they keep the varaition - Each popultions has the same probability of fixing for red or blue allele at the start (0.4 chnace of red and 0.6 chance of blue) – the most likley outcome is that 40% of then fix for red and 0.6 fox for blue –> within popultions p will be 0 or 1 BUT among the popultions p will stay 0.4
Quantofying loss of variation within popultions
Heterozygosity - Use the expected # of heterozygous NOT the observed number
Heterozygosity (H)
Metric for diversiity –> Genotypic frequency of heterozygotes in the population – It is the expected frequency of heterozygotesgiven the allele frequency and assuming HWE - Use the expected # of heterozygous NOT the observed number - Allows us to quantify the loss of variation within populations
Varaition in p = 0.4 vs. p = 0.15
There is more varaition in p = 0.4 than p = 0.15 – more even mix (cloer to 0.5 and 0.5) = more heterozygoues - If no go fixation = heterozygotsity decreases = less varaitionP = 0.4 –> H = 0.48 Q = 0.85; P = 0.15 –> H = 0.255
Where is drift inevitable
Drift = inevitable in population less than infantite in size – any popultions drift is always there ***Drift = based on Stnadrad error in gamete pool –> Smaller sample = more error - Change then sticks –> get new allele frequncey
What is drift based on
Drift = based on standrad error in gamate pool – error that you don’t have the same allele frequncey
Inevitable outcome of drift
Inevitable outcome = loss of varaition over time –> Now chnage in varaition in gene pool for good
Lose within subpopultion varaition –> decreases within subpopultion Maintain varaition acriss subdivided popultion – because stochastic = mainatin varaition acriss popultions
Chnage in H as P/Q changes
As allele frequcneies are more polarized (As p/q get closer to 0 and 1) = heteropzygosity decreases - Peak of varaition = when have 50:50 (P = 0.5; q = 0.5)When closer to even amount (Closer to 50:50) = increase varaition (Because increase in H and H is a measure of varaition)P = 0.4 (more 50:50 split) = higher H = more varaition p = 0.15 (more polarized) = Lower H = Less vraaition
How is Heterozygotsity Distrubuted
Peak = 0.5 –> Highest H value when we have 2 alleles in the population - IF we start with a popultion with all Heterozygotes –> expected frequncey stays the sameIN a two allele system expected Heterozygotsity is highets (Most amount of varaition is highest) at p = 0.5
How does Popultion size affect H
As N Increases = Avgerage chnage in H decreases - As N Increases = 1/2N gets small = less change in H over time because you are closer to being 1 (if 1/2N is smaller – 1-1/2N stays closer to 1 because you are substracting a smaller numebr from one) – If 1 - smaller number = 1 Times the current H = stays close to current H
As population size increases, the average change in heterozygosity decreases (1 minus a smaller fraction)
Example - Calculating H in the next generation Start with H = 0.5N = 1000
Hg+1 = Hg [1- 1/2N] Hg+1 = 0.5 [1 - 1/(2 X 1000) = 0.49975 - HERE = we get a number that is slightley less than 0.5 – expect small Sampel errow making 1000 zygotes = lose a little genetic varaition but not much We are likley to decrease in H from one generation to the next
Extending Chnage in H across generations
We can look at the effect of drift through time - Before = we were just looking at one generation - Heterozygostity is lost after many generations (Alleleic shake up but to drift over time) TO make it for across generations = Had T expeonent – add t to generations HERE = we assume that the popultion size is the same across multiple generations Hg+t = Hg [1 - 1/2N]^t t = numbver of generations
Affect of mutation vs. Affect of drift
Affect of mutations = needs more gernations to have allele freuqncey chnage BUT With drift = have substantaial frequcey change in shorter amount of timeWith drift – even with a population of a 100, over 10 generations, we’reexpecting an average allele frequency shift of over 0.1
Rate of drift
Often occurs much fater than epxcted (like in dropshilla experimnet) - Populations often druft faster than we would epxect based on their true popultion size – instead they dirft at the rate of a smaller population Drift = often occurs faster than censeus popultions – common –> often occurer faster thna expcted based on popultion size itself - Because often dirft is based on teh effective popultion size rather than the true/cecus size
Cesusus vs. effective popultions size
Census – actual popultion size – true popultion szie (Ex. in dropshilla experiment = 16)Effective popultion size – The popultion size expected to match the realized rate of driftDRift = based on Ne (Ne is smaller than N)
Effective popultions size
The popultion size expected to match the realized rate of drift - Ne = # of indiviuals actually reproducimng in popultion - Actual indovdiouals contributing to the gene pool that we are misampling from DRift = based on Ne (Ne is smaller than N)Example – Efefctive popultion size in dros[hila expeirment = 9 – only 9 breeding indidviaiuls on average –> 9 indidvuals on average reprdoucing (7 do not) - 16 = cencus size - 9 = Ne
Why don’t popultions actually drift at the census popultion size
Because of 3rd postulate of NS –> Varaition in popultion ins S/R even if unrelated to phenotypes/genes If dirfting at N –> All indiviaual probability of S/R = 1.0 – dorft at N = drifyoing at the actual counyd of Indiodvuals (Census popultion size) - Dirfting at N = everyone has probability of 1 - Phenotype is not affecting proibabiliyu Vs. Drifting at Ne (Effective popultion size) - HERE = 60% chnace of reproduction –> NOT per phenotype (Still uniform distrubution across all phenotypes) BUT all have probability of 60% = not all S/R - Actual breeding popultions = NOW subset of overall popultions –> NOT all repriduce = # of indiviualks reproducing = NeDRift = based on Ne (Ne is smaller than N = more dirft = drift occurs faster in most popultions than epxected) - Because variation in S/R = drift occurs more powerfully than if it was just based on census size alone
Calculating Ne
We will just look at sex ratio –> Sex ratio makes a big difference in effective popultion sizeEquation – Ne = 4NmNf/(Nm + Nf)
Things that affect sex ratio
- Polygamy – One male + many females - Very common in nature 2. Polyandry – One female + multiple males - Less common in natire but sometimes is still very important Throughs off the sex ratio = affects the rate of genetic drift
Exampe of calculating Ne
Example – rate of drift in popultion starts the same In mating popultions 1 Bull = mates with 25 females If have 500 in popultions –> 250 femnales – if have 1:25 mating ratio –> only 10 males mate 250 females (50:50 sex ratio) –> only 10 males mate - IN mating popultion only have 10 males and 250 females – sampling acros 250 females + smapling across 10 males = very little varaition Equation: Ne = 4NmNf/(Nm + Nf) Nm = # of mating males Nf = # of mationg females Ne = 4 X 10 X 250/ (10 + 250) = 38.5 - 38.5 = much smaller than 500 –> Sex ratio throughs off Ne a lot ***Sex ratio throughs off Ne a lot
N vs. Ne affect on H
If N = 500 (much bogger) = varaiotion stays for a while If N = 38.5 (Ne = 38.5) = much more drift occurs = lose varaition much faster
How do we quantofy the effect of drift across sub popultions
Compoare the within popultion varaition to the between sub popultion varaition - Look at varaition iwthin popultions vs. varaition between popultions to see how much drift has gone on - We can quantify the degree of genetic differentiation that drift hascaused between these populations by examining heterozygositywithin and between themCompare Observed vs. expected value to see how effective dirft i
Example – Affect of drift across subsivided popultions
Example – eaisest to look at 2 sub popultions of the same species - Look at the varaition loss within the popultion and compare that to the exoected between popultions - Look at varaition iwthin popultions vs. varaition between popultions to see how much drift has gone on We can quantify the degree of genetic differentiation that drift hascaused between these populations by examining heterozygositywithin and between themStart = find H within each popultions and take avergae of H of the two popultions - Start by calculating the expected Heterozygosity within bothpopulations and taking the average Population 1H = 2pqP = 0.35H = 0.455 Population 2P = 0.8H = 2pq = 0.32 THEN avergae varaition within the popultions – take Average of both H H pop 1 + H pop2/ 2 = 0.455 + 0.32/2 –> Hs = 0.38756 Hs = Average H across the two popultionsTHEN – We need tp turn to finidng the expected H if this was one big connected popultion –> Do so by combing the allele frequncies and Calculate H as before - Now we need H if one popultions –> Exopected H based on the avwerage allele frequcnies across the popultions - Find H if you take an avergae of the allele frequncies - If we assume equal popultion size = combined allele frequencey os just the average of the two Find Average P = 0.35 + 0.8/ 2 = 0.575 –> combine the alelle frequncies if in one popultions - Average alelle freqincey (assuming that they have teh same p[opultion szie) – get alelle frequncey if they were one popultion - Means that if all of the indiovdiuals belongs to the same big popultion – the allele frequcney would be o = 0.575 –> now we can use this to calculate the expected H across all popultions THEN find H with teh Average P –> Ht = 0.489 (2 X 0.575 X 0.425) - Gives you the expected H across all popultions THEN – we can use these numbers to quantify the degree of magnbitude of genetic difference between the popuotions using FST index - Now look at the difference between the two - Divide by the total amount of varaition in popultionHs = should always be equal to or smallter than HtFST = Ht-Hs/HtFST = 0.489 - 0.388 = 0.489FST = 0.207
What does FST show
FSt = indicates how close the popultions are from having drifted to fized differences from each otherFST = between 0-1
Meaning of FST
FST = 0 –> No difference in allele frequncey between popultions - - Most of the varaition is within popultions –> there is little vraaition between popultions - Can teat as one population – little variation between subpopulations
FST = 1 – Popiultion is fixed for allele = ALl of the varaition is between popultions - All of the varaition is between the popultions – none of the varaition is within the popultion – all of the varaition is exists between popultoons
Why find FST
FST = used very often –> Why calculate it Example 1 – baer survivorship population - If we are hunting baers in Florida = we might wnat to know how they connected they are to baer popultion in other places –> if have differences = might have to say they are different BUT if FST = 0 –> They you can treat them at one population - Little genetic varaition between them = treat as one popultion Example 2 – Used in epidemialogy –> Have a certain parasite that causes disease - Have a parasite that causes diseae + a vector (Parasite gets to misquitos and masiquiotes infect people) –> If want to control the disease = might need to think about popultion of parasite + vesctor popultion - Might have 2 popultions a a new variant of drug resistant nematodes –> need to know how worries are you that they will go to the other popultion – might look at genetic differences between popultions + look for the same thing for masquitos - Quation = are they sepearet or is the FST LOW – Look to see IF the two popultions are genetically differemt IF FST is low = the popultions are more in contact with each other
How do we know drift acts in large popultoiomns
We know because we can see chnage in nuetral loci ***Drift in large popultions dirves constant change at nuetal loci If only selection = should only have to do with things that affect fitness but since doesn’t affect fitness = can’t be selection = we know drift cuases chnage in large popultions
Why should we care about dirft in a large popultions
For a long time they didn’t thnk drift was important in a large popultion THEN when looking at the molecular level = they found controdictions to idea that drift isn’t acting in large popultions When looking at the molecular level they found that most varaition within/between species = selectivley nuetral –> no affect on phenotype = can’t affect surviva/reproduction = not chnaging due to natural selection = due to something else causinhg nuetral to change
What did they find about drift when looking at the molecular level
When looking at the molecular level they found that most varaition within/between species = selectivley nuetral –> no affect on phenotype = can’t affect surviva/reproduction = not chnaging due to natural selection = due to something else causinhg nuetral to change
What do we focus on with drift
With drift we focus on neutrality at the molecular level
What kind of mutations are nuetral
Synonymous mutations – nutral mutation –> means you get the same Amino Acid Mutation were if you change nucleotide sequence = can still specify the same amino acid = the secondary and tertiary structure is the same = function is the same = change to DNA that can’t affect genotype - Still herediatry change in coding region
Most varaition within and between species
Sequence genome – variation = mostly changes that don’t affect phenotype –> changes that don’t affect Amino acid = don’t affect phenotypeA lot of varaition in protein coding regions = synonomous mutations (Within and among species)
Issue with nuetral varaition
How would this varaition build if Natural selection can’t see it (no difference in S/R if can’t make a phenotype) ANSWER: due to druft
Rate that neutral varaition builds
Constant rate – Nuetral varaition appears to build at a constant rateVaraition builds in clocklike reptative rate
Evidence for drift building nuertal varaition
Varaition builds in clocklike reptative rate –> Variation builds in regular constant fashion = suggests it is NOT bottleneck
How do we get clockwork constant rate of change in nuetral
Done through nulceotide substitution (Mutation + Substitution) AND drift Some mutation goes to fixation because of drift
Mutation and Substitution
New varaition enters popultion as mutations but its converted tp varaition between popultions or species by substitition - Some mutation goes to fixation because of drift = mnucelotide substitution - Some new mutations randomly drift to fixation Substiution = fixation of one allele in plavce of other
How does mutation go to fixation
Because of drift (In mutation and substitution)
If drift is only strong in small populations why should this process occur constantly in large populations
Strength of drift = varies as a function of popultion size BUT mutation rate doesn’t vary BUT the number of mutations does vary with popultion size - Number of new nuetral alleles in each popultions = 2nvNumber of mutations increase as N increases Probability of any one alelle going to fixation = 1/2N (each alelle has equal probability) - 2nv = increases with N - 1/2N = decreases with N As N increase (popultion size increases) = probability of any one allele going to fixation decreases - As N increases = have more mutations but each one is less likley to drift to fixation (probability of going to fixation decreases as N increase) MEANS that the probability of getting a mutations AND the probability of that mutation going to fixation:2Nv X 1/2N = v – 2N cancles out = just get v as drive of nuertal substiutution in generation - Each generation the number of new mutations that are destined to become substititions –> 2nv X 1/2N = v – population size cancel out completley Popultion size affects the affect of drift + the affect of number of mutations –> decrease of affect of drift = counteracted by number of mutations that as v increases as N increases – effective population countercat of net resultIn small popultons = each allele has a higher chance of going to fixation VS. In large popultions more alelles come into existances –> The effect of popultion size balances out perfectley = only based on nuetral mutation rate = can occur in larger popultions
What is the drive of nuetral substitution in a popultion
v = drives of nuertal substiutution in generation
What is neutral substitution driven by?
- New mutations 2. Drift BOTH vary as a function of N
Expected rate of nuetral substitutions
The expected rate of nuertal substibution by drift is equal to the mutation rate In small popultons = each allele has a higher chance of going to fixation VS. In large popultions more alelles come into existances
The effect of popultion size on nutral substitution by drift rate
In small popultons = each allele has a higher chance of going to fixation VS. In large popultions more alelles come into existances –> The effect of popultion size balances out perfectley
What creates clocklike rate
The effect of popultion size balances out perfectley = rate is ONLY based on nutral mutation rate –> if we assume that the mutation rate stays constant over time then this shjould result in constant clocklike rate of nuetral evolutionary chnage - Mutation rate = the same through time = expect to build constantly over time = get constant rate
Synonmous vs. nonsynomous rates
Synomous = nuetral Nonsynomous = can be efefctvley nuetral (might not change protein in important way = doesn’t affect phenotype) BUT the nuetral ones occur at a lower rate than synomousExpect synomous and non-synomous to build at different rates - Non-synomous rate = lower – get more synomous per unit of time than non synonomous because all synomous are nuetral Looking at the rates = allows us to build a null model for expevctations for coding diferences should accumalte in the genome - Can see if constsint change is based on idea that something is driven based on dirft – gives evidence that NS is driving change
Why look at rates of synomous vs. non-synomous
Looking at the rates = allows us to build a null model for expevctations for coding diferences should accumalte in the genome - Can see if constsint change is based on idea that something is driven based on dirft – gives evidence that NS is driving change - At a given amount of accumulated neutral differentiation (amount of time separating species) - we should have an expected ratio of synonymous to nonsynonymous substitutions across the genome –> Selection favoring non-synonmous mutations should throw things off this expevctations (we can infer that patterns are the result of positive sel;ection by idetofying deviations from this ratio with higher than expected non-synonmous substitutions ratyes being driven by NS) - We expect this ratio to be pretty similar across the genome for sites that are evolving neutrally (with synonymous substitutions outnumbering nonsynonymous ones) IF not drift ratio of synomous to nonsynomous = allows us to have evidence that NS is driving change When looking at the rates = can get ratio of synomous vs. nonsynomousover seperating over time diverging = deviations form ratio = indicative that natural selection is driving chnage - Deviations from ratio = chnage is not due to drift = NS can be in process - (we can infer that patterns are the result of positive sel;ection by idetofying deviations from this ratio with higher than expected non-synonmous substitutions ratyes being driven by NS)
Example – looking at synomous vs. nonsynomous mutation ratio
Example – BRCA1 gene (Gene heavily implicated in breast cancer) –> Appears to have been under string postive selection relativley recetley – because the ratio of Non-synomous:synomous is > than 1.0 - Looking at the ratio – shows that most of the ratios are below 1 for most species until humans and chimps - Humans and chimps = have many non-synommous mutations compared to expectation of drift –> chnage = due to natural selection in common ancestor of humans and chimps Change see Natural selection is occuring nased on deviations from ratio based on drift alone = NS acted to shape varaition
What do we mean by migration
Emigration + imigration drynamics between sub popultions - When indiviual in one popultions contrubutes to genes in other popultion Movement of alleles from one breeding popultion to another breeding popultion - Indiviouals departing from or arriving in a new breeding popultion
What does migration violate in H-W
H-W = no one can go in or out –> when you are born into a popultion you contrubute to that popultion and no one comes in NOW the gentic makeup of the new generation is NOT just a function of within popultion reproductive dynamics – NOW it also includes some input from other popultions
Two models of migration
- Continent island model – larger popultion going to smaller 2. Two island model – recpiprcal gene flow (this is the one we use)
Migration model
Start = have 2 subdivided popultions with difefrent alelles frequnceiesAT H-W – Poplation 1 – P = 0.8 –> p1 = 0.8 Popultion 2 – P = 0.2 –> p2 = 0.2At H-W the allele frequencies would stay the same (no migration) –> BUT THEN we can swap individulas at a rate of mm = proportion of one population that will contrubute to reproduction pool of other popultion in the next generation
m in migration models
Proportion of popultion that contributes to the next generation of the other popultion HERE = we assume that m is the same for both popultions - We could do m1 and m2 for each popultion BUT we willl treat m symetrically
Things we need to account for in migration model
- m – proportion of popultion that contributes to the next generation of the other popultion - Input 2. m - 1 – need to account for individuals leaving popultion (need to take account loss) - Loss from original population - Loss of migrants from our focal popultions
Mathamatical model of migration
𝑝′1 = (1 − 𝑚) (𝑝1) + (𝑚)(𝑝2) - 1-m X p1 – only have individulas with p1 allele frequncey - m = migration rate - m X p2 – get indiviuals at a raye of m at P2 allele frequncey 𝑝′2 = (1 − 𝑚) (𝑝2) + (𝑚)(𝑝1)p’ = allele frequncey in the next generation
Change in alelle frequncey in both popultions
Because the system is symetrical migration chnage alelle in both popultion by same amount in different direction (because same m) - Since we set up the system to be symetrcal (same m for both) = they change by the same amount
What is happening with migration over time
Use general dP – dP = m(p2 - p1) - Assume m is the same - p2 - p1 – Frequcney of same allele in popultions 2 vs population 1 –> drives magnitude of change
End point of migration
if dP = 0 –> get equillirbium point - get an end point of popultion IF migration is ongoing dP = 0 when P2 = p1 (when the allele frequnceies are identical) - Have end point in middle + know what endpoint isEND – P2 = P1 –> end is when dP = 0
Migration is…
Determanistic – have an endpoint and know what the endpoint os –> if you know the start you can know what effect will be - Migration will go until p2 = p1
Effect of migration
Migration acts to decrease genetic diffeerntaion between popultions – homogenizes genetic varaition across subdivided popultions - If migration keeps occuring popultions will evenually have the same alelle frequncey - Makes allele requncies of popultion more similar through time until there is no difference between thwm Homogenize until get equal Pt (will end equal to the frequncey across popultions Pt)
Pt
If two popultions were in one popultions what would overall p be - Allelic varaition in total popultion if subdivided were in one popultion - Pt = used to find Ht = Heterpzygosity across popultions THIS is what migration pushes P to in each popultion – pushes to average of 2 alleles - Migration acts to drive P1 and P2 towards PT
FST of drift vs. migration
Drift = increases FST towards 1 - Drift = makes Pt less similar - Increases differences between popultions
Migration decreases FST towards 0 - decrwases difefrences between popultions - Make popultions converge of Pt
Migration and drift = in direct opposition - If migration acts to dirve P1 and P2 towards PT = acting in direct opposition of drift Can find an equillirbium between the two
Meaning of FST
Increase difefrence between popultions FST = 1 = no shared alleles (have P = 1 and P =0)FST = 0 – ni allele frequency differences (they are the same)
FST
The measure of genetic differentiation between subdivided popultions
Drift vs. migration equillbirum point
FST>
Size of m and FST>
If m is smalle (less than 10-15%)– can calculate FST at equillirbium between drift and migrayion –> 𝐹𝑆𝑇= 1/4𝑁𝑒𝑚+1Nem = efefctive popultion size times the migraytion rate = the numver of effective migrants per generation - Nem = improtant term - Assume that level of FST = due to equillibrum between drift and migration = can use FST to see number of migrants moving in generation ***Ne and m = hard ot gte iun popultionn but they are meaningful
Use of FST
Assume that level of FST = due to equillibrum between drift and migration = can use FST to see number of migrants moving in generationGiven a value of FST = can find Nem = can find expected number of effective migrants - Here we assuime that the popultions are at equillbirum
Changing Ne
If increase Ne – loosing FST vakue = FST decreases Increase Ne = FST decreases because ability for drift to maintain allele frequncey difefrences decreases and migration is strong
Increase Nem = favor of migration over drift = become similar
Decrease Nem = favor drift = miantain differences
Nem term
Effective population size times the migration rate – it is the number of effective migrants per generation
Meaning of FST
FST = equilibrium point between the effects of drift and effect of migration - It is a function of migration rate and strength of drift THIS IS A PUSHING EAQUILLIBRIUM = stable equillirbium between the two forces
Affect of drift vs. Affect of mutation
Drift = causes allele frequncey to be more different
Migration = Homogenoizes to Pt (Causes allele frequncey to be the same) We can define an equillrbium point
How do we find FST (overall)
Take observed vs. expected form - Compare heterozygosity in sub popultion to total variation FST = Ht - Hs/Ht - Ht = like epxected – expected H if they were one popultions - Hs = observed H (Avg of sub H) Observed parameter vs. expecteation given nullPt (all varaition – if were one popultion) vs. how varaition is distrubuted in subdivided popultions (Hs)
Observed in FST Vs. Expected in FST
Observed = looking at varaition within popultions (Hs – avergae varaition within sub popultions) Expected = null –> Null that 2 population stae part of one popultions - All alleles in both popultions that mized as if they were one popultions THEN what would H be (THIS IS Ht) - Null = treating as one popultion – Avg H if one popultion
FST (1 or 0)
FST = 1 – P = 1 and P = 0 –> H = 0 - Hs = 0.5 –> between the two of them - All of varaition is between the popultions NOT within FST = 0 – all varaition is within subdivided - P = 0.5 and P =0.5 – allele frequencey is the same in both popultions Low FST – allele frequncey in popultions are close together
Ht
Heterozygosity across both popultions - Equal to or greater than variation within subdivided popultions Uses Pt – Average between P of both populations
What can we use FST for
Given a value of FST – we can find the expected number of effective migrants (Can find Nem)
Pairwise effects of forces
- Mutation selection balance - Mutation adding alleles and NS getting rid of them - s and u 2. The dual effect of drift and mutation driving neutral molecular evolution - Drift regarless of popultion size drives genotypic variation between two species - u and N 3. The tension between drift and migration in subdivided populations - m and N
Mutation selection balance/Mutation and drift
Had fairly straight foward outcomes - Mutation creates varaition and drift Stable equillibriumExample: Mutation creates varaition and drift turns some of it into substitutions at a constant rate - Straight foward outcome in nucleotide substitution –: drift taking mutation and carrying it to fixation - u drives nucleotide substitution rate because Ne is in both equations = cancels out = only u affects the rate of process
Nature of drift and selection
Selection = determanistic –> predictable Drift = Stochastic –> don’t know direction Differences in nature of them = makes intercation complicated ***They are two of the strongest forces – how do they interact - ComplicatedSince drift = always happening –> means that selection is always at play with drift
Probability of allele going to fication
Probability of new allele going to fixation = 1/2N –> relative increase or decrease of probabiliy of going to fixation = based on 2N
What effects if selection can occur?
Selection needs to act alongside drift – relative strength pf 2 forces = affects if selection can occur
Importane of drift vs. selection
There are ranges of parameter space where one is much more important than the other
Drift + selection on adaptive topographies
NS = acts to push popultions uphill – increase average popultion fitness (will go to make w/) - Will keep going until get to a local maxiumum value of w/How will drift affect AT?***Drifts tendencey is not to go downhill it is just to move in either direction BUT at fitness peaks there is no where to go but down so drift pushes popultions off of fitness optima Drift = changes alelle frequnceies randomly back and forth - When at w/ (When at stable equillibrium) you can only go down in w/ BUT drift is not trying to decrease w/ it is just causing p to move randomly on X axis - Drift causes back and forth movement from one generation to the next BUT if at w/ then drift can only move w/ down = drift decreases w/ because only way to go BUT if popultion is not at w/ (not at equillirbioum) then drift is equally likley to go in both dorections (Can make it decrease w/ OR can make it go uphill to w/ fatser) - If no where to go but down drift can push popultion off of w/
Strength of NS + drift on AT
Steepness of slope = proportional to the strength of selection - If steep – a small chnage in P = bigger change in fitness = stronger repsonse of NS = NS can counteract affect of druft more readily (when steep drift is constrained on how much it can chnage things because NS can put it back to w/) - Steep – NS is going to give strong pushback as drift is moving the popultion around - If shallow (less steep) = can move p a lot without a big change in W/ –> when p chnages and effects w/ THEN drift can drive the system more stringly (because NS isn’t ask strong if less chnage in w/) –> drift has stronger effect here - If shallow – the NS pushback is much weaker = drift has a much freer hand Smaller popultion size = affect but drift is large = harder for NS to keep popultion at an optimium
Halanes seive
Overall: At low frequnceies when it is very shallow – drift can be strong –> the most likley outcome is that the benefical mutation will drift to zero before NS can get any traction - If beneficial mutations act in rescive fashion = most liklet will come and go to drift long before selection has a chance to act on them - Sometimes these mutations can reach high enough frequncies to start being visable to selection through drift Effect of drift on recssive benefical alleles at low frequencey –> most is lost due to drift before selection can take hold and do things (bring to fixation) - Affects rate of selection
Affect of drift on rate of selection
Already have low mutation rates –> mutations rates give us mutations but drift can make is lost – can lose it befgore selections can do things = even lower rates of increase benefical recssive mutations
Drift increase or decreasing beneficial mutation
Drift can inrease are decrease BUT it is more likley to lose it (to decrease) because once it hits zero its gone - If you have limit that affects increase/decrease for good + if have one copy –> if you lose one copy = its gone –> loss is more likley than increasing to go to fixation Creates limitations of NS acting on new alleles
Outcome for new reccessive benefical allele
Evolutionary outcome for new recessive benefical alleles is heavily influnces by drift
Migration + Natural Selection
The influx of alleles from other populations can also push populations off and adaptive peaks, or prevent them from getting tothem in the first place - Migration coming in adapted to different environments = can push population off of peak
Migration load
A lower average population fitness than predicted given selection regime due to migrants from populations with different selection regimes - Knowck off optimum because influc of alleles adapoted elsewhere - Espcially bad at edge of range
Can migration be good?
Migration is not always bad –> add variation IF the strength of selection = proportional to varaition - Selection = lose varaition - Waiting to get varaition through mutation (mutation is a slow source of new varaition) –> get little variation – low rate of mutation = doesn’t give varaition + lose a lot of mutation = ability to adapt if run out of varaition – wait a while to get varaition through mutation BUT migration can add varaition - Migration = can get new alleles into focal popultion that NS can act on –> might not need to worry about losing alleles because can have others with those alles come in - Migration can be very effective force for infusing popultions with new varaition on which selection can act
Is pushing of adopative peaks always a bad thing?
If have scenerio in image –> You can go to w/ = 0.35 OR can go to w/= 1IF p goes to p> = it will stay there – this is bad because low w/ – NS can’t move it from the valley BUT other evolutionary forces can - If went to the right of valley = would go to p = 1 – would end at w/ = 0.35 - If have other forces with selection = won’t get stuck there –> drift can chnage { and can drift acros equillirbium to the left and NS can then bring it to P = 1 - Selection can’t do this by itself but it can with other forces – drift opens up posibility - Selection + drift = two step process involoving chnage in predominance of first (first drift predominates then selection) OR migration can come in – other population with benefical allele can come in – they can send migrants –> migrants can go to the focal popultion and can bring P across equillibrium and allow NS to bring P = 0 and w/ = 1 NS by itself can’t bring the popultion to the global optimum but selection in conjuction with other forces can
Wrights theory (overall)
Back during the modern synthesis, Sewall Wright proposed a model for how the interplay of evolutionary forces might be a more powerful mode of evolutionary change than selection by itself - Thinkning about limits of NS and need other dorces to get complex traits It’s called ”shifting balance” between it involves shifts in the predominance of the evolutionary forces acting on a species - Think about how relative importance of forces might shift in system
When is wrights model useful
Particuly useful for thinkning about ways that evolution can act when adaptation is complex - Model on how to adapt across rigged AT –> how to cross adaptive valley if NS can’t do that by itself
Rugged adaptive topographies
Have more than one local maximum
What makes adaptive topographies rigged
Epistsis – due to epistasis –> intercation between loci and affect on fitness - Cam’t know affect of 1 locus on fitness without knowing the state of other trait - Affect of Trait one depends on state of trait 2
Epistasis + NS
Epistasis = majore hindernace on effects of NS Need other forces of have epistasisEpistatic rigged topography – multiple genes affect trait –> get both to move in right direction together – hard for NS to do
Example – 2 locus fitness epistasis
Has two fitness peaks + 2 low vallues A1 has high fitness in presence of B1 - A1 = only high fitness with B1A2 only has high fitness in the presence of B2 - High A2 and B2 = high fitness Wrong combo of alleles = decrease fitness If to the left of valley = fix for A1B1 BUT if to the firght of balue fix for A2B2 Have Adaptive topography for A with w/ and Adaptive topography with B for W/ –> put them together to see interaction
What does wrights model work best for
Wright’s model occurs takes place on rugged adaptive topographies, but also works best in subdivided population – works best in subdivided popultions Works best in subdivided because small popultion = increase drift + subdivided can have migration + mutation popultion drift independtley
Wrights model (Slides)
Step 1 – in individualsubpopulations – drift canpredominate if population sizesare small (relative to “height” offitness peak) - If this is happening in multiplesubpopulations, some of thesepopulations are likely to end up thebases of better optimaStep 2 – When drift brings thepopulation in contact with a steepfitness slope, the strength ofselection starts to outweigh theeffect of drift – selection pushesthe population up the peak - As mean population fitness increases,population size is likely also increasingStep 3 involves a shift towarddynamics among populations –large high fitness populations arelikely to produce a lot of migrants - These migrants can influence theevolution of other subpopulations - This influx of new variation pushes the 2nd population of its local peak –specifically in the direction of the peak in population 1Step 4 occurs when this push from migration allows selection to take overin the 2nd population – driving it towards the global optimumOverall: The interaction of evolutionary forces actingacross large subdivided populations might allow for complex adaptationthat natural selection can’t drive by itself
Wrights model (Mine)
Step1 – all subdivided popultions = stuck on bad fitness peak –> NS can’t act to get to a better position (because at a peak) -Height of peak is proprotional to W/ – depends on reproductive sucess - Low w/ = popultions staying small + low fitness = popultion decreases = cam drift heavily –> drift has string hand and NS will be weak –> can have random change in alelle frequncey - Random chnage in allele frequncey due to drift = csan be going on in 10 subdivided popultions independentley – all random change due to drift –> eventually popultions will leave peak - Drift will predominate unless the frequncey goes to a slope and NS can accelerate THEN – as popultion increases = drift acts less –> Selection will increase w/ to go up peak and get to a new peak = optimum fitness When increase w/ = now hvae high probability of S/R = increase popultion size = can be highest w in subdivided popultion = NOW need to send migrants to other popultions - Healthy popultion = only sending migrants out (not symetric) Migrants now influence P in other popultion – other popultions in other peak can have low drift out– if send migrants from original popultions chnage P in other popultion determansticallty towards higher peak (not random) –> NS can bvring to peak after migration – NS can take over and end at overall species wide adaptation in complex trait END = species wide adpatation in complex trait SHOWS – NS can drive complex adptation in com face of epistasis –> other droces play important role NOT just NS – need other mechansims to get adapative being
What does Wrights model explain
Explains how complex adaptation can occurUsing other forces = we can epxlain how complex adaptation can occur –> NS can’t do it by itself – relies on importance of forces shifting across process
Limitation of NS
NS can’t explor broader adaptive space itself (in rugged adaptive topographies) because only goes uphill – it can get stuck at a sub optimal peak Using other forces = we can epxlain how complex adaptation can occur –> NS can’t do it by itself – relies on importance of forces shifting across process
One shift within wright model
Shift from within sub populations to between sub population***Works when in sub popultionsStep 3 = Shift within sub popultion to between sub popultion
Quantative genetics (overall)
How does heritability operate in traits with continous quanative values - QG = study of continous penotypic varaition Subfeild of evolutionary biology focused on understanding the evolution of continous phenotypuc varaition
How do discrete units (genes) results in continuous phenotypic variation?
- Environmental condition acting on genetic variation - Environmental variation = Get continuous range of variation - Envirnmental vraition acting on top of genetoc varaition 2. Epistasis –> interaction between 2 loci –> interaction 3. More than one gene - Many genes = get continuous trait perhaps without environmental variation - Could be epistasis or additive –> could lead to continous - Additive = influnece indepndtley = contubute independentley Ex. Height – based on genotype and environment (condition of soil drive)
Distribution of traits (based on genotype)
If genotypes is only driver of phenotype = get continous bell curve distrabution based on genotype aloneMore loci = get complete continiuity amount of phenotypes - If increase genotypic states = get bell curve based on genotypic varaition alone
What affects most traits
Many traits = affecetd by both genetic and environmental - Many phenotype have contibution of envirnment and genetic - MOst traits = result of bot polygenic effects and envirnmntal effects Quanatative genetics = allows us to look at both simultaneously
Polygenic/Quantative
Many genes are determined by allelic varaition at multiple loci = Polygenic or qunatative traiots
Use of quanatative traits
Quanatative genetics = gives us the toolkut to understand the link between multi locus genotypes and phenotypes Allows us to understand how NS operates on these traitsQG = explains patterms of =heretability + explains how NS acts on fitness when have complex variation
Assumption in continous traits
Continous traits = assume traits influneced by multiple genes
Quanatative trait
Continous varaible + result of multiple genes = polygenic affects of the traits
What do we need to know to predict what will happen with NS in the next generation?
- How many phenotypes are possible (measure of varaition in trait)2. Need to know if phenotypic varaition is heretible + how heretible it is - Need to know how heretible traits are Overall: Need to know phenotypic varaition + how much varaiation is heretible - With this = can predict what will happen to phenotype across generations without having to know all genes and alleles involoved - Need to know the relationship between the phenotype and fitness AND the proportion of phenotypic varaition driven by fitness
Variation
Here we mean varaince in statistical sense - If it is contionous – how muc varaince is there in continous traits
Calculating Varaition
Overall: Square deviation from expected mean - Avergage deiviation of individula trait value vs. popultion mean –> avergae of how far off indiviudal is from mean Varaince = 1/n-1 X (SUm of (Xi - X/)^21/n-1 (Sum) – sum X 1/n –> Summing and dividing by number of samples = getting an average - The sum is the deviation of each data point X from the mean - squared (Xi-X/)^2 – - Difference if Xi trait value from mean trait value X/ –> Looking at average difference of deviation from mean - Looking at distance from meanSquare – because –> to make all posible numbers = can look at variation on both sides –> what matters is tge distance from mean not if it is larger or smallerVaraince = easy to measure in popultionCan do it for any trait value
Low vs. high varaince
Low varaince = everyone is closer to being the same High varaince = far from mean
Overall phenotypic varaition
Vp – overall trait varaince
What affects Vp
Vp = result of the additive effects of genetic and envirnmental components of phenotypic varaince - Result of polygenic affects + envirnmental affects Genes drive varaition + envirnment drives varaition –> Do so in ADDITIVE WAY - Varaition by genes and varaition by envirnment add together
Findiong Vp
Gene and envirnemnt affect Vp in adidtive way –> Additive relationship between genes and envirnment (easy to comvine genetic and envirnmental varaince) Vp = Vg + VE - Vg = variance effects due to genes - Ve –> Varaince due to envinment VGXE = can be determined too BUT that seperate source of variance also forms an additive component of Vp and is often lumped in with Ve - VGXE = another aditive effect –> also adds in additive way to total phenotypic variance Vp = Vg + Ve = VGXE
What can NS act on
NS can only act on heretible varaition to cause chnage in one generation to the next - Whether something is heritable or not is not just yes or no –> genes are heretible BUT envirnment is not heretible –> NEED to quantofy partial heretible effects to know how phenotype can change due to NS - The combination of Bg and Ve results in partial heredity Heretible varaition = part of Vp we are concerned with
Value of heretibility
0 -1 0 – there is no relationship between parent and offsrping phenotype - Offspring are not correlated to parents - All envirnment – no varaince due to genetics 1 – Offspring phenotypes are expactlet like parents - Phenotype is expactley like parents – if know parent know offspring phenotype
Broad vs. Narrow sense heretibility
Broad = encompasses all of the gentic input to the trait - Ecompasses all potential genetic affects acting on phenotype - All varaition due to any type of genetic affect - Includes genetic affects that it is hard for NS to act on - Braod = not measure of heretibiulity critical to making predictions H^2 = Vg/VpNarrow sense – proportion of phenotypic varaition that is passed between parents and offspring in a straight foward way (easily predictable way) - due to additive affects - Proportion of all genetic varaition due to additive affects - Part that matters –> component of Vp that is due additive h^2 = VA/Vp
Breaking down Vg
Vg = can be broken down into different components - Can break down genetic variance Vg = Va + Vd + ViVa = additive genetic varaince Vd = dominance genetic varaince Vi = epistatic interaction genetic varaince ***Not hard to measure them independently - Hard to get in nature
What drives narrow sense heretibility
Va = drives narrow sense heretibility - Affects parent offsrping relationshio Vd and Vi = interfere with direct parent phenotype to offspring phenotype relationshio - Interfere with some of parental contirbution to your phenotype = interfere with direct realtionship - they’re difficult to measure withoutknowing the genotypes. - More importantly – they interfere with the straightforward prediction of the effects of natural selection
Measuring h^2
Heretibility involoved the correlation between parent and offsrping phenotypes –> most straightfoward way to get h^2 = through midparent regressions - easy way to get h^2 = to compare parent to offspring phenotype using mid point regression
Mid point regression
Take parent average phenotype - Expect the offsrping the match the average of the parents –> use expectation to see gow well data matches - Estimates h^2h^2 = equal to the slope of a linear regression bteween the midpoint phenotype (average of mother and father) and the offsrping phenotype (or mean offsrping phenotype for multiple sublings)Overall in regression = looking at average parent vs. Avergae offspring
Results of mid point regression
Overall in regression = looking at average parent vs. Avergae offspringLeft = NO statistical coreelation –> parent phenotypoe dies not predict offspring phenotype - h = 0 Middle = Have trivial amount of heretibility - Parent phenotype predicts something but not completley - h = 0.5Right – h = 1 Value of heretibility = value of regression line - Slope = 0 –> like popultion mean not parent –> h = 0 - Slope = 1 –> h = 1 –> all varaince driven by additive genetics - h = 0.5 –> 1/2 varaince due to parent genotypes; 1/2 variance due to envirnmnetal non-additive components
Complications for measuring heretibility
- Might be hard to get data for mid-parent regression 2. Parent and offsrping are likley to share envirnmental varaition - parents and offsrping share envirnment that other members don’t share not only genes - Shared components of envirnments vraiation that conturbutes to parent vs. offsrping relationship pairs = complicates h^2 - Can be envirnment rather than genes Example – shared envirnment – might share where they live –> like if they gave a lot of food in area – parents get the food + kids get the food - Experiment – forced bird pairs to raise foster bids – can takie into account envirnmental varaition to rule out envirnmental contribution of varaibility – canges slope of regression h^2 = the upper limit of hereteibility in popultions –> because can be due to envirnment rather than genes = h^2 measured in mid-point regression = shows the upper limit of varaibility because some amount of slope is because of shared envirnment
What is h^2 specific to?
Prameters = refer only to the specific popultion (genetic makeup) in a specific envirnment - Specific to that envinment –> if put popultion in new envirnment = change Ve = chnage h^2 - Specific to popultion AND specific to popultion in that envirnment that it is in – only for popultion in that envirnment When we measure heretibility it is only value within that popultion – it is useless for making comparisons between popultions
Affect of dominance and epistasis
Interferes with ability of offspring/parent phenotype
Use of heretibility
if we know heritability of trait = we can predict how NS will affect it - Need to know h^2 to know what will happen to trait in next generation In order to know how something will change over time –> need to know h^2 -
Use of Breeder’s equation
We can predict a particular strength of selection using Breeder’s equation
Breeder’s equation
R = h^2sR = response to selection in the offspring generation - Phenotypic response –> change in mean trait value from one generation to next due to h^2 S = selection differential in the parental generation - Select subset from parental generation to S/R – get difference between overall mean and selected mean = get S Response to selection = due to strength of selection + heritability of trait h^2 – not actually squaring something – just put squared to differentate from other times h value is used (Called breeder’s equation because used in breeding in plants)
What restricts selection
restriction on selection – limited becaused h^2 is not perfect – not 1Response to selection = due to strength of selection + heritability of trait
What is S in breeders
Selection differential - Difference in traits mean in parents vs. the trait mean in the selected individuals
Selection Gradient
The relationship between the trait value and the relative fitness - Scalable into complex situations More flexible way to think about strength on phenotype Can account for continous fitness varaition + varying fitness varying as a function of the trait - Look at fecindity – relationship between varaibles = selection gradient ***Acts in the breeder’s equation in the same way
Use of selection gradient
Can use selection gradient to apply our quantitative genetics tools to more realistic complex fitness situationCan use technique in more complex settings
Selection Differential vs. Selection gradient
Differential = trait as binary survive or not survive - Just have number of individuals Selection gradient = Continuous relative fitness - Slope = selection gradient Can convert between the two
R in Breeders
Response to selection – change in the population mean from one generation to the next
S and R in graph
Look at Mid parent regression – parent phenotypes + offspring phenotypes – same plot used to get h^2 - Look at distance of parental phenotype and distance of offspring phenotype X Axis – push trait (pushing parent trait) by X much and see chnage in offspring (See how much Y chnages)Graph – pushing P –> P* and seeing chnage in Y value (Change in offsrping trait value) - When look at slope – look at how likley offspring match parent (slope is h^2) - Look to see for X push on X = get Y (get that much increase) in offspring - Run across mid parent regression that rise along using h^2 - See what happens if increase parent trait by X much – see chnage in Y value – change in offspring h^2 = slope
Rise / run in selection gradient
Really just looking at rise over run for change (-4) = run – look at how much rise there is - S = run - R = Rise 18 = 25h^2h^2 = 25/18 –> h^2 is slope = rise/run
What can we use artifical selection experiments for + Breeders equation?
We can also use equation to estimate the heretibility from artifical selection experiments - Can run selection experiment and use Breeders to calculate h^2 –> Might want to know if there is Va to select in the future Example – If running an agricultural breeding program to imrpove corn - Mean percent popped = 60% - You think there is a lot if varaince in phenotype and you want to know how much is genetic – want to know how much you can change trait - Breed – take from popultion with 85% popping GRAPH – parental vs. Offspring - Offspring increase popping rate (60% –> 78%) - Slope = h^2 – get slope with rise/run - Positive slope because increase trait value - S = run - R = Rise For 0.6 –> 0.85 increase in X (increase in S) = get 60 –> 78 increase in Y (increase in R) 18 = 25h^2h^2 = 25/18 –> h^2 is slope = rise/run
Finding R and S
S = selected mean - starting mean - Gives directionalityR = offsrping mean = starting mean - NOT using selected mean S = gives directionality R = gives response
Qunatative genetics (overall)
Know heretibility without knowing underlying genotypes
What does it mean when a complex trait has a genetic component? – How do we interpret when we find a gene for a trait
NEED TO BE CAREFUL – h^2 represtns the upper limit of heretuibility + other varaibles that are hard to break down - Over estimate h^2 because of shared envirnment (looks like heretibility but heretibility but really shared parent offsrping envrirnment not shared genes) Example – Birds that are in area (area might be warmer) = get more sunlight = birds in area are faster and bigger BUT they share the trait because of shared envirnmnet - Outcome is due to envirnmental similarity - Also applyes if have same diet in parent and offspring
Subtle envirnmnetal effects
- Maternal Effects2. Epigenetics
Maternal Effects
Envirnment of mom affects not gene - Resemble mom but resemblance is due to envirnmnet that shapes aspect of development Example – Egg size is based on food mom had not moms genes – due to moms envirnment not genes BUT different moms have different envirnments thats leads to difefrences that look genetic
Epigenetics
Trangenerations epigenetics – Trangenercation effects - opperates in the lab –> studies on lab organisms now studied in humans Affects that we are learning more about OVERALL – ssRNA for a gene regulates or RNAi regulates – the effect of RNAi is seen after generation after intial generation - Aleter gene expression that gets passed down to offspring not just in the 1st generation - Get connection between parent vs. offsrping dur to epigenetics not heretible changes
Effect of complex envinrmental effects + Shared envirnment efefcts
ALL complicated our ability to quantify genetic components of traits within human popultopms - Complicates ability to make inferences within popultions we measure – we often overestimate heretibility Measuring narrow sense heribility = far from trivial –> espcially complex traits in humans because can’t do the same experiments in humans Even when we feel we can draw string conlsuioons about heretibility we are still limited to talking about the role of genetics in THAT popultion - Strong heretibilility parameters = tell us about proportion of Va in popultion in THAT envirnment –> only have how much genetics drive phenotype in that envirnment - Doesn’t say anything about between popultions –> hard to look at heretibility across different asopects of human society
Example on heretibility in THAT envirnment
Even when we feel we can draw string conlsuioons about heretibility we are still limited to talking about the role of genetics in THAT popultion - Strong heretibilility parameters = tell us about proportion of Va in popultion in THAT envirnment –> only have how much genetics drive phenotype in that envirnment - Doesn’t say anything about between popultions –> hard to look at heretibility across different asopects of human societyExample – You are intersted in imporving beed yeilds through selective breeding Start = go to different cattle in popultion and look at phenotypic varaition in beef yeild in cattle to get an idea of Va - Get idea of genetic differences in popultion to leverage in experiment THEN – you gi to a couple of large farming operations to measure additive genetic varainceIF in popultion 1 – mean mass = 450; Vp = 10,000IF popultion 2 – Means mass = 850; Vp = 10,000 Difference in mean BUT similar amounts of phenotypic varaince GRAPH (off vs. Parent) – same slope but one mean is 450 (lowser) and other mean is 850 (higher) - Results –> heretible trait – strong relationship - h^2 = 1 in both popultions = within popultion all of the varaition is driven by Va (high heretibility but big difference in trait values) - Both slopes = 1 = h^2 = 1–> have 100% Vp explained by Va - If select to imporve beef yeild –> is one popultion genetically bettwe fir beef yeild (if H^2 is 100% in both) – is there genetic difefrence between popultions On the surface = seems to be geentic difefrence but the data does NOT suggest that Have we found genetic difefrence? h^2 us high = explained by Va –> tells us little about envirnmental varaince within popultion –> genetics could be the same but both popultions are in difefrent –> if foo is good in one and bad in other –> the differebnce is due to envirnmentWe don’t know if Va is what is difefrent between popultions
Appliying heretibility in THAT envirnment to humans
Compare humans popultions –> diferent traits value parameters don’t tell you difference in genesWe don’t know if Va is what is difefrent between popultions Human popultions = not in homeostasis –> have lots of envinmental variation - Envirnmental varaition can play out in one generation or across generations Envirnmental varaition makes saying things about genetic differnces meaningless If people say heretible and differnces is genetics —> They are using a misapplication of biology Each comparison only works for varaition in popultion you are measuring in that time
Sexual reproduction norm
Sexual reproduction seems like the norm but hard to explain why this is the case
Commanality of sexual reproduction
Very common in living world Just because we do it doesn’t mean it needs to be done
Sexual selection
Fitness is determined by variation in attracting males - Occasionally leads to outlandish phenotypes Phenotype relates to mate choice – not just survival and # of offspring Example – Bird + colors in monkeys + elk that has long antlers + Dopsin fly (has mandables that attracts mates)
Sexual conflict
Males and females have different optima for fitness - May be common in nature Example – 1. Lions –> Males kill all of the females pride that she had with another male to make the female fertile - Good for fitness of male; bad for fitness of female 2. Bed bugs = violent insemination – male stabs sperm into female body 3. Weird falas on falcan – corkskrew shapes – female has vaginal track that is corkscrew in the opposite direction –> Males change direction to control reproduction and females will chnage in response to control reproduction
Sex = NS
Sexual reprdouction poses a paradox for adaptation by NS –> it is reproductively costly to maintain two maintaining two mating types
Two fold cost of sex
It is reproductively costly to maintain two maintain two mating typesEach cost = doubling of fitness effect and 2 different aspects that come into play
Maintaining two mating types
Sometimes hard to explain Females making only females that also only make females = X2 reproduction rate = X2 fitnessExample – Each females makes 4 offsrping –> only the females will make more offspring IF 1/2 off are female – each female makes 4 offsrping –> end get 8 Grandchildren IF Females can make female offsrping without males –> each females makes 4 females –> End 16 grandchildren8 VS. 16 – BIG difference – X2 replication rate in only females = big fitness advantage - Females making only females = growth rate X2 = fitness X2
Genes in sexual reproduction Vs. Asexual
Sexual reproduction – only pass 1/2 genes to individuals –> offspring is 50% related to mom Asexual reproduction – offspring is 100% related to mom In sexual if have a mutations that leads to asexual –> allelle will be in all = spred fast (because asecual will pass mutation to all) Sexual –> if have gene that maintains sexual = only pass to 1/2 offspring
Loss of sex
Diploid organisms whose ancestors had sex – sex is lost rather infrequently Evolution of asexual out of sexual does happen enough that we know it can happen
Reprdouction in many dilpid
Parthenogensis
Parthenogenesis
Reproduction by females of other females via unfertilized eggs (females make other females via unfertilized egg) - Often facultative not obligative –> organisms can repduce in either mode depending on conditions – occurs depending on contexct - Colony of genetic recombination Might alternate like clockwork (one generation sexual; 1 generation aesexual)
Aesexual from sexual
Some organisms evolove asexual from sexual Example – SHOWS – asexual can evolove from sexual
Question in Sexual Reproduction
Given the 2 fold cost of sex why did sex evolve to begin with and why does it persist? 2 fold cost – decrease in reproductive rate + Lowers chance of passing on allele to offspring Potential Answer: maybe because once evolved –> can’t go back
What does parthenogensis show
Shows that asexual can evolve from sexual (Have sexual –> asexual where you stay asexual) New question = why doesn’t it sweep through given fitness advantage
Obligate Parthenogens
Fixed for asexual reproduction (need parthenogenesis) – tend to be single species or very small clased (NOT large raduioations of diversity) + They tend to be young - NOt very diverse - One or a handful of species - Lineages are young –> NOT long standing groups of aninals that reproduce asexually for long periods –> suggests that there are constraints that asxueal is not good long term
How can genomic data reveal that sex has taken place?
Recombination between lineages – shows that sexual reproduction goes on If a chromosome is the resulyt of reocmbination = evidencve that not purley clonal When have recombination = varaition from 2 parents that recombine = evidence for sexual reproduction
Why does sex persist
Recombination
Benefits of recombination
The 2 fold cost of sex can be made up for by the benefits of recombination acting to break up linkage disequilibrium that inevitabley forms in purely asexual popultions - Cost is counteracted by the benefit of recombinationProcess = Leads to ineviatble accumulation of deletrious mutations in asexual species Benefit in recombination = reshuffle variation to get new geentic combinations
Issue in clonal organisms
All new mutations = have linkage disequilibrium with other variation = have accumulations of deleterious mutations Process = Leads to ineviatble accumulation of deletrious mutations in asexual speciesInevitable accumulations of deleterious alleles = Muller’s ratchet
Muller’s Ratchet
Start: Well adapted high fitness genome – well adapted asexual organism OVER time – some mutations will arise - Some of the mutations will be good BUT most are nuetral or bad –> Most will make fitness decrease Mutations in lineage of asexual –> All offspring will have the mutations –> once mutation pops up it is there for good - The deletrious mutation that affects fitness a little but not enough to kill you –> Over time lose high fitness alleles until all that is left is deletrious mutations If only slightly deleterious = not bad enough that NS can act on it OVER time – initial high fitness genotype can drift out of existence
What drives the ratchet fowards
Combination of mutation and drift drives the ratchet forward
Why doesn’t NS act on deletrious mutations in ratchet
Because the mutations are only slightley deleterious = not bad enough that NS can act
Fitness change in Ratchet
Start with high fitness genotype –> get copies of self = have high fitness offsrping THEN have a mutation that decreases fitness a little but not a lot –> THEN pass mutation to offspring because clonal –> THEN have another mutations - We expect mutation in almost all generations HAVE more mutations in the background of the first mutations Each round the new mutations that occur stay for good in all decendents of mutated indiviauls –> the orginal high fitness genotypes become rare End = lose the zero mutation genotype – NOW only have 2 mutation vs. 1 mutation genotype –> ability of NS to seperate mutations get weaker End = the best fitness is 1 mutation (no longer have 0 deleterious) = have one click on ratched
Mutation in asexual
Once mutations pops up it is there for good
Ratchet moving foward a click
Perhaps NS can favor the no mutation high fitness genotype gainst the lineages with 4 mutations BUT if the high fitness genotype is lost due to drift OR of another mutations occurs in an individual with that genotype –> The high fitness genotype is gone for good and the best genotypes all have at least one deletrious mutations = the ratchet has moved foward a click
Genetic load
The accumulation of deletrious mutations weighs doen the fitness of a popultions - Over time popultion fitness decreases – decrease in fitness = genetoc load - Decrease in optimal fitness - Decrease fitness based on accumalation of deletrious alleles Major probelm in asexual organisms –> even driving them to extsiction
What breaks the ratchet
IN sexual popultions recombination breaks the ratchet - Sexual reproduction breaks ratchet (can go back and forth) –> can get high fitness genotype back If have a popultion that lost the high fitness genotyoe –> Can have crossing over in meiosis can give back the best genotype Example – if the highest fitness genotype is 1 mutations - IN asexual all mutations go to offspring - In sexual – by combining gametes in sexual reproduction + recombination –> we can get back the fitness with 0 mutations
Recombination + Novel phenotypes
Recombination via sex might speed up the rate at which novel phenotypes appearAbility of recombination to make novel genotyoes = important
Benefical mutations in Asexual
Purley clonal popultions have to wiat for benefical mutations to occur in sequntial order in the same lineages to get novel phenotyopes
What happens when lose high fitness genotype in Asexual
High fitness is gone for good unless have exact mutation backMutation rate = 10^-8 –> chance of exact mutation to high fitness is small
Reason most asexual lineages are young
Because of genetic load As mutations occur = fitness decreases over time
Adaptation in more than 1 gene
Adaptations often require more than one gene In asexual – need to wait for mutations to ccur in sequntial order in the same lineagese to get high fitness - To get high fitness need mutation in one indiviudal –> need to occur in occsrping in the background of the other mutations Sexual – Do not need mutation to occur in offspring in background of othe rmutations –> in sexual you can get the beneficial mutation in one organism and one beneficial in another organism and then they can combine through sex = get high fitness IMAGE – AB = high fitness - Top – Sexual – can get mutation in different lineagse and then mix lineages = get high fitness –> OCCURS much more rapidly – get more AB (more high fitness) – increase probability of polygenic adpatation - Bottom = Asexual – aB = dead end –> Need A and B –> Need ab THEN Ab –> THEN AB – need B mutation to occur in backgroun of A – need mutation in same genetic line
Sex + rapid evolution
Sex might be important beyond steady accumulation of deleterious mutations – might be needed for rapid evolution
What is constant evolution driven by
Driven by co-evolution – evolove to envirnmemnta + evolove to threats from other evolving organsims Overall – popultions don’t evolove in static envirnmmnets – have major evolutionary pressures coming from other species (predators + Prey + parasites + Competators) Example – evolove to parasites or pathogens (evolove fast) To deal with threat from other popultions = ability to evolve fast is important
Red Queen Hypothesis
Popultions are constantley evoloving to chnaging biologic chalelemnged and sex is crucial way for popultions to keep pace woth their eneminies To deal with defeinsive stredegy –> get niovel genetics in all generations (only can occur in sexual) Overall – popultions don’t evolove in static envirnmmnets – have major evolutionary pressures coming from other species (predators + Prey + parasites + Competators) - Threats to fitness from othe rorganisms that themsleves chnage (other organisms have chnaging adaptive topographies) –> Change in one organsims = have shift in other organisms adaptive topography - Sexual reproduction = gives organims ability to keep pack in co-evolutionary arms race
Sexual vs. Asexual experiments
Studies = done with organisms that can do both Low predator or parasites = asexual is maintained at higher rate Add predators –> Sexual reproductions = sweeps through populationExample – Snails + fish –> vary in degree of sexual reproduction - High predatores = increase rate of sexual - Low predators = maintain asexual SHows red queen hypothesisSlides: A number of natural andexperimental systems appear to back this up- asexual populations thrive in the absence of parasites, but sexual populations do better with intense coevolution More than making up for the 2-fold cost
