4 Introduction to Mendelian Genetics Flashcards

(72 cards)

1
Q

intro
mendelian Genetics

A

Population genetics is concerned with the collection of genes belonging to all members
of a population, called the “gene pool” or “genetic material/information” of a population.
Central questions in population genetics are: What is the structure of the gene pool?
How does it change in time? What causes these changes? In this chapter we introduce Mendelian genetics and use difference equations (maps) to study how the gene pool of a population changes over time.

: introduction to Mendelian genetics => laws of inheritance
=> theoretical formulation of natural selection

Mendel’s inheritance laws + Darwin’s theory => evolution, Maths: evolution with nonlinear maps

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2
Q

intro

A

in this chapter we assume that the trait under discussion
is produced by two alleles at a single locus. We are interested in whether genetic diversity
persists, or if one allele is lost and the other takes over the whole population. In other words,
for Mendel’s peas, we ask: in the long run, how likely is it that we will we end up with
only rounded peas? If both alleles persist the population is “polymorphic” at the locus. To
answer questions like this, we need to understand how the structure of the gene pool changes
over time at a given locus.
Mathematical population genetics was developed in the 1920s-1930s by Ronald Fisher,
John B. S. Haldane and Sewall Wright who laid out the mathematical formulation of Darwin’s
theory of evolution by natural selection. They built a theory based on Mendel’s observations
translated into his quantitative laws of inheritance (Mendelian genetics)

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3
Q

4.1 Mendelian population genetics
Mathematical population genetics was developed by Fisher, Wright and Haldane in 1920-30
based on Mendel’s work. Mendel derived empirically the laws of inheritance by observing peas

A

Mendel observed that 75% of peas were rounded and 25% wrinkled
=> 2 gene variants called alleles, say R or r, coding for “rounded” (R) or “wrinkled” (r), at a “locus” (specific position of a gene on a chromosome), with R “dominant” over r (“recessive”)

=> 1 allele from each parent are paired, giving 3 possible pairings called “genotypes”: RR, Rr or rr. These give the “trait expression” or “phenotype”. RR and Rr give rounded peas (75%) and rr wrinkled peas (25%)

=> genotype and phenotype frequencies can vary over time
=> if both alleles persist the population is “polymorphic”;
otherwise one allele takes over the entire population

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4
Q

How is genetic variability maintained?

A

The answer was provided by Mendel’s principles of inheritance (1866): organisms have discrete genotypes that get reshuffled and paired, not blended, during mating and this ensures genetic variability on which natural selection can operate, see Fig. 4.1. Nowadays, natural selection is broadly accepted as the biological explanation of adaptation.

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5
Q

4.2 Mendel’s inheritance laws

A

Chromosomes made up of strands of DNA carrying hereditary information. Genes are DNA segments
that determine specific traits. Based on observations, Mendel formulated the laws of inheritance:

segregation
during the formation of gametes (sexual cells) alleles segregate from each other => gametes carry 1 allele for each gene

  • Independent assortment:
    genes for different traits are sorted separately from one another=> inheritance of one trait independent of inheritance of another
  • Dominance:
    in heterozygous diploid organisms carrying two different alleles, the allele that would determine the trait is dominant. The other allele, that is entirely masked by the expression of the dominant allele, is the recessive allele. However, an allele is not necessary either dominant or recessive

Mendel’s laws + Darwin’s principles => population genetics => foundations of evolution

Modern interpretation: a new organism, zygote (“egg”), is formed from the fusion of sexual cells
(gametes) of both parents . Then cells duplicate => diploid organisms composed of many cells

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6
Q

Chromosomes

A

Chromosomes contain genes that can exist in different
variants (“alleles”). Same gene for both parents at same locus but possibly different alleles (A or a) => pairing and reshuffling lead to gentoypes AA or Aa or aA or aa
=> variability of traits, i.e. phenotypes
(e.g. color of wings in moths)

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7
Q

diploid
Haploid
gametes
locus
alleles
heterozygotes

A
  • diploid cells carry 2 instances of each gene, while haploid carry 1 such instance
  • gametes are sexual cells and haploid; zygotes are diploid
  • locus: position of a gene on a chromosome
  • for 2 alleles, A and a at a locus, there can be 3 genotypes: AA, aa and Aa
  • 2 same alleles (AA or aa) => homozygote; 2 different alleles (Aa or aA) => heterozygote
  • In heterozygotes, expressed trait is (generally) associated with the dominant allele (when there is one
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8
Q

assumptions

A

random mating, large population, diploid organisms,
discrete time t (generations) and non-overlapping generations

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9
Q

N diploid individuals, each with 2 instances of gene (allele) per locus

A

of AA,Aa and aa individuals respectively

2N genes, A or a, at a given locus

Frequency of allele A is
p (= fraction of A in the population of size 2N) and
frequency of allele a is
q=1-p
3 genotypes: AA, aa and Aa
#AA Aa and aa individuals
N_{AA}, N_Aₐ, and Nₐₐ

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10
Q

AA Aa and aa individuals

genotype frequencies
AA,Aa aa

N_{AA}, N_Aₐ, and Nₐₐ

A

D = N_AA/N (AA frequency),
H = N_Aa/N (Aa freq.),
R = N_aa/N (aa freq.).

When generations
matter, we write D_t, H_t R_t, p_t and q_t
t denotes the generation

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11
Q

Initial genotype frequencies

Allele frequency at t=0

A

D₀ = N_AA/N at t=0 (AA frequency),
H₀ = N_Aa/N at t=0 (Aa freq.),
R₀ = N_aa/N at t=0 (aa freq.).

p₀ = D₀ +(H₀/2) for A

q₀ = 1-p₀ = R₀ + (H₀/2) for a

Dt≥1, Ht≥1, Rt≥1 generally differ from D0, H0, R0

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12
Q

ALLELE FREQUENCIES

A

Since each AA contributes two A alleles and each Aa contributes a single A and a single a allele
=> allele frequencies:
p = [2N_AA + N_Aa]/2N = D +(H/2) ,

q = [2N_aa + N_Aa]/2N = R + (H/2)

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13
Q

4.3 Hardy-Weinberg principle (HWP)

A

When all genotypes have the same survival and reproduction potential (=fitness), HWP gives
simple expression for frequency of genotypes:

HWP: allele frequencies remain constant ⇒
p ≡ p₀ and q ≡ q₀ = 1 − p

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14
Q

HWP applied

allele frequencies

A

HWP: After 1 generation, the genotype frequencies remain constant = random pairing of A and a =>product of allele frequencies:

D ≡ Dt≥1 = p²,
H ≡ Ht≥1 = 2pq ,
R ≡ Rt≥1 = q²

Allele frequencies remain constant:
p = p₀ (A freq. at t > 0 = A freq. at t = 0)
q = qv (a freq. at t > 0 = a freq. at t = 0)

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15
Q

Example: black and white wings in moths. Allele A: black (dominant), allele a: white (recessive). At t=0: 45% of AA and 20% of Aa.
What fraction of population will have dark wings in the long run?

A

Assuming that HWP holds. Initial A frequency
p₀ = D₀ +(H₀/2) =0.45 +(0.2/2)=0.55

q₀ = 1-p₀ =0.45
initial frequency of allele a

According to HWP:
p = p₀ = 0.55 q = q₀ = 0.45 remain constant
so
frequencies are constant for t ≥ 1)
at t=1:
frequency of Aa is
H₁ = 2pq =2.0.55.0.45=0.495 =H_t for t ≥ 1)
frequency of AA is
D₁ = p²=0.55^2=0.3025=D_t for t ≥ 1)
frequency of aa is
R₁ = q²=0.452 t ≥ 1 = 0.2025 = R_t (for t ≥ 1)

DIAGRAMS
Since A (black wing) is dominant, the fraction of of moths with dark wings is D₁+H₁
⇒ Long-run fraction of dark months is 0.3025 + 0.495 = 0.7975, that is 79.75%

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16
Q

DIAGRAMS

A

lecture W4L1

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17
Q

HWP remark

A

pₜ = Dₜ + (Hₜ/2) = p²ₜ₋₁ + pₜ₋₁(1 − pₜ₋₁) = pₜ₋₁

for t ≥ 1 ⇒ pₜ = p₀ = p

=> allele frequencies remain constant:
pₜ = p₀ = p and
qₜ = q₀ = q = 1 − p

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18
Q

complete

A

4-43 notes lecture

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19
Q

Evolution with mutations but without selection pressure

A

Assumptions made in deriving the Hardy-Weinberg principle:
we assumed the absence of mutations. In reality, since duplication/reproduction is never perfect, mutations are always present. Here, we briefly discuss the influence of mutations on the allele frequencies in the absence of selection pressure

(We assumed no selection,, fitness etc)

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20
Q

Mutations

A

Mutations can be caused by transcription errors and may for example change allele A into a. They are likely to be deleterious and arise without reference to adaptive needs. They occur with small probabilities, but are important since they help maintain genetic variability.
Here, we consider two alleles at a locus, in large random-mating diploid populations.

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21
Q

4.4 Evolution with mutations but without selection pressure:

Since the allelic frequencies will now generally vary from one generation to another:

denote by p_t the frequency of allele A in generation t,
and by q_t the frequency of allele a in generation t (with qt = 1 − pt).

A

We consider the Hardy-Weinberg scenario of no selection,
and assume that in generation t a fraction upt of the genes, i.e. a fraction u of the organisms
carrying the allele A, at this locus mutates into allele a, according to A → a, with a mutation
probability u. We also assume that the reverse mutation, a → A, occurs with a probability
v, and therefore from generation t to t + 1 a fraction vqt of the genes at this locus (fraction
v of the organisms carrying the allele allele a) mutates into A. In practice, one typically has
u, v ≈ 10−6 − 10−4
.

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22
Q

Here: assume no selection, just mutations:
fraction u of A becomes a by error.
fraction v of a becomes A by error

pₜ₊₁ =

A

write the difference eq for the freq
pₜ₊₁ = (1 − u)pₜ + vqₜ
= (1 − u)pₜ + v(1 − pₜ)
= v + (1 − u − v)pₜ
fraction from A to A and a to A

A becomes a by small prob u etc

with initial cond given p_0

full sol
pₜ = p∗ + (p_0 − p∗)(1 − u − v)ᵗ
p∗ asymp stable
term (1 − u − v)ᵗ rapid decay in t as less than 1
approaches rapidly stationary state consisting
of finite fractions p* and q=1-p of both alleles

1st-order linear map solved:
p=v+(1-u-v)p
p=v/(u+v)
Thus Mutations: mechanism sustaining genetic variability as both variables coexist
(u and v typically small, p
becomes finite number)

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23
Q

4.5 Selection & fitness in population genetics

A

In population genetics, the fitness of an organism is
the expected number of offspring that an individual
contributes to the next generation
=>measures survival and reproduction potential
=>compute change in allele frequencies

(assumptions on HWP all genotypes have same repro and survival potential~ equally fit

natural selection: some traits render an organism more likely to survive and reproduce)

Pop genetics describes the influence of selection on evolution by introducing the concept of fitness

quantity reflects a propensity or probability to survive and reproduce in a given environment

To compute the change in gene frequencies, we need a measure of the survival and reproduction potential of the different genotypes. This is provided by the concept fitness

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24
Q

absolute fitness

A
  • The absolute fitness Wₓ of a genotype X is the average number of zygotes of type X in the next generation to which a type X in this generation contributes to.

Wₓ is the probability that a type X survives to breed, multiplied by its expected number of offspring

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25
relative fitness
The relative fitness wₓ ≡ Wₓ/Wᵧ of a genotype X relative to another genotype Y of absolute fitness Wᵧ is the ratio of the absolute fitness Wₓ of X relative to the absolute fitness Wᵧ of a reference genotype Y wₓ=Wₓ/Wᵧ with wᵧ= Wᵧ/Wᵧ
26
Example: Case of a simple diploid pop Geonotype A survival probability of 2/3 and 5 offspring=> Genotype B survival probability of 1/2 and surviving type B has 4 offspring=> relative fitness
diagram: not all survive, of those that survive produce offspring dep on probability W_A = (2/3) · 5 = 10/3 W_B = (1/2) · 4 = 2 Fitness of genotype B relative to genotype A: set (reference) define w_A=W_A/W_A=1 and w_b≡ W_B/W_A = 2/(10/3)=6/10=0.6 Hence w_A = 1, w_B = 0.6 or fitness of genotype B is 60% that of genotype A wB : wA = 0.6 In practice, natural selection operates at every stage of an individual’s life, e.g. there is viability selection and variable fecundity
27
4.6 Evolution in diploid populations under natural selection
Time-variation of the genetic material, driven by natural selection, is accompanied by changes in the allele frequencies. We are going to see that even “weak selection” can drastically change the frequency of alleles and genotypes through the generations. How genetic material is passed on over generations
28
4.6 Evolution in diploid populations under **natural selection** simple model
simple model for the evolution in diploid populations consider twoalleles, A and a at a specific locus, allele A is dominant and a is recessive resulting genotypes are AA, aa and Aa Natural selection occurs because organisms with different genotypes have generally different fitnesses hence differ in their potential to survive (viability) and to reproduce (fertility). Frequency of allele A pₜ in generation t ≥ 0 is Frequency of allele a in generation t ≥ 0 is qₜ = 1 − pₜ
29
4.6 Evolution in diploid populations under **natural selection** A first-order nonlinear map (Fisher-Haldane-Wright eq.) gives a relation between
A first-order nonlinear map (Fisher-Haldane-Wright eq.) gives a relation between p_{t+1} and p_t
30
4.6 Evolution in diploid populations under **natural selection** life cycle
Life cycle consists of different stages: different phases for which selection acts on Selection operates at each stage of life cycle: viability (survival) and fecundity (reproduction) selection 1) **zygotic phase** at time (generation) t when zygotes that have just been formed proceed to adulthood, (viability selection operated between differentiating between survival probability/potential; must survive into adulthood to pass on) 2) then the **breeding phase** (generation t), (how many offspring can be produced) 3) followed by the **gametic phase** (generation t), and then a new zygotic phase =>start of the new generation t+1 (fecundicity selection operates in this phase; organisms compete for mating and reproduction) gen t Zygotes to adults to parents to Gametes to zygotes gen t+1
31
4.6 Evolution in diploid populations under **natural selection** selection in life cycle
By analysing how the selection operates in each of the zygotic, breeding and gametic phases of the life cycle => Fisher-Haldane-Wright equation (detailed derivation in Appendix C of lecture notes Can produce equation relating to absolute fitness
32
Fisher-Haldane-Wright equation
pₜ₊₁ = [pₜ(pₜW_{AA}+qₜW_{Aa})]/[pₜ²W+2pₜqₜW_{Aa} +qₜ²W_{aa}] The Fisher-Haldane-Wright equation (FHWE) is a nonlinear (relation) first-order map shows that pₜ dep on **fitness** W responsible for change of allele freq, genome freq, total pop size No selection: fitness all the same W_AA=W_aa=W_Aa means pₜ₊₁= pₜ and pₜ=p back to Hardy-Weinberg (nonlinear scalar map giving a difference eq for allele A at the locus for a system of diploid individuals ,
33
Selection also responsible for changes of total population size Nₜ
Nₜ₊₁= (W_AApₜ² +2W_Aa pₜqₜ + W_aa qₜ²) Nₜ nonlinear first-order map nonlinear first-order map
34
Different ways of writing the FHWE
Using relative fitness with w_aa=1 as reference w_aa=W_AA/W_aa w_Aa= W_Aa/W_aa (dividing the absolute fitness) FHWE can be written: pₜ₊₁ = pₜ+ (pₜ(1-pₜ)/w¯ ) [ (w_{AA}-w_Aa)pₜ+(w_{Aa}-1)(1-pₜ)] where w¯= w_AApₜ² + 2pₜqₜw_{Aa} + qₜ² is the mean (average) relative fitness, in principle it depends on t as dep on p and q which dep on time
35
These different expressions of are equivalent expressions of the FHWE
FHWE can also be written as how freq changes from one gen to another pₜ₊₁ -pₜ≡ δp = pₜqₜ (w_A − w_a)/w¯ = pₜ(1 − pₜ)(w_A − w_a)/w¯ where the w_A(pₜ) = [w_AApₜ² + w_Aapₜqₜ]/[pₜ² + pₜqₜ] = w_AApₜ + w_Aaqₜ w_a(pₜ) = [qₜ² + w_Aapₜqₜ]/[qₜ² + pₜqₜ] = qₜ² + w_Aapₜ are the mean fitness of allele A and a, resp these quantities are the weighted average of the relative fitness, average RF by p
36
FHWE can also be written as how freq changes from one gen to another pₜ₊₁ -pₜ≡ δp = pₜqₜ (w_A − w_a)/w¯ = pₜ(1 − pₜ)(w_A − w_a)/w¯ Why is this convenient?
δp > 0: i.e. frequency of A increases, if w_A> w_a => the frequency of A increases if A has a higher mean fitness than a δp < 0: i.e. frequency of A decreases if w_A< w_a => the frequency of A decreases if A has a lower mean fitness than a (if the mean fitness of allele A is bigger than a's then frequency over time) delta p looks at this change in freq over one gen)
37
FIRST ORDER NON LINEAR SCALAR EQ FIXED POINTS OF FHWE pₜ₊₁ -pₜ≡ δp = pₜqₜ (w_A − w_a)/w¯ = pₜ(1 − pₜ)(w_A − w_a)/w¯
When δp = 0 ⇒ p_{t+1} = p_{t} = p∗ Clearly p=1, p=0 (or q=1) associated with A (for p=1) or a (for p=0) taking over the entire population (ENTIRE POP CONSIST OF ALLELE ONLY) when p=0 or 1 delta p vanishes thus these are fixed points of the map
38
Can there be a fixed point associated with the coexistence of alleles at respective frequencies p∗ and 1-p∗ ? When it is the case, the population is "polymorphic"
If both alleles persist the population is “polymorphic” at the locus. is there a coexistent fixed point
39
Fixed points of FHWE pₜ₊₁ -pₜ≡ δp = pₜqₜ (w_A − w_a)/w¯ = pₜ(1 − pₜ)(w_A − w_a)/w¯
by solving δp = 0 ⇒ lim_{t→∞} pₜ = p∗ (stationary frequency, independent of t) Stationary frequencies q∗ = 1 − p∗ physical points p∗ = 1,q∗ = 0 all carrying allele A p∗ = 0,q∗ = 1 all allele a physical coexistence point: 0< p∗<1 (fractions) given by w_A(p∗) = w_a(p∗) relating to mean fitness of both alleles being the same set wₐₐ=1 gives p∗= (1- w_Aa)/( 1+ w_AA - 2w_Aa) which is physical if 0 max(w_AA,1) or w_Aa < min(w_AA,1) , i.e. if heterozygotes are fitter than all homozygotes, or less fit then all homozygotes, then coexistence of both alleles and polymorphic population is possible (if p∗is stable)
40
Note when using FHWE
Idealized diploid non-overlapping populations evolve according to natural selection => we assume just two alleles at the locus, given p_0 and interested in evolution over many generations 2 alleles, A and a, at a single locus, and t=0,1,2,... (generations) allele frequencies obey the Fisher-Haldane-Wright equation (FHWE), a nonlinear first-order map
41
FHWE: P_{t} q_t
pₜ : frequency of allele A in generation t, qₜ = 1 − pt : frequency of allele a in generation t. p_0 = 1 − q_0 is known initial condition. and To be physical, for ∀t ≥ 0 we need 0 ≤ pₜ = 1 − qₜ ≤ 1
42
The principles of Darwin’s evolution by natural selection
Darwin’s theory assumes that slightly advantageous modifications in the genetic material are preserved and passed on through generations. For this, it necessitates heredity and variability. : (1) Not all produced offspring can survive; (2) traits vary among individuals; (3) rates of survival/reproduction differ; (4) traitdifferences are heritable; (5) offspring of parents better adapted replace deceased individuals.
43
Reminder: mean fitness of allele A and a are
w_A(pₜ) = w_AApₜ + w_Aaqₜ =(w_AApₜ + w_Aa pₜ) + w_Aa w_a(pₜ) = w_aa qₜ + w_Aapₜ =(w_Aa - w_aa pₜ) +w_aa
44
Which fixed points of the FHWE are stable? Various scenarios to be considered (see Q3 of Example Sheet 2
Which fixed points of the FHWE are stable? Various scenarios to be considered (see Q3 of Example Sheet 2
45
Example: selection favours a dominant advantageous allele (Scenario 1) Assume that A is dominant and advantageous, and a is recessive
so w_aa = 1 and WLOG w_AA = w_Aa = 1 + s > 1 (with s > 0) AA and Aa have same fitness because A is dominant and a is recessive 1+s > waa = 1 because A is advantageous over a By sub. into FHWE, we get: pₜ₊₁ = f(pₜ), where f(p) = p + (sp(1-p)²)/(1 +sp(2-p) looking at stability: ⇒ f′(p) = (1+s)(1+sp²)/[(1+sp(2-p))²] Fixed points are solutions of p=f(p) only fp are 0,1 no coexistence as fitness cond prev not satisfied Linear stability analysis f′(p = 0) = 1 + s > 1 UNSTABLE f′(p = 1) = 1 ⇒ no conclusion from linear stability analysis. Use another method For instance, a cobweb diagram shows that p = 1 is here asymptotically stable Other approach: note that in this case, the iterates of the FHWE form an increasing sequence (see Q3 of Example Sheet 2) => by monotone convergence theorem, p=1 is asymptotically stable
46
REMARK SELECTION STRENGTH
Time to reach p=1 greatly depends on the selection strength s: convergence to p=1 is ~100 faster when s=0.2 than when s=0.002 (weak selection) DIAGRAM closer to 1 S shapes but for less gens converge to fp for larger s, faster for larger s more advantegouse A then quicker to take over
47
Example: selection favours a recessive but advantageous allele (Scenario 2)
We consider that A is recessive but advantageous, and a dominant => fitnesses relative to aa: wAa = waa = 1 Aa and aa have same fitness, set to 1,because a is dominant and A is recessive and wAA = 1 + s with s > 0 > 1 because A is advantageous By sub. into FHWE, we get the map pₜ₊₁ = pₜ + spₜ (1-pₜ )²)[pₜ /[1 +spₜ²]] =f(pₜ) =pₜ+ spₜ²(1-pₜ)/[1+spₜ²] f′(p) = [1 + s(2 − p)p]/[(1 + sp²)²] for fixed points solve p=f(p) Only fixed points are p=1 (all carry A) and p=0 (all carry a), both physical. linear stability analysis f '(p=1)=1/(1+s)<1 => p=1 is asymptotically stable f '(p=0) =1 => no conclusion from linear analysis, but is clearly unstable. This can be checked using another method, like the cobweb diagram Alternatively, we can note that the iterates of the FHWE form an increasing sequence {p_0,p_1,p_2...} and bounded above by 1 => This is the unique asymptotically stable fixed point In this scenario, the advantageous allele A takes over, as in Scenario 1, even if it is recessive. However, for same value of s, this now takes longer than under Scenario 1. This is because heterozygotes now don't benefit from selection advantage (since A is here recessive)
48
difference in scenario 1 and 2
A takes over in scenario 2, stable even if recessive now we see since the heterozygote same fitness as small aa A recessive and for it to take over takes longer for similar value of s than in scenario 1 in 1: we have heterozygote have same fitness as A: wAA=w Aa allele A spreads when genotype AA is also carried by heterozygote not the case in scenario 2: only homozygotes carry 2 alleles and these are the ones that spread thus in both cases we get p=1 but how it converges is faster in scenario one because both heterozygotes and homozygotes carry A spread so convergence is faster for same s
49
Example: selection favours a semi-dominant & advantageous allele (Scenario 3
We now consider that A and a are neither completely dominant or recessive => fitness of heterozygotes Aa is between that of of homozygotes, say w_AA> w_Aa > w_aa For concreteness, we consider that A is advantageous and semi-dominant, and specify: w_AA = 1 + 2s, w_Aa = 1 + s and w_aa = 1 with s > 0 By sub. into FHWE, we get the map p_{t+1}= p_t + [sp_t(1-p_t)]/[1+sp_t] fixed points: p=f(p) sp(1-p)/(1+sp)=0 p=1,1 only fps both physical (no coexistence fp as the fitness is between doesnt meet prev condition) p=1 is asymptotically stable p=0 is unstable
50
Extra MATH5567M topic: evolution with selection and mutations
We discussed the influence of mutations in the absence of selection, and in we focused on the role of selection and ignored mutations. Here, we consider the joint influence of selection and mutations on the allele frequency changes over the generations. ( Fisher-Haldane-Wright equation but now modify it to take mutations into account).
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Allele frequencies vary due to the joint effect of selection and mutations. Evolution can lead to different forms of social behaviour => how to make sense of these?
Due to mutations, prior to starting a new zygotic phase and a new generation t + 1, a fraction 1 − u of the A alleles (a fraction (1 − u)pt of the population) consists of unmutated (genuine) A gametes and the remaining fraction upt consists mutated a gametes (mutated from A parent). Similarly, due to mutations a fraction (1 − v)qt of the population consists of unmutated a gametes (from a parent), while the remaining fraction vqt consists of mutated A gametes (mutated from a parent). Proceeding as in the absence of selection
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modified Fisher-Haldane-Wright eq. (FHWe) to account for mutations.
Diploid population, with allele A or a at a single locus assume that there are mutations from one generation to the next, with small probability u an A (from A parent) can become an alelle a, and an a (from a parent can become an allele A with a small prob. v small probability that A → a prob u a → A, prob v A → A prob 1−u a → a prob 1−v with 0 < u ≪ 1, 0 < v ≪ 1 in the next generation At end of gen. t: Fractions (1 − u)pₜ of unmutated A and vqₜ of mutated A
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FHWE without mutations: pₜ₊₁ Uses fitnesses
pₜ₊₁ = (w_A/w~)pₜ and qₜ₊₁=(w_a/w~)qₜ pₜ: A frequency in generation t = 0, 1, . . . qₜ=1- pₜ: a frequency in generation t = 0, 1, . w_A : mean fitness of allele A w_a : mean fitness of allele a w~ : average population fitness
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Usually u,v <
2 Usually u,v <
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Modified FHWE with mutations pₜ₊₁
pₜ₊₁= (1-u)(w_Apₜ/w~) +v(w_aqₜw~) fraction (1-u)pₜ of unmutated A: A-¹⁻ᵘ→ A Fraction vqt of mutated A : a -ᵛ→ A =pₜ + [(w_A-w~)pₜ]/w~ -**uw_Apₜ/w~** +**vw_aqₜ/w~** terms **A-ᵘ→ a a -ᵛ→ A**
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uw_A/w¯ ≈ u, vw_a/w¯ ≈ v and all w’s = 1 + O(s) ⇒ FHWE with mutations: δp
δp ≡ pₜ₊₁− pₜ = [w_A(pₜ) − w~(pₜ)]pₜ− (u + v)pₜ + v [First-order scalar nonlinear map for p] [We assume 0 < s ≪ 1, s is not as small as u and v
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FHWE with mutations: FIXED POINTS δp ≡ pₜ₊₁− pₜ = [w_A(pₜ) − w~(pₜ)]pₜ− (u + v)pₜ + v [First-order scalar nonlinear map for p]
Fixed point obtained by setting pₜ → p∗ and δp = 0 ⇒ [w_A(p∗) − w~(p∗)]p∗ − (u + v) p∗ + v = 0 FIXED POINT NO LONGER p=1, p=0 always mutation, never have A that takes over, always a chance mutated A into a etc fp depends on w's
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FHWE with mutations:Mutation-selection balance: say that is deleterious and recessive and is dominant, such that
A is not wiped out even if it is opposed by selection w_AA = 1 − s, with 0 < s ≪ 1, and w_aa = w_Aa = 1 (SAME FITNESS) w~ = w_AApₜ² + 2w_Aapₜqₜ + qₜ² = (1 − s)pₜ² + 2pₜqₜ + qₜ² = 1 − spₜ² Fitness of A w_A = w_AApₜ +w_Aaqₜ = (1 − s)pₜt + 1 − pₜ = 1 − spₜ, Fitness of a w_a = w_Aapₜ + w_aaqₜ = pₜ + 1 − pₜ = 1 by subbing eqs 4.9-4.12
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=> FHWE with mutations and weak selection: δp
δp= pₜ [w_A − w~]/[w~] − upₜ (w_A/w~)+ v[w_A/w~] qₜ ≈ **−spₜ²qₜ − upₜ(1 − spₜ)** + vqₜ **due to selection, frequency of decreases, negative contribution, mutations** + due to mutation , a-ᵛ→ A frequency of increases ≈ −spₜ²qₜ − upₜ + vqₜ this is simplified by using [w_A-w~]/w~ ≈ −spₜ(1 − pₜ) = −spₜqₜ and w_A/w~ ≈ 1 − spₜ w_a/w~ ≈ 1
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FHWE with mutations and weak selection: Coexistence fixed point given by
Selection is balanced by mutations: coexistence of and at small freq p* of A δp∗ = 0 ⇒ 0 = −s(p∗)²(1− p∗) − up∗ + v(1 − p∗) cubic Solve for p* (messy). Smarter: expect p* to be "small" 1 − p∗ ≈ 1, up∗ ≈ 0, vp∗ ≈ 0 p∗³≈ 0 (Since is deleterious, and thus opposed by selection, we expect p* to be small ⇒ Eq. for p∗ simplifies to 0 ≈ −s(p∗)² + v ⇒ p∗ ≈ sqrt(v/s) when u, v ≪ s ≪ 1 In fact, p∗ ≈ sqrt(v/s)+ O(v/s) which is small, as assumed, but generally not vanishingly small When u, v ≈ 10−4 and s ≈ 0.01: v/s ≈ 0.01 and p∗ ≈ 0.1 (small but not negligible)
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mutation-selection balance
This example shows how mutations help maintain genetic variability: deleterious allele is eliminated by selection but this is balanced by mutations (heterozygotes act as a reservoir of A alleles). This is called mutation-selection balance We have as expected mutations can have genetic variablity, if we have mutations we always end up with a non-negligible fraction from possible mutations Indeed A is deletirious but heterozygotes have the same fitness as those that have the advantageous allele, since it is deletirous and recessive. So they (heterozygotes) spread just like a , passing on both a and A that is why A remains in the pop and end up with deleterious allele in pop.
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4.9 Extra MATH5567M topic: kin selection & Hamilton’s rule What is the role of social behaviour on evolution? In fitness terms, there are four possible outcomes to interactions
There are different forms of social behaviour: **cooperation** when actions result in fitness gains for both participants; **altruism** is when the actor suffers a fitness cost and the receiver gets a fitness benefit; **selfishness** is when the actor gets a fitness gain and the receiver suffers a fitness loss; **spite** is when the action results in fitness losses for both participants. with some overlap between them.
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Kin selection:
Altruism can be explained if selection favours traits resulting in decrease of personal fitness to benefit kins => "evolutionary altruistic strategy" “Would I give my life to save a drowning brother? No, but I would lay down my life for two brothers or eight cousins”. (J. B. S. Haldane)
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“Would I give my life to save a drowning brother? No, but I would lay down my life for two brothers or eight cousins”. (J. B. S. Haldane)
This is because in a diploid population (no inbreeding) siblings share half of their genetic material (they have same parents), two full siblings share the same genes with a probability 1/2, while cousins are 1/8 identical while on average an uncle and his niece and first cousins are respectively 1/4 and 1/8 genetically identical => 2 full siblings or 8 cousins would carry the same genetic material as Haldane.
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The degree of relatedness
Coefficient of relatedness r (between 0 and 1) gives the probability that 2 individuals share an allele at a given locus by descending from a common ancestor. between two individuals Formally, the degree r between any two individuals is the probability that they share an allele at a given locus inherited from a common ancestor. In diploid organisms, the coefficient of relatedness r between a parent and child is 1/2, since half of the genetic material of the child is descended from that parent.
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pedigree chart
in which all minimal paths of descent starting from the actors and ending to the receiver are traced Since each parent contributes to 50% of the alleles of each offspring (one says that the probability that alleles are identical by descent is 50%), each step in the pedigree chart carries a probability 1/2. Hence, the coefficient of relatedness of two full siblings is the sum of the probabilities of being equal by descent through the mother (1/2 × 1/2) and through the father (1/2 × 1/2), resulting in r = 1/2
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Coefficient of relatedness r between parents and offspring siblings uncle/neice grandparent
Hence r=1/2 between parents and offspring, r=1/2 between full siblings, r=1/4 between uncle/niece and grandparent/ grandchild r=1/8 between cousins To obtain r: "pedigree chart" in which we draw all minimal paths of descent from actor to receiver, each path weighted 1/2.
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Haldane and his cousin have a coefficient r=1/8
Haldane and his cousin have a coefficient r=1/8 because they are related (i.e. received their common genes) via the following paths: Haldane -> Haldane's father -> Haldane's uncle -> Haldane's 0.5 x 0.5 x 0.5 = 1/8
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In 1964, W. D. Hamilton popularised the concept of kin selection and formulated what is known as **Hamilton’s rule**.
Hamilton’s rule says that kin selection causes genes to increase in frequency when the genetic relatedness of a recipient to an actor multiplied by the benefit to the recipient is greater than the reproductive cost to the actor. Hamilton’s rule says that the recipient’s genes should increase in frequency when rB > C. the frequency of the receiver's genes increases, where r: coefficient of relatedness, B: reproductive benefit to receiver, C: cost to actor (altruist)
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r for siblings
1/2 x 1/2 + 1/2 x 1/2 = 1/4 M F BOTH RELATES 1/2 x 1/2 two paths B S
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rB>C
If r is the coefficient of genetic relatedness of the recipient to the actor of the altruistic behaviour (individual dispensing the behaviour), B is the additional reproductive benefit gained by the recipient (individual receiving the behaviour), and C is the reproductive cost to the individual performing the act,
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hamiltons rule remark
In practice, it is hard to estimate B and C. Some recent observations (groups of red squirrels) seem to be in line with this "rule". Hamilton rule can be derived mathematically, under suitable assumptions, from the so-called "Price equation".