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Modern Evolutionary Biology
I. Population Genetics
A. Overview
B. The Genetic Structure of a Population
C. The Hardy-Weinberg Equilibrium Model
3. Utility:
- if a population is NOT in HWE, then one of the assumptions must be violated.
Sources of Variation
Recombination
- crossing over
VARIATION
Mutation
Agents of Change
- independent assortment
So, if NO AGENTS are acting on a population, then it
will be in equilibrium and WON'T change.
N.S.
Drift
Migration
Mutation
Non-random Mating
Modern Evolutionary Biology
I. Population Genetics
A. Overview
B. The Genetic Structure of a Population
C. The Hardy-Weinberg Equilibrium Model
D. Deviations from HWE
1. mutation
1. Consider a population with:
f(A) = p = 0.6
f(a) = q = 0.4
2. Suppose 'a' mutates to 'A' at a realistic rate of:
μ = 1 x 10-5
3. Well, what fraction of alleles will change?
'a' will decline by: qm = .4 x 0.00001 = 0.000004
'A' will increase by the same amount.
f(A) = p1 = 0.600004
f(a1) = q = 0.399996
Modern Evolutionary Biology
I. Population Genetics
A. Overview
B. The Genetic Structure of a Population
C. The Hardy-Weinberg Equilibrium Model
D. Deviations from HWE
1. mutation
2. migration
p2 = 0.7
p1 = 0.2
q2 = 0.3
q1 = 0.8
suppose migrants immigrate at a rate
such that the new immigrants represent
10% of the new population
Modern Evolutionary Biology
I. Population Genetics
A. Overview
B. The Genetic Structure of a Population
C. The Hardy-Weinberg Equilibrium Model
D. Deviations from HWE
1. mutation
2. migration
p2 = 0.7
p1 = 0.2
q2 = 0.3
q1 = 0.8
M = 10%
p(new) = p1(1-m) + p2(m)
= 0.2(0.9) + 0.7(0.1)
= 0.18 + 0.07 = 0.25
D. Deviations from HWE
1. mutation
2. migration
3. Non-random Mating
a. Positive Assortative Mating – “Like mates with Like”
offspring
F1
AA
Aa
aa
0.2
0.6
0.2
D. Deviations from HWE
1. mutation
2. migration
3. Non-random Mating
a. Positive Assortative Mating – “Like mates with Like”
offspring
F1
AA
Aa
aa
0.2
0.6
0.2
ALL AA
1/4AA:1/2Aa:1/4aa
ALL aa
D. Deviations from HWE
1. mutation
2. migration
3. Non-random Mating
a. Positive Assortative Mating – “Like mates with Like”
offspring
F1
AA
Aa
aa
0.2
0.6
0.2
ALL AA
1/4AA:1/2Aa:1/4aa
ALL aa
0.2
0.15 + 0.3 + 0.15
0.2
0.35
0.3
0.35
D. Deviations from HWE
1. mutation
2. migration
3. Non-random Mating
a. Positive Assortative Mating – “Like mates with Like”
b. Inbreeding: Mating with Relatives
Decreases heterozygosity across the genome, at a rate dependent on the degree
of relatedness among mates.
D. Deviations from HWE
1. mutation
2. migration
3. Non-random Mating
4. Finite Population Sizes: Genetic Drift
The organisms that actually reproduce in a population may not be representative
of the genetics structure of the population; they may vary just due to
sampling error
D. Deviations from HWE
1. mutation
2. migration
3. Non-random Mating
4. Finite Population Sizes: Genetic Drift
1 - small pops will differ more, just by chance, from the original
population
D. Deviations from HWE
1. mutation
2. migration
3. Non-random Mating
4. Finite Population Sizes: Genetic Drift
1 - small pops will differ more, just by chance, from the original
population
2 - small pops will vary more from one another than large
D. Deviations from HWE
1. mutation
2. migration
3. Non-random Mating
4. Finite Population Sizes: Genetic Drift
- “Founder Effect”
The Amish, a very small, close-knit
group decended from an initial
population of founders, has a high
incidence of genetic abnormalities
such as polydactyly
- “Founder Effect” and Huntington’s Chorea
HC is a neurodegenerative disorder caused by an
autosomal lethal dominant allele.
The fishing villages around Lake Maracaibo in
Venezuela have the highest incidence of
Huntington’s Chorea in the world, approaching
50% in some communities.
- “Founder Effect” and Huntington’s Chorea
HC is a neurodegenerative disorder caused by an
autosomal lethal dominant allele.
The fishing villages around Lake Maracaibo in
Venezuela have the highest incidence of
Huntington’s Chorea in the world, approaching
50% in some communities.
The gene was mapped to chromosome 4, and the HC
allele was caused by a repeated sequence of over
35 “CAG’s”. Dr. Nancy Wexler found homozygotes in
Maracaibo and described it as the first truly
dominant human disease (most are incompletely
dominant and cause death in the homozygous
condition).
- “Founder Effect” and Huntington’s Chorea
HC is a neurodegenerative disorder caused by an
autosomal lethal dominant allele.
The fishing villages around Lake Maracaibo in
Venezuela have the highest incidence of
Huntington’s Chorea in the world, approaching
50% in some communities.
By comparing pedigrees, she traced the incidence to
a single woman who lived 200 years ago. When the
population was small, she had 10 children who
survived and reproduced. Folks with HC now trace
their ancestry to this lineage.
- “Genetic Bottleneck”
If a population crashes (perhaps as the result of a plague) there will be both selection and
drift. There will be selection for those resistant to the disease (and correlated selection
for genes close to the genes conferring resistance), but there will also be drift at other loci
simply by reducing the size of the breeding population.
European Bison, hunted to
12 individuals, now number
over 1000.
Cheetah have very low
genetic diversity,
suggesting a severe
bottleneck in the past.
They can even
exchange skin grafts
without rejection…
Elephant seals fell to 100’s in
the 1800s, now in the
100,000’s
Modern Evolutionary Biology
I. Population Genetics
A. Overview
B. The Genetic Structure of a Population
C. The Hardy-Weinberg Equilibrium Model
D. Deviations From HWE:
1. Mutation
2. Migration
3. Non-Random Mating:
4. Populations of Finite Size and Sampling Error - "Genetic Drift"
5. Natural Selection
1. Fitness Components:
D. Deviations From HWE:
5. Natural Selection
1. Fitness Components:
Fitness = The mean number of reproducing offspring / genotype
- probability of surviving to reproductive age
- number of offspring
- probability that offspring survive to reproductive age
D. Deviations From HWE:
5. Natural Selection
1. Fitness Components:
Fitness = The mean number of reproducing offspring / genotype
- probability of surviving to reproductive age
- number of offspring
- probability that offspring survive to reproductive age
2. Constraints:
i. finite energy budgets and necessary trade-offs:
D. Deviations From HWE:
5. Natural Selection
1. Fitness Components:
Fitness = The mean number of reproducing offspring / genotype
- probability of surviving to reproductive age
- number of offspring
- probability that offspring survive to reproductive age
2. Constraints:
i. finite energy budgets and necessary trade-offs:
GROWTH
METABOLISM
REPRODUCTION
D. Deviations From HWE:
5. Natural Selection
1. Fitness Components:
2. Constraints:
i.
finite energy budgets and necessary trade-offs:
TRADE OFF #1: Survival vs. Reproduction
Maximize probability of survival
Maximize reproduction
GROWTH
GROWTH
METABOLISM
REPRODUCTION
METABOLISM
REPRODUCTION
D. Deviations From HWE:
5. Natural Selection
1. Fitness Components:
2. Constraints:
i.
finite energy budgets and necessary trade-offs:
TRADE OFF #1: Survival vs. Reproduction
TRADE OFF #2: Lots of small offspring vs. few large offspring
REPRODUCTION
METABOLISM
REPRODUCTION
METABOLISM
Lots of small, low
prob of survival
A few large, high
prob of survival
D. Deviations From HWE:
5. Natural Selection
1. Fitness Components:
2. Constraints:
i.
ii.
finite energy budgets and necessary trade-offs:
Contradictory selective pressures:
Photosynthetic potential
Water Retention
Leaf Size
D. Deviations From HWE:
5. Natural Selection
1. Fitness Components:
2. Constraints:
i.
ii.
finite energy budgets and necessary trade-offs:
Contradictory selective pressures:
Rainforest understory – dark, wet
Photosynthetic potential
Water Retention
Big leaves adaptive
Leaf Size
D. Deviations From HWE:
5. Natural Selection
1. Fitness Components:
2. Constraints:
i.
ii.
finite energy budgets and necessary trade-offs:
Contradictory selective pressures:
Desert – sunny, dry
Photosynthetic potential
Small leaves adaptive
Leaf Size
Water Retention
D. Deviations From HWE:
5. Natural Selection
1. Fitness Components:
2. Constraints:
3. Modeling Selection:
a. Calculating relative fitness
p = 0.4, q = 0.6
AA
Aa
aa
Parental "zygotes"
0.16
0.48
0.36
prob. of survival (fitness)
0.8
0.4
0.2
Relative Fitness
0.8/0.8=1
0.4/0.8 = 0.5 0.2/0.8=0.25
= 1.00
D. Deviations From HWE:
5. Natural Selection
1. Fitness Components:
2. Constraints:
3. Modeling Selection:
a. Calculating relative fitness
b. Modeling Selection
p = 0.4, q = 0.6
AA
Aa
aa
Parental "zygotes"
0.16
0.48
0.36
prob. of survival (fitness)
0.8
0.4
0.2
Relative Fitness
1
0.5
0.25
Survival to Reproduction
0.16
0.24
0.09
= 0.49
Freq’s in Breeding Adults
0.16/0.49
= 0.33
0.24/0.49
= 0.49
0.09/0.49
= 0.18
= 1.00
Gene Frequencies
F(A) = 0.575
Freq’s in F1 (p2, 2pq, q2)
0.33
0.49
= 1.00
F(a) = 0.425
0.18
= 1.00
Modern Evolutionary Biology
I. Population Genetics
A. Overview
B. The Genetic Structure of a Population
C. The Hardy-Weinberg Equilibrium Model
D. Deviations From HWE
E. Summary; The Modern Synthetic Theory of Evolution
Agents of Change
Mutation
Natural Selection
Recombination
- crossing over
- independent assortment
VARIATION
Sources of Variation
Genetic Drift
Migration
Mutation
Non-random Mating
Heredity, Gene Regulation, and Development
I. Mendel's Contributions
II. Meiosis and the Chromosomal Theory
III. Allelic, Genic, and Environmental Interactions
IV. Sex Determination and Sex Linkage
V. Linkage
VI. Mutation
VII. Gene Regulation
Heredity, Gene Regulation, and Development
I. Mendel's Contributions
II. Meiosis and the Chromosomal Theory
III. Allelic, Genic, and Environmental Interactions
IV. Sex Determination and Sex Linkage
V. Linkage
VI. Mutation
VII. Gene Regulation
A. Overview
All cells in an organism contain the same genetic information; the key to tissue
specialization is gene regulation – reading some genes in some cells and other
genes in other cells.
VII. Gene Regulation
A. Overview
All cells in an organism contain the same genetic information; the key to tissue
specialization is gene regulation – reading some genes in some cells and other
genes in other cells.
B. Terminology
Inducers turn a gene on…
Repressors turn a gene off…
VII. Gene Regulation
C. The lac Operon in E. coli
An “operon” is a region of genes that
are regulated as a unit – it typically
encodes > 1 protein involved in a
particular metabolic pathway.
VII. Gene Regulation
C. The lac Operon in E. coli
When lactose is present, E. coli produce three enzymes involved in lactose
metabolism. Lactose is broken into glucose and galactose, and galactose is
modified into glucose, too. Glucose is then metabolized in aerobic respiration
pathways to harvest energy (ATP). When lactose is absent, E. coli does not
make these enzymes and saves energy and amino acids.
How do these little bacteria KNOW? : )
VII. Gene Regulation
C. The lac Operon in E. coli
Lac Y - permease – increases absorption of lactose
Lac Z – B-galactosidase – cleaves lactose into glucose and galactose
Lac A – transacetylase – may code for enzymes that detoxify waste products of
lactose metabolism.
VII. Gene Regulation
C. The lac Operon in E. coli
1960 – Jacob and Monod proposed that this was an inducible system
under negative control. (Because the presence of the substrate INDUCES
transcription by SHUTTING OFF regulation).
Repressor
Gene
Repressor
Operator
RNA Poly
VII. Gene Regulation
C. The lac Operon in E. coli
1960 – Jacob and Monod proposed that this was an inducible system
under negative control. (Because the presence of the substrate INDUCES
transcription by SHUTTING OFF regulation).
LACTOSE
VII. Gene Regulation
C. The lac Operon in E. coli
The binding of lactose changes the shape of
the repressor (allosteric reaction) and it
can’t bind to the operator.
1960 – Jacob and Monod proposed that this was an inducible system
under negative control. (Because the presence of the substrate INDUCES
transcription by SHUTTING OFF regulation).
LACTOSE
VII. Gene Regulation
C. The lac Operon in E. coli
So, there are lots of genes that produce “regulatory proteins” which bind to
other genes, and influence whether those genes are turned on and off.
This allows cells to become very different from one another, with certain
subsets of genes turned on in some cells and off in others.