Genetics - Lecture 21: Evolutionary & Population Genetics

Industrial melanization

before industrial revolution, light colored moths were better suited for their environment - after industrial revolution, dark colored moths were favored

p frequency

frequency of dominant allele in a population

q frequency

frequency of recessive allele in a population

q^2

frequency of homozygous recessive

p^2

frequency of homozygous dominant

2pq

frequency of heterozygous genotype

For what traits is the Hardy-Weinberg Law invalid?

traits that affect reproductive fitness

Positive selection

natural selection that increases the frequency of a favorable allele

Negative selection

natural selection that decreases the frequency of a harmful allele

Example of selection in evolution

lactase persistence

Balancing selection

heterozygote has the advantage

Example of balancing selection

sickle cell anemia

Alleles that do not affect fitness

allele frequencies will stay the same from generation to generation

Genetic drift

A change in the allele frequency of a population as a result of chance events rather than natural selection.

Bottlenecks

the effective population becomes smaller, increasing the chances of skewing allelic frequencies from their "natural" values

Causes of bottlenecks

- migration

- catastrophe

Founder effect

A genetic bottleneck caused when a small group arrives in a new place

Disease that the Founder effect was useful in studying:

Huntington's disease

Repeat region disease

(Huntington's) variable repeated segments in the protein coding region of a gene - different number of repeats determines phenotype and time of onset

ALX1 gene

Gene for a transcription factor that affects the beak development associated with beak shape in Darwin's finches

CaM

protein that affects beak length

BMP4

protein that affects beak width

Genetic algorithms

represent solutions to complex problems as chromosomes

Start of a genetic algorithm

begin with a population of random solutions and allow them to recombine with each other based on fitness

Brute force solution

Consider all potential solutions and select the correct one - too many combinations

Steps of a genetic algorithm

- create a population of different combinations

- score the fitness for each combination

- mate combinations with each other

- repeat until no more improvements occur

Convergence

no more improvements by mutation