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Non-random mating n Mating in many species is often assortative Humans provide lots of examples Humans tend to marry people of similar intelligence Humans.

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Presentation on theme: "Non-random mating n Mating in many species is often assortative Humans provide lots of examples Humans tend to marry people of similar intelligence Humans."— Presentation transcript:

1 Non-random mating n Mating in many species is often assortative Humans provide lots of examples Humans tend to marry people of similar intelligence Humans tend to marry people of similar physical appearance  Height  Skin color

2 Inbreeding n Inbreeding is a type of non-random mating in which mates are chosen on the basis of relatedness n Inbreeding is typically non-adaptive - increase in likelihood of deleterious recessive alleles being expressed n The loss of vigor, health and survivorship from inbreeding is called inbreeding depression

3 Generation 1 Generation 2 Generation 3 Generation 4 A 1 Homozygote A 1 A 2 Heterozygote 100% 25% 50% 02550 Frequency of genotypes 25% 100% A 2 Homozygote 75100 Figure 22.6

4 Table 22.3

5 Non-random mating n In some species, pairing is dissassortative; mates are chosen that are different An example is the White-throated Sparrow, a common breeding bird throughout New England Two morphs of this bird are found Head with black and white stripes Head with black and tan stripes

6 White-throated Sparrow morphs 90% of the pairs are between tan- and white-striped individuals

7 Why is dissassortative mating seen in White-throated Sparrows? n The color of the head striping is correlated with parental qualities Tan-striped females tend to be good foragers White-striped males tend to be good defenders but relatively poor foragers Tan-striped males and white-striped females are intermediate in foraging ability Tan-striped males are less aggressive than white-striped males

8 Is non-random mating an evolutionary mechanism? n No. Allele frequencies in the population are not changed n Rather, the mix of genotypes differs from the Hardy- Weinberg prediction

9 Small population sizes n When populations are small, the phenomenon of genetic drift may come into play n Genetic drift is defined as any change in allele frequencies that is due to chance

10 Warwick Kerr and Sewall Wright experiments on genetic drift with Drosophila

11 Kerr and Wright experiments on genetic drift with Drosophila n The biologists concentrated on a single trait, the shape of leg bristles, controlled by a single locus n The wild type condition is normal, unbranched bristles; the mutant type is bristles that bend at the tip (forked) n The shape of the bristles does not affect the fitness of the flies so selection based on bristles did not occur

12 Experimental design n Used 96 cages, each of which received four female and four male Drosophila n Kerr and Wright began with p = q = 0.5 n After the parental generation bred, Kerr and Wright raised the offspring in each cage n From each cage, they randomly chose four males and four females to continue the line in each cage n They repeated this process for 16 generations

13 Experimental results n Three types of outcomes: In 29 of the populations, the allele for normal bristles had disappeared. All of the flies were fixed (homozygous) for forked bristles. In 41 populations, the flies were fixed for normal bristles (the forked allele had disappeared) In the remaining 26 populations, both alleles were still present The results are surprising: in 73% of the experimental populations, genetic drift reduced allelic diversity to zero!

14 Flipping a coin as a model of genetic drift n Let’s flip a fair coin (heads and tails are equally likely) 20 times n Let heads and tails be the two alleles in our population. We begin with p = q = 0.5 (since heads and tails are equally likely) n Here are the results of our 20 coin flips: H-T-T-T-T-T-H-H-T-T-H-T-H-H-T-T-H-H-T-H

15 The coin model n H-T-T-T-T-T-H-H-T-T-H-T-H-H-T-T-H-H-T-H n We therefore got 11 tails and 9 heads n By random events, we changed the gene frequencies: f(H) = p = 0.45 and f(T) = q = 0.55 n If we flipped our coin 1,000 times, p and q would be quite close to 0.50 n If we stopped flipping the coin after only six tosses, our new values of p would be 0.17 and q would be 0.83


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