1. Sources of VariationAgents of Change MutationN.S. RecombinationDrift - crossing overMigration - independent assortmentMutation Non-random Mating VARIATION So, if NO AGENTS are acting on a population, then it will be in equilibrium and WON'T change. 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.

2. 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

3. p1 = 0.2 q1 = 0.8 p2 = 0.7 q2 = 0.3 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 suppose migrants immigrate at a rate such that the new immigrants represent 10% of the new population

4. p2 = 0.7 q2 = 0.3 p1 = 0.2 q1 = 0.8 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 M = 10% p(new) = p1(1-m) + p2(m) = 0.2(0.9) + 0.7(0.1) = 0.18 + 0.07 = 0.25

5. AAAaaa 0.20.60.2 offspring F1 D. Deviations from HWE 1. mutation 2. migration 3. Non-random Mating a. Positive Assortative Mating – “Like mates with Like”

6. AAAaaa 0.20.60.2 offspringALL AA1/4AA:1/2Aa:1/4aaALL aa F1 D. Deviations from HWE 1. mutation 2. migration 3. Non-random Mating a. Positive Assortative Mating – “Like mates with Like”

7. AAAaaa 0.20.60.2 offspringALL AA1/4AA:1/2Aa:1/4aaALL aa 0.20.15 + 0.3 + 0.150.2 F10.350.30.35 D. Deviations from HWE 1. mutation 2. migration 3. Non-random Mating a. Positive Assortative Mating – “Like mates with Like”

8. 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.

9. 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

10. 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

11. 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

12. - “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 D. Deviations from HWE 1. mutation 2. migration 3. Non-random Mating 4. Finite Population Sizes: Genetic Drift

13. - “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.

14. - “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).

15. - “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.

16. - “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

17. 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:

18. 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

19. 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:

20. 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

21. Maximize probability of survival GROWTH METABOLISM REPRODUCTION GROWTH METABOLISM REPRODUCTION Maximize 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

22. METABOLISM REPRODUCTION 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.finite energy budgets and necessary trade-offs: TRADE OFF #1: Survival vs. Reproduction TRADE OFF #2: Lots of small offspring vs. few large offspring

23. D. Deviations From HWE: 5. Natural Selection 1.Fitness Components: 2. Constraints: i.finite energy budgets and necessary trade-offs: ii.Contradictory selective pressures: Leaf Size Photosynthetic potential Water Retention

24. D. Deviations From HWE: 5. Natural Selection 1.Fitness Components: 2. Constraints: i.finite energy budgets and necessary trade-offs: ii.Contradictory selective pressures: Leaf Size Photosynthetic potential Water Retention Rainforest understory – dark, wet Big leaves adaptive

25. D. Deviations From HWE: 5. Natural Selection 1.Fitness Components: 2. Constraints: i.finite energy budgets and necessary trade-offs: ii.Contradictory selective pressures: Leaf Size Photosynthetic potential Water Retention Desert – sunny, dry Small leaves adaptive

26. D. Deviations From HWE: 5. Natural Selection 1.Fitness Components: 2.Constraints: 3.Modeling Selection: a. Calculating relative fitness p = 0.4, q = 0.6AAAaaa Parental "zygotes"0.160.480.36 = 1.00 prob. of survival (fitness)0.80.40.2 Relative Fitness0.8/0.8=10.4/0.8 = 0.50.2/0.8=0.25

27. p = 0.4, q = 0.6AAAaaa Parental "zygotes"0.160.480.36 = 1.00 prob. of survival (fitness)0.80.40.2 Relative Fitness10.50.25 Survival to Reproduction0.160.240.09= 0.49 Freq’s in Breeding Adults0.16/0.49 = 0.33 0.24/0.49 = 0.49 0.09/0.49 = 0.18 = 1.00 Gene FrequenciesF(A) = 0.575F(a) = 0.425 Freq’s in F1 (p 2, 2pq, q 2 )0.330.490.18 = 1.00 D. Deviations From HWE: 5. Natural Selection 1.Fitness Components: 2.Constraints: 3.Modeling Selection: a. Calculating relative fitness b. Modeling Selection

28. Sources of VariationAgents of Change MutationNatural Selection RecombinationGenetic Drift - crossing overMigration - independent assortmentMutation Non-random Mating VARIATION 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

29. 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

30. 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.

31. 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…

32. 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

33. 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? : )

34. 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

35. 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). RepressorRNA Poly Repressor Gene Operator

36. 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

37. 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 The binding of lactose changes the shape of the repressor (allosteric reaction) and it can’t bind to the operator.

38. 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.