Topics

• Definitions
  • DNA, RNA, Protein
  • Allele, locus (loci)
  • For diploid: gametes join to from zygote; homozygous (aa), heterozygous (ab)
  • Haploid, diploid, tetrapoid, polyploid, haplodiploid
  • Genotype --> (environment, development) --> Phenotype
  • Dominant (AA Ab), Recessive (bb)
  • Gene frequency versus genotype frequency, phenotype frequency
    e. g., with a population of five individuals: aa, aa, aa, aB, BB

• Hardy-Weinberg Principle
  • Assumptions:
    • Random mating
    • No mutations
    • No genetic drift (large population size)
    • No migration
    • No natural selection
    Formulae:
    • Parental gene frequencies:
      p + q = 1, where p is frequency of allele-a; and q is frequency of allele-b
    • Parental genotype frequencies:
      are binomial expansion of (p + q)² = (p + q) x (p + q) = p² + pq + pq + q²
      aa: p²
      ab: 2pq
      bb: q²
      
    H-W Principle is used as a null model:
    • When assumptions met, frequencies in a parental generation predict frequencies in future generations.

• Evolution
  • Macro-evolution
    Speciation, Phylogenies, Taxon (taxa)
    All Living Things
    Taxonomy versus systematics -- natural groups, clades, monophyletic groups, polyphyletic groups
    Tree of Life
    Fossil record, paleontology
  • Micro-evolution
    Changes in gene frequencies, shorter ecological time spans
  • Relationship between Macro & Micro-evolution
    Punctuated equilibrium versus gradual change
    Reproductive isolation, vicariance, allopatric speciation, sympatric speciation

• Adaptation (or not)
  Just-so stories
  Ant and bullhorn acacia mutulism
  

  Assignment: Design experiment to show acacia's behaviors are adaptive, not just adaptations to past hebivory by now extict large Pliestocene mammals (1.8 million - 10,000 years ago)
  

• Experimental design -- statistical testing
  A rather silly, old professor wants to better understand his class. He hypothesizes that more motivated students are likely both to vote in national elections and to be first to form teams for their independent projects. There are 113 students registered for the class. He surveys the class, asking them two questions:
  1. Have you signed up for a group project yet?
  2. Did you vote in the last Pesidential election?
  He tabulates the results and finds the following:

  Observed results		IN GROUP PROJECT
  		Yes	No
  VOTED	Yes	35	28
  	No	13	10

  One student forgot to answer the survey questions!

  What can we conclude from these data? -- We need to do a statistical test to see if the observed results differ from the results that we would expect if the two variables were randomly associated. We will use a 2 x 2 contingency table and test our results with a Chi-square test.

  Total results		IN GROUP PROJECT
  		Yes	No	total
  VOTED	Yes	35	28	63
  	No	13	10	23
  total		48	38	86

  Expected results		IN GROUP PROJECT
  		Yes	No	total
  VOTED	Yes	48 * 63/86 = 35.16	38 * 63/86 = 27.83	63
  	No	48 * 23/86 = 12.84	38 * 23/86 = 10.16	23
  total		48	38	86

  (Obs. - Exp.)2 Exp.		IN GROUP PROJECT
  		Yes	No
  VOTED	Yes	(35 - 35.16)2 35.16	(28 - 27.83)2 27.83
  	No	(13 - 12.84)2 12.84	(10 - 10.16)2 10.16

  With Yates' correction for 2 x 2 contingency table, Χ² =

  N * ( |AD - BC| - N/2) 2

  (A+B) * (C+D) * (A+C) * (B+D)

  where, in this case, A=35, B=28, C=13, D=10, and N=86.

  • Does the value exceed a critical chi square value = 3.84, for p < 0.05 for df = 1?

    Online calculator

  • Assumptions? Errors? What are we missing? Honesty?

  • Might the professor's hypothesis still be correct?