Evolution and Genetic Variation - Quick Reference

Phylogeny and trait timing

• Deeper branches: more organisms share a trait; tips: newer traits shared by fewer organisms.

• Example: digit-bearing limbs (tetrapods) appear across mammals, lizards, birds; feathers are bird-specific and closer to the tips.

• Phylogenies have limitations:

  • Dates are often relative, not exact.

  • Trees are hypotheses that can be tested and may change with new data.

  • Choice of characters and data type affects the resulting tree (morphology vs molecular data).

  • More data can shift relationships (e.g., dog breeds vs wolves).

Homology, analogy, and convergent evolution

• Homologous structures: shared ancestry; may differ in form.

  • Forelimbs of horse and bat are homologous despite different function.

• Analogous structures: similar traits due to similar environments/selection pressures, not shared ancestry.

  • Wings of bat and bee; planes.

  • Convergent evolution: separate lineages arrive at similar solutions.

Biogeography and example of convergent evolution

• Large-scale distribution helps explain long-term evolution.

• Australian marsupials vs placental mammals show convergent forms (e.g., burrowing, ant-eating, nocturnal, climbing) due to similar ecological roles despite separate lineages.

Direct observation vs fossil record

• Direct observation (in real time): antibiotic resistance in bacteria (e.g., MRSA) can be seen within a lifetime due to fast generation times.

• Fossil record: shows changes over time; dating methods map trajectories (e.g., horse evolution) but is not direct observation; relies on dating and correlation.

• DNA data supplements fossils: ancient DNA helps confirm relationships (e.g., hippos and whales are closely related).

• When new data changes relationships, reassess whether a homologous vs analogous basis was used.

Evolution across scales and chapter transition

• Moving between microevolution (allele frequencies) and macroevolution (larger-scale changes) as we shift chapters.

• This unit focuses on genetic variation, allele frequencies, and the Hardy–Weinberg framework as a null model for population genetics.

Sources of genetic variation

• Mutations: changes in nucleotide sequence; can be neutral, deleterious, or rarely beneficial; most do not increase fitness.

• Gene duplication: multiple copies create redundancy; copies can diverge and contribute to variation.

• Epistasis and polygenic inheritance: interactions between genes affect phenotypes.

• Sexual reproduction: recombination, independent assortment, and random fertilization generate new combinations.

• Bacteria/viruses: fast generation times enable rapid evolution and adaptation; explains rapid changes like drug resistance.

• Relationship: phenotype = environment × genotype; natural selection acts on heritable variation in genotype.

• Key idea: genetic variation is necessary for evolution; without it, populations cannot adapt to changing environments.

Fitness and variation concepts

• Fitness concepts:

  • Zero fitness: no survival or reproduction.

  • Fitness = 1: baseline.

  • < 1: deleterious; > 1: advantageous.

• Most new mutations are not beneficial; variation is maintained through multiple mechanisms (mutation, recombination, etc.).

Population genetics and Hardy–Weinberg equilibrium

• Population: all individuals of the same species in a given area; unit of evolution (not the individual).

• Gene pool: all alleles present in the population.

• Allele frequency: proportion of a given allele in the gene pool.

• Genotype frequency: proportion of genotypes in the population.

• Hardy–Weinberg equilibrium (HW) as the null hypothesis: allele frequencies do not change over time, and the population is not evolving.

• HW equations:

  • Allele frequencies satisfy $p + q = 1$

  • Genotype frequencies satisfy $p^2,~~2pq,~~q^2$ for the genotypes corresponding to two alleles.

  • Overall genotype distribution: $p^2 + 2pq + q^2 = 1$

• HW conditions (no evolution):

  • No mutations

  • Random mating (no mate choice)

  • No natural selection

  • Very large population size

  • No gene flow

• If any condition is violated, allele frequencies may change and the population evolves.

• Gene flow, population size, and mating patterns influence how strongly HW expectations hold.

Practical example: allele-frequency counting around a two-allele gene

• Context: two alleles (CR and CW) in a population with two genotypes (e.g., red vs white flowers).

• Total alleles in population = 2 × N individuals.

• To compute allele frequencies:

  • If counts are: nRR, nRW, nWW with N = nRR + nRW + nWW,

  • Red allele count = 2·nRR + nRW,

  • White allele count = 2·nWW + nRW,

  • Total alleles = 2N,

  • $p = \frac{\text{Red allele count}}{2N},\quad q = \frac{\text{White allele count}}{2N},$

  • Check that $p + q = 1$.

• Note: with two alleles, genotype frequencies under HW would be $[p^2,\; 2pq,\; q^2]$.

Quick reminders for exam context

• Populations evolve, not individuals.

• HW equilibrium provides a baseline to test for evolution by comparing observed vs expected genotype frequencies.

• When studying allele frequencies, track changes over time to infer evolutionary processes (mutation, selection, drift, migration, nonrandom mating).