Evolution, Genetics, and the Scientific Method - Vocabulary Flashcards

Process of Science and the Learning Context

The plan for today centers on the Process of Science: why we learn science, what science is and how we study it, and an introduction to an evolution experiment. Reminders include a Background Quiz on ELMS/Canvas due tonight at midnight and starting next week’s SimBiotic assignment on Evolution, due Friday. The material emphasizes moving beyond memorization to thinking like a scientist: evaluating evidence, understanding context, and recognizing that scientists can disagree. The difference between a theory and a fact is defined: a fact is an observable phenomenon known to be true, while a theory is a well-substantiated explanation acquired through the scientific method and repeatedly tested through observation and experimentation. The scientific method is outlined in six steps: (1) Define a problem, (2) Research what is known, (3) Hypothesize a cause, (4) Test your hypothesis, (5) Analyze your results, (6) Support or refute your hypothesis. The emphasis is on testing ideas, interpreting data with awareness of context, and distinguishing correlation from causation. A key note discusses hypotheses as a structured sequence: Question -> BECAUSE of this reason -> Methods -> IF I do this -> THEN this will happen. Observational vs. experimental designs are contrasted, with observational studies collecting data without manipulation, and experimental designs manipulating one or more variables to test cause-and-effect relationships. The material also introduces systematic reviews and meta-analyses as strategies for addressing big questions by summarizing and reanalyzing existing studies. Negative data is valued as a useful part of science because science is iterative and can lead to new hypotheses. The role of mathematics in ecology is highlighted, including variability and data interpretation across multiple factors such as count data, environmental factors, and temporal variation. A growth mindset is encouraged: failure is an opportunity to grow, challenges help learning, effort and attitude matter, feedback is constructive, and success of others can be a source of inspiration. The “MegaPlate” evolution experiment is introduced as a notable example of rapid evolution in response to selective pressure and a prompt to consider what we already know about antibiotic resistance. Finally, the section sets the stage for an evolution module by introducing terminology, sources of genetic variation, and reminders about course deadlines.

Why Evolution Matters and Core Concepts

Evolution is foundational to understanding biology because it explains how populations change over time in response to genetic variation and environmental pressures. The material emphasizes that evolution operates on allele frequencies in populations, not on individuals. The plan for today covers the terminology of evolution, sources of genetic variation, and the mechanisms by which evolution occurs.
The notes introduce the basic concept that living organisms store information in nucleic acids (DNA and RNA) built from nucleotides. DNA is a double helix with a sugar-phosphate backbone and nucleobases: Adenine (A), Thymine (T), Cytosine (C), and Guanine (G); RNA uses Uracil (U) in place of T. Base pairing rules in DNA are A-T (with two hydrogen bonds) and C-G (with three hydrogen bonds). In RNA, A pairs with U, and C with G. The central dogma describes how DNA is transcribed into RNA, which is translated into a polypeptide (protein).
Genes are segments of DNA that provide information to make proteins. Proteins are built from amino acids (20 canonical amino acids with varying properties: nonpolar, polar, charged). The sequence of DNA base pairs dictates the sequence of amino acids, which then folds into functional proteins. Protein structure determines function (e.g., transporters, receptors, enzymes, anchors).
Alleles are variants of a gene. The genotype is the combination of alleles an individual carries for a gene, while the phenotype is the observable trait influenced by gene expression and the environment. Organisms have two alleles for most genes (except the sex chromosome in males for X-linked genes). Dominant alleles mask recessive alleles in heterozygotes, though there are exceptions (incomplete dominance, codominance).
The material covers how mutations create new alleles (point mutations, frameshift, chromosomal mutations) and how these can affect amino acid sequences, protein function, and phenotypes. Mutations can be heritable when they occur in germline cells (sperm/egg) and thus can be passed to offspring; somatic mutations are not inherited. Chromosomal mutations include deletions, insertions, translocations, and inversions, which can have substantial phenotypic consequences.

The Genetic Basis of Evolution: Variation, Heredity, and Selection

Variation in a population is essential for evolution. Variation can arise from mutation (new alleles), gene flow (movement of alleles between populations), and recombination during meiosis. Genetic drift (random changes in allele frequencies) and non-random mating also shape genetic variation. Natural selection acts on heritable variation, favoring traits that increase reproductive success in a given environment.
Fitness is a measure of reproductive success. Beneficial traits increase an individual’s chances of surviving to reproduce and passing on genes. Over time, allele frequencies change, leading to evolution. The notes emphasize that ecology and environmental conditions act as selecting forces that shape traits in populations (e.g., soil moisture, temperature, growing season, mutualists, competition, predators, parasites).
Darwin’s theory of natural selection rests on four main principles: 1) Heritable variation in populations; 2) Struggle/competition for limited resources; 3) Differential reproductive success; 4) Change in allele frequency over time. The idea of descent with modification underpins Darwin’s view that species are related through common ancestry and that adaptations arise as populations evolve.
Evolution can be traced using multiple lines of evidence: direct observation and strong inference, the fossil record, homologies and vestigial structures, biogeography and geology, and genetics. Direct observation includes artificial selection experiments (e.g., dogs from wolves) and natural observations (e.g., Darwin’s finches). The fossil record demonstrates the ordering of fossils in rock layers, with deeper layers being older. Homologous structures reveal shared ancestry, while analogous features illustrate convergent evolution. Vestigial structures reveal relatedness to ancestral forms. Biogeography supports patterns of descent with modification. Genetics provides a molecular basis for inheritance and variation.
The science of evolution also considers that evolution can occur via non-adaptive processes (mutation, gene flow, genetic drift, non-random mating) and adaptive processes (natural selection). The Red Queen hypothesis and Tangled Bank hypothesis are highlighted as theories for why sex and genetic variation are maintained in populations under environmental variability and co-evolution with parasites.

Coral Reefs as a Model: Defining Problems, Hypotheses, and Data Interpretation

A case study introduced is coral bleaching, where bleaching is driven by high water temperatures. The problem definition centers on understanding how environmental stressors such as temperature and light intensity affect zooxanthellae abundance and pigment content within coral tissues. The data include figures showing the effects of temperature (27°C vs 32°C) on zooxanthellae abundance and chlorophyll a pigment per zooxanthellae, and how light intensity influences these same variables. The experiments are designed to test whether high temperature reduces zooxanthellae abundance, pigment content, or both, and whether light intensity exacerbates bleaching.
Hypotheses are isolated in a structured way. For example: If we grow corals at normal and hot temperatures, then the number of zooxanthellae will be lower in corals grown at hot temperatures because bleaching is driven by high water temperatures. A complementary statement would be: If bleaching is driven by high water temperature, then the number of zooxanthellae will be lower in corals grown at hot temperatures. These hypotheses are tested using experiments that measure zooxanthellae abundance and pigment content under different temperature and light conditions.
Error bars on graphs reflect the variation around the mean and are used to assess statistical significance. When error bars overlap substantially, differences may not be statistically significant; when they do not, there is a hint of significance, but formal statistical tests are required to draw conclusions.
The data also introduce a second experiment that varies light intensity (ambient vs high light) to assess its separate and combined effects with temperature on bleaching. The combined data explore how temperature and light interact to influence zooxanthellae abundance and chlorophyll content, and how this informs our understanding of coral bleaching under multiple stressors.
Beyond the lab, satellite imagery and observational strategies are proposed to monitor coral bleaching and ocean temperatures. Questions are posed about causation vs correlation: Does high temperature cause bleaching, and does observational data alone prove causation? The discussion emphasizes the importance of considering alternative causes (light intensity, salinity, pollution, etc.) when interpreting correlations.
Observational vs experimental designs are contrasted: observational studies describe and summarize existing data without manipulation, whereas experiments involve deliberate manipulation of variables to test hypotheses. Systematic reviews and meta-analyses are introduced as methods to synthesize results across multiple studies and broader contexts.

Evolution, Heredity, and Molecular Foundations

The module introduces terminology for talking about evolution, including the genetic basis of inheritance and the flow of information from DNA to RNA to proteins. DNA and RNA are nucleic acids composed of nucleotides. The nucleotides comprise a sugar (deoxyribose in DNA, ribose in RNA), a phosphate group, and a nitrogenous base (adenine A, thymine T in DNA, uracil U in RNA, cytosine C, guanine G). In DNA, base pairing follows A with T and C with G via hydrogen bonds; in RNA, A pairs with U and C with G. The structure of DNA includes a sugar-phosphate backbone and base pairs that form a double helix, with base pairing enabling accurate replication.
DNA replication, transcription, and translation are the core steps of gene expression. DNA is replicated before cell division; transcription copies DNA into messenger RNA (mRNA); translation uses the mRNA sequence to assemble a polypeptide chain from amino acids. Proteins fold into functional three-dimensional structures, with the primary, secondary, tertiary, and quaternary structures arising from the sequence and chemical properties of amino acids. The 20 amino acids have different properties (non-polar, polar, charged) that influence folding and function. Protein function includes transporters, receptors, enzymes, antibodies, and structural roles like building muscle tissue or pigment transport (e.g., hemoglobin).
Genes encode proteins; a gene is a locus on a chromosome that provides information to synthesize a specific protein. The genotype refers to the allelic composition at a gene, while the phenotype is the expressed trait. Alleles can be dominant or recessive, and multiple forms (e.g., codominant or incompletely dominant) can exist for a given gene. The Punnett square is a tool to predict offspring genotypes and phenotypes based on parental genotypes. A genotype is denoted by two alleles (e.g., AA, Aa, aa); a phenotype may correspond to one or more genotypes depending on dominance relations.
Mutations create new alleles through changes in DNA sequences. They can be point mutations (single nucleotide changes), frameshift mutations (insertions or deletions that shift the reading frame), or chromosomal mutations (deletions, insertions, translocations, inversions). Point mutations can alter one amino acid in a protein, potentially changing function or introducing stop codons; frameshift mutations can drastically alter the protein sequence; chromosomal mutations affect larger genomic segments and can have substantial phenotypic consequences.
Non-coding regions of genes (introns, promoters, regulatory sequences) influence gene expression and genome structure. Exons encode the amino acid sequence, while introns are spliced out during processing of pre-mRNA. Regulation and expression determine when and where genes are active, contributing to cellular differentiation.
The organization of chromosomes and genes is described, including the concept of homologous chromosomes (same genes, different alleles) and the idea that crossing over during meiosis I can create recombinant chromosomes, increasing genetic diversity. Linkage (genes close together on a chromosome) can reduce recombination, while genetic distance corresponds to recombination frequency. Recombination rates can be translated into map units (centiMorgans), with 1 map unit corresponding to 1% recombination.

Mendelian Inheritance and Genetic Variation: From Genes to Traits

Mendel’s experiments with pea plants established particulate inheritance: traits are inherited as discrete units (now called genes) rather than blended in offspring. Alleles are variants of a gene, and the locus is the gene’s position on the chromosome. The Law of Segregation states that alleles separate randomly into gametes, so an offspring inherits one allele from each parent. The Law of Independent Assortment states that the way alleles of different genes line up in metaphase I is random, producing many allele combinations in offspring. Dominant alleles mask recessive alleles in heterozygotes, but there are exceptions (incomplete dominance and codominance).
A Punnett square can predict offspring genotypes and phenotypes based on parental genotypes. For a single gene with two alleles (A and a) and complete dominance, the genotype frequencies in the F1 and F2 generations often follow a 1:2:1 genotype ratio and a 3:1 phenotype ratio. For a dihybrid cross with two independently assorting genes (AaBb x AaBb), the classic phenotype ratio is 9:3:3:1 under independent assortment assumptions. Test crosses (crossing a dominant phenotype with a homozygous recessive) reveal genotypes and help determine dominance patterns.
The concepts of homozygous (two identical alleles) and heterozygous (two different alleles) genotypes are introduced, along with the distinction between genotype (allele composition) and phenotype (expressed trait). The frequency formulas for Hardy-Weinberg equilibrium are introduced as p and q, where p is the frequency of the dominant allele and q the frequency of the recessive allele, with p + q = 1 and p^2 + 2pq + q^2 = 1 describing the expected genotype frequencies in a non-evolving population.
Examples illustrate: (i) a cattle color problem with red (R) and white (r) alleles, where p^2, 2pq, and q^2 describe genotype frequencies; (ii) a PKU example where the recessive allele frequency q is derived from disease prevalence using q^2; (iii) a human eye color example where only phenotype data may be insufficient to deduce genotype without assuming Hardy-Weinberg equilibrium.
Linkage and recombination are discussed using Morgan’s work with fruit flies as a gateway to understanding how genes on the same chromosome can be linked and how crossing over in meiosis creates recombinants. The concept of recombination frequency as a measure of genetic distance is introduced, with the formula: r = rac{ ext{number of recombinant offspring}}{ ext{total offspring}} and the mapping distance in map units (1% recombination = 1 map unit).
Sex-linked inheritance is described: X-linked genes (on the X chromosome) show distinctive inheritance patterns in males (who have only one X) and females (two Xs). Practice problems show how to determine the proportion of daughters and sons affected by X-linked traits and how pedigrees can reveal carrier statuses.
The notes cover pleiotropy (one gene affecting multiple traits) and epistasis (one gene’s effect depends on another gene). Classic example: sickle cell disease exhibits pleiotropy and shows heterozygote advantage with malaria resistance. Epistasis is illustrated with coat color in Labrador retrievers, where one gene affects pigment production and another affects pigment deposition.
Genetic linkage and recombination are revisited to illustrate how recombination frequencies between linked genes provide a genetic map. Morgan’s work demonstrated that when genes are linked, the expected 9:3:3:1 ratio can deviate, depending on distance and recombination frequency. The concept of testcross data leading to conclusions about linkage and recombination is central to constructing genetic maps.

Population Genetics: The Hardy-Weinberg Framework

The Hardy-Weinberg Equilibrium describes a non-evolving population, assuming an infinitely large population, no mutation, random mating, no gene flow, and no natural selection. Under these conditions, allele and genotype frequencies remain constant across generations. The two key equations are:
Allele frequencies satisfy p + q = 1 where p is the frequency of the dominant allele and q is the frequency of the recessive allele.
Genotype frequencies satisfy p^2 + 2pq + q^2 = 1 corresponding to genotypes AA, Aa, and aa, respectively.
These equations allow us to estimate allele frequencies from genotype data or to test whether a population is evolving by comparing observed genotype frequencies to Hardy-Weinberg expectations. Stepwise examples show how to compute p and q from genotype counts, convert to expected numbers, and compare to observed numbers to assess HW equilibrium.
Practice problems cover multiple scenarios: calculating allele and genotype frequencies from genotype counts, differentiating among homozygous and heterozygous frequencies, and applying the p^2, 2pq, q^2 framework to real data (e.g., PKU carrier calculations, cat coat color, cattle roan genotype frequencies). In real data, deviations from HW expectations indicate that evolutionary forces may be acting (mutation, migration, selection, drift, non-random mating).
The probability rules underpinning Hardy-Weinberg calculations are reiterated, including the multiplication rule for independent events and the decomposition of p^2, 2pq, q^2 into genotypic frequencies. Some practice problems illustrate how to derive allele frequencies from observed genotype frequencies, and how to use p and q to predict future generation genotype frequencies.
The population genetics framework also addresses the construction of random mating assumptions, the interpretation of phenotypes vs. genotypes, and the limits of Hardy-Weinberg in natural populations where evolution is occurring due to the action of evolutionary forces.

Evolution in Real Populations: Case Studies and Data Visualization

Real-world population data illustrate how evolution can manifest in nature. Boxplots are used to summarize distributions of numerical traits (e.g., beak size, body mass). The Galápagos finches example (Grant & Grant) demonstrates how drought-induced shifts in resource availability can drive selection on beak size, producing measurable differences in survival and beak morphology between survivors and non-survivors. The boxplots capture central tendency and variability, with medians and quartiles, and illustrate how to interpret data distributions in an evolutionary context.
Another example examines human variation in salivary amylase gene (AMY1) copy number and enzyme activity across populations with different starch diets, highlighting how gene copy number variation can contribute to phenotypic differences and potential local adaptation.
Poaching and elephant populations are used as case studies to show how human-caused selective pressures can influence trait distributions (e.g., tusk length and circumference) and how long-term data can reveal shifts in populations under strong selective forces.
The MegaPlate antibiotic resistance experiment is revisited as a vivid demonstration of rapid adaptation to strong selection, where bacterial populations evolve resistance under exposure to antibiotics. This example emphasizes the speed of evolution in microbial systems and the role of selection in shaping allele frequencies across generations.

The Mechanisms of Evolution: Mutation, Gene Flow, Genetic Drift, Non-random Mating, and Natural Selection

Mutation introduces new genetic variation by altering DNA sequences. Mutations can be heritable when they occur in germline cells, providing novel alleles for selection to act upon. Mutation alone is not adaptive but provides the raw material for evolutionary change.
Gene flow (migration) introduces or removes alleles from a population through the movement of individuals between populations. Gene flow tends to homogenize allele frequencies between populations and is not inherently adaptive.
Genetic drift refers to random fluctuations in allele frequencies due to chance events, which can cause alleles to become fixed or lost in small populations (bottlenecks, founder effects). The strength of drift is inversely related to population size; smaller populations experience stronger drift.
Non-random mating includes assortative mating and inbreeding, where mate choice or geographic proximity biases mating patterns. It can change genotype frequencies without directly changing allele frequencies, and it can influence evolutionary trajectories.
Natural selection acts on heritable variation, leading to differential reproductive success. There are four main principles of natural selection: heritable variation, struggle for resources, differential reproductive success, and changes in allele frequency over time. In addition to directional, stabilizing, and disruptive selection, other dynamics include frequency-dependent selection and sexual selection.
The notes emphasize that ecology acts as the selecting force. Environmental factors such as soil moisture, temperature, predation, mutualisms, and competition influence which traits are advantageous and thus shape population evolution over generations.

The Evolution of Reproductive Strategies and Sexual Selection

Sexual reproduction introduces genetic diversity through meiosis, recombination, and fertilization. The biological imperative to reproduce drives the evolution of mating systems and sexual selection, which can involve male-male competition and female choice.
Sexual selection can operate strongly on males, where relative mating success drives trait evolution (e.g., elaborate plumage, weaponry, vocalization). Trade-offs can exist where traits that maximize mating success reduce survival. Female choice exerts control over male reproductive success and can drive rapid evolution of secondary sexual traits through direct benefits, good genes, or sexy sons/runaway selection dynamics.
Various examples illustrate sexual selection: male-male competition (combat, sperm competition), female choice (direct benefits like nuptial gifts, good genes, or runaway selection where female preference and male trait co-evolve), and phenomena such as sexual cannibalism or elaborate ornamentation. The concept of pleiotropy and epistasis can influence trait expression and the evolutionary trajectory of sexually selected characteristics.
The notes discuss the genetic architecture of complex traits, where multiple genes (polygenic) and environmental factors contribute to phenotypes like coloration, size, or reproductive traits. Epistasis can produce non-additive effects where one gene alters the expression of another, while linkage can constrain the independent mapping of alleles across loci.

Mitosis, Meiosis, and the Generation of Genetic Diversity

Mitosis produces two genetically identical diploid daughter cells from a single diploid parent, ensuring equal chromosome distribution and maintaining chromosome number through somatic cell divisions. The process includes DNA replication, followed by prophase, metaphase, anaphase, telophase, and cytokinesis.
Meiosis reduces chromosome number from diploid to haploid, creating genetic diversity via crossing over (recombination) during prophase I, independent assortment of homologous chromosome pairs during metaphase I, and random fertilization. Meiosis consists of two rounds of division (Meiosis I and Meiosis II) with interphase in between and results in four haploid gametes.
Homologous chromosomes pair and exchange genetic material at chiasmata during prophase I, producing recombinant chromatids. The law of segregation ensures that alleles separate into gametes, and the law of independent assortment states that the orientation of chromosome pairs during meiosis I is random, producing many possible gamete combinations.
When meiosis is coupled with fertilization, genetic diversity arises in offspring due to segregation, recombination, and fertilization randomness. Test crosses and dihybrid crosses illustrate how independent assortment operates for two or more genes. In linked genes, recombination frequency is used to map the distance between loci on a chromosome, measured in map units (percent recombination).

Population Genetics in Practice: HW Equilibrium, Frequencies, and Applications

The Hardy-Weinberg framework provides a null model to study evolution in populations. If a population is in HW equilibrium, allele and genotype frequencies remain constant across generations: p + q = 1 and p^2 + 2pq + q^2 = 1. The model assumes no mutation, random mating, large population size, no gene flow, and no selection. Deviations from HW equilibrium indicate evolutionary forces at work.
Allele frequencies (p and q) can be derived from observed genotype frequencies, and the expected genotype frequencies can be computed using p^2, 2pq, and q^2. If the observed genotype frequencies differ significantly from HW expectations, the population is evolving.
Examples illustrate how to calculate allele frequencies from counts (e.g., 700 FF, 200 Ff, 100 ff in a population -> p = 0.8, q = 0.2), and how to compute expected genotype frequencies for the next generation. Practice problems show how to calculate HH frequencies, test for HW equilibrium, and interpret deviations due to evolutionary forces like selection or drift.
Real-world applications include analyzing disease allele frequencies, carrier frequencies for recessive diseases (e.g., PKU), or pigmentation traits in animal populations. The HW framework also underpins population genetics in larger contexts, such as human genetic variation, allele frequency estimation from phenotype data, and the exploration of how migration and population structure affect allele frequencies.

Putting It All Together: Synthesis and Exam-Relevant Connections

The course integrates process of science with evolution, genetics, and population biology. It emphasizes thinking like a scientist, understanding how data support or refute hypotheses, and recognizing the roles of different mechanisms of evolution (mutation, gene flow, genetic drift, natural selection, non-random mating). It also highlights the difference between observational data and experimental manipulation, the importance of statistical interpretation (e.g., error bars and significance), and how to use models like Hardy-Weinberg as baseline comparisons.
The study of evolution is connected to real-world phenomena (e.g., coral bleaching, antibiotic resistance, Galápagos finches, amylase gene copy number) to illustrate how theory translates into empirical evidence. The content also covers ethical and societal implications of science, such as public trust in science and the role of science in policy decisions (e.g., climate policy, green technology, resource management).
Finally, the notes underscore that evolution is the change in allele frequency over time, and that the ultimate product of evolution is populations that are better adapted to their environments. The mathematical tools (p and q, HW equations, recombination rates, Punnett squares) provide a framework to quantify and predict evolutionary dynamics in populations across generations.

Key Equations and Notation

Hardy-Weinberg allele frequency relation: p + q = 1
Hardy-Weinberg genotype frequencies: p^2 + 2pq + q^2 = 1
Recombination frequency (genetic distance): r = \frac{\text{number of recombinant offspring}}{\text{total offspring}}
Map distance units: 1% recombination = 1 map unit (centiMorgan)
Probability rule (independence): P(A\ ext{and}\ B) = P(A) \times P(B)
Two-allele genotype frequencies: p^2,\ 2pq,\ q^2\; (\text{for genotypes } AA,\ Aa,\ aa)
Central dogma summary: DNA -> RNA -> Protein; base pairing rules: A\text{(DNA)}-T, C-G; A\text{(RNA)}-U, C-G; and the role of transcription and translation in producing polypeptides from mRNA.
Other core relationships: genotype vs phenotype, dominance/recessiveness, homozygous vs heterozygous, incomplete dominance, codominance, epistasis, and pleiotropy.

Notes on Real-World Implications and Study Practices

Evolutionary thinking applies to medicine, agriculture, conservation, and ecology. Antibiotic resistance demonstrates rapid evolution in microbial populations under strong selection. Human activities influence evolutionary trajectories in wildlife (e.g., poaching effects on tusk size in elephants, selection on beak depth in Galápagos finches during drought, and dietary effects on amylase gene copy number).
Understanding population genetics helps interpret data from pedigrees, cross-breeding experiments, and population surveys. Pedigree analysis can reveal dominant vs. recessive inheritance in humans and can help identify carriers of genetic diseases.
The concept of phenotypic plasticity highlights how genotype-environment interactions shape observed traits. Twin studies illustrate how genetics and environment contribute to phenotypes, such as susceptibility to complex conditions like schizophrenia or obesity.
Finally, the material emphasizes creative and critical thinking in science: formulating hypotheses, designing tests, evaluating data with appropriate statistical tools, and recognizing that multiple evolutionary processes can act simultaneously to shape populations.

Practice and Review Prompts (Representative Examples)

What is the difference between a theory and a fact? How do scientists use the scientific method to test theories?
Define a problem in coral bleaching and outline a testable hypothesis. How would you design an experiment to isolate the effects of temperature and light on zooxanthellae abundance?
Explain the four main principles of natural selection and provide an example for each.
What are the key differences between benign, neutral, and deleterious mutations? How do frameshift mutations differ from point mutations in terms of their impact on proteins?
Describe how meiosis generates genetic diversity via segregation, independent assortment, and recombination. What is the role of chiasmata in crossing over?
Compare and contrast Hardy-Weinberg equilibrium with real populations. What factors can violate HW assumptions, and how would you test for deviations?
Explain pleiotropy and epistasis with examples. How can these interactions influence evolutionary outcomes?
Discuss sex-linked inheritance and provide an example problem involving X-linked traits in humans or Drosophila.
Interpret a dihybrid cross and a test cross. What would you expect for phenotypic ratios if two genes assort independently? What if they are linked?
How does natural selection differ from genetic drift and gene flow? In what scenarios might drift overpower selection, and vice versa?

Real-World Data Interpretation: Quick Case References

Galápagos finches: drought selection changes beak size and shape, affecting survival and reproduction; boxplots summarize trait distributions and survival outcomes.
Amylase gene copy number (AMY1): higher copy number in high-starch diets, with corresponding changes in amylase protein levels.
Antibiotic resistance: cross-species examples show resistance spreads via mutation and selection; maps of resistance alleles illustrate rapid changes in allele frequencies.
Human disease genetics: HW calculations estimate carrier frequencies and predict disease prevalence in populations; pedigrees reveal inheritance patterns across generations.

If you want, I can tailor these notes further to focus on particular chapters or provide a condensed version for quick review, with emphasis on equations and problem-solving steps for HW, Punnett squares, and meiosis/meiosis-related calculations.