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About This Course

Hello from Dottie

Hello, fellow investigators! I'm Dottie the Drosophila, and I'll be your guide through this genetics course. Let's look at the evidence! From Mendel's peas to CRISPR gene editing, genetics is a story of careful observation, clever experiments, and beautiful logic. I pop up at key checkpoints in each chapter to welcome you to new topics, share helpful tips, flag common pitfalls, and celebrate your progress. With my compound eyes I can spot patterns from many angles at once — and I love helping you see the connections between genotype and phenotype!

Why Genetics Matters Now More Than Ever

We live in an era where a patient's genome can be sequenced in hours for under a thousand dollars, where CRISPR-based therapies are entering clinical trials, and where genome-wide association studies link thousands of variants to complex diseases. Yet translating raw sequence data into biological insight — understanding how genotype shapes phenotype — requires the kind of analytical reasoning that goes far beyond memorizing Mendel's laws.

Genetics sits at the intersection of biology, mathematics, and data science, and demand for practitioners who can think quantitatively about inheritance has never been higher:

In the United States (2025):

  • Clinical genetics and genomics roles have grown 40% over the past five years, driven by precision medicine adoption and direct-to-consumer testing1
  • The NIH invests more than $6 billion annually in genomics and genetic research2
  • Median salaries for genetic counselors exceed $90,000, with computational genomics roles in pharma surpassing $150,0003

Worldwide:

  • The global genomics market is projected to exceed $94 billion by 2030, creating sustained demand for genetics-literate professionals4
  • More than 100 million people have taken direct-to-consumer genetic tests, raising urgent questions about privacy, equity, and interpretation5

By studying with this open textbook you are joining a global community of learners preparing to analyze genomes, interpret genetic variation, understand gene regulation, and advance precision medicine.

Learning Through Interactive Visualization

Instead of memorizing disconnected facts, you will build intuition with browser-based MicroSims that let you explore real genetic data and models:

  • Trace inheritance patterns through interactive pedigree builders with Bayesian probability
  • Explore chromatin structure and epigenetic modifications with drag-to-zoom overlays
  • Perform genetic mapping by manipulating recombination frequencies and LOD scores
  • Visualize GWAS Manhattan plots and explore the connection between SNPs and phenotypes
  • Run population genetics simulations to observe drift, selection, and migration in real time
  • Analyze gene regulatory networks and predict expression changes from enhancer mutations

These simulations become laboratories you can reopen any time — no wet lab required — so conceptual understanding is paired with authentic computational experimentation.

What You Will Learn

Module Weeks Topic
1 1–2 Genetics as Inference — Bayesian reasoning, penetrance, expressivity, epistasis, complementation
2 3–4 Genome Organization — chromatin, structural variation, SNPs, STRs, transposable elements
3 5–6 Inheritance and Mapping — linkage, recombination, genetic mapping, gene discovery
4 7–8 Quantitative and Population Genetics — heritability, QTL, GWAS, population structure
5 9–10 Gene Expression — regulatory networks, enhancers, noncoding RNAs, chromatin state
6 11 Experimental Genetics — forward/reverse genetics, model organisms, mutagenesis
7 12 Genomics and Bioinformatics — sequence alignment, variant analysis, reproducible workflows
8 13 Human Genetics — complex traits, pharmacogenomics, cancer genetics, clinical interpretation
9 14 Ethics, Society, and Frontier Topics — genetic privacy, equity, CRISPR, AI in genomics

You Will Have Fun

Genetics is full of wonder — from the elegance of a complementation test revealing gene function to the detective work of tracing a disease variant through a multi-generation pedigree. This course keeps that sense of discovery alive. Case studies tie each module to real-world breakthroughs (pharmacogenomic dosing, cancer driver identification, CRISPR therapeutics), and the MicroSims encourage playful exploration. Whether you aspire to work in medicine, research, biotechnology, genetic counseling, or data science, the goal is the same: help you enjoy thinking like a geneticist while mastering practical analytical skills.

Background

This textbook was produced with Claude Code using Skills, with an emphasis on interactive MicroSims, probabilistic reasoning, and alignment to Bloom's Taxonomy learning objectives at all six cognitive levels. You can customize this textbook and create your own version using a tool such as Claude Code Pro ($20). See the Claude Skills Textbook for a detailed tutorial on how to create your own textbook.

About Dan McCreary

Dan McCreary is a semi-retired AI researcher, solution architect, and educator who has spent more than three decades helping Fortune 100 organizations reason over massive datasets. At Optum he founded the Generative AI Center of Excellence and led the team that built one of the world's largest healthcare knowledge graphs — spanning over 25 billion vertices — to unify member, provider, and patient insights. During his tenure at Optum Dan taught over 3,000 engineers how to model healthcare data using graph databases. Dan's deep background in knowledge representation and systems thinking underpins the precise learning graphs and intelligent textbook workflows used throughout this course.

He is the co-author of Making Sense of NoSQL (Manning Publications), the founding chair of the NoSQL Now! conference, and a frequent keynote speaker on semantic search, ontology strategy, and AI hardware. Beyond industry, Dan has mentored students as a STEM volunteer since 2014 and now applies the same rigor to building open educational resources. You can visit the Intelligent Textbooks Case Studies to see over 70 textbooks that Dan has created or co-created with other authors.

Selected Credentials

  • B.A. in Physics and Computer Science from Carleton College
  • M.S.E.E. from the University of Minnesota
  • MBA coursework at the University of St. Thomas (33 of 36 credits complete)
  • Patent holder in semantic search and ontology management techniques
  • Advocate for large-scale Enterprise Knowledge Graph adoption across healthcare and education
  • Long-time promoter of accessible, low-cost AI-powered learning experiences

References

  1. Burning Glass Technologies / Lightcast. Labor market analysis for genetics and genomics roles (2020–2025). ↩

  2. National Human Genome Research Institute. NHGRI Fact Sheet: Genomic Research Funding (2024). ↩

  3. U.S. Bureau of Labor Statistics. Occupational Outlook Handbook: Genetic Counselors (2024) and Glassdoor salary data for computational genomics roles. ↩

  4. Grand View Research. Genomics Market Size, Share & Trends Analysis Report (2023), projecting $94.2 billion by 2030. ↩

  5. MIT Technology Review. More than 100 million people have taken a consumer DNA test (2024). ↩