The Genomics of Human History
Instructor: William H. Piel
SMS: +65 9724 6482
Office Hours: Wednesdays 2PM-3:30, but best done with prior SMS or WhatsApp message to check that I didn’t step out for something urgent. Alternatively, feel free to schedule any other time.
Tuesdays: 13:00-14:30, 1.5-hour class
Fridays: 13:00-14:30, 1.5-hour class
This course explores the role of genomes in inferring human history and prehistory. Through secondary texts and primary literature, we will study what is known about human origins in Africa, East Asia, Europe, India, New World, and Oceania as inferred from contemporary and ancient genomes. We will learn how to analyze genomic data; make phylogenetic inferences of historical patterns; and how to apply phylogenetic methods to cultural traits, such as inferring language trees from shared lexical cognates. Finally, we will explore how genomics impacts issues of health, sex, inequality, and identity.
- Reich, D. 2018. Who We Are and How We Got Here. Oxford University Press.
- Additional readings from the primary literature
Synopsis and Motivation:
Much of human history and prehistory is obscured by incomplete historiography and the absence of written records. In its place, we must contend with patterns of DNA, archaeology, and linguistics to infer history. Early efforts to reconstruct the great migrations of the past using DNA of present-day people, together with archaeological evidence, initially produced an exciting consilience of evidence. However, later genomic data, particularly with the inclusion of ancient DNA, proved that many of these narratives were largely wrong. These new data reveal the existence of layers upon layers of large ghost populations: distinct prior populations that since blended themselves out of existence, yet there is often little or no surviving archaeological evidence other than what can be inferred from modern and ancient genomes. How do we know if our new models of history are true? Which of these theories are most liable to be overturned with additional data? What do these narratives tell us about broader patterns in human history? These are the kinds of questions we want to address in this module using primary and secondary literature along with hands-on access to genomic data and computational tools.
No prior knowledge of human history, phylogenetics, or genomics is expected of students other than a basic knowledge of DNA and evolutionary history as covered in Scientific Inquiry 1. However, students will probably perform better in the module if they are willing to learn genomic analysis using bioinformatics and computational tools.
Presentations â Students will give presentations on topical questions centered on a publication from the primary literature, e.g. How does the genetics of Fins dovetail with their non-Indo-European Uralic language? Or, why is the mitochondrial DNA of Fins largely Scandinavian but the Y-chromosome DNA mostly Siberian? By Sunday night, prior to the presentation, all remaining students are required to submit at least one question about the reading to the presenter, which helps the presenter focus on areas that others are having difficulty or that others are finding most interesting. The student presentation is followed by a class discussion.
Students will write an expository essay or a research proposal that focuses on a specific source of human DNA, or DNA and archeological evidence from a population of humans, and what can be learned about human history from this source. Rather than a review paper or synthesis paper, students may prefer to do a mini research project with de novo data analysis. Essay length is not set in stone, but most HI essays expect about 10-12 pages. As a launching point, I suggest reading articles and listening to podcasts in areas of interest. Here is a collection of example sources.
Reading quizzes are worth about 20%; Submitted questions are worth about 20%; Presentations are worth about 10%. Essays are worth about 50%. These allotments are subject to change, and their true weight ultimately depends on class variance.