Announcements and handouts

(30 Oct 2025)
In class we have a quick introduction to genetics on the board and with the following presentation: Class 1 presentation.
We then start discussing the problem of time estimation under molecular clock assumptions, cover this Class note.
Some general interest reading: This New Yorker article on using genetics to catch criminals.

Syllabus

The goal of this course is to introduce some of the major topics in Genetics, and gain a statistical perspective on them.
We will start with a brief introduction to Genetics concepts, and gradually start elaborating on statistical aspects of the questions that come up. As needed, we will introduce relevant areas of statistics in some detail.
In the latter part of the course we will pick a hot current research topic and concentrate on it for a few weeks.
The final grade will be based on a combination of homework (3-4), a final take home exam, and possibly a class presentation.
Tentative topics list (each topic 1-2 weeks):

• Introduction to Genetics and quantitative Genetics

• Mutation models: stochastic processes; estimation from data

• Phylogenetic analysis: algorithms and inference

• Human population genetics: statistical inference about human history

• Estimation of ancestry

• Principal component analysis in Genetics

• Genome-wide association studies (GWAS)

• Major public data sources like HapMap, 1000Genome project and their analysis

• Linear mixed models (LMM) in Genetics

Prerequisites

Basic knowledge of mathematical foundations: Calculus; Linear Algebra
Undergraduate courses in: Probability; Theoretical Statistics
Statistical programming experience in R is an advantage
Prior basic knowledge in Biology and Genetics is an advantage

Grading

There will be three or four homework assignments, which will count for about 30% of the final grade, and a final take-home project. Both the homework and the project will combine theoretical analysis with hands-on data analysis.

Some recommended books

Human Evolutionary Genetics by Jobling, Hurles and Tyler-Smith
An excellent introduction to Human Genetics, with a quantitative flavor
 
Principles of Population Genetics by Hartl and Clark
Comprehensive overview of computational methods in Genetics
 
Statistical Methods in Molecular Evolution edited by R. Nielsen
Collection of tutorials and reviews on major topics in Statistical Genetics

Computing

The course will require some use of statistical modeling software. Class examples will be given in R (freely available for PC/Unix/Mac).
R Project website also contains extensive documentation.
Modern Applied Statistics with Splus by Venables and Ripley is an excellent source for statistical computing help for R/Splus.
You are welcome to use Python or other tools as you wish.