Tuesday 14-16 or by appointment (coordination needed in any case).
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.
The final grade will be based on a combination of homework, 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
Estimation of heritability of disease
Announcements and handouts
(14 March)
R code
from class and
Mutation
counts in mtDNA coding region from the paper by Behar et al.
(2008).
(20 March) Homework 1 due on 18 April (class after Pesach). This will count as 1.5 exercises towards the final grade. Resources for this homework: mtDNA mutation counts for problem 1. mtDNA loci list for problem 1.
The paper by Whittaker et al. (2003) for problem 3 is available in pdf or html. PHYLIP homepage for problem 4. The
primate data for problem 4.
(19 April)
Homework 2
due on 2 May in class. It uses the
Chromosome
22 HapMap dataset.
We will discuss HapMap in class, for
the HW all you need to know is this is a dataset of haplotypes (=two
copies of each SNP for each individual, already aligned by
chromosome). The format is self explanatory: rows are SNPs, columns
are chromosomes (two per individual), except column 2, which is the
location of the SNP along the chromosome.
(19 May)
Homework 3
due on 6 June in class. It uses the source codes I prepared for
EM and
Data
generation.
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
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