Organiser

Michel Georges, University of Liège, Belgium

Introduction

The Summer Institute in Statistical Genetics is a world re-knowned advanced course in statistical genetics. It was initiated in 1995 by Professor Bruce Weir, formerly William Neal Reynolds Professor of Statistics and Genetics at North Carolina State University, and – since 2006 – head of the Department of Biostatistics at the University of Washington in Seattle. The objectives are to introduce biologists to modern methods of statistical analysis of genomic data and to introduce statisticians to statistical problems arising in modern genomics. The programme is composed of a series of 2-½ day modules covering evolving topics in biostatistics and bioinformatics. The course targets PhD students, postdoctoral fellows and senior scientists both from academia and industry. Each module is thought by a team of 2-3 world leading experts in the corresponding field. Most modules comprise both theoretical and practical (computer) sessions. Students are encouraged to bring their own datasets to the course. Individuals attending the Summer Institute receive certificates of completion recognising their participation.

Programme

The Summer Institute will comprise three series of three concomitant modules as illustrated below.

Module 1.1: Introduction to quantitative genetics
Instructors: W. Muir & B. Walsh
Covers quantitative trait models, variances and covariances of relatives, estimation of variance components, response to selection, and the effects of mutation.

Module 1.2: Introduction to population genetics
Instructors: K.Holsinger & B. Weir
Covers estimates and sample variance of allele frequencies, Hardy-Weinberg and linkage disequilibrium, characterization of population structure with F-statistics, a Bayesian approach to disequilibrium and population structure, haplotype determination, parentage and relationship estimation, and use of computer software including GDA, PwoerMaker and Hickory.

Module 1.3: Introduction to bioinformatics
Instructors: S. Muse & E. Stone
Covers sequence analysis, including pairwise alignments, fast database searches, multiple alignments, and the use of profiles; hidden Markov models and gene finding; text mining; computational and statistical aspects of bioinformatics; and corresponding web-based resources.

Module 2.1: QTL mapping
Instructors: R. Doerge & Z.-B. Zeng
Covers linkage map construction, single-marker analyses; multiple and partial regression methods; interval, composite-interval, and multiple-interval mapping; model selection and determining significance levels.

Module 2.2: MCMC in genetics
Instructors: E. Anderson & M. Stephens
Covers the mathematical preliminaries of of Monte-Carlo and MCMC methods; Metropolis Hastings and Gibbs samplers and their use in Bayesian and frequentist analyses; methods for assessing convergence and diagnosing mixing problems of MCMC samplers; use of MCMC output for model selection and model checking; and use of computer software including Structure and R.

Module 2.3: Microarray analysis
Instructors: G. Gibson & J. Storey
Covers an overview of array technologies, image analysis and normalization, experimental design, statistical modeling and inference (e.g., detecting differential gene expression, ANOVA, multiple testing. false discovery rate, clustering, and classification), and expression QTL. Applications to evolutionary biology. Use of computer software, including BioConductor.

Module 3.1: Association mapping
Instructors: L. Cardon & M. Georges
Covers an introduction to the theory of linkage disequilibrium; haplotype structure; determining haplotypes from population data; population and family-based association techniques for discrete and quantitative traits; detecting and accounting for population structure; multiple testing issues; cladistic strategies for haplotype grouping; combined linkage and linkage disequilibrium mapping using variance component methods.

Module 3.2: Coalescent theory
Instructors: G. McVean & P. Awadalla
Derivation and properties of basic coalescent model and extension to include factors such as recombination, geographic structure and natural selection. Use of the coalescent in analyzing data for disease gene mapping, recombination rate estimation, and detection of recent adaptive evolution. Use of coalescent methodologies in large-scale surveys of genetic variation. Use of computer software.

Module 3.3: Molecular phylogenetics
Instructors: Felsenstein & Holland
Overview of methods for analysis of inter-specific DNA and protein sequence data. Parsimony, maximum likelihood, distance-based, and Bayesian methods for phylogenetic estimation. The comparative methods, divergence time estimation, phylogenetic hypothesis testing, detection of positive selection. Statistical methodology is emphasized, with computational algorithms and software being introduced. Use of computer software, including PHYLIP and MrBayes.

Venue

Liège is a city of ~ 250,000 inhabitants located ~ 90 kms south east of Brussels International airport, 75 Kms
from Charleroi airport (e.g. Ryan Air), 25 Kms from Maastricht airport, and has its own Bierset airport. Liège is on the TGV road enabling participants to come by “Tallys” high speed train.

The Summer Institute will be held on the Sart Tilman Campus of the ULg. This remarkable 500 ha forested domain is located at less than 10 Kms from the center of Liège and easily accessible by public transportation. The courses will be held in the centrally located “Amphithéâtres de l’Europe”. .

Registration

Registration is closed.