NATURE AND SCOPE OF THE POSITIONS:

Applications are invited for postdoctoral fellowships and/or research associate positions at the University of Toronto in the Department of Public Health Sciences and the Samuel Lunenfeld Research Institute and at McGill University in the Department of Human Genetics and the Montreal General Hospital Research Institute.

This research will be conducted within the MITACS project "STATISTICAL MODELING AND ANALYSIS OF COMPLEX TRAITS IN HUMAN POPULATIONS" which involves investigators at the Universities of Toronto and Alberta, and McGill and Dalhousie Universities. There may be opportunities for exchanges among the research groups in these centres.

Our objectives are to develop new statistical concepts and tools for ongoing genetic research that are sufficiently robust to handle both existing and anticipated multidimensional data and to model complex traits in individuals, families, and populations.

The positions are designed with a 50% time allocation to statistical collaboration with biomedical scientists in research studies of the genetics of complex traits, including study design and data analysis, and 50% to statistical research developing and testing new tools for statistical data analysis relevant to the collaborative studies.

The position in Toronto is to develop methods to examine multiple genomic regions in combination, and evaluate the ability of alternative multidimensional searching strategies to screen and detect complex gene-gene and gene-environment interactions. The position in Montreal is to develop new approaches to the modeling of complex traits and common chronic diseases in Canadian founder populations that incorporate information on the population history and genealogies. A second position in Toronto will use statistical methods to develop expression profiles from high dimensional DNA microarray measures of gene expression that may serve to define distinct biological pathways in the development of cancer.

See below for more information about the project.

ESSENTIAL SKILLS, KNOWLEDGE, AND ABILITIES:

Candidates must possess a Ph.D. in statistics, biostatistics, mathematics, population genetics, computer science, or a related discipline, with a research background in computational statistical methods. Background in the following areas will be considered an asset: genetic epidemiologic analysis; statistical data analysis; statistical theory underlying maximum likelihood segregation and linkage models; Markov chain Monte Carlo estimation; pattern recognition techniques; artificial neural networks; resampling methods such as the bootstrap; scientific software development.

LENGTH OF EMPLOYMENT:

Appointments are for one year, with expected renewal for a second year, and are subject to the availability of funds and approval of the MITACS Board of Directors. Appointments can commence any time between March 1, 1999 and July 1, 1999.

FOR FURTHER INFORMATION, PLEASE CONTACT:

MITACS Positions
c/o Dr. S.B. Bull
Samuel Lunenfeld Research Institute
600 University Avenue Toronto, Ontario M5G 1X5
fax: 416-586-8404
email: [email protected]

or

MITACS Positions
c/o Dr. K. Morgan
Montreal General Hospital Research Institute
1650 Cedar Avenue Montreal, Quebec H3G 1A4
email: [email protected]

Applications should include a Curriculum Vitae (including information on academic and research experience, academic standing, programming experience, and relevant publications) and the names of three references.

STATISTICAL MODELING AND ANALYSIS OF COMPLEX TRAITS IN HUMAN POPULATIONS

1. Purpose: Complex traits, including disease and disabilities, that vary in human populations are determined by multiple genetic and environmental factors that interact with one another in complicated, often non-linear ways. The nature and complexity of these interactions depend on characteristics of the population as well as characteristics of the individual and the family. With continuing advances in molecular biologic technology and the prospect of a complete reference sequence of the entire human genome in the near future, biomedical investigators face an explosion in data that are highly dimensional and have complex structure. Appropriate analysis is required to direct scientific energy and resources into feasible and effective medical and health interventions.

2. Project Members

Project Leader: SHELLEY BULL (SAMUEL LUNENFELD RESEARCH INSTITUTE OF MOUNT SINAI HOSPITAL and DEPT OF PUBLIC HEALTH SCIENCES, UNIVERSITY OF TORONTO). Expertise in Statistical genetic methods, Multivariate statistical modeling of categorical outcomes, Analysis of family data, Generalized linear models, Jackknife methods

UNIVERSITY OF TORONTO

DAVID ANDREWS (STATISTICS). Monte Carlo methods, Computation of complex pedigree likelihoods, Robust statistical inference, Symbolic computation

MARY COREY (PUBLIC HEALTH SCIENCES and HOSPITAL FOR SICK CHILDREN RESEARCH INSTITUTE). Genetic epidemiology, Epidemiology of cystic fibrosis, asthma, and Crohn's disease, Analysis of longitudinal data

GERARDA DARLINGTON (PUBLIC HEALTH SCIENCES). Likelihood based modeling of human sibship data, Statistical modeling of gene-environment interactions, Epidemiologic study design

MICHAEL ESCOBAR (PUBLIC HEALTH SCIENCES). Bayesian computation, Gibbs sampling methods, Analysis of DNA fingerprint data, Mixture models

DAVID TRITCHLER (PUBLIC HEALTH SCIENCES and ONTARIO CANCER INSTITUTE). Nonparametric inference, Bayesian networks, Graphical models, Statistical modeling of quantitative traits

MCGILL UNIVERSITY

KENNETH MORGAN (HUMAN GENETICS AND MEDICINE and MONTREAL GENERAL HOSPITAL RESEARCH INSTITUTE). Population genetics, Complex pedigree analysis, Analysis of complex traits in Canadian founder populations, Genetic epidemiology

DALHOUSIE UNIVERSITY

CHRIS FIELD (MATHEMATICS and STATISTICS). Small sample asymptotics, Robust statistics, Markov chain Monte Carlo methods, Genetic analysis of natural populations

BRUCE SMITH (MATHEMATICS and STATISTICS). Hidden Markov models, Genetic analysis of natural populations, Modeling of neurophysiological systems, Mixture models

UNIVERSITY OF ALBERTA

PETER HOOPER (MATHEMATICAL SCIENCES). Multivariate statistics, Statistical pattern recognition techniques, Analysis of DNA sequence data

3. Industrial Affiliates

SAMUEL LUNENFELD RESEARCH INSTITUTE
ELLIPSIS BIOTHERAPEUTICS
MONTREAL GENERAL HOSPITAL RESEARCH INSTITUTE
HOSPITAL FOR SICK CHILDREN RESEARCH INSTITUTE
ONTARIO CANCER INSTITUTE
CANCER CARE ONTARIO