Faculty Member

Laurent Briollais PhD, Statistical Genetics

Email Address(es)
Office Phone
Office Address
Lunenfeld-Tanenbaum Research Institute 60 Murray St., Rm 5-218 Toronto, ON M5T 3L9
Curriculum Vitae
Biostatistics Division
Associate Professor
SGS Status
Full Member
Appointment Status
Status Only

Research Interests

  • Statistical Genetics
  • Robust Statistics
  • Multivariate Survival Analysis in Family Design
  • Sequential Design/Sequential Method
  • Application of Bayesian Graphical Models in Genetic Association Studies
  • Longitudinal Data Analysis in Family Design
  • Measurement Error Models for the Analysis of Gene-Environment Interactions

Education & Training History

  • 1990 B.Sc. (Statistics and Economy) ENSAE, France
  • 1994 M.Sc. (Statistical Genetics) University Paris XI, Dept. of Public Health Sciences, France
  • 1998 Ph.D. (Statistical Genetics) University Paris XI, Dept. of Public Health Sciences, France

Primary Teaching Responsibilities

  • CHL 5210: Categorical Data Analysis (Instructors: Lei Sun and Laurent Briollais)

Professional Summary & Appointments

  • 2000 to – Principal Investigator Samuel Lunenfeld Research Institute, Toronto, Canada
  • 2000 to 2013 Assistant Professor (Status only) Dalla Lana School of Public Health, University of Toronto, Canada
  • 2013 to – Associate Professor (Status only) Dalla Lana School of Public Health, University of Toronto, Canada

Honours & Awards

  • 2002 Petro-Canada Young Investigator Award
  • 2012 Student’s Post-doctoral Fellowship Award (Yan Yan Wu, CIHR Stage – 2 years)
  • 2010 Student’s Post-doctoral Fellowship Award (Taraneh Abarin, MITACS – 1 year)
  • 2006 Student’s Post-doctoral Fellowship Award (Yun-Hee Choi, Canadian Breast Cancer Foundation- 2 years)

Current Research Projects

  • Joint analysis of SNP effects in GWAS using Bayesian graphical model
  • Development of multistate models for risk estimation in Lynch Syndrome families
  • Modeling of growth child trajectories and search for associated genetic variants and epidemiological risk factors
  • Development of robust statistical methods for ‘omics data
  • Modeling of sequencing data in child brain tumors
  • Estimation of predictive models for prostate cancer progression based on genetic, epigenetic and proteiomic data

Representative Publications

  • Choi Y, Kopciuk K, Briollais L (2008) Bias and efficiency in family-based designs for estimating the risk associated with mutated genes involved in complex diseases. Hum Hered. 66:238-251.
  • Durrieu G, Briollais L (2009). Sequential designs for microarray experiments. Journal of the American Statistical Association 104:650-660.
  • Kopciuk KA, Choi YH, Parkhomenko E, Parfrey P, McLaughlin J, Green J, Briollais L. (2009) Penetrance of HNPCC-related cancers in a retrolective cohort of 12 large Newfoundland families carrying a MSH2 founder mutation: an evaluation using modified segregation models. Hered Cancer Clin Pract 7(1):16
  • Choi YH, Briollais L (2011) An EM Composite likelihood for multistage sampling of family data. Statistica Sinica 21: 231-253.
  • Sow M, Durrieu G, Briollais L, Ciret P, Massabuau JC (2011) Modeling high-frequency serial valvometry data: a kernel-regression approach. Environmental Monitoring and Assessment 182(1-4):155-70.
  • Dobra, A., Briollais, L., Jarnazi H, Ozcelik H, Massam H. Applications of the mode oriented stochastic search (MOSS) algorithm for discrete multi-way data to genomewide studies. In Bayesian Modeling in Bioinformatics Taylor & Francis, D. Dey, S. Ghosh and B. Mallick (eds.), 2011, Pages 63-94.
  • Abarin T, Wu Y, Warrington N, Lye S, Pennell C, Briollais L. (2013) The impact of breastfeeding on FTO-related BMI growth trajectories. International Journal of Epidemiology 41(6):1650-60.
  • Warrington NM, Wu Y, Pennell CE, Marsh JA, Beilin LJ, Palmer LJ, Lye SJ, Briollais L. (2013) Modelling BMI trajectories in children for genetic association studies. PloS One 8(1):e53897.
  • Choi YH, Briollais L, Parfrey P, Green J, Kopciuk K. Estimating successive cancer risk in Lynch Syndrome families using a progressive three-state model. Statistics in Medicine 2013. doi: 10.1002/sim.5938.