Faculty Member

Laurent Briollais PhD, Statistical Genetics

Email Address(es)
laurent(at)lunenfeld.ca
Office Phone
416-586-8863
Office Address
Lunenfeld-Tanenbaum Research Institute 60 Murray St., Rm 5-218 Toronto, ON M5T 3L9
Curriculum Vitae
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Division(s)/Institute(s)
Biostatistics Division
Position
Professor
SGS Status
Full Member
Appointment Status
Status Only
Currently Accepting Doctoral Students?
Yes

Research Interests

  • Statistical Genetics
  • Biostatistics
  • Bayesian methods
  • Multivariate survival analysis
  • Joint modelling
  • Quantile regression

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

  • CHL8001H F2: Introduction to Joint Modeling in Health Research

Professional Summary & Appointments

  • 2000 to – Scientist – Lunenfeld Tanenbaum Research Institute, Sinai Health System
  • 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

  • Development of joint modelling approaches for cancer research
  • Extending risk prediction models for hereditary breast ovarian cancer
  • Development of polygenic risk score approaches for longitudinal studies
  • Development and applications of Bayesian approaches for high-dimensional  genetic/genomic studies
  • Development of quantile regression methods in genetics

Representative Publications

  • Xu J, Xu W, Briollais L. A Novel Bayesian Region-Based Analysis for Next Generation Sequencing Data. Biometrics, In Press, 2020.
  • Rustand D, Briollais L, Tournigang C, Rondeau V. Two-part joint model for a longitudinal semicontinuous marker and a terminal event with application to metastatic colorectal cancer data. Biostatistics, In Press, 2020.
  • Choi YH, Briollais L, He W, Kopciuk K. FamEvent: An R Package for Generating and Modeling Time-to-Event Data in Family Designs. Journal of Statistical Software, In Press, 2020.
  • Choi YH, Jacqmin-Gadda H, Król A, Parfrey P, Briollais L, Rondeau V. Joint nested frailty models for clustered recurrent and terminal events: An application to colonoscopy screening visits and colorectal cancer risks in Lynch Syndrome families. Stat Methods Med Res. 2020; 29(5):1466-1479
  • Dimitromanolakis A, Xu J, Krol A, Briollais L. sim1000G: a user-friendly genetic variant simulator in R for unrelated individuals and family-based designs. BMC Bioinformatics. 2019; 20(1):26.
  • Choi YC, Lakhal-Chaieb L, Krol A, Yu B, Buchanan D, Ahnen D, Le Marchand L, Newcomb PA, Win AK, Jenkins M, Lindor NM, Briollais L. Risks of colorectal cancer and cancer-related mortality in Familial Colorectal Cancer Type X and Lynch Syndrome families. J Natl Cancer Inst. 2018. Oct 30. doi: 10.1093/jnci/djy159. Online ahead of print.
  • Choi YH, BriollaisL, Win AK, Hopper J, Buchanan D, Jenkins M, Lakhal-Chaieb L. Modelling of Successive Cancer Risks in Lynch Syndrome Families in the presence of competing risks using Copulas. Biometrics 2017; 73(1): 271-282.
  • Briollais L, Dobra A, Liu J, Friedlander M, Ozcelik H, Massam H. A Bayesian graphical model for genome-wide association studies (GWAS). Annals of Applied Statistics 2016; 10(2): 786-811.
  • Wu YY, Wong A, Monette G and Briollais L. Evaluation of Third-order Method for the Test of Variance Component in Linear Mixed Models. Open Journal of Statistics 2015; 5: 233-244.
  • Briollais L, Durrieu G. Application of Quantile Regression to Recent Genetic and -omic Studies. Human Genetics 2014; 133(8): 951-66.
  • Abarin T, Li H, Wang L, Briollais L (2014) On Method of Moments Estimation in Linear Mixed Effects Models with Measurement Error on Covariates and Response with Application to Longitudinal studies of Gene-Environment Interaction. Statistics in Biosciences 6:1-18. doi:10.1007/s12561-012-9074-5.
  • 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; 33: 618-38. doi: 10.1002/sim.5938.
  • Choi YH, Briollais L.  An EM Composite likelihood for multistage sampling of family data. Statistica Sinica 2011: 21: 231-253.
  • Durrieu G, Briollais L. Sequential designs for microarray experiments. Journal of the American Statistical Association 2009; 104:650-660.
Bronskill, Susan
Bright, Kristin
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Bronskill, Susan
Bright, Kristin