Collaborative Research Team Project #08
Rare DNA Variants and Human Complex Traits: Improving Analyses of Family Studies by Better Modeling the Dependence Structures
This project explores familial dependence structures in DNA sequence data. This is used in investigating rare genetic mutations that may be involved in complex diseases.
Research Category: Health & Biology
Why Study Genetic Mutations in Families?
The advent of high-throughput DNA sequencing has opened the possibility of detecting rare genetic mutations that may be involved in complex diseases.
Family samples are better suited to establish the involvement of rare mutations in complex traits than samples of unrelated subjects. This is because in a family, due to the basic principles of inheritance, multiple affected members may carry the same rare mutation.
Areas of Exploration
Includes investigating the various forms of dependence structures in familial DNA sequence data.
Sources of dependence can include:
- the relationships among family members, either known or unknown to the investigators;
- the association among mutations located at nearby genomic regions, detectable through DNA-sequence familial and population patterns;
- dependence among multiple traits.
Solving Global Challenges
Research Team’s Goals
To bring together statisticians, genetic epidemiologists and complex trait experts to better integrate and model the various forms of dependence.
To develop statistical inference approaches that are more general and adapted (with gains in power and validity) than the few methods currently applicable.
People Behind the Project
Alexandre Bureau | Université Laval
Karim Oualkacha | Université du Québec à Montréal
From the fields of statistics and genetic epidemiology:
Marie-Hélène Roy-Gagnon | University of Ottawa
Kelly Burkett | University of Ottawa
Fabrice Larribe | Université du Québec à Montréal
Aurélie Labbe | HEC Montréal
Jinko Graham | Simon Fraser University
Celia Greenwood | McGill University
M’Hamed Lajmi Lakhal Chaieb | Université Laval
Ingo Ruczinski | Johns Hopkins Bloomberg School of Public Health in Baltimore, USA
Eleftheria Zeggini | Wellcome Trust Sanger Institute in Cambridge, UK
In addition, five Canadian experts will contribute data and insights on the genetics of complex traits.
- J. Sun, K. Oualkacha, C. Greenwood and L. Lakhal-Chaieb (10/2017). Multivariate association test for rare variants controlling for cryptic and family relatedness. Canadian Journal of Statistics, special issue featuring the CANSSI CRTs, 32 pages, (under revision).
- Zhao K, Jiang L, Klein K, Greenwood CMT, Oualkacha K. (11/2017). CpG-set association assessment of lipid concentration changes and DNA methylation on chromosome 11. BMC Proceeding, 6 pages, in press.
- Jiang L, Zhao K, Klein K, Canty AJ, Oualkacha K, Greenwood CMT. (11/2017). Investigating potential causal relationships between SNPs, DNA methylation and HDL. BMC Proceeding, 6 pages, in press.
- C. Nieuwoudt (12/2017). R package, SimRVPedigree. Submitted to the Comprehensive R Archive Network (CRAN).
- J. Sun, K. Oualkacha, C. Greenwood and L. Lakhal-Chaieb (09/2018). Multivariate association test for rare variants controlling for cryptic and family relatedness. Canadian Journal of Statistics, special issue featuring the CANSSI CRTs, 32 pages, (in press).
- C. Nieuwoudt, S. Jones, A. Brooks-Wilson, and J. Graham (10/2018). Simulating pedigrees ascertained for multiple disease-affected relatives. Source Code for Biology and Medicine, 2018, 13:2. https://doi.org/10.1186/s13029-018-0069-6.
- Kaiqiong Zhao, Karim Oualkacha, Lajmi Lakhal-Chaieb, Marie Hudson, and Celia MT Greenwood (2019). Smooth modeling of covariate effects in bisulfite sequencing-derived measures of DNA methylation. Paper in progress.
Find Related Programs
Rare DNA Variants and Human Complex Traits: Improving Analyses of Family Studies by Better Modeling the Dependence Structures is a Collaborative Research Team project. This program tackles complex problems through a three-year research and training agenda.
CANSSI offers approximately $200,000 for this type of project, which requires a team of faculty, postdocs, and students.