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Genetics and epigenetics of common complex diseases including peanut allergy, food allergy, asthma, atopy, healthy aging, aortic valve stenosis. Longitudinal analyses of childhood birth cohorts. The lab is currently initiating several targeted whole genome bisulfate sequencing projects and we plan on integrating data from a variety of omics approaches. We are also developing genetic risk indexes for a variety of common complex disease.
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Graduate Student Supervision
Master's Student Supervision
Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.
The Horvath epigenetic clock was developed using Illumina HumanMethylation 450K and 27K array data with the purpose of predicting epigenetic age. In this study targeted methylation sequencing using Illumina’s MethylCapture sequencing library was completed on 932 samples from three Canadian studies - The Canadian Asthma Primary Prevention Study (CAPPS, n=632 samples); the Saguenay-Lac-Saint-Jean study (SLSJ, n=180 samples) and the Canadian Peanut Allergy Registry (CanPAR, n=120). CAPPS is a longitudinal cohort that follows children at high-risk for developing asthma from birth to year 15, with targeted methylation sequencing done on at least one of three occasions- birth, and/or years 7 and 15. Maternal samples for CAPPS were also included in the methylation study. SLSJ consists of three-generational triads from families of French-Canadian descent. CanPAR consists of individuals with peanut allergy. The objective of this study was three-fold: Assess the accuracy of the Horvath epigenetic clock in children To evaluate its applicability and its utility as a quality control (QC) metric in targeted methylation sequencing experiments Identify age informative CpG sites in datasetThe Horvath age prediction algorithm was shown to be accurate in this data set with a mean absolute error (MAE±S.D.= 7.00±7.03, median absolute error=4.57). Additional QCs included principal component analyses to assess age, ethnicity and cell counts, and genotype concordance checks using overlap SNPs from GWAS and sequencing studies. These steps identified possibly swapped samples which were also noted as outliers by the age prediction with their relative difference (RD, RD = abs (predicted age-expected age)/expected age)) > mean + 2 S.D. for their reported groups.Linear and mixed Effects models that utilize the CAPPS longitudinal data structure identified hundreds of thousands of new CpG sites significantly associated with age in children. We have demonstrated the applicability of the Horvath age algorithm to targeted methylation sequencing studies and the opportunity for its improvement.
While data generation has been, and will remain crucial to making scientific discoveries, our ability to analyze data has not been at par with data generation. Therefore, it is important to direct our efforts towards making sense of the data already produced. In this thesis, the harmonization of single nucleotide polymorphism (SNP) identifiers is investigated. Harmonization of SNP identifiers means having the same identifier for a SNP every time it occurs. Harmonizing SNP identifiers would allow the genetic data from different datasets to become comparable, which would allow re-purposing of existing datasets in public repositories.Genetic data helps in associating genetic alterations with disease and health. Genetic data is being generated at a rate faster than Moore’s law. With the intention of making generated data available to all researchers in the world, public repositories like the UK Biobank, European Genome-phenome archive (EGA), and database of Genotypes and Phenotypes (dbGaP) have been set up to host public data and disseminate it according to protocols established. The data in these repositories is from different time points, is generated using different genotyping arrays, and is submitted by researchers all over the world. This leads to a large degree of heterogeneity in the data. In order to make the most of the data, they need to be harmonized. The greater the overlap between two datasets, the easier it is to harmonize them. Thus, in order to assess the extent to which datasets can be harmonized, it is important to perform an overlap between them. SNPs are of most interest in genetic datasets. Because of the numerous kinds of identifiers a SNP may have, determining the number and identity of overlapping SNPs between datasets is challenging and increases in complexity with the number of comparisons (SNPs and datasets). There is no tool available to perform on-the-fly harmonization of SNP identifiers. The SNP Overlap Tool (SPOT) was designed to harmonize SNP identifiers using the SNP chromosomal locations, and subsequently calculate the overlap of SNPs between two datasets. It is a web-based tool, coded in Java programming language.