Master of Science in Medical Genetics (MSc)
Investigating sex differences in the polygenic risk of major depressive disorder and shared associations with cardiovascular disease
The last decade has seen an unprecedented explosion of data. In medicine, data are increasingly being generated and linked across electronic health records, administrative databases, and biobanked samples. These resources hold tremendous promise for improving human health and achieving precision medicine, which will only be realized by thoughtful study designs and innovative analyses.
My lab uses novel computational methods grounded in genetic epidemiology and statistical genetics to capitalize on today’s big data resources. We aim to understand how genetic and epigenetic differences between people contribute to variation in disease susceptibility, response to treatment, and recovery. A primary goal of our research is to reduce the suffering associated with psychiatric disorders, many of which first manifest in childhood and adolescence. We conduct studies in large population datasets, with a major interest in electronic health records and biobanks, and we work at the intersection of genetics, epidemiology, statistics, bioinformatics, and computer science.
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Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.
Major depressive disorder (MDD) is a leading cause of morbidity and disability worldwide, with approximately twice as many women reported to have a lifetime occurrence of MDD than men. MDD is a polygenic trait, wherein hundreds to thousands of common genetic variants with small effect sizes contribute to risk of disease. This study investigated sex differences in the risk factor comorbidity and genetic architecture of MDD in over 16,000 people aged 45-85 from the Canadian Longitudinal Study on Aging (CLSA), with 21% of females (n=1,741) and 12% of males (n=1,055) coded with MDD. Polygenic risk scores (PRS) for individuals were made using sex-stratified and non-sex-specific (“both-sexes”) UK Biobank genome-wide association study summary statistics data. The female sex-specific PRS had higher associations with MDD in females in the top decile of PRS risk (OR = 1.68 (1.32-2.14), p = 2.8E-05) than the male-specific PRS in males (OR = 1.43 (1.07-1.93), p = 0.017) and the both-sexes PRS applied to both sexes (OR = 1.51 (1.25-1.83), p = 2.5E-05). Odds of MDD for the sex-specific PRSs, socioeconomic, lifestyle and clinical risk factors were assessed using a multivariable logistic regression model for each sex. Sex-specific risk factor associations with odds of MDD were found in females (hypothyroidism (OR = 1.42 (1.25-1.63), p = 1.74E-07), not being partnered (OR = 1.34 (1.17-1.52), p = 1.26E-05), having diabetes (OR = 1.30 (1.11-1.52), p = 1.03E-03), higher sex-specific autosomal PRS (OR = 1.10 (1.04-1.16), p = 6.15E-04) and a history of ischemic heart disease (OR = 1.52 (1.14-2.01), p = 3.39E-03)) and males (high blood pressure, OR = 1.35 (1.04-1.47), p = 4.55E-05). Significant differences were observed in the proportion of variables that contributed to the most to each model, evaluated by relative pseudo-R2 values. Age contributed the most to the model for both sexes (73% for females, 57% for males), wherein younger age was associated with higher odds of MDD. The results of this thesis underscore the relevance for sex-disaggregating analyses of complex traits, like MDD, and the incorporation of clinical variables into models of MDD, in applications such as early detection and primary prevention.
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