Open Collections

Integration of genomic and metabolomic data for the prioritization of rare disease variants

University of British Columbia

Many inborn errors of metabolism (IEMs) are amenable to treatment, therefore early diagnosis and treatment is imperative. Despite recent advances, the genetic basis of many metabolic phenotypes remains unknown. For discovery purposes, Whole Exome Sequencing (WES) variant prioritization coupled with clinical and bioinformatics expertise is currently the primary method used to identify novel disease-causing variants; however, causation is often difficult to establish due to the number of plausible variants. Integrated analysis of untargeted metabolomics (UM) and WES or Whole Genome Sequencing (WGS) data is a promising systematic approach for prioritizing causal variants from a list of candidates. In this thesis, we present an automated network-based bioinformatics approach to the integration of WES with UM data from 13 neurometabolic patients with known IEMs and 25 controls. We perform label propagation on the STRING network initialized using an integrated genomic and metabolomic score, and use the results to rank candidate genes in order of their likely relevance to the disease. Integrated genomic and metabolomic evidence was able to prioritize the causative gene in the top 20th percentile of candidate genes for 61.5% (8 of 13) of patients, 75% of which achieved a percentile prioritization score at least one standard deviation above a permuted percentile. Combining genomic and metabolomic evidence resulted in the prioritization of the causative gene in 30.7% more patients than was possible with genomic evidence alone. The results of this study indicate that for diagnostic and gene discovery purposes, metabolomics can lend support to WES gene discovery methods. This is the first method that uses UM and WES data to rank candidate variants in order of their biological relevance. To improve this method, expansion of gene-metabolite annotations and metabolomic feature-to-metabolite mapping methods are needed.

Text

2018-11-26

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/67890

Bioinformatics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/