Integration of genomics and metabolomics for prioritization of rare disease variants: a 2018 literature review

Abstract

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 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 identifying disease-causing variants. In this review, we provide a literature-based overview of UM methods utilizing liquid chromatography mass spectrometry (LC-MS), and assess approaches to integrating WES/WGS and LC-MS UM data for the discovery and prioritization of variants causing IEMs. To embed this integrated -omics approach in the clinic, expansion of gene-metabolite annotations and metabolomic feature-to-metabolite mapping methods are needed.

Keywords: Genomics; Inborn errors of metabolism; Metabolomics; Omic integration; Variant prioritization.

Fig. 1

WES rare variant analysis pipeline for the detection of inborn errors of metabolism causing neurometabolic disorders, as used in Tarailo-Graovac et al . Given raw sequencing reads for each patient, this pipeline identifies a conservative list of candidate variants (MAF ≤ 0.01). First, raw reads (FASTQ files) are aligned to the human genome (hg19 or equivalent). Second, variants are annotated using published software programs like ANNOVAR. Third, variants that do not map to protein-coding regions, or that do not pass QC steps are removed. Fourth, variants that do not agree with multiple inheritance models and that would not agree with the observed phenotypic effect are removed. Finally, rare variants are selected by removing variants with annotated minor allele frequencies (MAF) greater than 0.01

Fig. 2

Sample LC-MS metabolomics analysis pipeline. Briefly, raw metabolomics data can be processed using freely available processing software (e.g., XCMS), annotated (e.g., CAMERA), normalized (e.g., through use of internal standards), and filtered. Differentially abundant metabolites can be isolated using univariate or multivariate tests. Biological interpretation such as pathway analysis can be performed using published metabolomic databases (e.g., HMDB, BioCyc, METLIN)

Fig. 3

Untargeted metabolomics pre-processing pipeline. A combination of automated and manual steps are used to prepare metabolomics data for downstream analysis. The algorithms listed are only examples of tools that could be used in each step