Detection of outlier methylation from bisulfite sequencing data with novel Bioconductor package BOREALIS

• Published in: bioRxiv
• Main Authors: Oliver, Gavin R, William Garrett Jenkinson, Olson, Rory J, Schultz-Rogers, Laura E, Klee, Eric W
• Format: Paper
• Language: English
• Published: Cold Spring Harbor Cold Spring Harbor Laboratory Press 20.05.2022; Cold Spring Harbor Laboratory
• Edition: 1.1
• Subjects: Bioinformatics; Bisulfite; CpG islands; Decision making; Disease; DNA methylation; DNA sequencing; Genetic screening; Genomic imprinting; Patients; Phenotypes; DNA methylation; outlier analysis; rare disease; bisulfite sequencing
• ISSN: 2692-8205; 2692-8205
• DOI: 10.1101/2022.05.19.492700

DNA sequencing results in genetic diagnosis of 18-40% of previously unsolved cases, while the incorporation of RNA-Seq analysis has more recently been shown to generate significant numbers of previously unattainable diagnoses. Multiple inborn diseases resulting from disorders of genomic imprinting are well characterized and a growing body of literature suggest the causative or correlative role of aberrant DNA methylation in diverse rare inherited conditions. Therefore, the systematic application of genomic-wide methylation-based sequencing for undiagnosed cases of rare disease is a logical progression from current testing paradigms. Following the rationale previously exploited in RNA-based studies of rare disease, we can assume that disease-associated methylation aberrations in an individual will demonstrate significant differences from individuals with unrelated phenotypes. Thus, aberrantly methylated sites will be outliers from a heterogeneous cohort of individuals. Based on this rationale, we present BOREALIS: Bisulfite-seq OutlieR MEthylation At SingLe-SIte ReSolution. BOREALIS uses a beta binomial model to identify outlier methylation at single CpG site resolution from bisulfite sequencing data. This method addresses a need unmet by standard differential methylation analyses based on case-control groups. Utilizing a heterogeneous cohort of 94 rare disease patients undiagnosed following DNA-based testing we show that BOREALIS can successfully identify outlier methylation linked to phenotypically relevant genes, providing a new avenue of exploration in the quest for increased diagnostic rates in rare disease patients. We highlight the case of a patient with previously undetected hypermethylation patterns that are informing clinical decision-making. BOREALIS is implemented in R and is freely available as a Bioconductor package. Competing Interest Statement The authors have declared no competing interest.