Leveraging DNA Methylation Towards Comprehensive Rare Disease Diagnostics
Education and Research Strategies
-
Primary Categories:
- Basic Research
-
Secondary Categories:
- Basic Research
Introduction:
The diagnosis of rare diseases often presents significant challenges due to the genetic heterogeneity and complexity inherent in these conditions. Recent advances in epigenetic research, particularly DNA methylation analysis, have opened new avenues for enhancing diagnostic accuracy and complementing traditional genetic testing. In this study, we leveraged our in-house rare disease program to explore the utilization of DNA methylation analysis, focusing on the identification of methylation outliers and allele-specific methylation (ASM) patterns within individual rare disease patients.
Methods:
The study cohort included 270 patients presented to the Mayo Clinic Department of Clinical Genomics with phenotypes consistent with the presence of rare genetic disease and returned with an inconclusive genetic test. The blood samples were subjected to Whole-Genome Bisulfite Sequencing (WGBS). All sequenced reads were aligned to human genome reference build hg38 using BISMARK. Methylation values at each site were calculated using the tool BOREALIS to infer aberrant DNA methylation and detect methylation outliers. ASM events were calculated using MethHaplo for individual samples, and bimodal values were calculated to detect outlier ASM events.
Results:
The study produced three primary datasets: (1) A set of genome-wide discordances in methylation states between two alleles, driven by genetic variants rather than parental origin on single sample level (allele-specific methylation; ASM); (2) Abnormal methylation pattern at cytosine base in the promoter region of a protein coding gene from individual samples within the cohort (1 vs n comparisons); and (3) Rare ASM events identified through bimodal values and population Z score. To better understand each sample’s methylation landscape, the datasets were integrated to generate a list of rare, hyper-methylated CpG events in the promoter (pAdj ≦ 0.05) in sample-cohort comparisons that are also allele-specific, and further filtered for ASM rarity.
Conclusion:
This combined approach offers a more comprehensive understanding of the molecular mechanisms of rare disease on case-to-case basis compared to traditional group vs. group comparisons, enabling a more personalized approach to diagnosis. The rare promoters hyper-mCpGs that are also ASM outliers identified in this study showed diagnostic potentials if combine with transcriptomic and phenotypic data. Ultimately, this method may lead to earlier detection, better-informed treatment strategies, and improved patient outcomes.
The diagnosis of rare diseases often presents significant challenges due to the genetic heterogeneity and complexity inherent in these conditions. Recent advances in epigenetic research, particularly DNA methylation analysis, have opened new avenues for enhancing diagnostic accuracy and complementing traditional genetic testing. In this study, we leveraged our in-house rare disease program to explore the utilization of DNA methylation analysis, focusing on the identification of methylation outliers and allele-specific methylation (ASM) patterns within individual rare disease patients.
Methods:
The study cohort included 270 patients presented to the Mayo Clinic Department of Clinical Genomics with phenotypes consistent with the presence of rare genetic disease and returned with an inconclusive genetic test. The blood samples were subjected to Whole-Genome Bisulfite Sequencing (WGBS). All sequenced reads were aligned to human genome reference build hg38 using BISMARK. Methylation values at each site were calculated using the tool BOREALIS to infer aberrant DNA methylation and detect methylation outliers. ASM events were calculated using MethHaplo for individual samples, and bimodal values were calculated to detect outlier ASM events.
Results:
The study produced three primary datasets: (1) A set of genome-wide discordances in methylation states between two alleles, driven by genetic variants rather than parental origin on single sample level (allele-specific methylation; ASM); (2) Abnormal methylation pattern at cytosine base in the promoter region of a protein coding gene from individual samples within the cohort (1 vs n comparisons); and (3) Rare ASM events identified through bimodal values and population Z score. To better understand each sample’s methylation landscape, the datasets were integrated to generate a list of rare, hyper-methylated CpG events in the promoter (pAdj ≦ 0.05) in sample-cohort comparisons that are also allele-specific, and further filtered for ASM rarity.
Conclusion:
This combined approach offers a more comprehensive understanding of the molecular mechanisms of rare disease on case-to-case basis compared to traditional group vs. group comparisons, enabling a more personalized approach to diagnosis. The rare promoters hyper-mCpGs that are also ASM outliers identified in this study showed diagnostic potentials if combine with transcriptomic and phenotypic data. Ultimately, this method may lead to earlier detection, better-informed treatment strategies, and improved patient outcomes.