Epigenetic disease markers in primary sclerosing cholangitis and primary biliary cholangitis-methylomics of cholestatic liver disease.

BACKGROUND: The epigenome, the set of modifications to DNA and associated molecules that control gene expression, cellular identity, and function, plays a major role in mediating cellular responses to outside factors. Thus, evaluation of the epigenetic state can provide insights into cellular adaptions occurring over the course of disease. METHODS: We performed epigenome-wide association studies of primary sclerosing cholangitis (PSC) and primary biliary cholangitis (PBC) using the Illumina MethylationEPIC Bead Chip. RESULTS: We found evidence of increased epigenetic age acceleration and differences in predicted immune cell composition in patients with PSC and PBC. Epigenetic profiles demonstrated differences in predicted protein levels including increased levels of tumor necrosis factor receptor superfamily member 1B in patients with cirrhotic compared to noncirrhotic PSC and PBC. Epigenome-wide association studies of PSC discovered strongly associated 5'-C-phosphate-G-3' sites in genes including vacuole membrane protein 1 and SOCS3, and epigenome-wide association studies of PBC found strong 5'-C-phosphate-G-3' associations in genes including NOD-like receptor family CARD domain containing 5, human leukocyte antigen-E, and PSMB8. Analyses identified disease-associated canonical pathways and upstream regulators involved with immune signaling and activation of macrophages and T-cells. A comparison of PSC and PBC data found relatively little overlap at the 5'-C-phosphate-G-3' and gene levels with slightly more overlap at the level of pathways and upstream regulators. CONCLUSIONS: This study provides insights into methylation profiles of patients that support current concepts of disease mechanisms and provide novel data to inspire future research. Studies to corroborate our findings and expand into other -omics layers will be invaluable to further our understanding of these rare diseases with the goal to improve and individualize prognosis and treatment.

A supervised learning method for classifying methylation disorders.

BACKGROUND: DNA methylation is one of the most stable and well-characterized epigenetic alterations in humans. Accordingly, it has already found clinical utility as a molecular biomarker in a variety of disease contexts. Existing methods for clinical diagnosis of methylation-related disorders focus on outlier detection in a small number of CpG sites using standardized cutoffs which differentiate healthy from abnormal methylation levels. The standardized cutoff values used in these methods do not take into account methylation patterns which are known to differ between the sexes and with age. RESULTS: Here we profile genome-wide DNA methylation from blood samples drawn from within a cohort composed of healthy controls of different age and sex alongside patients with Prader-Willi syndrome (PWS), Beckwith-Wiedemann syndrome, Fragile-X syndrome, Angelman syndrome, and Silver-Russell syndrome. We propose a Generalized Additive Model to perform age and sex adjusted outlier analysis of around 700,000 CpG sites throughout the human genome. Utilizing z-scores among the cohort for each site, we deployed an ensemble based machine learning pipeline and achieved a combined prediction accuracy of 0.96 (Binomial 95% Confidence Interval 0.868[Formula: see text]0.995). CONCLUSION: We demonstrate a method for age and sex adjusted outlier detection of differentially methylated loci based on a large cohort of healthy individuals. We present a custom machine learning pipeline utilizing this outlier analysis to classify samples for potential methylation associated congenital disorders. These methods are able to achieve high accuracy when used with machine learning methods to classify abnormal methylation patterns.

Corrected QT Interval-Polygenic Risk Score and Its Contribution to Type 1, Type 2, and Type 3 Long-QT Syndrome in Probands and Genotype-Positive Family Members.

BACKGROUND: Long-QT syndrome (LQTS) is characterized by a prolonged heart rate-corrected QT interval (QTc). Genome-wide association studies identified common genetic variants that collectively explain ≈8% to 10% of QTc variation in the general population. METHODS: Overall, 423 patients with LQT1, LQT2, or LQT3 were genotyped for 61 QTc-associated genetic variants used in a prototype QTc-polygenic risk score (QTc-PRS). A weighted QTc-PRS (range, 0-154.8 ms) was calculated for each patient, and the FHS (Framingham Heart Study) population-based reference cohort (n=853). RESULTS: The average QTc-PRS in LQTS was 88.0±7.2 and explained only ≈2.0% of the QTc variability. The QTc-PRS in LQTS probands (n=137; 89.3±6.8) was significantly greater than both FHS controls (87.2±7.4, difference-in-means±SE: 2.1±0.7, P<0.002) and LQTS genotype-positive family members (87.5±7.4, difference-in-mean, 1.8±.7, P<0.009). There was no difference in QTc-PRS between symptomatic (n=156, 88.6±7.3) and asymptomatic patients (n=267; 87.7±7.2, difference-in-mean, 0.9±0.7, P=0.15). LQTS patients with a QTc≥480 ms (n=120) had a significantly higher QTc-PRS (89.3±6.7) than patients with a QTc<480 ms (n=303, 87.6±7.4, difference-in-mean, 1.7±0.8, P<0.05). There was no difference in QTc-PRS or QTc between genotypes. CONCLUSIONS: The QTc-PRS explained <2% of the QTc variability in our LQT1, LQT2, and LQT3 cohort, contributing 5× less to their QTc value than in the general population. This prototype QTc-PRS does not distinguish/predict the clinical outcomes of individuals with LQTS.