Identification, replication and characterization of epigenetic remodelling in the aging genome:A cross population analysis

Aging is a complex biological process regulated by multiple cellular pathways and molecular mechanisms including epigenetics. Using genome-wide DNA methylation data measured in a large collection of Scottish old individuals, we performed discovery association analysis to identify age-methylated CpGs and replicated them in two independent Danish cohorts. The double-replicated CpGs were characterized by distribution over gene regions and location in relation to CpG islands. The replicated CpGs were further characterized by involvement in biological pathways to study their functional implications in aging. We identified 67,604 age-associated CpG sites reaching genome-wide significance of FWE

The frailty index outperforms DNA methylation age and its derivatives as an indicator of biological age

The measurement of biological age as opposed to chronological age is important to allow the study of factors that are responsible for the heterogeneity in the decline in health and function ability among individuals during aging. Various measures of biological aging have been proposed. Frailty indices based on health deficits in diverse body systems have been well studied, and we have documented the use of a frailty index (FI(34)) composed of 34 health items, for measuring biological age. A different approach is based on leukocyte DNA methylation. It has been termed DNA methylation age, and derivatives of this metric called age acceleration difference and age acceleration residual have also been employed. Any useful measure of biological age must predict survival better than chronological age does. Meta-analyses indicate that age acceleration difference and age acceleration residual are significant predictors of mortality, qualifying them as indicators of biological age. In this article, we compared the measures based on DNA methylation with FI(34). Using a well-studied cohort, we assessed the efficiency of these measures side by side in predicting mortality. In the presence of chronological age as a covariate, FI(34) was a significant predictor of mortality, whereas none of the DNA methylation age-based metrics were. The outperformance of FI(34) over DNA methylation age measures was apparent when FI(34) and each of the DNA methylation age measures were used together as explanatory variables, along with chronological age: FI(34) remained significant but the DNA methylation measures did not. These results indicate that FI(34) is a robust predictor of biological age, while these DNA methylation measures are largely a statistical reflection of the passage of chronological time

Model-based clustering of DNA methylation array data: a recursive-partitioning algorithm for high-dimensional data arising as a mixture of beta distributions

<p>Abstract</p> <p>Background</p> <p>Epigenetics is the study of heritable changes in gene function that cannot be explained by changes in DNA sequence. One of the most commonly studied epigenetic alterations is cytosine methylation, which is a well recognized mechanism of epigenetic gene silencing and often occurs at tumor suppressor gene loci in human cancer. Arrays are now being used to study DNA methylation at a large number of loci; for example, the Illumina GoldenGate platform assesses DNA methylation at 1505 loci associated with over 800 cancer-related genes. Model-based cluster analysis is often used to identify DNA methylation subgroups in data, but it is unclear how to cluster DNA methylation data from arrays in a scalable and reliable manner.</p> <p>Results</p> <p>We propose a novel model-based recursive-partitioning algorithm to navigate clusters in a beta mixture model. We present simulations that show that the method is more reliable than competing nonparametric clustering approaches, and is at least as reliable as conventional mixture model methods. We also show that our proposed method is more computationally efficient than conventional mixture model approaches. We demonstrate our method on the normal tissue samples and show that the clusters are associated with tissue type as well as age.</p> <p>Conclusion</p> <p>Our proposed recursively-partitioned mixture model is an effective and computationally efficient method for clustering DNA methylation data.</p

Identification of Methylated Genes Associated with Aggressive Bladder Cancer

Approximately 500,000 individuals diagnosed with bladder cancer in the U.S. require routine cystoscopic follow-up to monitor for disease recurrences or progression, resulting in over $2 billion in annual expenditures. Identification of new diagnostic and monitoring strategies are clearly needed, and markers related to DNA methylation alterations hold great promise due to their stability, objective measurement, and known associations with the disease and with its clinical features. To identify novel epigenetic markers of aggressive bladder cancer, we utilized a high-throughput DNA methylation bead-array in two distinct population-based series of incident bladder cancer (n = 73 and n = 264, respectively). We then validated the association between methylation of these candidate loci with tumor grade in a third population (n = 245) through bisulfite pyrosequencing of candidate loci. Array based analyses identified 5 loci for further confirmation with bisulfite pyrosequencing. We identified and confirmed that increased promoter methylation of HOXB2 is significantly and independently associated with invasive bladder cancer and methylation of HOXB2, KRT13 and FRZB together significantly predict high-grade non-invasive disease. Methylation of these genes may be useful as clinical markers of the disease and may point to genes and pathways worthy of additional examination as novel targets for therapeutic treatment

Differences in smoking associated DNA methylation patterns in South Asians and Europeans

This is a freely-available open access publication. Please cite the published version which is available via the DOI link in this record.Background DNA methylation is strongly associated with smoking status at multiple sites across the genome. Studies have largely been restricted to European origin individuals yet the greatest increase in smoking is occurring in low income countries, such as the Indian subcontinent. We determined whether there are differences between South Asians and Europeans in smoking related loci, and if a smoking score, combining all smoking related DNA methylation scores, could differentiate smokers from non-smokers. Results Illumina HM450k BeadChip arrays were performed on 192 samples from the Southall And Brent REvisited (SABRE) cohort. Differential methylation in smokers was identified in 29 individual CpG sites at 18 unique loci. Interaction between smoking status and ethnic group was identified at the AHRR locus. Ethnic differences in DNA methylation were identified in non-smokers at two further loci, 6p21.33 and GNG12. With the exception of GFI1 and MYO1G these differences were largely unaffected by adjustment for cell composition. A smoking score based on methylation profile was constructed. Current smokers were identified with 100% sensitivity and 97% specificity in Europeans and with 80% sensitivity and 95% specificity in South Asians. Conclusions Differences in ethnic groups were identified in both single CpG sites and combined smoking score. The smoking score is a valuable tool for identification of true current smoking behaviour. Explanations for ethnic differences in DNA methylation in association with smoking may provide valuable clues to disease pathways.Wellcome Trust Enhancement grantMedical Research CouncilDiabetes UKthe British Heart Foundatio