Integration of genomic and epigenomic DNA methylation data in common complex diseases by haplotype-specific methylation analysis

Statistical methods for assessing the effects of de novo variants on birth defects

With the development of next-generation sequencing technology, de novo variants (DNVs) with deleterious effects can be identified and investigated for their effects on birth defects such as congenital heart disease (CHD). However, statistical power is still limited for such studies because of the small sample size due to the high cost of recruiting and sequencing samples and the low occurrence of DNVs. DNV analysis is further complicated by genetic heterogeneity across diseased individuals. Therefore, it is critical to jointly analyze DNVs with other types of genomic/biological information to improve statistical power to identify genes associated with birth defects. In this review, we discuss the general workflow, recent developments in statistical methods, and future directions for DNV analysis.

Human-specific CpG 'beacons' identify human-specific prefrontal cortex H3K4me3 chromatin peaks

BACKGROUND

Targeted recruitment of chromatin-modifying enzymes to clusters of CpG dinucleotides contributes toward the formation of accessible chromatin. By interprimate comparison we previously identified the set of nonpolymorphic human-specific CpGs (CpG 'beacons') and revealed that these loci were enriched for human disease traits. Due to their human-specific CpG density change, extreme CpG 'beacon' clusters (≥20 CpG beacons/kb) were predicted to identify permissive chromatin peaks within the human genome.

AIM

We set out to explore these sequence-defined regions for evidence of an active chromatin signature.

RESULTS

Using available comparative primate epigenomic data from neurons of the prefrontal cortex, we show that these CpG 'beacon' clusters are indeed enriched for being human-specific H3K4me3 peaks (χ(2): p < 2.2 × 10(-16)) and thus predictive of permissive chromatin states. These sequence regions had a higher predictive value than previous selective analyses. We also show that both human-specific H3K4me3 and CpG 'beacon' clusters are increased within current and ancestral telomeric regions, supporting an association with recombination, which is higher towards the distal ends of chromosomes.

CONCLUSION

Therefore, CpG-focused comparative sequence analysis can precisely pinpoint chromatin structures that contribute to the human-specific phenotype and further supports an integrated approach in genomic and epigenomic studies.

Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease

The burgeoning interest in the field of epigenetics has precipitated the need to develop approaches to strengthen causal inference when considering the role of epigenetic mediators of environmental exposures on disease risk. Epigenetic markers, like any other molecular biomarker, are vulnerable to confounding and reverse causation. Here, we present a strategy, based on the well-established framework of Mendelian randomization, to interrogate the causal relationships between exposure, DNA methylation and outcome. The two-step approach first uses a genetic proxy for the exposure of interest to assess the causal relationship between exposure and methylation. A second step then utilizes a genetic proxy for DNA methylation to interrogate the causal relationship between DNA methylation and outcome. The rationale, origins, methodology, advantages and limitations of this novel strategy are presented.

Epidemiology, epigenetics and the 'Gloomy Prospect': embracing randomness in population health research and practice

Epidemiologists aim to identify modifiable causes of disease, this often being a prerequisite for the application of epidemiological findings in public health programmes, health service planning and clinical medicine. Despite successes in identifying causes, it is often claimed that there are missing additional causes for even reasonably well-understood conditions such as lung cancer and coronary heart disease. Several lines of evidence suggest that largely chance events, from the biographical down to the sub-cellular, contribute an important stochastic element to disease risk that is not epidemiologically tractable at the individual level. Epigenetic influences provide a fashionable contemporary explanation for such seemingly random processes. Chance events-such as a particular lifelong smoker living unharmed to 100 years-are averaged out at the group level. As a consequence population-level differences (for example, secular trends or differences between administrative areas) can be entirely explicable by causal factors that appear to account for only a small proportion of individual-level risk. In public health terms, a modifiable cause of the large majority of cases of a disease may have been identified, with a wild goose chase continuing in an attempt to discipline the random nature of the world with respect to which particular individuals will succumb. The quest for personalized medicine is a contemporary manifestation of this dream. An evolutionary explanation of why randomness exists in the development of organisms has long been articulated, in terms of offering a survival advantage in changing environments. Further, the basic notion that what is near-random at one level may be almost entirely predictable at a higher level is an emergent property of many systems, from particle physics to the social sciences. These considerations suggest that epidemiological approaches will remain fruitful as we enter the decade of the epigenome.