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Modern Applied Statistics With S

CA : A Cancer Journal for Clinicians

Results from genome-wide association studies (GWAS) can be used to infer causal relationships between phenotypes, using a strategy known as 2-sample Mendelian randomization (2SMR) and bypassing the need for individual-level data. However, 2SMR methods are evolving rapidly and GWAS results are often insufficiently curated, undermining efficient implementation of the approach. We therefore developed MR-Base ( http://www.mrbase.org ): a platform that integrates a curated database of complete GWAS results (no restrictions according to statistical significance) with an application programming interface, web app and R packages that automate 2SMR. The software includes several sensitivity analyses for assessing the impact of horizontal pleiotropy and other violations of assumptions. The database currently comprises 11 billion single nucleotide polymorphism-trait associations from 1673 GWAS and is updated on a regular basis. Integrating data with software ensures more rigorous application of hypothesis-driven analyses and allows millions of potential causal relationships to be efficiently evaluated in phenome-wide association studies.

/pdf/the-mr-base-platform-supports-systematic-causal-inference-c759oagatn.pdf

The MR-Base platform supports systematic causal inference across the human phenome

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

Integrative analysis of 111 reference human epigenomes

Observational epidemiological studies are prone to confounding, reverse causation and various biases and have generated findings that have proved to be unreliable indicators of the causal effects of modifiable exposures on disease outcomes. Mendelian randomization (MR) is a method that utilizes genetic variants that are robustly associated with such modifiable exposures to generate more reliable evidence regarding which interventions should produce health benefits. The approach is being widely applied, and various ways to strengthen inference given the known potential limitations of MR are now available. Developments of MR, including two-sample MR, bidirectional MR, network MR, two-step MR, factorial MR and multiphenotype MR, are outlined in this review. The integration of genetic information into population-based epidemiological studies presents translational opportunities, which capitalize on the investment in genomic discovery research.

/pdf/mendelian-randomization-genetic-anchors-for-causal-inference-v9zk0vevsg.pdf

Mendelian randomization: genetic anchors for causal inference in epidemiological studies

/pdf/epigenetic-epidemiology-karin-b-michels-ed-11ymzsjs1f.pdf

Epigenetic Epidemiology. Karin B. Michels (ed).

What is it about boys

Rationale and objectivesLittle evidence exists on the health effects of e-cigarette use. DNA methylation may serve as a biomarker for exposure and could be predictive of future health risk. We aimed to investigate the DNA methylation profile of e-cigarette use. MethodsAmong 117 smokers, 117 non-smokers and 116 non-smoking vapers, we evaluated associations between e-cigarette use and epigenome-wide methylation from saliva. We tested associations between e-cigarette use and methylation scores known to predict smoking and smoking-related disease. We assessed the ability of a methylation score for predicting e-cigarette use and for discriminating lung cancer. Measurements and Main Results7 CpGs were identified in relation to e-cigarette use at p<1x10-5 and none at p<5.91x10-8. 13 CpGs were associated with smoking at p<1x10-5 and one at p<5.91x10-8. CpGs associated with e-cigarette use were largely distinct from those associated with smoking. There was strong enrichment of known smoking-related CpGs in the smokers but not the vapers. A methylation score for e-cigarette use showed poor prediction internally (AUC 0.55, 0.41-0.69) and externally (AUC 0.57, 0.36-0.74) compared with a smoking score (AUCs 0.80) and was less able to discriminate lung squamous cell carcinoma from adjacent normal tissue (AUC 0.64, 0.52-0.76 versus AUC 0.73, 0.61-0.85). ConclusionsThe DNA methylation profile for e-cigarette use is largely distinct from that of cigarette smoking, did not replicate in independent samples, and was unable to discriminate lung cancer from normal tissue. The extent to which methylation related to long-term e-cigarette use translates into chronic effects requires further investigation. Key MessagesO_ST_ABSWhat is the key question?C_ST_ABSIs there a DNA methylation signature of e-cigarette use and is it distinct from that of smoking? What is the bottom line?Smoke exposure is known to lead to widespread changes in DNA methylation which have been identified in different populations and samples, persist for many years after smoking cessation, and may act as a biomarker for smoking-related disease risk and mortality. Whether a similar methylation profile exists in relation to e-cigarette use has not been widely investigated. Why read on?We obtained saliva samples from 116 e-cigarette users and compared their DNA methylation profile with 117 smokers and 117 non-smokers. The e-cigarette users in this study had a minimal smoking history, and so we were able to distinguish the effects of e-cigarette use from those of smoke exposure. Overall, we found that the methylation profile associated with e-cigarette use is less pronounced and distinct from that associated with cigarette smoking.

/pdf/investigating-the-dna-methylation-profile-of-e-cigarette-use-ncbtxygmcq.pdf

Investigating the DNA methylation profile of e-cigarette use

The relationship between gestational age and pulse pressure in children

Background: Smoking remains one of the leading preventable causes of death. Smoking leaves a strong signature on the blood methylome as shown in multiple studies using the Infinium HumanMethylation450 BeadChip. Here, we explore novel blood methylation smoking signals on the Illumina MethylationEPIC BeadChip (EPIC) array, which also targets novel CpG‑sites in enhancers. Method: A smoking‑methylation meta‑analysis was carried out using EPIC DNA methylation profiles in 1407 blood samples from four UK population‑based cohorts, including the MRC National Survey for Health and Development (NSHD) or 1946 British birth cohort, the National Child Development Study (NCDS) or 1958 birth cohort, the 1970 British Cohort Study (BCS70), and the TwinsUK cohort (TwinsUK). The overall discovery sample included 269 current, 497 former, and 643 never smokers. Replication was pursued in 3425 trans‑ethnic samples, including 2325 American Indian individuals participating in the Strong Heart Study (SHS) in 1989–1991 and 1100 African‑American participants in the Genetic Epidemiology Network of Arteriopathy Study (GENOA). Results: Altogether 952 CpG‑sites in 500 genes were differentially methylated between smokers and never smok‑ ers after Bonferroni correction. There were 526 novel smoking‑associated CpG‑sites only profiled by the EPIC array, of which 486 (92%) replicated in a meta‑analysis of the American Indian and African‑American samples. Novel CpG sites mapped both to genes containing previously identified smoking‑methylation signals and to 80 novel genes not previously linked to smoking, with the strongest novel signal in SLAMF7 . Comparison of former versus never smokers identified that 37 of these sites were persistently differentially methylated after cessation, where 16 represented novel signals only profiled by the EPIC array. We observed a depletion of smoking‑associated signals in CpG islands and an enrichment in enhancer regions, consistent with previous results. Conclusion: This study identified novel smoking‑associated signals as possible biomarkers of exposure to smoking and may help improve our understanding of smoking‑related disease risk.

Open Novel DNA methylation signatures of tobacco smoking with trans-ethnic eﬀects

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