Baoshan Ma

Predicting DNA methylation level across human tissues

Differences in methylation across tissues are critical to cell differentiation and are key to understanding the role of epigenetics in complex diseases. In this investigation, we found that locus-specific methylation differences between tissues are highly consistent across individuals. We developed a novel statistical model to predict locus-specific methylation in target tissue based on methylation in surrogate tissue. The method was evaluated in publicly available data and in two studies using the latest IlluminaBeadChips: a childhood asthma study with methylation measured in both peripheral blood leukocytes (PBL) and lymphoblastoid cell lines; and a study of postoperative atrial fibrillation with methylation in PBL, atrium and artery. We found that our method can greatly improve accuracy of cross-tissue prediction at CpG sites that are variable in the target tissue [R2 increases from 0.38 (original R2 between tissues) to 0.89 for PBL-to-artery prediction; from 0.39 to 0.95 for PBL-to-atrium; and from 0.81 to 0.98 for lymphoblastoid cell line-to-PBL based on cross-validation, and confirmed using cross-study prediction]. An extended model with multiple CpGs further improved performance. Our results suggest that large-scale epidemiology studies using easy-to-access surrogate tissues (e.g. blood) could be recalibrated to improve understanding of epigenetics in hard-to-access tissues (e.g. atrium) and might enable non-invasive disease screening using epigenetic profiles.

The Case‐Only Test for Gene–Environment Interaction is Not Uniformly Powerful: An Empirical Example

The case‐only test has been proposed as a more powerful approach to detect gene–environment (G × E) interactions. This approach assumes that the genetic and environmental factors are independent. Although it is well known that Type I error rate will increase if this assumption is violated, it is less widely appreciated that G × E correlation can also lead to power loss. We illustrate this phenomenon by comparing the performance of the case‐only test to other approaches to detect G × E interactions in a genome‐wide association study (GWAS) of esophageal squamous‐cell carcinoma (ESCC) in Chinese populations. Some of these approaches do not use information on the correlation between exposure and genotype (standard logistic regression), whereas others seek to use this information in a robust fashion to boost power without increasing Type I error (two‐step, empirical Bayes, and cocktail methods). G × E interactions were identified involving drinking status and two regions containing genes in the alcohol metabolism pathway, 4q23 and 12q24. Although the case‐only test yielded the most significant tests of G × E interaction in the 4q23 region, the case‐only test failed to identify significant interactions in the 12q24 region which were readily identified using other approaches. The low power of the case‐only test in the 12q24 region is likely due to the strong inverse association between the single nucleotide polymorphism (SNPs) in this region and drinking status. This example underscores the need to consider multiple approaches to detect G × E interactions, as different tests are more or less sensitive to different alternative hypotheses and violations of the G × E independence assumption.

Risk prediction of esophageal squamous-cell carcinoma with common genetic variants and lifestyle factors in Chinese population

Genome-wide association studies have identified multiple genetic variants associated with risk of esophageal squamous-cell carcinoma (ESCC) in Chinese populations. We examined whether these genetic factors, along with non-genetic factors, can contribute to ESCC risk prediction. We examined 25 single nucleotide polymorphisms (SNPs) and 4 non-genetic factors (sex, age, smoking and drinking) associated with ESCC risk in 9805 cases and 10 493 controls from Chinese populations. Weighted genetic risk score (wGRS) was calculated and logistic regression was used to analyze the association between wGRS and ESCC risk. We calculated the area under the curve (AUC) using receiver operating characteristic curve analysis to measure the discrimination after adding genetic variants to the model with only non-genetic factors. Net reclassification improvement (NRI) was used to quantify the degree of correct reclassification using different models. wGRS of the combined 17 SNPs with significant marginal effect (G SNPs) increased ~4-fold ESCC risk (P = 1.49 × 10(-) (164)) and the associations were significant in both drinkers and non-drinkers. However, wGRS of the eight SNPs with significant effect in gene × drinking interaction (GE SNPs) increased ~4-fold ESCC risk only in drinkers (P interaction = 8.76 × 10(-) (41)). The AUC for a risk model with 4 non-genetic factors, 17 G SNPs, 8 GE SNPs and their interactions with drinking was 70.1%, with the significant improvement of 7.0% compared with the model with only non-genetic factors (P < 0.0001). Our results indicate that incorporating genetic variants, lifestyle factors and their interactions in ESCC risk models can be useful for identifying patients with ESCC.

RTeQTL: Real-Time Online Engine for Expression Quantitative Trait Loci Analyses.

Our database tool, called Real-Time Engine for Expression Quantitative Trait Loci Analyses (RTeQTL), can efficiently provide eQTL association results that are not available in existing eQTL databases browsers. These functions include (i) single SNP (single-nucleotide polymorphism) and (ii) two-SNP conditional eQTL effects on gene expression regardless of the magnitude of P-values. The database is based on lymphoblastoid cell lines from >900 samples with global gene expression and genome-wide genotyped and imputed SNP data. The detailed result for any pairs of gene and SNPs can be efficiently computed and browsed online, as well as downloaded in batch mode. This is the only tool that can assess the independent effect of a disease- or trait-associated SNP on gene expression conditioning on other SNPs of interest, such as the top eQTL of the same gene. It is also useful to identify eQTLs for candidate genes, which are often missed in existing eQTL browsers, which only store results with genome-wide significant P-value. Additional analyses stratifying by gender can also be easily achieved by this tool. Database URL: http://eqtl.rc.fas.harvard.edu/