Background Altered microbiome composition and aberrant promoter hypermethylation of tumor suppressor genes (TSGs) are two important hallmarks of colorectal cancer (CRC). Here we performed concurrent 16S rRNA gene sequencing and methyl-CpG binding domain-based capture sequencing in 33 tissue biopsies (5 normal colonic mucosa tissues, 4 pairs of adenoma and adenoma-adjacent tissues, and 10 pairs of CRC and CRC-adjacent tissues) to identify significant associations between TSG promoter hypermethylation and CRC-associated bacteria, followed by functional validation of the methylation-associated bacteria. Results Fusobacterium nucleatum and Hungatella hathewayi were identified as the top two methylation-regulating bacteria. Targeted analysis on bona fide TSGs revealed that H. hathewayi and Streptococcus spp . significantly correlated with CDX2 and MLH1 promoter hypermethylation, respectively. Mechanistic validation with cell-line and animal models revealed that F. nucleatum and H. hathewayi upregulated DNA methyltransferase. H. hathewayi inoculation also promoted colonic epithelial cell proliferation in germ-free and conventional mice. Conclusion Our integrative analysis revealed previously unknown epigenetic regulation of TSGs in host cells through inducing DNA methyltransferase by F. nucleatum and H. hathewayi , and established the latter as CRC-promoting bacteria.

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BackgroundWith the increasing amount of high-throughput genomic sequencing data, there is a growing demand for a robust and flexible tool to perform interaction analysis. The identification of SNP-SNP, SNP-CpG, and higher order interactions helps explain the genetic etiology of human diseases, yet genome-wide analysis for interactions has been very challenging, due to the computational burden and a lack of statistical power in most datasets.ResultsThe wtest R package performs association testing for main effects, pairwise and high order interactions in genome-wide association study data, and cis-regulation of SNP and CpG sites in genome-wide and epigenome-wide data. The software includes a number of post-test diagnostic and analysis functions and offers an integrated toolset for genetic epistasis testing.ConclusionsThe wtest is an efficient and powerful statistical tool for integrated genetic epistasis testing. The package is available in CRAN: https://CRAN.R-project.org/package=wtest.

An increasing number of studies are focused on the epigenetic regulation of DNA to affect gene expression without modifications to the DNA sequence. Methylation plays an important role in shaping disease traits; however, previous studies were mainly experiment, based, resulting in few reports that measured gene–methylation interaction effects via statistical means. In this study, we applied the data set adaptive W-test to measure gene–methylation interactions. Performance was evaluated by the ability to detect a given set of causal markers in the data set obtained from the GAW20. Results from simulation data analyses showed that the W-test was able to detect most markers. The method was also applied to chromosome 11 of the experimental data set and identified clusters of genes with neuronal and retinal functions, including MPPED2I, GUCY2E, NAV2, and ZBTB16. Genes from the TRIM family were also identified; these genes are potentially related to the regulation of triglyceride levels. Our results suggest that the W-test could be an efficient and effective method to detect gene–methylation interactions. Furthermore, the identified genes suggest an interesting relationship between lipid levels and the etiology of neurological disorders.

Genetic data consists of a wide range of marker types, including common, low frequency, and rare variants. Multiple genetic markers and their interactions play central roles in the heritability of complex disease. In this study, we propose an algorithm that uses a stratified variable selection design by genetic architectures and interaction effects, achieved by a data-set adaptive W-test. The polygenic sets in all strata were integrated to form a classification rule. The algorithm was applied to the Critical Assessment of Genome Interpretation 4 bipolar challenge sequencing data. The prediction accuracy was 60% using genetic markers on an independent test set. We found that epistasis among common genetic variants contributed most substantially to prediction precision. However, the sample size was not large enough to draw conclusions for the lack of predictability of low frequency variants and their epistasis.

BackgroundObservational studies report inconclusive effects of tea consumption on the risk of Alzheimer’s disease (AD), and the mechanisms are unclear. This study aims to investigate the effects of genetically predicted tea intake (cups of tea consumed per day) on AD, brain volume, and cerebral small vessel disease (CSVD) using the two-sample Mendelian randomization (MR) method.MethodsSummary statistics of tea intake were obtained from UK Biobank (N = 447,485), and AD was from the International Genomics of Alzheimer’s Project (N = 54,162). Genetic instruments were retrieved from UK Biobank using brain imaging-derived phenotypes for brain volume outcomes (N > 33,224) and genome-wide association studies for CSVD (N: 17,663–48,454).ResultsIn the primary MR analysis, tea intake significantly increased the risk of AD using two different methods (ORIVW = 1.48, 95% CI: [1.14, 1.93]; ORWM = 2.00, 95% CI: [1.26, 3.18]) and reached a weak significant level using MR-Egger regression (p < 0.1). The result passed all the sensitivity analyses, including heterogeneity, pleiotropy, and outlier tests. In the secondary MR analysis, per extra cup of tea significantly decreased gray matter (βWM = −1.63, 95% CI: [−2.41, −0.85]) and right hippocampus volume (βWM = −1.78, 95% CI: [−2.76, −0.79]). We found a nonlinear association between tea intake and AD in association analysis, which suggested that over-drinking with more than 13 cups per day might be a risk factor for AD. Association analysis results were consistent with MR results.ConclusionThis study revealed a potential causal association between per extra cup of tea and an increased risk of AD. Genetically predicted tea intake was associated with a decreased brain volume of gray matter and the right hippocampus, which indicates that over-drinking tea might lead to a decline in language and memory functions. Our results shed light on a novel possible mechanism of tea intake to increase the risk of AD by reducing brain volume.

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BackgroundAn accumulation of evidence has revealed the important role of epigenetic factors in explaining the etiopathogenesis of human diseases. Several empirical studies have successfully incorporated methylation data into models for disease prediction. However, it is still a challenge to integrate different types of omics data into prediction models, and the contribution of methylation information to prediction remains to be fully clarified.ResultsA stratified drug-response prediction model was built based on an artificial neural network to predict the change in the circulating triglyceride level after fenofibrate intervention. Associated single-nucleotide polymorphisms (SNPs), methylation of selected cytosine-phosphate-guanine (CpG) sites, age, sex, and smoking status, were included as predictors. The model with selected SNPs achieved a mean 5-fold cross-validation prediction error rate of 43.65%. After adding methylation information into the model, the error rate dropped to 41.92%. The combination of significant SNPs, CpG sites, age, sex, and smoking status, achieved the lowest prediction error rate of 41.54%.ConclusionsCompared to using SNP data only, adding methylation data in prediction models slightly improved the error rate; further prediction error reduction is achieved by a combination of genome, methylation genome, and environmental factors.