Gene-based Higher Criticism methods for large-scale exonic single-nucleotide polymorphism data

DNA hypomethylation mediates immune response in pan-cancer

Abnormal DNA methylation is a fundamental characterization of epigenetics in cancer. Here we demonstrate that aberrant DNA methylating can modulate the tumour immune microenvironment in 16 cancer types. Differential DNA methylation in promoter region can regulate the transcriptomic pattern of immune-related genes and DNA hypomethylation mainly participated in the processes of immunity, carcinogenesis and immune infiltration. Moreover, many cancer types shared immune-related functions, like activation of innate immune response, interferon gamma response and NOD-like receptor signalling pathway. DNA methylation can further help identify molecular subtypes of kidney renal clear cell carcinoma. These subtypes are characterized by DNA methylation pattern, major histocompatibility complex, cytolytic activity and cytotoxic t lymphocyte and tumour mutation burden, and subtype with hypomethylation pattern shows unstable immune status. Then, we investigate the DNA methylation pattern of exhaustion-related marker genes and further demonstrate the role of hypomethylation in tumour immune microenvironment. In summary, our findings support the use of hypomethylation as a biomarker to understand the mechanism of tumour immune environment.

Detecting epistasis in human complex traits

Genome-wide association studies (GWASs) have become the focus of the statistical analysis of complex traits in humans, successfully shedding light on several aspects of genetic architecture and biological aetiology. Single-nucleotide polymorphisms (SNPs) are usually modelled as having additive, cumulative and independent effects on the phenotype. Although evidently a useful approach, it is often argued that this is not a realistic biological model and that epistasis (that is, the statistical interaction between SNPs) should be included. The purpose of this Review is to summarize recent directions in methodology for detecting epistasis and to discuss evidence of the role of epistasis in human complex trait variation. We also discuss the relevance of epistasis in the context of GWASs and potential hazards in the interpretation of statistical interaction terms.

Effect of linkage disequilibrium on the identification of functional variants

We summarize the contributions of Group 9 of Genetic Analysis Workshop 17. This group addressed the problems of linkage disequilibrium and other longer range forms of allelic association when evaluating the effects of genotypes on phenotypes. Issues raised by long-range associations, whether a result of selection, stratification, possible technical errors, or chance, were less expected but proved to be important. Most contributors focused on regression methods of various types to illustrate problematic issues or to develop adaptations for dealing with high-density genotype assays. Study design was also considered, as was graphical modeling. Although no method emerged as uniformly successful, most succeeded in reducing false-positive results either by considering clusters of loci within genes or by applying smoothing metrics that required results from adjacent loci to be similar. Two unexpected results that questioned our assumptions of what is required to model linkage disequilibrium were observed. The first was that correlations between loci separated by large genetic distances can greatly inflate single-locus test statistics, and, whether the result of selection, stratification, possible technical errors, or chance, these correlations seem overabundant. The second unexpected result was that applying principal components analysis to genome-wide genotype data can apparently control not only for population structure but also for linkage disequilibrium.