SUMMARY: GLINT is a user-friendly command-line toolset for fast analysis of genome-wide DNA methylation data generated using the Illumina human methylation arrays. GLINT, which does not require any programming proficiency, allows an easy execution of Epigenome-Wide Association Study analysis pipeline under different models while accounting for known confounders in methylation data. AVAILABILITY AND IMPLEMENTATION: GLINT is a command-line software, freely available at https://github.com/cozygene/glint/releases . It requires Python 2.7 and several freely available Python packages. Further information and documentation as well as a quick start tutorial are available at http://glint-epigenetics.readthedocs.io . CONTACT: [email protected] or [email protected].

MOTIVATION: Epigenome-wide association studies can provide novel insights into the regulation of genes involved in traits and diseases. The rapid emergence of bisulfite-sequencing technologies enables performing such genome-wide studies at the resolution of single nucleotides. However, analysis of data produced by bisulfite-sequencing poses statistical challenges owing to low and uneven sequencing depth, as well as the presence of confounding factors. The recently introduced Mixed model Association for Count data via data AUgmentation (MACAU) can address these challenges via a generalized linear mixed model when confounding can be encoded via a single variance component. However, MACAU cannot be used in the presence of multiple variance components. Additionally, MACAU uses a computationally expensive Markov Chain Monte Carlo (MCMC) procedure, which cannot directly approximate the model likelihood. RESULTS: We present a new method, Mixed model Association via a Laplace ApproXimation (MALAX), that is more computationally efficient than MACAU and allows to model multiple variance components. MALAX uses a Laplace approximation rather than MCMC based approximations, which enables to directly approximate the model likelihood. Through an extensive analysis of simulated and real data, we demonstrate that MALAX successfully addresses statistical challenges introduced by bisulfite-sequencing while controlling for complex sources of confounding, and can be over 50% faster than the state of the art. AVAILABILITY AND IMPLEMENTATION: The full source code of MALAX is available at https://github.com/omerwe/MALAX . CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Gene-gene interactions are of potential biological and medical interest, as they can shed light on both the inheritance mechanism of a trait and on the underlying biological mechanisms. Evidence of epistatic interactions has been reported in both humans and other organisms. Unlike single-locus genome-wide association studies (GWAS), which proved efficient in detecting numerous genetic loci related with various traits, interaction-based GWAS have so far produced very few reproducible discoveries. Such studies introduce a great computational and statistical burden by necessitating a large number of hypotheses to be tested including all pairs of single nucleotide polymorphisms (SNPs). Thus, many software tools have been developed for interaction-based case-control studies, some leading to reliable discoveries. For quantitative data, on the other hand, only a handful of tools exist, and the computational burden is still substantial. RESULTS: We present an efficient algorithm for detecting epistasis in quantitative GWAS, achieving a substantial runtime speedup by avoiding the need to exhaustively test all SNP pairs using metric embedding and random projections. Unlike previous metric embedding methods for case-control studies, we introduce a new embedding, where each SNP is mapped to two Euclidean spaces. We implemented our method in a tool named EPIQ (EPIstasis detection for Quantitative GWAS), and we show by simulations that EPIQ requires hours of processing time where other methods require days and sometimes weeks. Applying our method to a dataset from the Ludwigshafen risk and cardiovascular health study, we discovered a pair of SNPs with a near-significant interaction (P = 2.2 × 10(-13)), in only 1.5 h on 10 processors. AVAILABILITY: https://github.com/yaarasegre/EPIQ