Modern data sets in various domains often include units that were sampled non-randomly from the population and have a latent correlation structure. Here we investigate a common form of this setting, where every unit is associated with a latent variable, all latent variables are correlated, and the probability of sampling a unit depends on its response. Such settings often arise in case-control studies, where the sampled units are correlated due to spatial proximity, family relations, or other sources of relatedness. Maximum likelihood estimation in such settings is challenging from both a computational and statistical perspective, necessitating approximations that take the sampling scheme into account. We propose a family of approximate likelihood approaches which combine composite likelihood and expectation propagation. We demonstrate the efficacy of our solutions via extensive simulations. We utilize them to investigate the genetic architecture of several complex disorders collected in case-control genetic association studies, where hundreds of thousands of genetic variants are measured for every individual, and the underlying disease liabilities of individuals are correlated due to genetic similarity. Our work is the first to provide a tractable likelihood-based solution for case-control data with complex dependency structures.

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: The use of dense single nucleotide polymorphism (SNP) data in genetic linkage analysis of large pedigrees is impeded by significant technical, methodological and computational challenges. Here we describe Superlink-Online SNP, a new powerful online system that streamlines the linkage analysis of SNP data. It features a fully integrated flexible processing workflow comprising both well-known and novel data analysis tools, including SNP clustering, erroneous data filtering, exact and approximate LOD calculations and maximum-likelihood haplotyping. The system draws its power from thousands of CPUs, performing data analysis tasks orders of magnitude faster than a single computer. By providing an intuitive interface to sophisticated state-of-the-art analysis tools coupled with high computing capacity, Superlink-Online SNP helps geneticists unleash the potential of SNP data for detecting disease genes. RESULTS: Computations performed by Superlink-Online SNP are automatically parallelized using novel paradigms, and executed on unlimited number of private or public CPUs. One novel service is large-scale approximate Markov Chain-Monte Carlo (MCMC) analysis. The accuracy of the results is reliably estimated by running the same computation on multiple CPUs and evaluating the Gelman-Rubin Score to set aside unreliable results. Another service within the workflow is a novel parallelized exact algorithm for inferring maximum-likelihood haplotyping. The reported system enables genetic analyses that were previously infeasible. We demonstrate the system capabilities through a study of a large complex pedigree affected with metabolic syndrome. AVAILABILITY: Superlink-Online SNP is freely available for researchers at http://cbl-hap.cs.technion.ac.il/superlink-snp. The system source code can also be downloaded from the system website. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Vol. 29, No. 2, 2013, pp. 197–205 doi:10.1093/bioinformatics/bts658 The publishers regret that the author affiliations for this paper should appear as follows: Mark Silberstein1,2, Omer Weissbrod1,*, Lars Otten3, Anna Tzemach1, Andrei Anisenia1,4, Oren Shtark1, Dvir Tuberg1, Eddie Galfrin1, Irena Gannon1, Adel Shalata5,6,7, Zvi U. Borochowitz5,8, Rina Dechter3, Elizabeth Thompson9 and Dan Geiger1 1Department of Computer Science, Technion-Israel Institute of Technology, Haifa, Israel, 2Department of Computer Science, University of Texas at Austin, Austin, TX, USA, 3Donald Bren School of Information and Computer Sciences, UC Irvine, CA, USA, 4Department of Computer Science, University of Ottawa, Ottawa, Canada, 5The Simon Winter Institute for Human Genetics, Bnai-Zion Medical Center, Haifa, Israel, 6Research and Development Center, The Galilee Society, Shefa-Amr, Israel, 7Holy Family Hospital, Nazareth, Israel, 8The Rappaport Faculty of Medicine and Research Institute, Technion-Israel Institute of Technology, Haifa, Israel and 9Department of Statistics, University of Washington, Seattle, WA, USA