High costs and technical limitations of cell sorting and single-cell techniques currently restrict the collection of large-scale, cell-type-specific DNA methylation data. This, in turn, impedes our ability to tackle key biological questions that pertain to variation within a population, such as identification of disease-associated genes at a cell-type-specific resolution. Here, we show mathematically and empirically that cell-type-specific methylation levels of an individual can be learned from its tissue-level bulk data, conceptually emulating the case where the individual has been profiled with a single-cell resolution and then signals were aggregated in each cell population separately. Provided with this unprecedented way to perform powerful large-scale epigenetic studies with cell-type-specific resolution, we revisit previous studies with tissue-level bulk methylation and reveal novel associations with leukocyte composition in blood and with rheumatoid arthritis. For the latter, we further show consistency with validation data collected from sorted leukocyte sub-types.

BackgroundPhosphorylation is the most prevalent post-translational modification on eukaryotic proteins. Multisite phosphorylation enables a specific combination of phosphosites to determine the speed, specificity and duration of biological response. Until recent years, the lack of high quality data limited the possibility for analyzing the properties of phosphorylation at the proteome scale and in the context of a wide range of conditions. Thanks to advances of mass spectrometry technologies, thousands of phosphosites from in-vivo experiments were identified and archived in the public domain. Such resource is appropriate to derive an unbiased view on the phosphosites properties in eukaryotes and on their functional relevance.ResultsWe present statistically rigorous tests on the spatial and functional properties of a collection of ~70,000 reported phosphosites. We show that the distribution of phosphosites positioning along the protein tends to occur as dense clusters of Serine/Threonines (pS/pT) and between Serine/Threonines and Tyrosines, but generally not as much between Tyrosines (pY) only. This phenomenon is more ubiquitous than anticipated and is pertinent for most eukaryotic proteins: for proteins with ≥ 2 phosphosites, 54% of all pS/pT sites are within 4 amino acids of another site. We found a strong tendency for clustered pS/pT to be activated by the same kinase. Large-scale analyses of phosphopeptides are thus consistent with a cooperative function within the cluster.ConclusionsWe present evidence supporting the notion that clusters of pS/pT but generally not pY should be considered as the elementary building blocks in phosphorylation regulation. Indeed, closely positioned sites tend to be activated by the same kinase, a signal that overrides the tendency of a protein to be activated by a single or only few kinases. Within these clusters, coordination and positional dependency is evident. We postulate that cellular regulation takes advantage of such design. Specifically, phosphosite clusters may increase the robustness of the effectiveness of phosphorylation-dependent response.ReviewersReviewed by Joel Bader, Frank Eisenhaber, Emmanuel Levy (nominated by Sarah Teichmann). For the full reviews, please go to the Reviewers' comments section.

BackgroundGenetic data are known to harbor information about human demographics, and genotyping data are commonly used for capturing ancestry information by leveraging genome-wide differences between populations. In contrast, it is not clear to what extent population structure is captured by whole-genome DNA methylation data.ResultsWe demonstrate, using three large-cohort 450K methylation array data sets, that ancestry information signal is mirrored in genome-wide DNA methylation data and that it can be further isolated more effectively by leveraging the correlation structure of CpGs with cis-located SNPs. Based on these insights, we propose a method, EPISTRUCTURE, for the inference of ancestry from methylation data, without the need for genotype data.ConclusionsEPISTRUCTURE can be used to infer ancestry information of individuals based on their methylation data in the absence of corresponding genetic data. Although genetic data are often collected in epigenetic studies of large cohorts, these are typically not made publicly available, making the application of EPISTRUCTURE especially useful for anyone working on public data. Implementation of EPISTRUCTURE is available in GLINT, our recently released toolset for DNA methylation analysis at: http://glint-epigenetics.readthedocs.io.Electronic supplementary materialThe online version of this article (doi:10.1186/s13072-016-0108-y) contains supplementary material, which is available to authorized users.

We introduce a Bayesian semi-supervised method for estimating cell counts from DNA methylation by leveraging an easily obtainable prior knowledge on the cell-type composition distribution of the studied tissue. We show mathematically and empirically that alternative methods which attempt to infer cell counts without methylation reference only capture linear combinations of cell counts rather than provide one component per cell type. Our approach allows the construction of components such that each component corresponds to a single cell type, and provides a new opportunity to investigate cell compositions in genomic studies of tissues for which it was not possible before.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1513-2) contains supplementary material, which is available to authorized users.

Motivation: Much of the large-scale molecular data from living cells can be represented in terms of networks. Such networks occupy a central position in cellular systems biology. In the protein–protein interaction (PPI) network, nodes represent proteins and edges represent connections between them, based on experimental evidence. As PPI networks are rich and complex, a mathematical model is sought to capture their properties and shed light on PPI evolution. The mathematical literature contains various generative models of random graphs. It is a major, still largely open question, which of these models (if any) can properly reproduce various biologically interesting networks. Here, we consider this problem where the graph at hand is the PPI network of Saccharomyces cerevisiae. We are trying to distinguishing between a model family which performs a process of copying neighbors, represented by the duplication–divergence (DD) model, and models which do not copy neighbors, with the Barabási–Albert (BA) preferential attachment model as a leading example.Results: The observed property of the network is the distribution of maximal bicliques in the graph. This is a novel criterion to distinguish between models in this area. It is particularly appropriate for this purpose, since it reflects the graph's growth pattern under either model. This test clearly favors the DD model. In particular, for the BA model, the vast majority (92.9%) of the bicliques with both sides ≥4 must be already embedded in the model's seed graph, whereas the corresponding figure for the DD model is only 5.1%. Our results, based on the biclique perspective, conclusively show that a naïve unmodified DD model can capture a key aspect of PPI networks.Contact: regevs01@cs.huji.ac.il; michall@cc.huji.ac.il; nati@cs.huji.ac.ilSupplementary information: Supplementary data are available at Bioinformatics online.

elior.rahmani@gmail.com or ehalperin@cs.ucla.edu.

Estimation of heritability is fundamental in genetic studies. Recently, heritability estimation using linear mixed models (LMMs) has gained popularity because these estimates can be obtained from unrelated individuals collected in genome-wide association studies. Typically, heritability estimation under LMMs uses the restricted maximum likelihood (REML) approach. Existing methods for the construction of confidence intervals and estimators of SEs for REML rely on asymptotic properties. However, these assumptions are often violated because of the bounded parameter space, statistical dependencies, and limited sample size, leading to biased estimates and inflated or deflated confidence intervals. Here, we show that the estimation of confidence intervals by state-of-the-art methods is inaccurate, especially when the true heritability is relatively low or relatively high. We further show that these inaccuracies occur in datasets including thousands of individuals. Such biases are present, for example, in estimates of heritability of gene expression in the Genotype-Tissue Expression project and of lipid profiles in the Ludwigshafen Risk and Cardiovascular Health study. We also show that often the probability that the genetic component is estimated as 0 is high even when the true heritability is bounded away from 0, emphasizing the need for accurate confidence intervals. We propose a computationally efficient method, ALBI (accurate LMM-based heritability bootstrap confidence intervals), for estimating the distribution of the heritability estimator and for constructing accurate confidence intervals. Our method can be used as an add-on to existing methods for estimating heritability and variance components, such as GCTA, FaST-LMM, GEMMA, or EMMAX.

Testing for the existence of variance components in linear mixed models is a fundamental task in many applicative fields. In statistical genetics, the score test has recently become instrumental in the task of testing an association between a set of genetic markers and a phenotype. With few markers, this amounts to set-based variance component tests, which attempt to increase power in association studies by aggregating weak individual effects. When the entire genome is considered, it allows testing for the heritability of a phenotype, defined as the proportion of phenotypic variance explained by genetics. In the popular score-based Sequence Kernel Association Test (SKAT) method, the assumed distribution of the score test statistic is uncalibrated in small samples, with a correction being computationally expensive. This may cause severe inflation or deflation of -values, even when the null hypothesis is true. Here, we characterize the conditions under which this discrepancy holds, and show it may occur also in large real datasets, such as a dataset from the Wellcome Trust Case Control Consortium 2 ( = 13,950) study, and, in particular, when the individuals in the sample are unrelated. In these cases, the SKAT approximation tends to be highly overconservative and therefore underpowered. To address this limitation, we suggest an efficient method to calculate exact -values for the score test in the case of a single variance component and a continuous response vector, which can speed up the analysis by orders of magnitude. Our results enable fast and accurate application of the score test in heritability and in set-based association tests. Our method is available in http://github.com/cozygene/RL-SKAT.