MOTIVATION: The interactions among various types of cells play critical roles in cell functions and the maintenance of the entire organism. While cell-cell interactions are traditionally revealed from experimental studies, recent developments in single-cell technologies combined with data mining methods have enabled computational prediction of cell-cell interactions, which have broadened our understanding of how cells work together, and have important implications in therapeutic interventions targeting cell-cell interactions for cancers and other diseases. Despite the importance, to our knowledge, there is no database for systematic documentation of high-quality cell-cell interactions at the cell type level, which hinders the development of computational approaches to identify cell-cell interactions. RESULTS: We develop a publicly accessible database, CITEdb (Cell-cell InTEraction database, https://citedb.cn/), which not only facilitates interactive exploration of cell-cell interactions in specific physiological contexts (e.g. a disease or an organ) but also provides a benchmark dataset to interpret and evaluate computationally derived cell-cell interactions from different tools. CITEdb contains 728 pairs of cell-cell interactions in human that are manually curated. Each interaction is equipped with structured annotations including the physiological context, the ligand-receptor pairs that mediate the interaction, etc. Our database provides a web interface to search, visualize and download cell-cell interactions. Users can search for cell-cell interactions by selecting the physiological context of interest or specific cell types involved. CITEdb is the first attempt to catalogue cell-cell interactions at the cell type level, which is beneficial to both experimental, computational and clinical studies of cell-cell interactions. AVAILABILITY AND IMPLEMENTATION: CITEdb is freely available at https://citedb.cn/ and the R package implementing benchmark is available at https://github.com/shanny01/benchmark. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Identification and interpretation of non-coding variations that affect disease risk remain a paramount challenge in genome-wide association studies (GWAS) of complex diseases. Experimental efforts have provided comprehensive annotations of functional elements in the human genome. On the other hand, advances in computational biology, especially machine learning approaches, have facilitated accurate predictions of cell-type-specific functional annotations. Integrating functional annotations with GWAS signals has advanced the understanding of disease mechanisms. In previous studies, functional annotations were treated as static of a genomic region, ignoring potential functional differences imposed by different genotypes across individuals. RESULTS: We develop a computational approach, Openness Weighted Association Studies (OWAS), to leverage and aggregate predictions of chromosome accessibility in personal genomes for prioritizing GWAS signals. The approach relies on an analytical expression we derived for identifying disease associated genomic segments whose effects in the etiology of complex diseases are evaluated. In extensive simulations and real data analysis, OWAS identifies genes/segments that explain more heritability than existing methods, and has a better replication rate in independent cohorts than GWAS. Moreover, the identified genes/segments show tissue-specific patterns and are enriched in disease relevant pathways. We use rheumatic arthritis and asthma as examples to demonstrate how OWAS can be exploited to provide novel insights on complex diseases. AVAILABILITY AND IMPLEMENTATION: The R package OWAS that implements our method is available at https://github.com/shuangsong0110/OWAS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Mapping expression quantitative trait loci (eQTLs) has provided insight into gene regulation. Compared to cis-eQTLs, the regulatory mechanisms of trans-eQTLs are less known. Previous studies suggest that trans-eQTLs may regulate expression of remote genes by altering the expression of nearby genes. Trans-association has been studied in the mediation analysis with a single mediator. However, prior applications with one mediator are prone to model misspecification due to correlations between genes. Motivated from the observation that trans-eQTLs are more likely to associate with more than one cis-gene than randomly selected SNPs in the GTEx dataset, we developed a computational method to identify trans-eQTLs that are mediated by multiple mediators. RESULTS: We proposed two hypothesis tests for testing the total mediation effect (TME) and the component-wise mediation effects (CME), respectively. We demonstrated in simulation studies that the type I error rates were controlled in both tests despite model misspecification. The TME test was more powerful than the CME test when the two mediation effects are in the same direction, while the CME test was more powerful than the TME test when the two mediation effects are in opposite direction. Multiple mediator analysis had increased power to detect mediated trans-eQTLs, especially in large samples. In the HapMap3 data, we identified 11 mediated trans-eQTLs that were not detected by the single mediator analysis in the combined samples of African populations. Moreover, the mediated trans-eQTLs in the HapMap3 samples are more likely to be trait-associated SNPs. In terms of computation, although there is no limit in the number of mediators in our model, analysis takes more time when adding additional mediators. In the analysis of the HapMap3 samples, we included at most 5 cis-gene mediators. Majority of the trios we considered have one or two mediators. CONCLUSIONS: Trans-eQTLs are more likely to associate with multiple cis-genes than randomly selected SNPs. Mediation analysis with multiple mediators improves power of identification of mediated trans-eQTLs, especially in large samples.