SUMMARY: Tens of thousands of reproducibly identified GWAS (Genome-Wide Association Studies) variants, with the vast majority falling in non-coding regions resulting in no eventual protein products, call urgently for mechanistic interpretations. Although numerous methods exist, there are few, if any methods, for simultaneously testing the mediation effects of multiple correlated SNPs via some mediator (e.g. the expression of a gene in the neighborhood) on phenotypic outcome. We propose multi-SNP mediation intersection-union test (SMUT) to fill in this methodological gap. Our extensive simulations demonstrate the validity of SMUT as well as substantial, up to 92%, power gains over alternative methods. In addition, SMUT confirmed known mediators in a real dataset of Finns for plasma adiponectin level, which were missed by many alternative methods. We believe SMUT will become a useful tool to generate mechanistic hypotheses underlying GWAS variants, facilitating functional follow-up. AVAILABILITY AND IMPLEMENTATION: The R package SMUT is publicly available from CRAN at https://CRAN.R-project.org/package=SMUT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: High throughput chromatin conformation capture (3C) technologies, such as Hi-C and ChIA-PET, have the potential to elucidate the functional roles of non-coding variants. However, most of published genome-wide unbiased chromatin organization studies have used cultured cell lines, limiting their generalizability. RESULTS: We developed a web browser, HUGIn, to visualize Hi-C data generated from 21 human primary tissues and cell lines. HUGIn enables assessment of chromatin contacts both constitutive across and specific to tissue(s) and/or cell line(s) at any genomic loci, including GWAS SNPs, eQTLs and cis-regulatory elements, facilitating the understanding of both GWAS and eQTL results and functional genomics data. AVAILABILITY AND IMPLEMENTATION: HUGIn is available at http://yunliweb.its.unc.edu/HUGIn. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: The majority of variation identified by genome wide association studies falls in non-coding genomic regions and is hypothesized to impact regulatory elements that modulate gene expression. Here we present a statistically rigorous software tool GREGOR (Genomic Regulatory Elements and Gwas Overlap algoRithm) for evaluating enrichment of any set of genetic variants with any set of regulatory features. Using variants from five phenotypes, we describe a data-driven approach to determine the tissue and cell types most relevant to a trait of interest and to identify the subset of regulatory features likely impacted by these variants. Last, we experimentally evaluate six predicted functional variants at six lipid-associated loci and demonstrate significant evidence for allele-specific impact on expression levels. GREGOR systematically evaluates enrichment of genetic variation with the vast collection of regulatory data available to explore novel biological mechanisms of disease and guide us toward the functional variant at trait-associated loci. AVAILABILITY AND IMPLEMENTATION: GREGOR, including source code, documentation, examples, and executables, is available at http://genome.sph.umich.edu/wiki/GREGOR. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Genotype imputation has become an indispensible step in genome-wide association studies (GWAS). Imputation accuracy, directly influencing downstream analysis, has shown to be improved using re-sequencing-based reference panels; however, this comes at the cost of high computational burden due to the huge number of potentially imputable markers (tens of millions) discovered through sequencing a large number of individuals. Therefore, there is an increasing need for access to imputation quality information without actually conducting imputation. To facilitate this process, we have established a publicly available SNP and indel imputability database, aiming to provide direct access to imputation accuracy information for markers identified by the 1000 Genomes Project across four major populations and covering multiple GWAS genotyping platforms. RESULTS: SNP and indel imputability information can be retrieved through a user-friendly interface by providing the ID(s) of the desired variant(s) or by specifying the desired genomic region. The query results can be refined by selecting relevant GWAS genotyping platform(s). This is the first database providing variant imputability information specific to each continental group and to each genotyping platform. In Filipino individuals from the Cebu Longitudinal Health and Nutrition Survey, our database can achieve an area under the receiver-operating characteristic curve of 0.97, 0.91, 0.88 and 0.79 for markers with minor allele frequency >5%, 3-5%, 1-3% and 0.5-1%, respectively. Specifically, by filtering out 48.6% of markers (corresponding to a reduction of up to 48.6% in computational costs for actual imputation) based on the imputability information in our database, we can remove 77%, 58%, 51% and 42% of the poorly imputed markers at the cost of only 0.3%, 0.8%, 1.5% and 4.6% of the well-imputed markers with minor allele frequency >5%, 3-5%, 1-3% and 0.5-1%, respectively. AVAILABILITY: http://www.unc.edu/∼yunmli/imputability.html

MOTIVATION: Identification of the genetic variation underlying complex traits is challenging. The wealth of information publicly available about the biology of complex traits and the function of individual genes permits the development of informatics-assisted methods for the selection of candidate genes for these traits. RESULTS: We have developed a computational system named CAESAR that ranks all annotated human genes as candidates for a complex trait by using ontologies to semantically map natural language descriptions of the trait with a variety of gene-centric information sources. In a test of its effectiveness, CAESAR successfully selected 7 out of 18 (39%) complex human trait susceptibility genes within the top 2% of ranked candidates genome-wide, a subset that represents roughly 1% of genes in the human genome and provides sufficient enrichment for an association study of several hundred human genes. This approach can be applied to any well-documented mono- or multi-factorial trait in any organism for which an annotated gene set exists. AVAILABILITY: CAESAR scripts and test data can be downloaded from http://visionlab.bio.unc.edu/caesar/