SUMMARY: Expression quantitative trait loci (eQTLs) characterize the associations between genetic variation and gene expression to provide insights into tissue-specific gene regulation. Interactive visualization of tissue-specific eQTLs or splice QTLs (sQTLs) can facilitate our understanding of functional variants relevant to disease-related traits. However, combining the multi-dimensional nature of eQTLs/sQTLs into a concise and informative visualization is challenging. Existing QTL visualization tools provide useful ways to summarize the unprecedented scale of transcriptomic data but are not necessarily tailored to answer questions about the functional interpretations of trait-associated variants or other variants of interest. We developed FIVEx, an interactive eQTL/sQTL browser with an intuitive interface tailored to the functional interpretation of associated variants. It features the ability to navigate seamlessly between different data views while providing relevant tissue- and locus-specific information to offer users a better understanding of population-scale multi-tissue transcriptomic profiles. Our implementation of the FIVEx browser on the EBI eQTL catalogue, encompassing 16 publicly available RNA-seq studies, provides important insights for understanding potential tissue-specific regulatory mechanisms underlying trait-associated signals. AVAILABILITY AND IMPLEMENTATION: A FIVEx instance visualizing EBI eQTL catalogue data can be found at https://fivex.sph.umich.edu. Its source code is open source under an MIT license at https://github.com/statgen/fivex. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: LocusZoom.js is a JavaScript library for creating interactive web-based visualizations of genetic association study results. It can display one or more traits in the context of relevant biological data (such as gene models and other genomic annotation), and allows interactive refinement of analysis models (by selecting linkage disequilibrium reference panels, identifying sets of likely causal variants, or comparisons to the GWAS catalog). It can be embedded in web pages to enable data sharing and exploration. Views can be customized and extended to display other data types such as phenome-wide association study (PheWAS) results, chromatin co-accessibility, or eQTL measurements. A new web upload service harmonizes datasets, adds annotations, and makes it easy to explore user-provided result sets. AVAILABILITY AND IMPLEMENTATION: LocusZoom.js is open-source software under a permissive MIT license. Code and documentation are available at: https://github.com/statgen/locuszoom/. Installable packages for all versions are also distributed via NPM. Additional features are provided as standalone libraries to promote reuse. Use with your own GWAS results at https://my.locuszoom.org/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Summary: Estimating linkage disequilibrium (LD) is essential for a wide range of summary statistics-based association methods for genome-wide association studies. Large genetic datasets, e.g. the TOPMed WGS project and UK Biobank, enable more accurate and comprehensive LD estimates, but increase the computational burden of LD estimation. Here, we describe emeraLD (Efficient Methods for Estimation and Random Access of LD), a computational tool that leverages sparsity and haplotype structure to estimate LD up to 2 orders of magnitude faster than current tools. Availability and implementation: emeraLD is implemented in C++, and is open source under GPLv3. Source code and documentation are freely available at http://github.com/statgen/emeraLD. Supplementary information: Supplementary data are available at Bioinformatics online.

Genotype imputation is a computational method where probable genotypes are inferred to a lower-density dataset (target) based on a higher-density reference panel and it is considered to be a cost-effective approach to prepare data for various purposes, including genome-wide association studies (GWAS). In order for the final outcomes of any downstream analysis to be trustworthy, it is important to choose the most optimal method available for the imputation step. The main focus of this study is to compare and evaluate different available genotype imputation algorithms and their overall performance together with their respective quality metrics using the data obtained through SISu and FinMetSeq projects. Of special interest is the accuracy of low-frequency and rare variants.

SUMMARY: To enable direct comparison of ancestry background in different studies, we developed LASER to estimate individual ancestry by placing either sezquenced or genotyped samples in a common ancestry space, regardless of the sequencing strategy or genotyping array used to characterize each sample. Here we describe the LASER server to facilitate application of the method to a wide range of genetic studies. The server provides genetic ancestry estimation for different geographic regions and user-friendly interactive visualization of the results. AVAILABILITY AND IMPLEMENTATION: The LASER server is freely accessible at http://laser.sph.umich.edu/. CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Recent genetic studies as well as recorded history point to massive growth in human population sizes during the recent past. To model and understand this growth accurately we introduce FTEC, an easy-to-use coalescent simulation program capable of simulating haplotype samples drawn from a population that has undergone faster than exponential growth. Samples drawn from a population that has undergone faster than exponential growth show an excess of very rare variation and more rapid LD decay when compared with samples drawn from a population that has maintained a constant size over time. AVAILABILITY: Source code for FTEC is freely available for download from the University of Michigan Center for Statistical Genetics Wiki at http://genome.sph.umich.edu/wiki/FTEC

UNLABELLED: Genome-wide association studies (GWAS) have revealed hundreds of loci associated with common human genetic diseases and traits. We have developed a web-based plotting tool that provides fast visual display of GWAS results in a publication-ready format. LocusZoom visually displays regional information such as the strength and extent of the association signal relative to genomic position, local linkage disequilibrium (LD) and recombination patterns and the positions of genes in the region. AVAILABILITY: LocusZoom can be accessed from a web interface at http://csg.sph.umich.edu/locuszoom. Users may generate a single plot using a web form, or many plots using batch mode. The software utilizes LD information from HapMap Phase II (CEU, YRI and JPT+CHB) or 1000 Genomes (CEU) and gene information from the UCSC browser, and will accept SNP identifiers in dbSNP or 1000 Genomes format. Single plots are generated in approximately 20 s. Source code and associated databases are available for download and local installation, and full documentation is available online.

MOTIVATION: Genome-wide association (GWA) studies may identify multiple variants that are associated with a disease or trait. To narrow down candidates for further validation, quantitatively assessing how identified genes relate to a phenotype of interest is important. RESULTS: We describe an approach to characterize genes or biological concepts (phenotypes, pathways, diseases, etc.) by ontology fingerprint--the set of Gene Ontology (GO) terms that are overrepresented among the PubMed abstracts discussing the gene or biological concept together with the enrichment p-value of these terms generated from a hypergeometric enrichment test. We then quantify the relevance of genes to the trait from a GWA study by calculating similarity scores between their ontology fingerprints using enrichment p-values. We validate this approach by correctly identifying corresponding genes for biological pathways with a 90% average area under the ROC curve (AUC). We applied this approach to rank genes identified through a GWA study that are associated with the lipid concentrations in plasma as well as to prioritize genes within linkage disequilibrium (LD) block. We found that the genes with highest scores were: ABCA1, lipoprotein lipase (LPL) and cholesterol ester transfer protein, plasma for high-density lipoprotein; low-density lipoprotein receptor, APOE and APOB for low-density lipoprotein; and LPL, APOA1 and APOB for triglyceride. In addition, we identified genes relevant to lipid metabolism from the literature even in cases where such knowledge was not reflected in current annotation of these genes. These results demonstrate that ontology fingerprints can be used effectively to prioritize genes from GWA studies for experimental validation.