MOTIVATION: The number of significantly associated regions reported in genome-wide association studies (GWAS) for polygenic traits typically increases with sample size. A traditional tool for quality control and identification of significant regions has been a visual inspection of how significant and correlated genetic variants cluster within a region. However, while inspecting hundreds of regions, this subjective method can misattribute significance to some loci or neglect others that are significant. RESULTS: The GWAS quality score (GQS) identifies suspicious regions and prevents erroneous interpretations with an objective, quantitative and automated method. The GQS assesses all measured single nucleotide polymorphisms (SNPs) that are linked by inheritance to each other [linkage disequilibrium (LD)] and compares the significance of trait association of each SNP to its LD value for the reported index SNP. A GQS value of 1.0 ascribes a high level of confidence to the entire region and its underlying gene(s), while GQS values <1.0 indicate the need to closely inspect the outliers. We applied the GQS to published and non-published genome-wide summary statistics and report suspicious regions requiring secondary inspection while supporting the majority of reported regions from large-scale published meta-analyses. AVAILABILITY AND IMPLEMENTATION: The GQS code/scripts can be cloned from GitHub (https://github.com/Xswapnil/GQS/). The analyst can use whole-genome summary statistics to estimate GQS for each defined region. We also provide an online tool (http://35.227.18.38/) that gives access to the GQS. The quantitative measure of quality attributes by GQS and its visualization is an objective method that enhances the confidence of each genomic hit. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Genome-wide association study (GWAS) analyses, at sufficient sample sizes and power, have successfully revealed biological insights for several complex traits. RICOPILI, an open-sourced Perl-based pipeline was developed to address the challenges of rapidly processing large-scale multi-cohort GWAS studies including quality control (QC), imputation and downstream analyses. The pipeline is computationally efficient with portability to a wide range of high-performance computing environments. RICOPILI was created as the Psychiatric Genomics Consortium pipeline for GWAS and adopted by other users. The pipeline features (i) technical and genomic QC in case-control and trio cohorts, (ii) genome-wide phasing and imputation, (iv) association analysis, (v) meta-analysis, (vi) polygenic risk scoring and (vii) replication analysis. Notably, a major differentiator from other GWAS pipelines, RICOPILI leverages on automated parallelization and cluster job management approaches for rapid production of imputed genome-wide data. A comprehensive meta-analysis of simulated GWAS data has been incorporated demonstrating each step of the pipeline. This includes all the associated visualization plots, to allow ease of data interpretation and manuscript preparation. Simulated GWAS datasets are also packaged with the pipeline for user training tutorials and developer work. AVAILABILITY AND IMPLEMENTATION: RICOPILI has a flexible architecture to allow for ongoing development and incorporation of newer available algorithms and is adaptable to various HPC environments (QSUB, BSUB, SLURM and others). Specific links for genomic resources are either directly provided in this paper or via tutorials and external links. The central location hosting scripts and tutorials is found at this URL: https://sites.google.com/a/broadinstitute.org/RICOPILI/home. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: Here we describe an R library for genome-wide association (GWA) analysis. It implements effective storage and handling of GWA data, fast procedures for genetic data quality control, testing of association of single nucleotide polymorphisms with binary or quantitative traits, visualization of results and also provides easy interfaces to standard statistical and graphical procedures implemented in base R and special R libraries for genetic analysis. We evaluated GenABEL using one simulated and two real data sets. We conclude that GenABEL enables the analysis of GWA data on desktop computers. AVAILABILITY: http://cran.r-project.org.