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Holistic Analysis with Pre and Post Integration GWAS. HAPPI GWAS is a genome-wide association study (GWAS) tool written in R.

A tool for GWAS analysis on tetraploid organisms by integrating results of four GWAS software.

fastSKAT provides a faster implementation of SKAT, which is a tool to map the effects of rare genomic variants.

An efficient genome-wide fine-mapping method integrating summary statistics and functional annotations.

A rapid polygenic score calculator for summary GWAS data without validation dataset. Polygenic scores (PGS) aim to predict complex traits at an individual level based on genetic data. Computation of PGS is based on simply ascertained genome-wide association summary statistics but typically requires an independent test dataset to tune PGS parameters, and for the more sophisticated methods, the computation of LD matrices. Internally tuned methods have recently been proposed that obviate the need for a test dataset, but they remain computationally intensive to run. Here we present RápidoPGS, a flexible and fast method to compute PGS without the need to compute LD-matrices, requiring only summary-level GWAS datasets. Based on fine-mapping principles, RápidoPGS computes the posterior probability that each variant is causal, which in turn is used to shrink effect sizes adaptively as a function of LD and strength of association

Data pre-processing for more informative bacterial GWAS. Manuscript in support of prewas software. R package to perform data pre-processing for more informative bacterial GWAS. The prewas R package allows users to create a binary SNP matrix from a whole genome alignment. The SNP matrix includes the following features: (1) multiple line representation of multiallelic sites, (2) multiple line represention for SNPs present in overlapping genes, and (3) choice over the reference allele. Additionally, users can collapse SNPs into genes so the output is a binary gene matrix. Output from the prewas package should be used as the input to bacterial GWAS tools such as hogwash. The package is currently under development by the Snitkin Lab - please check for a vignette/tutorial in January 2020

The penalized inverse-variance weighted (pIVW) estimator is a Mendelian randomization method for estimating the causal effect of an exposure variable on an outcome of interest based on summary-level GWAS data. The pIVW estimator accounts for weak instruments and horizontal pleiotropy simultaneously.

INFIMA leverages multi-omics model organism data to identify effector genes of human GWAS variants.

FINEMAP-miss is a software package designed to refine fine-mapping in genome-wide association studies (GWAS), particularly when dealing with missing genotype information. It extends the FINEMAP model to account for variant-specific missingness and offers an open-source implementation as an R package.

A model blood cell trait focused annotation tool for interpretation and prioritization of complex trait genome-wide association study results. Variant Annotation Method Pointing to Interesting Regulatory Effects.

Estimating colocalization probability from limited summary statistics. Implements POEMColoc colocalization method described in King et al. 2020. This method is based on the coloc R package, but takes as input datasets that may have full summary statistics, or may have summary statistics for a single top SNP.

Target illumination GWAS analytics. Genome-wide association studies (GWAS) generate large volumes of associations between genomic variations and phenotypic traits. However, assessing strength, specificity and relevance of associations, and semantically valid aggregation of associations, for applications such as drug target prioritization, is challenging. This project addresses this challenge. Beta coefficients require units and thus are not comparable between non-convertible units (e.g. mg vs mm). Nor are beta coefficients comparable with OR, so it is questionable that these values are combined in one field OR_or_BETA. Current TIGA workaround is to use simple count of beta values supporting gene-trait association.

Pipeline for running colocalization tests between GTEx version 8 single-tissue eQTLs or sQTLs and GWAS signals using COLOC.

An R package and shiny application for sample size and power calculation of bulk tissue and single-cell eQTL analysis. Power and sample size calculation for eQTL analysis based on ANOVA or simple linear regression. It can also calculate power/sample size for testing the association of a SNP to a continuous type phenotype.

eQTpLot is an intuitive and user-friendly R package developed for the visualization of colocalization between eQTL and GWAS data. eQTpLot takes as input standard GWAS and eQTL summary statistics, and optional pairwise LD information, to generate a series of plots visualizing colocalization, correlation, and enrichment between eQTL and GWAS signals for a given gene-trait pair.

Multi-Omic Strategies for Transcriptome-Wide Association Studies.

Identifying causal genes mediating a trait through Bayesian estimation of allelic heterogeneity. Code for analyses in MRLocus paper. MRLocus is a statistical method providing Mendelian Randomization analysis per locus, leveraging eQTL and GWAS summary statistics, for estimation of gene-to-trait effect size and dispersion.

A Memory-efficient, Visualization-enhanced, and Parallel-accelerated tool for Genome-Wide Association Study.