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GRMA is a novel method for genetic association studies that improves upon standard GWAS by allowing analysis in ancestrally diverse populations. It reduces bias by matching individuals to controls based on pairwise relatedness.

mtag is a Python-based command line tool for jointly analyzing multiple sets of GWAS summary statistics as described by Turley et. al. (2018). It can also be used as a tool to meta-analyze GWAS results.

a python tool to evaluate genomic prediction in complex scenarios | SeqBreed.py is a python3 software to simulate populations under (genomic) selection. It inherits most of funcionality from SBVB (Perez-Enciso et al., 2017, https://github.com/miguelperezenciso/sbvb1) and pSBVB (Zingaretti et al. 2018, https://github.com/lauzingaretti/pSBVB) softwares but code has been completely rewritten. It is now much more user friendly and adds numerous new features such as easy selection implementation (BLUP, GBLUP, SSTEP), plots (PCA, GWAS)..

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

GNExT is a web-based platform for systematically exploring genetic associations from GWAS datasets. It integrates variant-level effects into broader biological context using network medicine approaches, facilitating drug repurposing and disease mechanism mining.

tstrait is an open-source Python library for simulating quantitative traits on ancestral recombination graphs (ARGs). It addresses the limitation of existing methods by directly simulating traits on ARGs, avoiding genotype data export and enabling analysis of biobank-scale datasets.

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

Mixed Logistic Regression in Genome-Wide Association Studies. milorGWAS: MIxed LOgistic Regression in GWAS. This package is for Genome Wide Association Studies using a logistic mixed model, using fast approximate methods as described in (Milet and Perdry, 2020). One of these methods extends the GMMAT method by Chen et al. (Chen et al., 2016). A similar method was described in (Zhou et al., 2020).

LOCATER is an R package for testing tree structures (represented via discrete clades or relatedness matrices) for association with traits. It leverages genealogy-based methods to improve trait association power, especially in regions with allelic heterogeneity.

KCFtools is a Java-based toolkit for rapid, alignment-free introgression screening and GWAS using k-mer profiles. It identifies the presence and absence of k-mers in genomic or transcriptomic windows, enabling efficient detection of introgressed or variable regions and facilitating genotype matrix generation for GWAS.

An FPGA-accelerated implementation of GWAS permutation testing designed primarily for AWS EC2 F1 (f1.2xlarge) instances.

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.

Identification of SNP Targeted Pathways From Genome-wide Association Study (GWAS) Data. Abstract The identification of the variants that explain familial risk of a specific disease is important since it enables the development of genetic risk prediction tests, diagnosis tools and therapeutical applications. One possible reason of multifactorial diseases is the alterations in the activity of biological pathways, where a series of mutations occur in distinct genes. While each of these variations extends slightly the likelihood of having the disease, they work together to give birth to the perturbations in normal biological processes. We provide a protocol \(termed PANOGA, Pathway and Network Oriented GWAS \(Genome-wide association study) Analysis) to devise functionally important pathways through the identification of genes within these pathways, where these genes are targeted by single nucleotide polymorphisms \(SNPs) obtained from the GWAS analysis ||| HOMEPAGE BROKEN!

Gene prioritization at GWAS loci using phenotypic enrichment of Mendelian disease genes. Initial set-up for standalone MendelVar usage. A new tool that blends your everyday work apps into one. It's the all-in-one workspace for you and your team.

A web-based colocalization tool for the annotation and functional follow-up of GWAS. Explore GWAS Results with GTEx's eQTL Association Results. A Colocalization Tool to Prioritize Genes and Tissues from GWAS and eQTL Integration.

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 tool for finding all significant de Bruijn subgraphs for bacterial GWAS.

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.

A scalable high-throughput pipeline for inferring molecular mechanisms of non-coding genetic variants.

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.

High Performance Computing tool for the genome-wide detection of pathogenic variants.

microGWAS is a computational pipeline for performing large-scale bacterial genome-wide association studies. It integrates state-of-the-art tools into a user-friendly and reproducible workflow, allowing for the identification of genetic variants associated with bacterial phenotypes.

Collaborative mixed model to dissecting genetic contributions to complex traits by leveraging regulatory information.

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.

Repository for transferGWAS, a deep learning method for performing genome-wide association studies on full medical imaging data.

Efficient TWAS Tool with Nonparametric Bayesian eQTL Weights of 49 Tissue Types from GTEx V8.

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.

A biologically drop-connect deep neural network model for prioritizing disease risk variants and genes. Population studies such as GWAS have identified a variety of genomic variants associated with human diseases. To further understand potential mechanisms of disease variants, recent statistical methods associate functional omic data (e.g., gene expression) with genotype and phenotype and link variants to individual genes. However, how to interpret molecular mechanisms from such associations, especially across omics is still challenging. To address this, we develop an interpretable deep learning method, Varmole to simultaneously reveal genomic functions and mechanisms while predicting phenotype from genotype. In particular, Varmole embeds multi-omic networks into a deep neural network architecture and prioritizes variants, genes and regulatory linkages via drop-connect without needing prior feature selections.

CandiHap is a user-friendly local software, that can fast preselect candidate causal SNPs from Sanger or next-generation sequencing data, and report results in table and exquisite vector-graphs within a minute. Investigators can use CandiHap to specify a gene or linkage sites based on GWAS and explore favourable haplotypes of candidate genes for target traits.

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.

The Parkinson’s Disease GWAS Locus Browser. The NHGRI-EBI GWAS Catalog: a curated collection of all human genome-wide association studies, produced by a collaboration between EMBL-EBI and NHGRI. Summary Statistics from Other GWASes:.

The variant call format provides efficient and robust storage of GWAS summary statistics. VCF vs plain text GWAS storage formats.

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

Software for family based association tests