TACTICAL: Tissue of ACTion scores for Investigating Complex trait-Associated Loci

This package takes genetic associations or fine-mapped genetic credible sets and systemically integrates them with functional annotations to obtain tissue of action (TOA) scores that can guide mechanistic investigation of genetic signals from genome-wide association studies.

Prerequisites

The package was developed in R (version 3.6.0) and requires the following R packages:

• data.table (1.12.8)
• dplyr (0.8.4)
• GenomicRanges (1.36.1)
• devtools (2.2.2)
  • install_github function used for package installation
• Note: functions from ggplot2 (3.3.0) and gridExtra (2.3) are used to plot PCA output in this tutorial

Installation

The package can be directly installed from GitHub using this R command:

devtools::install_github("jmtorres138/TACTICAL")

Suggested usage

TACTICAL provides a simple way to systematically integrate genetic information from trait-associated loci with functional genomic annotations and gene expression to obtain tissue-of-action (TOA) scores. These scores can be used to:

• classify signals to most likely tissues of action.
• prioritise genetic signals for experimental validation in a particular cell or tissue type.
• guide the identification of causal gene(s) - at specific loci - by informing which tissue(s) are most appropriate for analyses such as eQTL colocalisation or integration with chromatin conformation capture (3C) approaches.
• inform process-specific polygenic risk scores.

TACTICAL is simple to run and flexible; you need only provide the input data you are interested in evaluating. The data accepted include:

• Genetic information: Credible sets from bayesian fine-mapping or index variants from GWAS. If using index SNPs, we recommended using conditionally independent SNPs or LD-pruned SNPs.
• Genomic annotations: Any type of interval data that can be formatted in a BED file. This can include chromatin segmentation states, ChIP-seq peaks, chromatin accessible regions (i.e. DHS or ATAC-seq peaks), lowly/highly methylated regions, coding sequence, untranslated regions, etc.
• Expression specificity scores: When comparing across a set of tissues or cell types, you can provide expression specificity scores (described below) that inform the extent of tissue-specific gene expression in each evaluated tissue.

Although there are many possible data combinations the user may wish to explore, we offer a few suggestions:

1. As some annotations are more enriched for genome-wide significant SNPs (or SNP heritability), you may consider explicitly using these enrichment values as weights within TACTICAL. Although this package does not estimate enrichment per se, there are many software programs available that do, such as fgwas, GARFIELD, and GoShifter.
2. If you are interested in prioritising genetic signals within a particular tissue, then we suggest using as many relevant annotations as available (i.e. from a variety of molecular assays); though you may not want to include "repressed" or low-signal annotations in your tissue or cell-type of interest.
3. On the other hand, if you are interested in profiling signals across a range of candidate tissues, then we strongly recommend restricting the provided annotations to those are available for all evaluated tissues. For example, you may not want to use ATAC-seq peaks if they are only available for a subset of tissues.