Function reference

Targets

Build a simple {targets} project

populate_targets_proj()

Create a minimal targets template in the current project

use_crew_lsf()

Use crew lsf to execute a targets pipeline using the LSF HPC scheduler

Genomics

Genomics utility functions and wrappers around other tools/programs/packages to perform GWAS meta-analysis, fine-mapping, colocalization, and more.

Annotation

Functions that are useful for annotating GWAS summary statistics

annotate_rsids()

Annotate a dataframe containing genomic coordinates with rsids

GWAS

Functions that are useful for performing GWAS/GWAS meta-analysis

metal_config()

Create a configuration file for METAL

metal_run()

Use METAL to run a GWAS meta-analysis

mr_mega()

Perform multi-ancestry GWAS meta-analysis using MR-MEGA

Linkage disequilibrium

Functions that are useful for linkage disequilibrium

plink_extract_ld()

Extract an LD matrix from a reference panel using plink 1.9

Colocalization

Functions that are useful for performing colocalization. Colocalization is a technique for evaluating the evidence supporting the presence of shared causal variant(s) at a given locus across two or more traits. Several methods of colocalization have been described, which generally leverage GWAS summary statistics across multiple traits. The methods use either proportional or enumeration approaches, which make different assumptions and test different hypotheses.

coloc_run()

Run Bayesian enumeration colocalization using Coloc

hyprcoloc_df()

Run multi-trait colocalization using HyPrColoc

Finemapping

Functions that are useful for finemapping. Finemapping is an approach for identifying the putative causal variant(s) at a locus identified in a GWAS. Like colocalization, finemapping methods make different assumptions about the configuration of causal variant(s) at the locus.

calc_credset()

Perform Bayesian finemapping using the Approximate Bayes Factor approach

run_carma()

Perform Bayesian finemapping using CARMA

Heritability

Functions that are useful for estimating heritability. Several tools for performing heritability estimation using GWAS summary statistics have been developed, including LDSC and LDAK. The ldscr package provides a native R implemtation of LDSC.

ldak_h2()

Calculate heritability using LDAK

TWAS/Gene-based testing

Functions that are useful for identifying trait-associated genes from GWAS.

s_multixcan()

Integrate PrediXcan data across tissues

s_predixcan()

Run a TWAS using S-PrediXcan

magmar()

Run MAGMA gene-based analysis

Miscellaneous

Miscellaneous functions useful for manipulating/presenting data.

render_datatable()

Render DataTable to HTML

gg_manhattan_df()

Create a Manhattan Plot

gg_qq_df()

Create a QQ plot