1 Installation

To install this package, run the following code chunk (in R 4.3 or later):

if (!require("BiocManager", quietly = TRUE))


2 Methodology Reference

For a comprehensive description of the probabilistic framework behind INTACT please refer to:

Okamoto, Jeffrey, et al. “Probabilistic integration of transcriptome-wide association studies and colocalization analysis identifies key molecular pathways of complex traits.” The American Journal of Human Genetics 110.1 (2023): 44-57.

3 Introduction

Integrative genetic association methods have shown great promise in post-GWAS (genome-wide association study) analyses, in which one of the most challenging tasks is identifying putative causal genes and uncovering molecular mechanisms of complex traits. Prevailing computational approaches include transcriptome-wide association studies (TWASs) and colocalization analysis. TWASs aim to assess the correlation between predicted gene expression of a target gene and a GWAS trait. Common output for TWASs include gene-level z-statistics. Colocalization analysis attempts to determine whether genetic variants that are causal for a molecular phenotype (such as gene expression) overlap with variants that are causal for a GWAS trait. Common output for colocalization analysis often include gene-level colocalization probabilities, which provide evidence regarding whether there exists a colocalized variant for the expression of a target gene and GWAS trait. Recent studies suggest that TWASs and colocalization analysis are individually imperfect, but their joint usage can yield robust and powerful inference results. INTACT is a computational framework to integrate probabilistic evidence from these distinct types of analyses and implicate putative causal genes. This procedure is flexible and can work with a wide range of existing integrative analysis approaches. It has the unique ability to quantify the uncertainty of implicated genes, enabling rigorous control of false-positive discoveries. INTACT-GSE is an efficient algorithm for gene set enrichment analysis based on the integrated probabilistic evidence. This package is intended for performing integrative genetic association analyses in tandem with other Bioconductor packages such as biomaRt or GO.db, which could be used to obtain gene set annotations for gene set enrichment analysis.

4 Included Data Sets

To illustrate the functionality of the INTACT package, we include a simulated data set simdat. See the methodology reference for an explanation of the simulation design. The data is organized as a 1197 row by 3 column data frame, where rows correspond to genes, the GLCP column provides
gene-level colocalization probabilities, and the TWAS_z column provides TWAS scan z-scores.

Additionally, we include a simulated gene set list gene_set_list, which contains two gene sets. The first gene set has 503 gene members and is significantly enriched among the genes included in simdat, based on the probabilistic INTACT output. The second gene set has 200 gene members and is not enriched among the simdat genes. We include gene_set_list to show how to perform gene set enrichment estimation using INTACT-GSE.

5 INTACT: Integrating TWAS Scan and Colocalization Analysis Results

The first main functionality of this package is integrating results from a transcriptome-wide association study (TWAS) scan and a colocalization analysis. The TWAS scan results must be in the form of gene-level z scores, and the colocalization analysis results should be in the form of gene-level colocalization probabilities. These are provided as output by most popular TWAS and colocalization methods.

Below, we include an example of how to use INTACT to integrate TWAS scan and colocalization results for a simulated data set simdat.

## Attaching package: 'INTACT'
## The following object is masked from 'package:stats':
##     step

rst <- INTACT::intact(GLCP_vec=simdat$GLCP, 

The intact function takes a vector of gene-level colocalization probabilities GLCP_vec and TWAS scan z-scores z_vec. It outputs gene-level posterior probabilities of putative causality. The example included above uses default settings for the prior function and truncation threshold \(t\) (prior_fun = linear and t=0.05). There are three additional prior functions implemented in the INTACT software, including expit, step, and hybrid. The expit and hybrid options have an additional curvature shrinkage parameter D, with a default value of 0.1. The default truncation parameter value for the step prior function is 0.5, while the default value is 0.05 for all other prior functions. Below are three additional examples of how to integrate the TWAS z scores and colocalization probabilies from the simulated data using different prior function, truncation threshold, and curvature shrinkage settings:

rst1 <- INTACT::intact(GLCP_vec=simdat$GLCP, 
                       z_vec = simdat$TWAS_z,
                       t = 0.02,D = 0.09)
rst2 <- INTACT::intact(GLCP_vec=simdat$GLCP, 
                       z_vec = simdat$TWAS_z,
                       t = 0.49)
rst3 <- INTACT::intact(GLCP_vec=simdat$GLCP, 
                       z_vec = simdat$TWAS_z,
                       t = 0.49,D = 0.05)

If the user wishes to specify TWAS Bayes factors instead of z-scores, they can do so through the argument twas_BFs. The Bayes factors should be a numeric vector with genes in the same order as the colocalization probabilities vector. If the user wishes to specify gene-specific TWAS priors, they can do so through the argument twas_priors. If no input is supplied, INTACT computes a scalar prior using the TWAS data (see the methodology reference for more details).

We provide an additional function fdr_rst that is useful if the user wishes to perform Bayesian FDR control on the INTACT output. An example of how to apply this function at a target control level of 0.05 is shown below.

fdr_example <- fdr_rst(rst1, alpha = 0.05)
##   posterior  sig
## 1         1 TRUE
## 2         1 TRUE
## 3         1 TRUE
## 4         1 TRUE
## 5         1 TRUE
## 6         1 TRUE

6 INTACT-GSE: Gene Set Enrichment Estimation Using INTACT results

The INTACT package provides the intactGSE function to perform gene set enrichment estimation and inference using integrated TWAS scan z-scores and colocalization probabilities. This function requires a data frame gene_data containing gene names and corresponding colocalization probabilities and TWAS z-scores for each gene. Column names should be “gene”, “GLCP”, and “TWAS_z’. If the user wishes to specify TWAS Bayes factors instead of z-scores, use the column name”TWAS_BFs“. If the user wishes to specify gene-specific TWAS priors, use the column name”TWAS_priors".

In addition to gene_data, the user must provide a list of gene sets gene_sets. The format of gene_sets must match the included example gene_set_list: it must named list of gene sets for which enrichment is to be estimated. List items should be character vectors of gene IDs. Gene ID format should match the gene column in gene_data.

The user can specify the same prior-related arguments as in the intact function, including prior_fun, t, and D(only when the prior function is specified as expit or hybrid).

The user can specify the method by which the standard error of the enrichment estimate is computed. Options include a numerical differentiation of the score function (default): NDS; a profile likelihood approach: profile_likelihood, and bootstrapping: bootstrap. For hypothesis testing, the user can specify a significance threshold, which is 0.05 by default.

An example of how to estimate gene set enrichment in the gene sets provided in gene_set_list (using default settings) is shown below:

INTACT::intactGSE(gene_data = simdat,gene_sets = gene_set_list)
##    Gene_Set    Estimate        SE           z         pval CI_Leftlim
## 1 gene_set1  1.01981520 0.1808068  5.64035913 1.696958e-08  0.6654404
## 2 gene_set2 -0.01650172 0.2314519 -0.07129655 9.431617e-01 -0.4701391
##   CI_Rightlim CONVERGED
## 1   1.3741900         1
## 2   0.4371357         1

The output of intactGSE includes one row per gene set and eight columns: the gene set name, the enrichment parameter \(\alpha_1\) estimate, the enrichment parameter estimate standard error, the z-score, the p-value, the left and right CIs, and the convergence flag (if CONVERGED = 1, then the enrichment estimation algorithm converged. If not, CONVERGED = 0). Some data sets are not informative for gene set enrichment estimation; in this case, the algorithm will fail to converge. We emphasize that failure of the algorithm to converge does not provide information regarding the enrichment (or lack thereof) for a given gene set.

Finally, we include three additional examples of how to estimate enrichment for the same data sets using non-default prior parameters:

INTACT::intactGSE(gene_data = simdat,prior_fun = INTACT::step,
                  t = 0.45,gene_sets = gene_set_list)
##    Gene_Set   Estimate        SE          z         pval CI_Leftlim CI_Rightlim
## 1 gene_set1  0.9897163 0.1828463  5.4128322 6.203562e-08  0.6313442   1.3480884
## 2 gene_set2 -0.1320656 0.2404421 -0.5492617 5.828259e-01 -0.6033235   0.3391922
## 1         1
## 2         1
INTACT::intactGSE(gene_data = simdat,prior_fun = INTACT::expit,
                  t = 0.08,D = 0.08, gene_sets = gene_set_list)
##    Gene_Set   Estimate        SE          z         pval CI_Leftlim CI_Rightlim
## 1 gene_set1  1.0198388 0.1818588  5.6078591 2.048446e-08  0.6634020   1.3762756
## 2 gene_set2 -0.0424509 0.2348399 -0.1807652 8.565518e-01 -0.5027287   0.4178269
## 1         1
## 2         1
INTACT::intactGSE(gene_data = simdat,prior_fun = INTACT::hybrid,
                  t = 0.08,D = 0.08, gene_sets = gene_set_list)
##    Gene_Set    Estimate        SE          z         pval CI_Leftlim
## 1 gene_set1  1.02016151 0.1822905  5.5963496 2.189120e-08  0.6628786
## 2 gene_set2 -0.04068458 0.2351970 -0.1729809 8.626665e-01 -0.5016622
##   CI_Rightlim CONVERGED
## 1   1.3774444         1
## 2   0.4202931         1

7 Integrating Additional Gene Product Data

Although INTACT achieves higher power than colocalization alone and better false discovery control than TWAS alone, it is not robust to scenarios in which a causal gene impacts the complex trait, but not through gene expression. Motivated by recent protein and splicing QTL studies suggesting these scenarios are quite common, we introduce Multi-INTACT, an empirical Bayes framework that extends the INTACT model to jointly consider multiple molecular gene products (e.g. encoded RNA transcript levels and protein levels). Multi-INTACT comprises two stages: a scanning stage and model selection stage. The scanning stage integrates GWAS results and QTL data for two or more molecular gene products to implicate putative causal genes. The model selection stage aims to determine which of the measured molecular gene products for a gene implicated in the scanning stage directly affects the complex trait-of-interest. We employ a Bayesian procedure to compare possible underlying molecular mechanisms using probabilistic evidence. While leveraging additional molecular gene product information, Multi-INTACT shares strengths with INTACT such as computational efficiency and probabilistic uncertainty quantification for causal gene nomination. This implementation of Multi-INTACT can jointly consider up to 2 molecular gene products at a time.

8 Included Data Sets to Demonstrate Multi-INTACT Functionality

To illustrate the functionality of the Multi-INTACT functions, we include a simulated data set multi_simdat. See the methodology reference for an explanation of the simulation design. The data is organized as a 1197 row by 6 column data frame, where rows correspond to genes, the GLCP_1 and GLCP_2 columns provide pairwise gene-level colocalization probabilities (between the complex trait/gene expression and complex trait/protein levels, respectively), z_1 and z_2 columns provide TWAS and protein-TWAS (PWAS) z-scores, respectively, and chisq provides multivariate Wald chi-square test statistics from the joint regression of the complex trait on predicted protein and expression levels.

If individual-level data are available, recommend using the function wald.test from the aod package in order to generate the chi-square test statistics. If individual-level data are not available, it is still possible to approximate the statistic. We include simulated data sets ld_sumstats, exprwt_sumstats, protwt_sumstats, z_sumstats, to show how to approximate the multivariate Wald statistic using summary-level data.

9 Estimating the Chi-square Statistic From Summary-level Data

To estimate the chi-square statistic, for each candidate gene, we will need a set of SNPs used to predict each gene product level, an estimated linkage disequilibrium (LD) correlation matrix for these SNPs, and prediction weights for each gene product type, and TWAS z-scores for each gene product type. The example data ld_sumstats contains the LD matrix, exprwt_sumstats and protwt_sumstats contain TWAS and PWAS prediction weights, and z_sumstats contains the TWAS and PWAS z-scores for the candidate gene.

To estimate the chi-square statistic, run:


INTACT::chisq_sumstat(z_vec = z_sumstats,
              w = cbind(protwt_sumstats,exprwt_sumstats),
              R = ld_sumstats)
##          [,1]
## [1,] 1.171374

We highly recommend running this step in parallel if you have many candidate gene-trait pairs.

10 Running Multi-INTACT

Once chi-square statistics, pairwise colocalization data, and marginal z-scores for each gene product are available, we are ready to run Multi-INTACT. To compute gene probabilities of putative causality (GPPCs) and gene product relevance probabilities (GPRPs), run:


rst <- INTACT::multi_intact(df = multi_simdat)

The output from the multi_intact function is a list object containing 3 items. The first is a data frame with the GPPC, GPRP for expression (GPRP_1), and for protein (GPRP_2). The second is a numeric 3-vector containing conditional prior parameter estimates (denoted \(h_e,h_p,h_{e+p}\) in the methodology reference). The third is a Boolean indicating whether the EM algorithm converged. The a preview of the output is shown below:

##                gene GPPC     GPRP_1     GPRP_2
## 15  ENSG00000038274    1 0.06825044 1.00000000
## 20  ENSG00000039319    1 1.00000000 0.08780404
## 130 ENSG00000113161    1 1.00000000 0.09504486
## 143 ENSG00000113269    1 0.98648370 1.00000000
## 168 ENSG00000113456    1 0.78760649 0.28634966
## 236 ENSG00000123643    1 1.00000000 0.09710129
## [1] 0.2918381 0.3665981 0.3412209
## [1] TRUE

If you want to see the model posteriors for expression-only (posterior_1), protein-only (posterior_2), and expression-and-protein (posterior_12), run:

rst <- INTACT::multi_intact(df = multi_simdat,return_model_posteriors = TRUE)
##                gene GPPC  posterior_0  posterior_1  posterior_2 posterior_12
## 15  ENSG00000038274    1 1.152317e-26 4.676668e-27 9.317496e-01   0.06825044
## 20  ENSG00000039319    1 1.850717e-40 9.121960e-01 3.668790e-41   0.08780404
## 130 ENSG00000113161    1 1.330650e-18 9.049551e-01 2.707217e-19   0.09504486
## 143 ENSG00000113269    1 1.415389e-17 1.625467e-16 1.351630e-02   0.98648370
## 168 ENSG00000113456    1 3.416643e-29 7.136503e-01 2.123935e-01   0.07395614
## 236 ENSG00000123643    1 6.386325e-32 9.028987e-01 4.644891e-33   0.09710129
##         GPRP_1     GPRP_2
## 15  0.06825044 1.00000000
## 20  1.00000000 0.08780404
## 130 1.00000000 0.09504486
## 143 0.98648370 1.00000000
## 168 0.78760649 0.28634966
## 236 1.00000000 0.09710129

If you only want to compute GPPCs (and not run the EM algorithm to compute GPRPs), run:

rst <- INTACT::multi_intact(df = multi_simdat,em_algorithm = FALSE)
##               gene GPPC
## 2  ENSG00000215630    1
## 5  ENSG00000113269    1
## 37 ENSG00000271824    1
## 39 ENSG00000113456    1
## 44 ENSG00000145740    1
## 45 ENSG00000248489    1

Session information is included below:

## R version 4.4.0 RC (2024-04-16 r86468)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## time zone: America/New_York
## tzcode source: system (glibc)
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## other attached packages:
## [1] INTACT_1.5.0     BiocStyle_2.33.0
## loaded via a namespace (and not attached):
##  [1] vctrs_0.6.5         cli_3.6.2           knitr_1.46         
##  [4] rlang_1.1.3         xfun_0.43           purrr_1.0.2        
##  [7] generics_0.1.3      jsonlite_1.8.8      glue_1.7.0         
## [10] htmltools_0.5.8.1   SQUAREM_2021.1      sass_0.4.9         
## [13] fansi_1.0.6         rmarkdown_2.26      tibble_3.2.1       
## [16] evaluate_0.23       jquerylib_0.1.4     fastmap_1.1.1      
## [19] yaml_2.3.8          lifecycle_1.0.4     numDeriv_2016.8-1.1
## [22] bookdown_0.39       BiocManager_1.30.22 compiler_4.4.0     
## [25] dplyr_1.1.4         pkgconfig_2.0.3     tidyr_1.3.1        
## [28] digest_0.6.35       R6_2.5.1            tidyselect_1.2.1   
## [31] utf8_1.2.4          pillar_1.9.0        magrittr_2.0.3     
## [34] bslib_0.7.0         withr_3.0.0         tools_4.4.0        
## [37] bdsmatrix_1.3-7     cachem_1.0.8