This package integrates colocalization probabilities from colocalization analysis with transcriptome-wide association study (TWAS) scan summary statistics to implicate genes that may be biologically relevant to a complex trait. Given gene set annotations, this package can estimate gene set enrichment using posterior probabilities from the TWAS-colocalization integration step.
INTACT 1.4.0
To install this package, run the following code chunk (in R 4.3 or later):
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("INTACT")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.
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.
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.
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.
library(INTACT)##
## Attaching package: 'INTACT'## The following object is masked from 'package:stats':
##
## stepdata(simdat)
rst <- INTACT::intact(GLCP_vec=simdat$GLCP,
z_vec=simdat$TWAS_z)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$GLCP,
prior_fun=INTACT::expit,
z_vec = simdat$TWAS_z,
t = 0.02,D = 0.09)
rst2 <- INTACT::intact(GLCP_vec=simdat$GLCP,
prior_fun=INTACT::step,
z_vec = simdat$TWAS_z,
t = 0.49)
rst3 <- INTACT::intact(GLCP_vec=simdat$GLCP,
prior_fun=INTACT::hybrid,
z_vec = simdat$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)
head(fdr_example)## posterior sig
## 1 1 TRUE
## 2 1 TRUE
## 3 1 TRUE
## 4 1 TRUE
## 5 1 TRUE
## 6 1 TRUEThe 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:
data(gene_set_list)
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 1The 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
## CONVERGED
## 1 1
## 2 1INTACT::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
## CONVERGED
## 1 1
## 2 1INTACT::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 1Although 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.
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.
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:
data(z_sumstats)
data(exprwt_sumstats)
data(protwt_sumstats)
data(ld_sumstats)
INTACT::chisq_sumstat(z_vec = z_sumstats,
w = cbind(protwt_sumstats,exprwt_sumstats),
R = ld_sumstats)## [,1]
## [1,] 1.171374We highly recommend running this step in parallel if you have many candidate gene-trait pairs.
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:
data(multi_simdat)
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] TRUEIf 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 0.09710129If 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 1Session information is included below:
sessionInfo()## R version 4.4.0 beta (2024-04-15 r86425)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
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## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
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## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
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## time zone: America/New_York
## tzcode source: system (glibc)
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## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
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## other attached packages:
## [1] INTACT_1.4.0 BiocStyle_2.32.0
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