# Genetics and population analysis

# PaintorPipe: a pipeline for genetic variant fine-mapping using functional annotations

Zoé Gerber<fnref n="1" />,<fnref n="2" />, Michel Fisun<fnref n="3" />, Hugues Aschard ID <fnref n="3" />,<fnref n="4" />, Sarah Djebali ID <fnref n="1" />,*

Associate Editor: Nicola Mulder

## Abstract

**Motivation:** Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with common diseases. These results include a mix of causal and non-causal variants related through strong linkage disequilibrium (LD, i.e. highly correlated). Fine-mapping methods have been developed to decipher the causal from non-causal variants using GWAS results and LD information, assigning to each variant a probability of being causal. In this field, the PAINTOR program has become a standard, one of its advantages being its ability to take into account functional annotations. This approach requires many pre- and post-processing steps. Here, we developed a Nextflow pipeline called PaintorPipe that wraps all these steps and the fine-mapping itself together. PaintorPipe uses three independent sources of information: GWAS summary statistics, LD information and functional annotations, to rank the variants according to their susceptibility to be involved in the disease development. The PAINTOR framework is used to calculate the posterior probability of each variant (single nucleotide polymorphism) to be causal (a.k.a. Bayesian fine-mapping). The resulting credible sets of variants are annotated with their biological functions and visualized using CANVIS. This pipeline requires minimal input from users (a GWAS summary statistics file and a set of functional annotation files) and is designed to be modular and customizable, allowing for an easy integration of diverse functional annotations.

**Availability and implementation:** PaintorPipe is implemented in the Nextflow pipeline specific language, can be run locally or on a slurm cluster and handles containerization using Singularity. PaintorPipe is freely available on GitHub (https://github.com/sdjebali/PaintorPipe).

## 1 Introduction

Most common human diseases, such as diabetes, obesity, cardiovascular disease/stroke, cancer, neuropsychiatric, autoimmunity, and inflammatory diseases, are multifactorial, depending on numerous environmental and genetic factors. The genetic component is typically highly polygenic, likely involving hundreds to thousands of causal variants spread across the entire genome. The identification of genetic variants, mostly single nucleotide polymorphisms (SNPs), associated with those common polygenic diseases heavily relies on genome-wide association studies (GWAS). The GWAS consists in a massively parallel univariate testing of millions of variants using a standard linear additive model. This approach has proven quite efficient, identifying thousands of associations, but also has some limitations. In particular, the extent of linkage disequilibrium (LD, i.e. correlation) across variants in the human genome implies that numerous non-causal variants can capture signal through their correlation with causal ones. Deciphering the causal variants from non-causal ones found associated because of LD, has proven extremely difficult.

To address this challenge, in silico fine-mapping methods have been developed (Schaid et al. 2018) to prioritize genetic variants more likely to be causal. Most fine-mapping methods are built on a Bayesian framework which jointly models the effects of multiple SNPs on a trait and use LD information to assign a probability of causality to each variant (posterior probability, PP). PAINTOR (Kichaev et al. 2014) is one of such Bayesian fine-mapping methods. It is a popular approach that has been used for the prioritization of variants in hundreds of fine-mapping studies. One advantage of PAINTOR is its ability to incorporate functional annotations. It builds on the assumption that causal variants might act through similar biological pathways, so that enrichment for a given annotation might be leveraged to improve the accuracy of fine-mapping results. However, running PAINTOR requires several pre- and post-processing steps, making it time-consuming and cumbersome.

In this paper, we describe the Nextflow (Di Tommaso et al. 2017) pipeline called PaintorPipe, that aims at overcoming this issue for fast and efficient fine-mapping analyses. PaintorPipe performs all the pre- and post-processing steps required for a fine-mapping, apply PAINTOR, and

<footnote n="1">¹IRSD, Université de Toulouse, INSERM, INRAE, ENVT, Université Toulouse III - Paul Sabatier (UPS), 31024 Toulouse, France</footnote>

<footnote n="2">²Bordeaux Bioinformatics Master, Université de Bordeaux, 33405 Talence, France</footnote>

<footnote n="3">³Department of Computational Biology, Pasteur Institute, Université Paris Cité, 75015 Paris, France</footnote>

<footnote n="4">⁴Program in Genetic Epidemiology and Statistical Genetics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States</footnote>

<footnote>*Corresponding author. IRSD, Université de Toulouse, INSERM, INRAE, ENVT, Université Toulouse III - Paul Sabatier (UPS), U1220 - Bat B - CHU Purpan Place du Docteur Baylac - CS 60039, 31024 Toulouse Cedex 3, France. E-mail: sarah.djebali@inserm.fr</footnote>
<figure><img src="image_1.png" /><figcaption>**Figure 1.** The PaintorPipe Workflow. The PaintorPipe workflow consists of three main parts, indicated on the left of the figure: pre-processing, fine-mapping, and post-processing. Each part is itself divided into modules represented as large horizontal boxes. The pre-processing part is made of three modules: *Split GWAS into loci*, *Calculate LD*, and *Integrate annotation*; the fine-mapping part is made of a single PAINTOR module; and the post-processing part is made of two modules, *Interpret results* and *Visualise* that uses the CANVIS tool. In practice, each module is composed of several Nextflow processes, but for clarity purposes, only the module name is indicated on the figure. The required input files, including the GWAS file and the functional annotation files, are represented as small green boxes, while the intermediate files, that are used by other steps of the pipeline and that can optionally be saved, are represented as small yellow boxes. Finally, the output files are represented as small red boxes.</figcaption></figure>
<figure><img src="image_2.png" /><figcaption>**Figure 2.** Illustration of the effect of genetic variants located in gene enhancers. (Top) Each enhancer can be linked to multiple genes, and conversely each gene can be linked to multiple enhancers. (Bottom) Here, an SNP variant located in an enhancer disrupts the activity of a distant gene, leading to increased disease risk.</figcaption></figure>

SNPs to be causal. Its ability to incorporate functional annotations makes it a powerful tool for identifying causal variants underlying polygenic traits and diseases. To address its time-consuming and burdensome pre- and post-processing parts, we developed PaintorPipe, a Nextflow DSL2 pipeline that automates the entire process for fast and user-friendly analysis. By reducing the number of candidate variants to a more manageable and meaningful set, PaintorPipe can be used to prioritize candidate variants for further investigation.

Indeed, the variants with the highest probability of being causal can be more carefully inspected before experimental validation. For instance their location with respect to genes and regulatory elements can be computed (or derived from the pipeline outputs in case such functional annotations were used) and for the case of variants located in enhancers, the genes they are susceptible to affect can be retrieved from predicted enhancer/gene relationships [for instance from the ABC model (Fulco et al. 2019, Nasser et al. 2021) at https://www.engreitzlab.org/resources].

In short, PaintorPipe greatly facilitates the use of the PAINTOR fine-mapping program, allowing to run it from a minimum set of inputs (GWAS and annotation location files) in a reproducible way, and producing exploitable results for interpretation and visualization.

Finally, while we focused our development on PAINTOR, most fine-mapping methods use similar information (Z-scores, LD matrix across loci, and for some of them functional annotations), and produce similar outputs. We thus expect future versions of PaintorPipe to be able to use some of these other tools (Wang et al. 2020, Yang et al. 2023).

## Acknowledgements

The authors would like to thank Cervin Guyomar for providing expertise on NextFlow, and the genotoul bioinformatics platform (https://bioinfo.genotoul.fr/) for help with software installation.

<figure><img src="image_3.png" /><figcaption>**Figure 3.** Example of a CANVIS visualization produced by PaintorPipe on a specific locus. The plot is made of three parts (from top to bottom): (i) a dotplot showing for each SNP of the locus, its genomic position on the x axis and its posterior probability to be causal (PP) on the y axis, with the credible set highlighted in red. The non-causal SNPs (not significant) are plotted in blue, and the different types of annotations used in this run are indicated by horizontal bars below the plot; (ii) a dotplot representing for each SNP of the locus, its genomic position on the x axis and its $-\log10(P$ value) on the y axis. The most probable causal variant is represented by a black diamond, and the color gradient represents the linkage disequilibrium (correlation) between this variant and the other SNPs of the locus; (iii) a heatmap corresponding to the linkage disequilibrium (SNP pair correlation) of the locus. Here, PaintorPipe version 1.2 was run with the following parameters `--chromosome_header Chr --pvalue_nonlead 0.01 --snp 100000` on the 2018 Coronary Atery Disease (CAD) GWAS study Van Der Harst and Verweij (2018) using four annotations from Finucane et al. (2015). This run produced 149 loci. This figure presents the sixth locus of chromosome 6.</figcaption></figure>
## Author contributions

Zoé Gerber (Formal analysis [lead], Methodology [equal], Project administration [lead], Software [lead], Visualization [lead], Writing—original draft [equal], Writing—review & editing [supporting]), Michel Fisun (Methodology [supporting], Software [supporting]), Hugues Aschard (Conceptualization [equal], Funding acquisition [supporting], Methodology [equal], Writing—original draft [supporting], Writing—review & editing [supporting]), and Sarah Djebali (Conceptualization [equal], Funding acquisition [lead], Methodology [equal], Project administration [supporting], Software [supporting], Supervision [lead], Writing—original draft [equal], Writing—review & editing [lead])

## Conflict of interest

None declared.

## Funding

This work was mainly supported by INSERM, and partly supported by Institut Pasteur.

## Data availability

The PaintorPipe code and small test dataset are freely available from GitHub at https://github.com/sdjebali/PaintorPipe.

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