Benchmarking post-GWAS analysis tools in major depression: Challenges and implications

Abstract

Our knowledge of complex disorders has increased in the last years thanks to the identification of genetic variants (GVs) significantly associated with disease phenotypes by genome-wide association studies (GWAS). However, we do not understand yet how these GVs functionally impact disease pathogenesis or their underlying biological mechanisms. Among the multiple post-GWAS methods available, fine-mapping and colocalization approaches are commonly used to identify causal GVs, meaning those with a biological effect on the trait, and their functional effects. Despite the variety of post-GWAS tools available, there is no guideline for method eligibility or validity, even though these methods work under different assumptions when accounting for linkage disequilibrium and integrating molecular annotation data. Moreover, there is no benchmarking of the available tools. In this context, we have applied two different fine-mapping and colocalization methods to the same GWAS on major depression (MD) and expression quantitative trait loci (eQTL) datasets. Our goal is to perform a systematic comparison of the results obtained by the different tools. To that end, we have evaluated their results at different levels: fine-mapped and colocalizing GVs, their target genes and tissue specificity according to gene expression information, as well as the biological processes in which they are involved. Our findings highlight the importance of fine-mapping as a key step for subsequent analysis. Notably, the colocalizing variants, altered genes and targeted tissues differed between methods, even regarding their biological implications. This contribution illustrates an important issue in post-GWAS analysis with relevant consequences on the use of GWAS results for elucidation of disease pathobiology, drug target prioritization and biomarker discovery.

Genopyc: a python library for investigating the genomic basis of complex diseases

“…However, 90% of the genetic variation associated to complex diseases are non-coding type and a benchmark of methods to interpret how they alter genes, perturb biological pathways and ultimately lead to disease is still missing (Li 2021). Moreover, the application and integration of different tools to analyze GWAS data lead to discordant results, thus an unbiased assessment of the methods available is still required (Pérez-Granado 2022). An advancement in associating genes to non-coding variants has been made by the Open Target Ge-netics platform, which implemented a pipeline consisting of a machine learning model that uses heterogeneous features such as distance from variant to the gene, expression quantitative trait loci, chromatin conformation and variant effect predictor.…”

Genopyc: a Python library for investigating the functional effects of genomic variants associated to complex diseases

“…However, 90% of the genetic variation associated to complex diseases are noncoding type and a benchmark of method to interpret how they alter genes, perturb biological pathways and ultimately lead to disease is still missing ( Li and Ritchie 2021 ). Moreover, the application and integration of different tools to analyze GWAS data lead to discordant results, thus an unbiased assessment of the methods available is still required (P é rez-Granado et al 2022 ). An advancement in associating genes to noncoding variants has been made by the Open Target Genetics platform, which implemented a pipeline consisting of a machine learning model that uses heterogeneous features such as distance from variant to the gene, expression quantitative trait loci, chromatin conformation and variant effect predictor.…”