Cargando…

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

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 pathogenes...

Descripción completa

Detalles Bibliográficos
Autores principales: Pérez-Granado, Judith, Piñero, Janet, Furlong, Laura I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9579284/
https://www.ncbi.nlm.nih.gov/pubmed/36276939
http://dx.doi.org/10.3389/fgene.2022.1006903
_version_ 1784812146361630720
author Pérez-Granado, Judith
Piñero, Janet
Furlong, Laura I.
author_facet Pérez-Granado, Judith
Piñero, Janet
Furlong, Laura I.
author_sort Pérez-Granado, Judith
collection PubMed
description 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.
format Online
Article
Text
id pubmed-9579284
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-95792842022-10-20 Benchmarking post-GWAS analysis tools in major depression: Challenges and implications Pérez-Granado, Judith Piñero, Janet Furlong, Laura I. Front Genet Genetics 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. Frontiers Media S.A. 2022-10-05 /pmc/articles/PMC9579284/ /pubmed/36276939 http://dx.doi.org/10.3389/fgene.2022.1006903 Text en Copyright © 2022 Pérez-Granado, Piñero and Furlong. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Pérez-Granado, Judith
Piñero, Janet
Furlong, Laura I.
Benchmarking post-GWAS analysis tools in major depression: Challenges and implications
title Benchmarking post-GWAS analysis tools in major depression: Challenges and implications
title_full Benchmarking post-GWAS analysis tools in major depression: Challenges and implications
title_fullStr Benchmarking post-GWAS analysis tools in major depression: Challenges and implications
title_full_unstemmed Benchmarking post-GWAS analysis tools in major depression: Challenges and implications
title_short Benchmarking post-GWAS analysis tools in major depression: Challenges and implications
title_sort benchmarking post-gwas analysis tools in major depression: challenges and implications
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9579284/
https://www.ncbi.nlm.nih.gov/pubmed/36276939
http://dx.doi.org/10.3389/fgene.2022.1006903
work_keys_str_mv AT perezgranadojudith benchmarkingpostgwasanalysistoolsinmajordepressionchallengesandimplications
AT pinerojanet benchmarkingpostgwasanalysistoolsinmajordepressionchallengesandimplications
AT furlonglaurai benchmarkingpostgwasanalysistoolsinmajordepressionchallengesandimplications