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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...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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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 |
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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 |
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