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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| Format: | Article |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.1006903/full |
| _version_ | 1811243328809730048 |
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| author | Judith Pérez-Granado Janet Piñero Janet Piñero Laura I. Furlong Laura I. Furlong |
| author_facet | Judith Pérez-Granado Janet Piñero Janet Piñero Laura I. Furlong Laura I. Furlong |
| author_sort | Judith Pérez-Granado |
| collection | DOAJ |
| 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. |
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| id | doaj.art-7b74157caa9c48559e25ce327791f8c9 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-12T14:05:31Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-7b74157caa9c48559e25ce327791f8c92022-12-22T03:30:06ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-10-011310.3389/fgene.2022.10069031006903Benchmarking post-GWAS analysis tools in major depression: Challenges and implicationsJudith Pérez-Granado0Janet Piñero1Janet Piñero2Laura I. Furlong3Laura I. Furlong4Research Programme on Biomedical Informatics (GRIB), Hospital Del Mar Medical Research Institute (IMIM), Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra (UPF), Barcelona, SpainResearch Programme on Biomedical Informatics (GRIB), Hospital Del Mar Medical Research Institute (IMIM), Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra (UPF), Barcelona, SpainMedBioinformatics Solutions SL, Barcelona, SpainResearch Programme on Biomedical Informatics (GRIB), Hospital Del Mar Medical Research Institute (IMIM), Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra (UPF), Barcelona, SpainMedBioinformatics Solutions SL, Barcelona, SpainOur 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.https://www.frontiersin.org/articles/10.3389/fgene.2022.1006903/fullfine-mappingcolocalizationpost-GWASmajor depressioneQTLs |
| spellingShingle | Judith Pérez-Granado Janet Piñero Janet Piñero Laura I. Furlong Laura I. Furlong Benchmarking post-GWAS analysis tools in major depression: Challenges and implications Frontiers in Genetics fine-mapping colocalization post-GWAS major depression eQTLs |
| 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 | fine-mapping colocalization post-GWAS major depression eQTLs |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.1006903/full |
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