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GPT (text-davinci-edit-001) used to revise manuscript #69

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16 changes: 11 additions & 5 deletions content/02.introduction.md
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## Introduction

Genes work together in context-specific networks to carry out different functions [@pmid:19104045; @doi:10.1038/ng.3259].
Genes work together in context-specific networks to carry out different functions [@pmid:19104045].
Variations in these genes can change their functional role and, at a higher level, affect disease-relevant biological processes [@doi:10.1038/s41467-018-06022-6].
In this context, determining how genes influence complex traits requires mechanistically understanding expression regulation across different cell types [@doi:10.1126/science.aaz1776; @doi:10.1038/s41586-020-2559-3; @doi:10.1038/s41576-019-0200-9], which in turn should lead to improved treatments [@doi:10.1038/ng.3314; @doi:10.1371/journal.pgen.1008489].
Previous studies have described different regulatory DNA elements [@doi:10.1038/nature11247; @doi:10.1038/nature14248; @doi:10.1038/nature12787; @doi:10.1038/s41586-020-03145-z; @doi:10.1038/s41586-020-2559-3] including genetic effects on gene expression across different tissues [@doi:10.1126/science.aaz1776].
Integrating functional genomics data and GWAS data [@doi:10.1038/s41588-018-0081-4; @doi:10.1016/j.ajhg.2018.04.002; @doi:10.1038/s41588-018-0081-4; @doi:10.1038/ncomms6890] has improved the identification of these transcriptional mechanisms that, when dysregulated, commonly result in tissue- and cell lineage-specific pathology [@pmid:20624743; @pmid:14707169; @doi:10.1073/pnas.0810772105].
In this context, determining how genes influence complex traits requires mechanistically understanding expression regulation across different cell types [@doi:10.1126/science.aaz1776], which in turn should lead to improved treatments [@doi:10.1038/ng.3314; @doi:10.1371/journal.pgen.1008489].
Previous studies have described different regulatory DNA elements [@doi:10.1038/nature11247; @doi:10.1038/nature14248; @doi:10.1038/nature12787] including genetic effects on gene expression across different tissues [@doi:10.1126/science.aaz1776].
Integrating functional genomics data and GWAS data [@doi:10.1038/s41588-018-0081-4; @doi:10.1016/j.ajhg.2018.04.002; @doi:10.1038/s41588-018-0081-4] has improved the identification of these transcriptional mechanisms that, when dysregulated, commonly result in tissue- and cell lineage-specific pathology [@pmid:20624743; @pmid:14707169; @doi:10.1073/pnas.0810772105].


Given the availability of gene expression data across several tissues [@doi:10.1038/nbt.3838; @doi:10.1038/s41467-018-03751-6; @doi:10.1126/science.aaz1776; @doi:10.1186/s13040-020-00216-9], an effective approach to identify these biological processes is the transcription-wide association study (TWAS), which integrates expression quantitative trait loci (eQTLs) data to provide a mechanistic interpretation for GWAS findings.
TWAS relies on testing whether perturbations in gene regulatory mechanisms mediate the association between genetic variants and human diseases [@doi:10.1371/journal.pgen.1009482; @doi:10.1038/ng.3506; @doi:10.1371/journal.pgen.1007889; @doi:10.1038/ng.3367], and these approaches have been highly successful not only in understanding disease etiology at the transcriptome level [@pmid:33931583; @doi:10.1101/2021.10.21.21265225; @pmid:31036433] but also in disease-risk prediction (polygenic scores) [@doi:10.1186/s13059-021-02591-w] and drug repurposing [@doi:10.1038/nn.4618] tasks.
However, TWAS works at the individual gene level, which does not capture more complex interactions at the network level.


These gene-gene interactions play a crucial role in current theories of the architecture of complex traits, such as the omnigenic model [@doi:10.1016/j.cell.2017.05.038], which suggests that methods need to incorporate this complexity to disentangle disease-relevant mechanisms.
Gene-gene interactions play a crucial role in current theories of the architecture of complex traits, such as the omnigenic model [@doi:10.1016/j.cell.2017.05.038], which suggests that methods need to incorporate this complexity to disentangle disease-relevant mechanisms.
Widespread gene pleiotropy, for instance, reveals the highly interconnected nature of transcriptional networks [@doi:10.1038/s41588-019-0481-0; @doi:10.1038/ng.3570], where potentially all genes expressed in disease-relevant cell types have a non-zero effect on the trait [@doi:10.1016/j.cell.2017.05.038; @doi:10.1016/j.cell.2019.04.014].
One way to learn these gene-gene interactions is using the concept of gene module: a group of genes with similar expression profiles across different conditions [@pmid:22955619; @pmid:25344726; @doi:10.1038/ng.3259].
In this context, several unsupervised approaches have been proposed to infer these gene-gene connections by extracting gene modules from co-expression patterns [@pmid:9843981; @pmid:24662387; @pmid:16333293].
Matrix factorization techniques like independent or principal component analysis (ICA/PCA) have shown superior performance in this task [@doi:10.1038/s41467-018-03424-4] since they capture local expression effects from a subset of samples and can handle modules overlap effectively.
Therefore, integrating genetic studies with gene modules extracted using unsupervised learning could further improve our understanding of disease origin [@pmid:25344726] and progression [@pmid:18631455].


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Here we propose PhenoPLIER, an omnigenic approach that provides a gene module perspective to genetic studies.
The flexibility of our method allows integrating different data modalities into the same representation for a joint analysis.
In this work, we show that this module perspective can infer how groups of functionally-related genes influence complex traits, detect shared and distinct transcriptomic properties among traits, and predict how pharmacological perturbations affect genes' activity to exert their effects.
Expand All @@ -38,3 +43,4 @@ In summary, instead of considering single genes associated with different comple
This approach improves robustness in detecting and interpreting genetic associations, and here we show how it can prioritize alternative and potentially more promising candidate targets even when known single gene associations are not detected.
The approach represents a conceptual shift in the interpretation of genetic studies.
It has the potential to extract mechanistic insight from statistical associations to enhance the understanding of complex diseases and their therapeutic modalities.

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