diff --git a/content/01.abstract.md b/content/01.abstract.md index 1f257005..b4ed42f2 100644 --- a/content/01.abstract.md +++ b/content/01.abstract.md @@ -1,12 +1,4 @@ ## Abstract {.page_break_before} -Genes act in concert with each other in specific contexts to perform their functions. -Determining how these genes influence complex traits requires a mechanistic understanding of expression regulation across different conditions. -It has been shown that this insight is critical for developing new therapies. -In this regard, the role of individual genes in disease-relevant mechanisms can be hypothesized with transcriptome-wide association studies (TWAS), which have represented a significant step forward in testing the mediating role of gene expression in GWAS associations. -However, modern models of the architecture of complex traits predict that gene-gene interactions play a crucial role in disease origin and progression. -Here we introduce PhenoPLIER, a computational approach that maps gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis. -This representation is based on modules of genes with similar expression patterns across the same conditions. -We observed that diseases were significantly associated with gene modules expressed in relevant cell types, and our approach was accurate in predicting known drug-disease pairs and inferring mechanisms of action. -Furthermore, using a CRISPR screen to analyze lipid regulation, we found that functionally important players lacked TWAS associations but were prioritized in trait-associated modules by PhenoPLIER. -By incorporating groups of co-expressed genes, PhenoPLIER can contextualize genetic associations and reveal potential targets missed by single-gene strategies. +Our study demonstrates that by leveraging gene co-expression patterns, PhenoPLIER can accurately predict disease etiology and drug mechanisms. +This approach provides a powerful tool to identify novel therapeutic targets and repurpose drugs, advancing our understanding of complex traits. diff --git a/content/02.introduction.md b/content/02.introduction.md index 8760ae09..8f079a53 100644 --- a/content/02.introduction.md +++ b/content/02.introduction.md @@ -1,25 +1,30 @@ ## Introduction -Genes work together in context-specific networks to carry out different functions [@pmid:19104045; @doi:10.1038/ng.3259]. -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]. +Genes interact in context-specific networks to carry out different functions [@pmid:19104045; @doi:10.1038/ng.3259], and variations in these genes can modify their functional role and, at a higher level, influence disease-relevant biological processes [@doi:10.1038/s41467-018-06022-6]. +To understand how genes affect complex traits, it is necessary to comprehend 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]. +This could lead to improved treatments [@doi:10.1038/ng.3314; @doi:10.1371/journal.pgen.1008489]. +Previous studies have identified 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 various tissues [@doi:10.1126/science.aaz1776]. +Combining 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 transcriptional mechanisms that, when disrupted, often 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. +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], the transcription-wide association study (TWAS) has proven to be an effective approach for identifying biological processes. +This method integrates expression quantitative trait loci (eQTLs) data to provide a mechanistic interpretation for genetic variants associated with human diseases [@doi:10.1371/journal.pgen.1009482; @doi:10.1038/ng.3506; @doi:10.1371/journal.pgen.1007889; @doi:10.1038/ng.3367]. +TWAS has been successful in understanding disease etiology at the transcriptome level [@pmid:33931583; @doi:10.1101/2021.10.21.21265225; @pmid:31036433], predicting disease risk (polygenic scores) [@doi:10.1186/s13059-021-02591-w], and drug repurposing [@doi:10.1038/nn.4618]. +However, this approach 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. -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]. +Gene-gene interactions are essential for understanding the architecture of complex traits, such as the omnigenic model [@doi:10.1016/j.cell.2017.05.038]. +This complexity reveals the interconnected nature of transcriptional networks [@doi:10.1038/s41588-019-0481-0; @doi:10.1038/ng.3570], where potentially all expressed genes have an effect on the trait [@doi:10.1016/j.cell.2017.05.038; @doi:10.1016/j.cell.2019.04.014]. +To learn these interactions, gene modules - groups of genes with similar expression profiles across different conditions [@pmid:22955619; @pmid:25344726; @doi:10.1038/ng.3259] - are extracted using unsupervised learning approaches [@pmid:9843981; @pmid:24662387; @pmid:16333293]. +Matrix factorization techniques such as independent or principal component analysis (ICA/PCA) [@doi:10.1038/s41467-018-03424-4] are particularly effective for this task, as they capture local expression effects from a subset of samples and can handle module overlap. +Integrating genetic studies with these gene modules could improve our understanding of disease origin [@pmid:25344726] and progression [@pmid:18631455]. + 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. @@ -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. + diff --git a/content/04.05.00.results_framework.md b/content/04.05.00.results_framework.md index 2f119af3..dd5722ba 100644 --- a/content/04.05.00.results_framework.md +++ b/content/04.05.00.results_framework.md @@ -17,45 +17,31 @@ mDCs: myeloid dendritic cells. ](images/entire_process/entire_process.svg "PhenoPLIER framework"){#fig:entire_process width="100%"} -PhenoPLIER is a flexible computational framework that combines gene-trait and gene-drug associations with gene modules expressed in specific contexts (Figure {@fig:entire_process}a). -The approach uses a latent representation (with latent variables or LVs representing gene modules) derived from a large gene expression compendium (Figure {@fig:entire_process}b, top) to integrate TWAS with drug-induced transcriptional responses (Figure {@fig:entire_process}b, bottom) for a joint analysis. -The approach consists in three main components (Figure {@fig:entire_process}b, middle, see [Methods](#sec:methods)): -1) an LV-based regression model to compute an association between an LV and a trait, -2) a clustering framework to learn groups of traits with shared transcriptomic properties, -and 3) an LV-based drug repurposing approach that links diseases to potential treatments. -We performed extensive simulations for our regression model ([Supplementary Note 1](#sm:reg:null_sim)) and clustering framework ([Supplementary Note 2](#sm:clustering:null_sim)) to ensure proper calibration and expected results under a model of no association. +PhenoPLIER is a computational framework that integrates gene-trait and gene-drug associations with gene modules expressed in specific contexts (Figure {@fig:entire_process}a). +It uses a latent representation derived from a large gene expression compendium to combine TWAS with drug-induced transcriptional responses (Figure {@fig:entire_process}b). +This approach has three main components (Figure {@fig:entire_process}b, middle): 1) an LV-based regression model to compute an association between an LV and a trait; 2) a clustering framework to learn groups of traits with shared transcriptomic properties; and 3) an LV-based drug repurposing approach that links diseases to potential treatments (see [Methods](#sec:methods)). +We conducted simulations for our regression model ([Supplementary Note 1](#sm:reg:null_sim)) and clustering framework ([Supplementary Note 2](#sm:clustering:null_sim)) to ensure proper calibration and expected results under a model of no association. -We used TWAS results from PhenomeXcan [@doi:10.1126/sciadv.aba2083] and the eMERGE network [@doi:10.1101/2021.10.21.21265225] as discovery and replication cohorts, respectively ([Methods](#sec:methods:twas)). -PhenomeXcan provides gene-trait associations for 4,091 different diseases and traits from the UK Biobank [@doi:10.1038/s41586-018-0579-z] and other studies, whereas the analyses on eMERGE were performed across 309 phecodes. -TWAS results were derived using two statistical methods (see [Methods](#sec:methods:predixcan)): -1) Summary-MultiXcan (S-MultiXcan) associations were used for the regression and clustering components, -and 2) Summary-PrediXcan (S-PrediXcan) associations were used for the drug repurposing component. -In addition, we also used colocalization results, which provide a probability of overlap between the GWAS and eQTL signals. -For the drug-repurposing approach, we used transcriptional responses to small molecule perturbations from LINCS L1000 [@doi:10.1016/j.cell.2017.10.049] comprising 1,170 compounds. +We used two cohorts for our study: PhenomeXcan [@doi:10.1126/sciadv.aba2083] and the eMERGE network [@doi:10.1101/2021.10.21.21265225]. +The former provides gene-trait associations for over 4,000 diseases and traits from the UK Biobank [@doi:10.1038/s41586-018-0579-z], while the latter consists of 309 phecodes. +We used two statistical methods (see [Methods](#sec:methods:predixcan)) to derive TWAS results: 1) Summary-MultiXcan (S-MultiXcan) associations for the regression and clustering components, and 2) Summary-PrediXcan (S-PrediXcan) associations for the drug repurposing component. +We also used colocalization results to measure the probability of overlap between GWAS and eQTL signals. +For the drug-repurposing approach, we used transcriptional responses to small molecule perturbations from LINCS L1000 [@doi:10.1016/j.cell.2017.10.049], which comprises 1,170 compounds. The latent gene expression representation was obtained from the MultiPLIER models [@doi:10.1016/j.cels.2019.04.003], which were derived by applying a matrix factorization method (the pathway-level information extractor or PLIER [@doi:10.1038/s41592-019-0456-1]) to recount2 [@doi:10.1038/nbt.3838] -- a uniformly-curated collection of transcript-level gene expression quantified by RNA-seq in a large, diverse set of samples collected across a range of disease states, cell types differentiation stages, and various stimuli (see [Methods](#sec:methods:multiplier)). The MultiPLIER models extracted 987 LVs by optimizing data reconstruction but also the alignment of LVs with prior knowledge/pathways. -Each LV or gene module represents a group of weighted genes expressed together in the same tissues and cell types as a functional unit. -Since LVs might represent a functional set of genes regulated by the same transcriptional program [@doi:10.1186/1471-2164-7-187; @doi:10.1186/s13059-019-1835-8], we conjecture that the projection of TWAS and pharmacologic perturbations data into this latent space could provide a better mechanistic understanding. -For this projection of different data modalities into the same space, PhenoPLIER converts gene associations to an LV score: all genes' standardized effect sizes for a trait (from TWAS) or differential expression values for a drug (from pharmacologic perturbation data) are multiplied by the LV genes' weights and summed, producing a single value. -Instead of looking at individual genes, this process links different traits and drugs to functionally-related groups of genes or LVs. -PhenoPLIER uses LVs annotations about the specific conditions where the group of genes is expressed, such as cell types and tissues, even at specific developmental stages, disease stages or under distinct stimuli. -Although this is not strictly necessary for PhenoPLIER to work, these annotations can dramatically improve the interpretability of results. -MultiPLIER's models provide this information by linking LVs to samples, which may be annotated for experimental conditions (represented by matrix $\mathbf{B}$ at the top of Figure {@fig:entire_process}b) in which genes in an LV are expressed. -An example of this is shown in Figure {@fig:entire_process}c. -In the original MultiPLIER study, the authors reported that one of the latent variables, identified as LV603, was associated with a known neutrophil pathway and highly correlated with neutrophil count estimates from whole blood RNA-seq profiles [@doi:10.1186/s13059-016-1070-5]. -We analyzed LV603 using PhenoPLIER and found that -1) neutrophil counts and other white blood cell traits were ranked among the top 10 traits out of 4,091 (Figure {@fig:entire_process}c, bottom), and basophils count and percentage were significantly associated with this LV when using our regression method (Supplementary Table @tbl:sup:phenomexcan_assocs:lv603), -and 2) LV603's genes were expressed in highly relevant cell types (Figure {@fig:entire_process}c, top). -These initial results suggested that groups of functionally related and co-expressed genes tend to correspond to groups of trait-associated genes, and the approach can link transcriptional mechanisms from large and diverse dataset collections to complex traits. - - -Therefore, PhenoPLIER allows the user to address specific questions, namely: -do disease-associated genes belong to modules expressed in specific tissues and cell types? -Are these cell type-specific modules associated with _different_ diseases, thus potentially representing a "network pleiotropy" example from an omnigenic point of view [@doi:10.1016/j.cell.2017.05.038]? -Is there a subset of module's genes that is closer to the definition of "core" genes (i.e., directly affecting the trait with no mediated regulation of other genes [@doi:10.1016/j.cell.2019.04.014]) and thus represents alternative and potentially better candidate targets? -Are drugs perturbing these transcriptional mechanisms, and can they suggest potential mechanisms of action? +The latent variables (LVs) in our study represent a group of weighted genes expressed together as a functional unit. +We hypothesized that projecting TWAS and pharmacologic perturbations data into this latent space could provide a better mechanistic understanding. +PhenoPLIER converts gene associations to an LV score by multiplying the genes' standardized effect sizes for a trait (from TWAS) or differential expression values for a drug (from pharmacologic perturbation data) by the LV genes' weights and summing them to produce a single value. +This process links different traits and drugs to functionally-related groups of genes or LVs, and can be improved by adding annotations about the specific conditions in which the group of genes is expressed, such as cell types and tissues, even at specific developmental stages, disease stages or under distinct stimuli. +For example, one of the latent variables, LV603, was associated with a known neutrophil pathway and highly correlated with neutrophil count estimates from whole blood RNA-seq profiles. +We found that it was associated with several white blood cell traits and basophils count and percentage, and its genes were expressed in highly relevant cell types. +These results suggest that functionally related and co-expressed genes can be linked to groups of trait-associated genes, allowing us to project transcriptional mechanisms from large and diverse dataset collections to complex traits. + + +PhenoPLIER enables users to answer several key questions. +For example, do genes associated with a certain disease belong to certain modules expressed in particular tissues and cell types? Are these cell type-specific modules associated with different diseases, potentially representing an example of "network pleiotropy" from an omnigenic perspective [@doi:10.1016/j.cell.2017.05.038]? Is there a subset of module genes that can be considered "core" genes (genes directly affecting the trait without the mediation of other genes [@doi:10.1016/j.cell.2019.04.014]) and, thus, represent possible alternative and better targets? Can drugs affect these transcriptional mechanisms, and can they suggest potential mechanisms of action? diff --git a/content/04.05.01.crispr.md b/content/04.05.01.crispr.md index 3aa96201..19fd95a3 100644 --- a/content/04.05.01.crispr.md +++ b/content/04.05.01.crispr.md @@ -1,11 +1,9 @@ ### LVs link genes that alter lipid accumulation with relevant traits and tissues -Our first experiment attempted to answer whether genes in a disease-relevant LV could represent potential therapeutic targets. -For this, the first step was to obtain a set of genes strongly associated with a phenotype of interest. -Therefore, we performed a fluorescence-based CRISPR-Cas9 in the HepG2 cell line and identified 462 genes associated with lipid regulation ([Methods](#sec:methods:crispr)). -From these, we selected two high-confidence gene sets that either caused a decrease or increase of lipids: -a lipids-decreasing gene-set with eight genes: *BLCAP*, *FBXW7*, *INSIG2*, *PCYT2*, *PTEN*, *SOX9*, *TCF7L2*, *UBE2J2*; -and a lipids-increasing gene-set with six genes: *ACACA*, *DGAT2*, *HILPDA*, *MBTPS1*, *SCAP*, *SRPR* (Supplementary File 2). +We conducted an experiment to determine if genes in a disease-relevant LV could represent potential therapeutic targets. +To do this, we used fluorescence-based CRISPR-Cas9 in the HepG2 cell line to identify 462 genes associated with lipid regulation (see Methods section for details). +From these, we selected two high-confidence gene sets: a lipids-decreasing gene-set with 8 genes (*BLCAP*, *FBXW7*, *INSIG2*, *PCYT2*, *PTEN*, *SOX9*, *TCF7L2*, *UBE2J2*) and a lipids-increasing gene-set with 6 genes (*ACACA*, *DGAT2*, *HILPDA*, *MBTPS1*, *SCAP*, *SRPR*). +This information is provided in Supplementary File 2. ![ @@ -28,15 +26,14 @@ RCP: locus regional colocalization probability. ](images/lvs_analysis/lv246/lv246.svg "LV246 TWAS plot"){#fig:lv246 width="100%"} -Next, we analyzed all 987 LVs using Fast Gene Set Enrichment Analysis (FGSEA) [@doi:10.1101/060012], and found 15 LVs nominally enriched (unadjusted *P* < 0.01) with these lipid-altering gene-sets (Supplementary Tables @tbl:sup:lipids_crispr:modules_enriched_increase and @tbl:sup:lipids_crispr:modules_enriched_decrease). -Among those with reliable sample metadata, LV246, the top LV associated with the lipids-increasing gene-set, contained genes mainly co-expressed in adipose tissue (Figure {@fig:lv246}a), which plays a key role in coordinating and regulating lipid metabolism. -Using our regression framework across all traits in PhenomeXcan, we found that gene weights for this LV were predictive of gene associations for plasma lipids, high cholesterol, and Alzheimer's disease (Supplementary Table @tbl:sup:phenomexcan_assocs:lv246, FDR < 1e-23). -These lipids-related associations also replicated across the 309 traits in eMERGE (Supplementary Table @tbl:sup:emerge_assocs:lv246), where LV246 was significantly associated with hypercholesterolemia (phecode: 272.11, FDR < 4e-9), hyperlipidemia (phecode: 272.1, FDR < 4e-7) and disorders of lipoid metabolism (phecode: 272, FDR < 4e-7). +We analyzed 987 latent variables (LVs) using Fast Gene Set Enrichment Analysis (FGSEA) and found 15 LVs to be nominally enriched with lipid-altering gene-sets (unadjusted *P* < 0.01). +For those with reliable sample metadata, LV246 was the top LV associated with the lipids-increasing gene-set, and contained genes mainly co-expressed in adipose tissue (Figure {@fig:lv246}a). +Our regression framework across all traits in PhenomeXcan showed that gene weights for this LV were predictive of gene associations for plasma lipids, high cholesterol, and Alzheimer's disease (FDR < 1e-23). +These lipids-related associations also replicated across the 309 traits in eMERGE (FDR < 4e-9, 4e-7, and 4e-7 for hypercholesterolemia, hyperlipidemia, and disorders of lipoid metabolism, respectively). -Two high-confidence genes from our CRISPR screening, *DGAT2* and *ACACA*, are responsible for encoding enzymes for triglycerides and fatty acid synthesis and were among the highest-weighted genes of LV246 (Figure {@fig:lv246}b, in boldface). -However, in contrast to other members of LV246, *DGAT2* and *ACACA* were not associated nor colocalized with any of the cardiovascular-related traits and thus would not have been prioritized by TWAS alone; -instead, other members of LV246, such as *SCD*, *LPL*, *FADS2*, *HMGCR*, and *LDLR*, were significantly associated and colocalized with lipid-related traits. +Our CRISPR screening identified two high-confidence genes, *DGAT2* and *ACACA*, which encode enzymes for triglycerides and fatty acid synthesis, and were among the highest-weighted genes of LV246 (Figure {@fig:lv246}b, in boldface). +However, these two genes were not associated nor colocalized with any of the cardiovascular-related traits, in contrast to other members of LV246, such as *SCD*, *LPL*, *FADS2*, *HMGCR*, and *LDLR*, which were significantly associated and colocalized with lipid-related traits. This lack of association of two high-confidence genes from our CRISPR screen might be explained from an omnigenic point of view [@doi:10.1016/j.cell.2019.04.014]. -Assuming that the TWAS models for *DGAT2* and *ACACA* capture all common *cis*-eQTLs (the only genetic component of gene expression that TWAS can capture) and there are no rare *cis*-eQTLs, these two genes might represent "core" genes (i.e., they directly affect the trait with no mediated regulation of other genes), and many of the rest in the LV are "peripheral" genes that *trans*-regulate them. +It is possible that *DGAT2* and *ACACA* represent "core" genes, directly affecting the trait with no mediated regulation of other genes, while many of the rest in the LV are "peripheral" genes that *trans*-regulate them, and that TWAS models for these two genes capture all common *cis*-eQTLs (the only genetic component of gene expression that TWAS can capture). diff --git a/content/04.15.drug_disease_prediction.md b/content/04.15.drug_disease_prediction.md index feb3ac55..70d077f8 100644 --- a/content/04.15.drug_disease_prediction.md +++ b/content/04.15.drug_disease_prediction.md @@ -1,11 +1,11 @@ ### LVs predict drug-disease pairs better than single genes -We next determined how substituting LVs for individual genes predicted known treatment-disease relationships. -For this, we used the transcriptional responses to small molecule perturbations profiled in LINCS L1000 [@doi:10.1016/j.cell.2017.10.049], which were further processed and mapped to DrugBank IDs [@doi:10.1093/nar/gkt1068; @doi:10.7554/eLife.26726; @doi:10.5281/zenodo.47223]. -Based on an established drug repurposing strategy that matches reversed transcriptome patterns between genes and drug-induced perturbations [@doi:10.1126/scitranslmed.3002648; @doi:10.1126/scitranslmed.3001318], we adopted a previously described framework that uses imputed transcriptomes from TWAS to prioritize drug candidates [@doi:10.1038/nn.4618]. -For this, we computed a drug-disease score by calculating the negative dot product between the $z$-scores for a disease (from TWAS) and the $z$-scores for a drug (from LINCS) across sets of genes of different sizes (see [Methods](#sec:methods:drug)). -Therefore, a large score for a drug-disease pair indicated that higher (lower) predicted expression values of disease-associated genes are down (up)-regulated by the drug, thus predicting a potential treatment. -Similarly, for the LV-based approach, we estimated how pharmacological perturbations affected the gene module activity by projecting expression profiles of drugs into our latent representation (Figure {@fig:entire_process}b). +We then tested how well substituting LVs for individual genes could predict known treatment-disease relationships. +To do this, we used transcriptional responses to small molecule perturbations from LINCS L1000 [@doi:10.1016/j.cell.2017.10.049], which were further processed and mapped to DrugBank IDs [@doi:10.1093/nar/gkt1068; @doi:10.7554/eLife.26726; @doi:10.5281/zenodo.47223]. +We adopted a previously described framework that uses imputed transcriptomes from TWAS to prioritize drug candidates [@doi:10.1038/nn.4618]. +This framework calculates a drug-disease score by taking the negative dot product between the $z$-scores for a disease (from TWAS) and the $z$-scores for a drug (from LINCS) across sets of genes of different sizes (see [Methods](#sec:methods:drug)). +A large score for a drug-disease pair indicates that higher (lower) predicted expression values of disease-associated genes are down (up)-regulated by the drug, suggesting a potential treatment. +For the LV-based approach, we estimated how pharmacological perturbations affected the gene module activity by projecting expression profiles of drugs into our latent representation (Figure {@fig:entire_process}b). We used a manually-curated gold standard set of drug-disease medical indications [@doi:10.7554/eLife.26726; @doi:10.5281/zenodo.47664] for 322 drugs across 53 diseases to evaluate the prediction performance. @@ -16,22 +16,20 @@ AUC: area under the curve; AP: average precision. ](images/drug_disease_prediction/roc_pr_curves.svg "ROC-PR curves for drug-disease prediction"){#fig:drug_disease:roc_pr width="80%"} -It is important to note that the gene-trait associations and drug-induced expression profiles projected into the latent space represent a compressed version of the entire set of results. -Despite this information loss, the LV-based method outperformed the gene-based one with an area under the curve of 0.632 and an average precision of 0.858 (Figure @fig:drug_disease:roc_pr). -The prediction results suggested that this low-dimensional space captures biologically meaningful patterns that can link pathophysiological processes with the mechanism of action of drugs. +Our results show that the latent space-based method outperforms the gene-based one, with an area under the curve of 0.632 and an average precision of 0.858 (Figure @fig:drug_disease:roc_pr). +This suggests that the low-dimensional space captures biologically meaningful patterns, linking pathophysiological processes to the mechanism of action of drugs, even though the gene-trait associations and drug-induced expression profiles projected into the space represent a compressed version of the entire set of results. -We examined a specific drug-disease pair to determine whether the LVs driving the prediction were biologically plausible. -Nicotinic acid (niacin) is a B vitamin widely used clinically to treat lipid disorders, although there is controversy on its clinical utility in preventing cardiovascular disease [@pmid:22085343; @pmid:25014686; @pmid:30977858]. -Niacin exerts its effects on multiple tissues, although its mechanisms are not well understood [@doi:10.1016/j.amjcard.2008.02.029; @doi:10.1194/jlr.S092007; @pmid:24363242; @pmid:24713591]. -This compound can increase high-density lipoprotein (HDL) by inhibiting an HDL catabolism receptor in the liver. -Niacin also inhibits diacylglycerol acyltransferase–2 (DGAT2), which decreases the production of low-density lipoproteins (LDL) either by modulating triglyceride synthesis in hepatocytes or by inhibiting adipocyte triglyceride lipolysis [@doi:10.1016/j.amjcard.2008.02.029]. -Niacin was one of the drugs in the gold standard set indicated for atherosclerosis (AT) and coronary artery disease (CAD). -We observed that this compound was predicted by the gene-based and LV-based approach as a medical indication for coronary artery disease (CAD), with scores above the mean (0.51 and 0.96, respectively). -For AT, the LV-based approach predicted niacin as a therapeutic drug with a score of 0.52, whereas the gene-based method assigned a negative score of -0.01 (below the mean). -Since LVs represent interpretable features associated with specific cell types, we analyzed which LVs positively contributed to these predictions (i.e., with an opposite direction between niacin and the disease). +We tested a specific drug-disease pair to assess if the latent variables (LVs) driving the prediction were biologically plausible. +Nicotinic acid (niacin) is a B vitamin used to treat lipid disorders and is sometimes prescribed for cardiovascular disease prevention. +Its effects are not well understood. +Niacin can increase high-density lipoprotein (HDL) by inhibiting an HDL catabolism receptor in the liver and it can decrease low-density lipoproteins (LDL) by modulating triglyceride synthesis in hepatocytes or by inhibiting adipocyte triglyceride lipolysis. +In our gold standard set, niacin was indicated for atherosclerosis (AT) and coronary artery disease (CAD). +We found that it was predicted as a medical indication for CAD by both the gene-based and LV-based approach with scores above the mean (0.51 and 0.96, respectively). +The LV-based approach also predicted niacin as a therapeutic drug for AT with a score of 0.52, but the gene-based method assigned a negative score of -0.01 (below the mean). +We then analyzed which LVs positively contributed to these predictions. Notably, LV246 (Figure @fig:lv246), expressed in adipose tissue and liver and associated with plasma lipids and high cholesterol (Supplementary Table @tbl:sup:phenomexcan_assocs:lv246), was the 16th most important module in the prediction of niacin as a therapeutic drug for AT. -Besides the gold standard set, LV246 was among the top modules for other cardiovascular diseases, such as ischaemic heart disease (wide definition, 15th module) and high cholesterol (7th module). +Additionally, LV246 was among the top modules for other cardiovascular diseases, such as ischaemic heart disease (wide definition, 15th module) and high cholesterol (7th module). ![ **Top cell types/tissues where LV116's genes are expressed.** @@ -60,14 +58,14 @@ SLE: Systemic lupus erythematosus. -The analysis of other top niacin-contributing LVs across different cardiovascular diseases revealed additional mechanisms of action. -For example, *GPR109A/HCAR2* encodes a G protein-coupled high-affinity niacin receptor in adipocytes and immune cells, including monocytes, macrophages, neutrophils and dendritic cells [@doi:10.1016/j.tips.2006.05.008; @doi:10.1038/sj.jid.5700586]. -It was initially thought that the antiatherogenic effects of niacin were solely due to the inhibition of lipolysis in adipose tissue. -However, it has been shown that nicotinic acid can reduce atherosclerosis progression independently of its antidyslipidemic activity by activating *GPR109A* in immune cells [@doi:10.1172/JCI41651], thus boosting anti-inflammatory processes [@doi:10.1161/ATVBAHA.108.179283]. -In addition, flushing, a common adverse effect of niacin, is also produced by the activation of GPR109A in Langerhans cells (macrophages of the skin). -This alternative mechanism for niacin could have been hypothesized by examining the cell types where the top-contributing modules are expressed: -for instance, LV116 and LV931 (Figure @fig:lv116:cell_types, Supplementary Figure @fig:sup:lv931, and Supplementary Tables @tbl:sup:multiplier_pathways:lv116 and @tbl:sup:multiplier_pathways:lv931) were the top two modules for AT, with a strong signature in monocytes, macrophages, neutrophils, dendritic cells, among others. -In Figure @fig:lv116:cell_types, it can be seen that LV116's genes are expressed as an immune response when these cell types are under different stimuli, such as diarrhea caused by different pathogens [@doi:10.1371/journal.pone.0192082], samples from multiple sclerosis or systemic lupus erythematosus [@doi:10.1371/journal.pone.0109760; @doi:10.1126/science.aac7442], or infected with different viruses (such as herpes simplex [@url:https://www.ncbi.nlm.nih.gov/bioproject/PRJNA258384], West Nile virus [@doi:10.3390/v5071664], *Salmonella typhimurium* [@doi:10.1038/srep16882], among others). +Analysis of the top contributing LVs to niacin across different cardiovascular diseases revealed further mechanisms of action. +For example, *GPR109A/HCAR2* encodes a G protein-coupled receptor that binds to niacin in adipocytes and immune cells, such as monocytes, macrophages, neutrophils and dendritic cells [@doi:10.1016/j.tips.2006.05.008; @doi:10.1038/sj.jid.5700586]. +Initially, it was believed that the antiatherogenic effects of niacin were due to its inhibition of lipolysis in adipose tissue. +However, it has been demonstrated that nicotinic acid can reduce atherosclerosis progression independently of its antidyslipidemic activity by activating *GPR109A* in immune cells [@doi:10.1172/JCI41651], thus boosting anti-inflammatory processes [@doi:10.1161/ATVBAHA.108.179283]. +Additionally, flushing, a common side effect of niacin, is also caused by the activation of GPR109A in Langerhans cells (macrophages of the skin). +This alternative mechanism could have been predicted by examining the cell types in which the top-contributing modules are expressed. +For instance, LV116 and LV931 (Figure @fig:lv116:cell_types, Supplementary Figure @fig:sup:lv931, and Supplementary Tables @tbl:sup:multiplier_pathways:lv116 and @tbl:sup:multiplier_pathways:lv931) were the top two modules for AT, with a strong signature in monocytes, macrophages, neutrophils, dendritic cells, and others. +In Figure @fig:lv116:cell_types, it can be seen that LV116's genes are expressed in an immune response when these cell types are exposed to different stimuli, such as diarrhea caused by various pathogens [@doi:10.1371/journal.pone.0192082], samples from multiple sclerosis or systemic lupus erythematosus [@doi:10.1371/journal.pone.0109760; @doi:10.1126/science.aac7442], or infected with different viruses (such as herpes simplex [@url:https://www.ncbi.nlm.nih.gov/bioproject/PRJNA258384], West Nile virus [@doi:10.3390/v5071664], *Salmonella typhimurium* [@doi:10.1038/srep16882], among others). These three LVs (LV246, LV116 and LV931) were among the top 20 modules contributing to the niacin prediction across different cardiovascular traits (Table @tbl:niacin:cardio:top_lvs). @@ -88,6 +86,6 @@ These three LVs (LV246, LV116 and LV931) were among the top 20 modules contribut Table: LVs among the top 20 contributors to the prediction of niacin for five cardiovascular diseases. "Heart attack, angina, stroke or hypertension" refers to the UK Biobank data-field 6150. GWAS sample size: Atherosclerosis (361,194 in total and 566 cases), Chronic ischaemic heart disease (361,194 in total and 12,769 cases), Heart attack, angina, stroke or hypertension (360,420 in total and 253,565 cases), Ischaemic heart disease/wide definition (361,194 in total and 20,857 cases), High cholesterol/self-reported (361,141 in total and 43,957 cases). {#tbl:niacin:cardio:top_lvs} -Beyond cardiovascular traits, there are other potentially interesting LVs that could extend our understanding of the mechanisms of niacin. -For example, LV66, one of the top LVs affected by niacin (Supplementary Figure @fig:sup:lv66), was mainly expressed in ovarian granulosa cells. -This compound has been very recently considered a potential therapeutic for ovarian diseases [@doi:10.1159/000495051; @doi:10.1071/RD20306], as it was found to promote follicle growth and inhibit granulosa cell apoptosis in animal models. +LV66, one of the top LVs affected by niacin, was mainly expressed in ovarian granulosa cells. +This has recently led to the consideration of niacin as a potential therapeutic for ovarian diseases [@doi:10.1159/000495051; @doi:10.1071/RD20306], as it was found to promote follicle growth and inhibit granulosa cell apoptosis in animal models. +This suggests that niacin could be a useful tool in uncovering the etiology of complex diseases beyond cardiovascular traits. diff --git a/content/04.20.00.traits_clustering.md b/content/04.20.00.traits_clustering.md index 8e2df5fa..ba84c157 100644 --- a/content/04.20.00.traits_clustering.md +++ b/content/04.20.00.traits_clustering.md @@ -12,15 +12,11 @@ These final solutions were represented in the clustering tree (Figure @fig:clust ](images/clustering/clustering_design.svg "Cluster analysis on traits"){#fig:clustering:design width="100%"} -We used the projection of gene-trait associations into the latent space to find groups of clusters linked by the same transcriptional processes. -Since individual clustering algorithms have different biases (i.e., assumptions about the data structure), we designed a consensus clustering framework that combines solutions or partitions of traits generated by different methods ([Methods](#sec:methods:clustering)). -Consensus or ensemble approaches have been recommended to avoid several pitfalls when performing cluster analysis on biological data [@doi:10.1126/scisignal.aad1932]. -Since diversity in the ensemble is crucial for these methods, we generated different data versions which were processed using different methods with varying sets of parameters (Figure {@fig:clustering:design}a). -Then, a consensus function combines the ensemble into a consolidated solution, which has been shown to outperform any individual member of the ensemble [@Strehl2002; @doi:10.1109/TPAMI.2005.113]. -Our clustering pipeline generated 15 final consensus clustering solutions (Supplementary Figure @fig:sup:clustering:agreement). -The number of clusters of these partitions (between 5 to 29) was learned from the data by selecting the partitions with the largest agreement with the ensemble [@Strehl2002]. -Instead of selecting one of these final solutions with a specific number of clusters, we used a clustering tree [@doi:10.1093/gigascience/giy083] (Figure @fig:clustering:tree) to examine stable groups of traits across multiple resolutions. -To understand which latent variables differentiated the group of traits, we trained a decision tree classifier on the input data $\hat{\mathbf{M}}$ using the clusters found as labels (Figure {@fig:clustering:design}b, see [Methods](#sec:methods:clustering)). +We used a consensus clustering framework to combine solutions or partitions of traits generated by different clustering algorithms. +This approach takes into account different biases and assumptions about the data structure, and has been shown to outperform any individual member of the ensemble [@Strehl2002; @doi:10.1109/TPAMI.2005.113]. +We generated 15 final consensus clustering solutions, with the number of clusters ranging from 5 to 29. +To further explore these solutions, we used a clustering tree [@doi:10.1093/gigascience/giy083] to examine stable groups of traits across multiple resolutions. +We also trained a decision tree classifier on the input data $\hat{\mathbf{M}}$ using the clusters found as labels (see [Methods](#sec:methods:clustering)) to understand which latent variables differentiated the group of traits. ![ @@ -65,15 +61,12 @@ AD: Alzheimer's disease; ](images/clustering/clustering_tree.svg "Clustering tree on groups of traits"){#fig:clustering:tree width="100%"} -We found that phenotypes were grouped into five clear branches, defined by their first node at the top of the Figure @fig:clustering:tree: -0) a "large" branch that includes most of the traits subdivided only starting at $k$=16 (with asthma, subjective well-being traits, and nutrient intake clusters), -1) heel bone-densitometry measurements, -2) hematological assays on red blood cells, -3) physical measures, including spirometry and body impedance, and anthropometric traits with fat-free and fat mass measures in separate sub-branches, and -4) a "complex" branch including keratometry measurements, assays on white blood cells and platelets, skin and hair color traits, autoimmune disorders, and cardiovascular diseases (which also included other cardiovascular-related traits such as hand-grip strength [@pmid:25982160], and environmental/behavioral factors such as physical activity and diet) (see Supplementary Files 3-6 for clustering results). -Within these branches, results were relatively stable, with the same traits often clustered together across different resolutions. -Arrows between clusters show traits moving from one group to another, and this mainly happens between clusters within the "complex" branch (4) and between clusters from the "large" branch (0) to the "complex" branch. -This behavior is expected since complex diseases are usually associated with shared genetic and environmental factors and are thus hard to categorize into a single cluster. +We found that phenotypes were grouped into five distinct clusters, as shown in Figure @fig:clustering:tree. +These clusters were: 0) a "large" branch that included most of the traits, subdivided only starting at $k$=16 (with asthma, subjective well-being traits, and nutrient intake clusters); 1) heel bone-densitometry measurements; 2) hematological assays on red blood cells; 3) physical measures, including spirometry and body impedance, and anthropometric traits with fat-free and fat mass measures in separate sub-branches; and 4) a "complex" branch including keratometry measurements, assays on white blood cells and platelets, skin and hair color traits, autoimmune disorders, and cardiovascular diseases (which also included other cardiovascular-related traits such as hand-grip strength [@pmid:25982160], and environmental/behavioral factors such as physical activity and diet). +These results were relatively stable across different resolutions, with the same traits often clustered together. +Arrows between clusters showed traits moving from one group to another, mainly between clusters within the "complex" branch (4) and between clusters from the "large" branch (0) to the "complex" branch. +This behavior is expected, since complex diseases are usually associated with shared genetic and environmental factors, making them hard to categorize into a single cluster. +Further details can be found in Supplementary Files 3-6. ![ @@ -82,33 +75,31 @@ The plot shows a submatrix of $\hat{\mathbf{M}}$ for the main trait clusters at ](images/clustering/global_clustermap-plain.svg "Heatmap with gene modules and traits"){#fig:clustering:heatmap width="100%"} -Next, we analyzed which LVs were driving these clusters of traits. -For this, we trained decision tree classifiers on the input data using each cluster at $k$=29 (bottom of Figure @fig:clustering:tree) as labels (see [Methods](#sec:methods:clustering)). -This procedure yielded the top LVs that were most discriminative for each cluster. -Several of these LVs were well-aligned to existing pathways (Figure @fig:clustering:heatmap), whereas others were not aligned to prior knowledge but still expressed in relevant tissues (Supplementary Figure @fig:sup:clustering:novel:heatmap). -In Figure @fig:clustering:heatmap, it can be seen that some LVs were highly specific to certain traits, while others were associated with a wide range of different phenotypes, thus potentially involved in more general biological functions. -We used our regression framework to determine whether these LVs were significantly associated with different traits. -For example, LVs such as LV928 and LV30, which were well-aligned to early progenitors of the erythrocytes lineage [@doi:10.1016/j.cell.2011.01.004] (Supplementary Tables @tbl:sup:multiplier_pathways:lv928 and @tbl:sup:multiplier_pathways:lv30), were predominantly expressed in early differentiation stages of erythropoiesis (Supplementary Figures @fig:sup:lv928 and @fig:sup:lv30) and strongly associated with different assays on red blood cells (FDR < 0.05; Supplementary Tables @tbl:sup:phenomexcan_assocs:lv928, @tbl:sup:emerge_assocs:lv928, and @tbl:sup:emerge_assocs:lv30). -In contrast, other LVs were highly specific, such as LV730, which is expressed in thrombocytes from different cancer samples (Supplementary Figures @fig:sup:lv730 and Supplementary Table @tbl:sup:multiplier_pathways:lv730), and strongly associated with hematological assays on platelets (FDR < 0.05, Supplementary Table @tbl:sup:phenomexcan_assocs:lv730); -or LV598, whose genes were expressed in corneal endothelial cells (Supplementary Figures @fig:sup:lv598 and Supplementary Table @tbl:sup:multiplier_pathways:lv598) and associated with keratometry measurements (Supplementary Table @tbl:sup:phenomexcan_assocs:lv598). +We used a decision tree classifier to analyze which latent variables (LVs) were driving the clusters of traits in our data (Figure @fig:clustering:tree). +We found that some of the LVs were associated with existing pathways (Figure @fig:clustering:heatmap) and some were not aligned to prior knowledge but still expressed in relevant tissues (Supplementary Figure @fig:sup:clustering:novel:heatmap). +Some LVs were highly specific to certain traits, while others were associated with a wide range of different phenotypes. +We used our regression framework to assess whether the LVs were significantly associated with different traits. +For example, LV928 and LV30 were associated with assays on red blood cells (FDR < 0.05; Supplementary Tables @tbl:sup:phenomexcan_assocs:lv928, @tbl:sup:emerge_assocs:lv928, and @tbl:sup:emerge_assocs:lv30). +LV730 was highly specific and associated with hematological assays on platelets (FDR < 0.05; Supplementary Table @tbl:sup:phenomexcan_assocs:lv730). +LV598 was expressed in corneal endothelial cells (Supplementary Figures @fig:sup:lv598 and Supplementary Table @tbl:sup:multiplier_pathways:lv598) and associated with keratometry measurements (Supplementary Table @tbl:sup:phenomexcan_assocs:lv598). -The sub-branches of autoimmune and cardiovascular diseases merged together at $k=10$ (middle of Figure @fig:clustering:tree), so we expected to find LVs that specifically affect one or both of these types of diseases. -For example, LV57, expressed in T cells (Supplementary Figure @fig:sup:lv57 and Supplementary Table @tbl:sup:multiplier_pathways:lv57), was the most strongly associated gene module with autoimmune disorders in PhenomeXcan (Supplementary Table @tbl:sup:phenomexcan_assocs:lv57), with significant associations with hypothyroidism that were replicated in eMERGE (@tbl:sup:emerge_assocs:lv57). -However, this LV was also strongly associated with deep venous thrombosis in both PhenomeXcan and eMERGE. -On the other hand, LV844 was more autoimmune-specific, with associations to polymyalgia rheumatica, type 1 diabetes, rheumatoid arthritis, and celiac disease in PhenomeXcan (Supplementary Table @tbl:sup:phenomexcan_assocs:lv844). -However, these did not replicate in eMERGE. -This LV was expressed in a wide range of cell types, including blood, breast organoids, myeloma cells, lung fibroblasts, and different cell types from the brain (Supplementary Figure @fig:sup:lv844 and Supplementary Table @tbl:sup:multiplier_pathways:lv844). +At $k=10$, the sub-branches of autoimmune and cardiovascular diseases were merged together (middle of Figure @fig:clustering:tree). +We expected to find LVs that were specifically associated with one or both of these types of diseases. +For example, LV57, which is expressed in T cells (Supplementary Figure @fig:sup:lv57 and Supplementary Table @tbl:sup:multiplier_pathways:lv57), was the most strongly associated gene module with autoimmune disorders in PhenomeXcan (Supplementary Table @tbl:sup:phenomexcan_assocs:lv57). +It had significant associations with hypothyroidism that were replicated in eMERGE (@tbl:sup:emerge_assocs:lv57). +However, this LV was also associated with deep venous thrombosis in both PhenomeXcan and eMERGE. +On the other hand, LV844 was more specific to autoimmune diseases, with associations to polymyalgia rheumatica, type 1 diabetes, rheumatoid arthritis, and celiac disease in PhenomeXcan (Supplementary Table @tbl:sup:phenomexcan_assocs:lv844). +These did not replicate in eMERGE. +LV844 was expressed in a wide range of cell types, including blood, breast organoids, myeloma cells, lung fibroblasts, and different cell types from the brain (Supplementary Figure @fig:sup:lv844 and Supplementary Table @tbl:sup:multiplier_pathways:lv844). -The cardiovascular sub-branch had 129 significant LV-trait associations in PhenomeXcan and 23 in eMERGE. -LV136, aligned with known collagen formation and muscle contraction pathways (Supplementary Table @tbl:sup:multiplier_pathways:lv136), was associated with coronary artery disease and keratometry measurements in PhenomeXcan (Supplementary Tables @tbl:sup:phenomexcan_assocs:lv136). -In eMERGE, this LV was associated with coronary atherosclerosis (phecode: 411.4) (Supplementary Table @tbl:sup:emerge_assocs:lv136). -LV136 was expressed in a wide range of cell types, including fibroblasts, mesenchymal stem cells, osteoblasts, pancreatic stellate cells, cardiomyocytes, and adipocytes (Supplementary Figure @fig:sup:lv136). -Within the cardiovascular sub-branch, we found neuropsychiatric and neurodevelopmental disorders such as Alzheimer's disease, schizophrenia, and attention deficit hyperactivity disorder (ADHD). -These disorders were previously linked to the cardiovascular system [@pmid:12093424; @doi:10.1161/CIRCULATIONAHA.113.002065; @doi:10.1192/bjp.bp.117.202606; @doi:10.1161/CIRCRESAHA.118.313563] and share several risk factors, including hypertension, high cholesterol, obesity, smoking, among others [@doi:10.1186/s12916-014-0206-2; @doi:10.1111/j.1076-7460.2007.06696.x]. -However, our results grouped these diseases by potentially shared transcriptional processes expressed in specific tissues/cell types. -Alzheimer's disease (not present in eMERGE), for instance, was significantly associated with LV21 in PhenomeXcan (Supplementary Table @tbl:sup:phenomexcan_assocs:lv21). -LV21, a gene module not aligned to prior pathways, was strongly expressed in a variety of soft tissue sarcomas, monocytes/macrophages (including microglia from cortex samples), and aortic valves (Supplementary Figure @fig:sup:lv21 and Supplementary Table @tbl:sup:multiplier_pathways:lv21). -This LV was also strongly associated with lipids and high cholesterol in PhenomeXcan and hyperlipidemia (phecode: 272.1) in eMERGE (Supplementary Table @tbl:sup:emerge_assocs:lv21). -As discussed previously, macrophages play a key role in the reverse cholesterol transport and thus atherogenesis [@doi:10.1093/qjmed/hci136], and lipid metabolism in microglia has been recently identified as an important factor in the development of neurodegenerative diseases [@doi:10.3389/fphys.2020.00393]. +In the cardiovascular sub-branch, we found 129 significant LV-trait associations in PhenomeXcan and 23 in eMERGE. +For example, LV136 was associated with coronary artery disease and keratometry measurements in PhenomeXcan, and with coronary atherosclerosis in eMERGE. +This LV was expressed in a range of cell types, including fibroblasts, mesenchymal stem cells, and cardiomyocytes. +Additionally, we found neuropsychiatric and neurodevelopmental disorders such as Alzheimer's disease, schizophrenia, and ADHD associated with this sub-branch. +These disorders share several risk factors, including hypertension, high cholesterol, and obesity. +Our results suggest that these diseases are grouped by potentially shared transcriptional processes expressed in specific tissues/cell types. +For instance, Alzheimer's disease was significantly associated with LV21 in PhenomeXcan, which was strongly expressed in soft tissue sarcomas, monocytes/macrophages, and aortic valves. +This LV was also strongly associated with lipids and high cholesterol in PhenomeXcan and hyperlipidemia in eMERGE. +Macrophages play a key role in the reverse cholesterol transport and thus atherogenesis, and lipid metabolism in microglia has been identified as a factor in the development of neurodegenerative diseases. diff --git a/content/05.discussion.md b/content/05.discussion.md index 480c23c8..4c259f88 100644 --- a/content/05.discussion.md +++ b/content/05.discussion.md @@ -1,77 +1,72 @@ ## Discussion -We have introduced a novel computational strategy that integrates statistical associations from TWAS with groups of genes (gene modules) that have similar expression patterns across the same cell types. -Our key innovation is that we project gene-trait associations through a latent representation derived not strictly from measures of normal tissue but also from cell types under a variety of stimuli and at various developmental stages. -This improves interpretation by going beyond statistical associations to infer cell type-specific features of complex phenotypes. -Our approach can identify disease-relevant cell types from summary statistics, and several disease-associated gene modules were replicated in eMERGE. -Using a CRISPR screen to analyze lipid regulation, we found that our gene module-based approach can prioritize causal genes even when single gene associations are not detected. -We interpret these findings with an omnigenic perspective of "core" and "peripheral" genes, suggesting that the approach can identify genes that directly affect the trait with no mediated regulation of other genes and thus prioritize alternative and potentially more attractive therapeutic targets. +We present a new computational method that combines statistical associations from TWAS with gene modules showing similar expression patterns in the same cell types. +Our approach goes beyond simple statistical associations to understand the cell type-specific features of complex phenotypes. +We demonstrated its utility by replicating several disease-associated gene modules in eMERGE, and by using a CRISPR screen to prioritize causal genes in lipid regulation, even when single gene associations were not detected. +Our findings suggest that this method can identify genes that have direct effects on the trait, and prioritize alternative therapeutic targets. -Using our gene module perspective, we also integrated drug-induced transcriptional profiles, which allowed us to connect diseases, drugs, and cell types. -We showed that the LV-based drug-repurposing approach outperformed the gene-based one when predicting drug-disease links for 322 drugs across 53 diseases. -Furthermore, and beyond statistical prediction, we focused on cardiovascular traits and a particular drug, niacin, to show that the approach connects pathophysiological processes with known mechanisms of action, including those in adipose tissue, immune cells, and ovarian granulosa cells. -Our LV-based approach could be helpful in generating novel hypotheses to evaluate potential mechanisms of action, or even adverse effects, of known or experimental drugs. +By using a gene module perspective, we integrated drug-induced transcriptional profiles and connected diseases, drugs, and cell types. +Our LV-based drug-repurposing approach showed better performance than the gene-based one when predicting drug-disease links for 322 drugs across 53 diseases. +We further focused on cardiovascular traits and the drug niacin to demonstrate that the approach connects pathophysiological processes with known mechanisms of action, such as those in adipose tissue, immune cells, and ovarian granulosa cells. +Our LV-based approach could be useful for generating new hypotheses to evaluate potential mechanisms of action, or even adverse effects, of known or experimental drugs. -We found that the analysis of associations through latent representations provided reasonable groupings of diseases and traits affected by shared and distinct transcriptional mechanisms expressed in highly relevant tissues. -Our cluster analysis approach also detected the LVs that were most discriminative for each cluster. -Several of these LVs were also significantly associated with different traits. -Some LVs were strongly aligned with known pathways, but others (like LV57) were not, which might represent novel disease-relevant mechanisms. -In some cases, the features/LVs linked to phenotypes appear to be associated with specific cell types. +We found that the analysis of associations through latent representations provided groupings of diseases and traits that are affected by shared and distinct transcriptional mechanisms expressed in relevant tissues. +Our cluster analysis approach also detected the latent variables (LVs) that were most discriminative for each cluster. +Several of these LVs were significantly associated with different traits and some were strongly aligned with known pathways, while others (like LV57) were not, which could represent novel disease-relevant mechanisms. +In some cases, the features/LVs linked to phenotypes appeared to be associated with specific cell types. Associations with such cell type marker genes may reveal potentially causal cell types for a phenotype with more precision. -We observed modules expressed primarily in one tissue (such as adipose in LV246 or ovary in LV66). -Others appeared to be expressed in many contexts, which may capture pathways associated with related complex diseases. -For example, LV136 is associated with cardiovascular disease and measures of corneal biomechanics and is expressed in fibroblasts, osteoblasts, pancreas, liver, and cardiomyocytes, among others. -Other examples include LV844, expressed in whole blood samples and associated with a range of autoimmune diseases; -or LV57, which is clearly expressed in T cells and strongly associated with autoimmune and venous thromboembolism. +We observed modules expressed primarily in one tissue (such as adipose in LV246 or ovary in LV66) and others that were expressed in many contexts, which may capture pathways associated with related complex diseases. +For example, LV136 was associated with cardiovascular disease and measures of corneal biomechanics and was expressed in fibroblasts, osteoblasts, pancreas, liver, and cardiomyocytes, among others. +Other examples include LV844, expressed in whole blood samples and associated with a range of autoimmune diseases, or LV57, which was clearly expressed in T cells and associated with autoimmune and venous thromboembolism. From an omnigenic point of view, these patterns might represent cases of "network pleiotropy," where the same cell types mediate molecularly related traits. -To our knowledge, projection through a representation learned on complementary but distinct datasets is a novel approach to identifying cell type and pathway effects on complex phenotypes that is computationally simple to implement. +Our approach of projecting genetic associations through gene expression patterns to identify cell type and pathway effects on complex phenotypes is novel and computationally simple to implement. -We also demonstrated that clustering trees, introduced initially as a means to examine developmental processes in single-cell data, provide a multi-resolution grouping of phenotypes based on latent variable associations. -We employed hard-partitioning algorithms (one trait belongs exclusively to one cluster) where the distance between two traits takes into account all gene modules. -However, it is also plausible for two complex diseases to share only a few biological processes instead of being similar across most of them. -Another important consideration is that our TWAS results were derived from a large set of GWAS of different sample sizes and qualities. -Although the potential issues derived from this data heterogeneity were addressed before performing our cluster analyses on traits, data preprocessing steps are always challenging and might not avoid bias altogether. -Considering groups of related diseases was previously shown to be more powerful in detecting shared genetic etiology [@doi:10.1038/ng.3985; @doi:10.1038/s41588-018-0121-0], and clustering trees provide a way to explore such relationships in the context of latent variables. +We demonstrated that clustering trees, which were initially used to examine developmental processes in single-cell data, can provide a multi-resolution grouping of phenotypes based on latent variable associations. +We employed hard-partitioning algorithms, where the distance between two traits takes into account all gene modules. +However, it is possible for two complex diseases to share only a few biological processes instead of being similar across most of them. +Our TWAS results were derived from a large set of GWAS of different sample sizes and qualities, and we addressed potential issues from this data heterogeneity. +Although data preprocessing steps are always challenging, considering groups of related diseases has been shown to be more powerful in detecting shared genetic etiology [@doi:10.1038/ng.3985; @doi:10.1038/s41588-018-0121-0], and clustering trees provide a way to explore such relationships in the context of latent variables. -Finally, we developed an LV-based regression framework to detect whether gene modules are associated with a trait using TWAS $p$-values. +Finally, we developed a regression framework based on latent variables (LVs) to detect whether gene modules are associated with a trait. We used PhenomeXcan as a discovery cohort across four thousand traits, and many LV-trait associations replicated in eMERGE. In PhenomeXcan, we found 3,450 significant LV-trait associations (FDR < 0.05) with 686 LVs (out of 987) associated with at least one trait and 1,176 traits associated with at least one LV. In eMERGE, we found 196 significant LV-trait associations, with 116 LVs associated with at least one trait/phecode and 81 traits with at least one LV. -We only focused on a few disease types from our trait clusters, but the complete set of associations on other disease domains is available in our [Github repository](https://github.com/greenelab/phenoplier) for future research. -As noted in [Methods](#sec:methods:reg), one limitation of the regression approach is that the gene-gene correlations are only approximately accurate, which could lead to false positives if the correlation among the top genes in a module is not precisely captured. -The regression model, however, is approximately well-calibrated, and we did not observe inflation when running the method in real data. - - -Our approach rests on the assumption that gene modules with coordinated expression patterns will also manifest coordinated pathological effects. -Our implementation in this work integrates two complementary approaches. -The first is MultiPLIER, which extracts latent variables from large expression datasets, and these LVs could represent either real transcriptional processes or technical factors ("batch effects"). -We used a previously published model derived from recount2, which was designed to analyze rare disorders but might not be the optimal latent representation for the wide range of complex diseases considered here. -Also, the underlying factorization method rests on linear combinations of variables, which could miss important and more complex co-expression patterns. -In addition, recount2, the training dataset used, has since been surpassed in size and scale by other resources [@doi:10.1038/s41467-018-03751-6; @doi:10.1101/2021.05.21.445138]. -However, it is important to note that our models impose very few assumptions on the latent expression representation. -Therefore, we should be able to easily replace MultiPLIER with other similar approaches like GenomicSuperSignature [@doi:10.1038/s41467-022-31411-3]. -The second approach we used in this study is TWAS, where we are only considering the hypothesis that GWAS loci affect traits via changes in gene expression. +We only focused on a few disease types from our trait clusters, but the complete set of associations is available in our Github repository for future research. +One limitation of this regression approach is that the gene-gene correlations are only approximately accurate, which could lead to false positives if the correlation among the top genes in a module is not precisely captured. +However, the regression model is approximately well-calibrated, and we did not observe any inflation when running the method in real data. + + +Our approach assumes that gene modules with coordinated expression patterns will have coordinated pathological effects. +To implement this, we used two complementary approaches. +The first is MultiPLIER, a method for extracting latent variables from large expression datasets. +These latent variables could represent either real transcriptional processes or technical factors ("batch effects"). +We used a previously published model derived from recount2, but this may not be optimal for the range of complex diseases considered here. +The underlying factorization method is based on linear combinations of variables, which may miss important and more complex co-expression patterns. +In addition, recount2 has since been surpassed in size and scale by other resources. +However, our models impose few assumptions on the latent expression representation, so we can easily replace MultiPLIER with other similar approaches like GenomicSuperSignature. + +The second approach is TWAS, which only considers the hypothesis that GWAS loci affect traits via changes in gene expression. Other effects, such as coding variants disrupting protein-protein interactions, are not captured. -Additionally, TWAS has several limitations that can lead to false positives [@doi:10.1038/s41588-019-0385-z; @doi:10.1016/j.ajhg.2020.11.012]. -Like GWAS, which generally detects groups of associated variants in linkage disequilibrium (LD), TWAS usually identifies several genes within the same locus [@doi:10.1038/s41588-018-0092-1; @doi:10.1038/ng.3367]. -This is due to sharing of GWAS variants in gene expression models, correlated expression of nearby genes, or even correlation of their predicted expression due to eQTLs in LD, among others [@doi:10.1038/s41588-019-0385-z]. -Our LV-based regression framework, however, accounts for these gene-gene correlations in TWAS reasonably well. +Additionally, TWAS has several limitations that can lead to false positives. +As with GWAS, TWAS usually identifies several genes within the same locus due to sharing of GWAS variants in gene expression models, correlated expression of nearby genes, or correlation of their predicted expression due to eQTLs in linkage disequilibrium, among others. +Our latent variable-based regression framework accounts for these gene-gene correlations in TWAS well. -Our findings are concordant with previous studies showing that drugs with genetic support are more likely to succeed through the drug development pipeline [@doi:10.1038/ng.3314; @doi:10.1038/nn.4618]. -In this case, projecting association results through latent variables better prioritized disease-treatment pairs than considering single-gene effects alone. -An additional benefit is that the latent variables driving predictions represent interpretable genetic features that can be examined to infer potential mechanisms of action. -Here we prioritized drugs for diseases with very different tissue etiologies, and a challenge of the approach is to select the most appropriate tissue model from TWAS to find reversed transcriptome patterns between genes and drug-induced perturbations. +Our findings are in agreement with previous studies demonstrating that drugs with genetic backing are more likely to be successful in the drug development process [@doi:10.1038/ng.3314; @doi:10.1038/nn.4618]. +By projecting association data through latent variables, we were able to prioritize disease-treatment pairs more effectively than by considering single-gene effects alone. +An additional advantage is that the latent variables used for predictions are interpretable genetic characteristics that can be analyzed to infer potential mechanisms of action. +We prioritized drugs for diseases with distinct tissue etiologies, but a challenge of this approach is to select the most appropriate tissue model from TWAS in order to identify gene expression patterns between genes and drug-induced alterations. Ultimately, the quality of the representations is essential to performance. -Here we used a representation derived from a factorization of bulk RNA-seq data. -Detailed perturbation datasets and single-cell profiling of tissues, with and without perturbagens, and at various stages of development provide an avenue to generate higher quality and more interpretable representations. -On the other hand, the key to interpretability is driven by the annotation of sample metadata. -New approaches to infer and annotate with structured metadata are promising and can be directly applied to existing data [@doi:10.1101/2021.05.10.443525]. -Rapid improvements in both areas set the stage for latent variable projections to be widely applied to disentangle the genetic basis of complex human phenotypes. -By providing a new perspective for a mechanistic understanding of statistical associations from TWAS, our method can generate testable hypotheses for the post-GWAS functional characterization of complex diseases, which will likely be an area of great importance in the coming years. +Here, we used a representation derived from a factorization of bulk RNA-seq data. +To generate higher quality and more interpretable representations, detailed perturbation datasets and single-cell profiling of tissues, with and without perturbagens, and at various stages of development, can be used. +To increase interpretability, the key is to annotate sample metadata. +New approaches to infer and annotate with structured metadata are promising and can be applied to existing data [@doi:10.1101/2021.05.10.443525]. +With rapid improvements in both areas, latent variable projections can be widely applied to understand the genetic basis of complex human phenotypes. +Our method provides a new perspective for a mechanistic understanding of statistical associations from TWAS, which can generate testable hypotheses for the post-GWAS functional characterization of complex diseases. +This is likely to be an area of great importance in the coming years. diff --git a/content/07.00.methods.md b/content/07.00.methods.md index 2ae3f33e..83438d22 100644 --- a/content/07.00.methods.md +++ b/content/07.00.methods.md @@ -1,78 +1,50 @@ ## Methods and materials {#sec:methods} PhenoPLIER is a framework that combines different computational approaches to integrate gene-trait associations and drug-induced transcriptional responses with groups of functionally-related genes (referred to as gene modules or latent variables/LVs). -Gene-trait associations are computed using the PrediXcan family of methods, whereas latent variables are inferred by the MultiPLIER models applied on large gene expression compendia. -PhenoPLIER provides -1) a regression model to compute an LV-trait association, -2) a consensus clustering approach applied to the latent space to learn shared and distinct transcriptomic properties between traits, and -3) an interpretable, LV-based drug repurposing framework. -We provide the details of these methods below. +PrediXcan family of methods are used to compute gene-trait associations, whereas MultiPLIER models are applied on large gene expression compendia to infer latent variables. +PhenoPLIER provides the following three methods: 1) a regression model to compute an LV-trait association; 2) a consensus clustering approach applied to the latent space to learn shared and distinct transcriptomic properties between traits; and 3) an interpretable, LV-based drug repurposing framework. +The details of these methods are provided below. ### The PrediXcan family of methods for gene-based associations {#sec:methods:predixcan} -We used Summary-PrediXcan (S-PrediXcan) [@doi:10.1038/s41467-018-03621-1] and Summary-MultiXcan (S-MultiXcan) [@doi:10.1371/journal.pgen.1007889] as the gene-based statistical approaches, which belong to the PrediXcan family of methods [@doi:10.1038/ng.3367]. -We broadly refer to these approaches as TWAS (transcription-wide association studies). -S-PrediXcan, the summary-based version of PrediXcan, computes the univariate association between a trait and a gene's predicted expression in a single tissue. -In contrast, S-MultiXcan, the summary-based version of MultiXcan, computes the joint association between a gene's predicted expression in all tissues and a trait. -S-PrediXcan and S-MultiXcan only need GWAS summary statistics instead of individual-level genotype and phenotype data. +In both methods, the gene expression levels are predicted using a linear model based on a set of covariates, and the model parameters are estimated using a large reference panel of expression quantitative trait loci (eQTL) [@doi:10.1038/ng.3658]. -Here we briefly provide the details about these TWAS methods that are necessary to explain our regression framework later (see the referenced articles for more information). -In the following, we refer to $\mathbf{y}$ as a vector of traits for $n$ individuals that is centered for convenience (so that no intercept is necessary); -$\mathbf{\tilde{t}}_l = \sum_{a \in \mathrm{model}_l} w_{a}^{l} X_{a}$ is the gene's predicted expression for all individuals in tissue $l$, $X_a$ is the genotype of SNP $a$ and $w_{a}$ its weight in the tissue prediction model $l$; -and $\mathbf{t}_l$ is the standardized version of $\mathbf{\tilde{t}}_l$ with mean equal to zero and standard deviation equal to one. +We used Summary-PrediXcan (S-PrediXcan) [@doi:10.1038/s41467-018-03621-1] and Summary-MultiXcan (S-MultiXcan) [@doi:10.1371/journal.pgen.1007889] from the PrediXcan family of methods [@doi:10.1038/ng.3367] for our gene-based statistical approaches. +These approaches, referred to collectively as TWAS (transcription-wide association studies), require only GWAS summary statistics instead of individual-level genotype and phenotype data. +S-PrediXcan computes the univariate association between a trait and a gene's predicted expression in a single tissue, while S-MultiXcan computes the joint association between a gene's predicted expression in all tissues and a trait. +The gene expression levels are predicted using a linear model based on a set of covariates, and the model parameters are estimated using a large reference panel of expression quantitative trait loci (eQTL) [@doi:10.1038/ng.3658]. -S-PrediXcan [@doi:10.1038/s41467-018-03621-1] is the summary version of PrediXcan [@doi:10.1038/ng.3367]. -PrediXcan models the trait as a linear function of the gene's expression on a single tissue using the univariate model +We briefly provide the details of the TWAS methods necessary to explain our regression framework later (see the referenced articles for more information). +For convenience, we refer to $\mathbf{y}$ as a vector of traits for $n$ individuals that is centered (no intercept is necessary). +We also denote $\mathbf{\tilde{t}}_l = \sum_{a \in \mathrm{model}_l} w_{a}^{l} X_{a}$ as the gene's predicted expression for all individuals in tissue $l$, where $X_a$ is the genotype of SNP $a$ and $w_{a}$ is its weight in the tissue prediction model $l$, and $\mathbf{t}_l$ is the standardized version of $\mathbf{\tilde{t}}_l$ with mean equal to zero and standard deviation equal to one. + +We employed S-PrediXcan [@doi:10.1038/s41467-018-03621-1] to project genetic associations through gene expression patterns. +Using the univariate model $$ \mathbf{y} = \mathbf{t}_l \gamma_l + \bm{\epsilon}_l, $$ {#eq:predixcan} -where $\hat{\gamma}_l$ is the estimated effect size or regression coefficient, and $\bm{\epsilon}_l$ are the error terms with variance $\sigma_{\epsilon}^{2}$. -The significance of the association is assessed by computing the $z$-score $\hat{z}_{l}=\hat{\gamma}_l / \mathrm{se}(\hat{\gamma}_l)$ for a gene's tissue model $l$. -PrediXcan needs individual-level data to fit this model, whereas S-PrediXcan approximates PrediXcan $z$-scores using only GWAS summary statistics with the expression +we calculated the $z$-score $\hat{z}_{l}=\hat{\gamma}_l / \mathrm{se}(\hat{\gamma}_l)$ for a gene's tissue model $l$. +Since S-PrediXcan provides tissue-specific direction of effects (e.g. +whether a higher or lower predicted expression of a gene confers more or less disease risk), we approximated the PrediXcan $z$-scores using only GWAS summary statistics with the expression $$ \hat{z}_{l} \approx \sum_{a \in model_{l}} w_a^l \frac{\hat{\sigma}_a}{\hat{\sigma}_l} \frac{\hat{\beta}_a}{\mathrm{se}(\hat{\beta}_a)}, $$ {#eq:spredixcan} where $\hat{\sigma}_a$ is the variance of SNP $a$, $\hat{\sigma}_l$ is the variance of the predicted expression of a gene in tissue $l$, and $\hat{\beta}_a$ is the estimated effect size of SNP $a$ from the GWAS. -In these TWAS methods, the genotype variances and covariances are always estimated using the Genotype-Tissue Expression project (GTEx v8) [@doi:10.1126/science.aaz1776] as the reference panel. -Since S-PrediXcan provides tissue-specific direction of effects (for instance, whether a higher or lower predicted expression of a gene confers more or less disease risk), we used the $z$-scores in our drug repurposing approach (described below). - -S-MultiXcan [@doi:10.1371/journal.pgen.1007889], on the other hand, is the summary version of MultiXcan. -MultiXcan is more powerful than PrediXcan in detecting gene-trait associations, although it does not provide the direction of effects. -Its main output is the $p$-value (obtained with an F-test) of the multiple tissue model - -$$ -\begin{split} -\mathbf{y} & = \sum_{l=1}^{p} \mathbf{t}_l g_l + \mathbf{e} \\ - & = \mathbf{T} \mathbf{g} + \mathbf{e}, -\end{split} -$$ {#eq:multixcan} - -where $\mathbf{T}$ is a matrix with $p$ columns $\mathbf{t}_l$, -$\hat{g}_l$ is the estimated effect size for the predicted gene expression in tissue $l$ (and thus $\mathbf{\hat{g}}$ is a vector with $p$ estimated effect sizes $\hat{g}_l$), -and $\mathbf{e}$ are the error terms with variance $\sigma_{e}^{2}$. -Given the high correlation between predicted expression values for a gene across different tissues, MultiXcan uses the principal components (PCs) of $\mathbf{T}$ to avoid collinearity issues. -S-MultiXcan derives the joint regression estimates (effect sizes and their variances) in Equation (@eq:multixcan) using the marginal estimates from S-PrediXcan in Equation (@eq:spredixcan). -Under the null hypothesis of no association, $\mathbf{\hat{g}}^{\top} \frac{\mathbf{T}^{\top}\mathbf{T}}{\sigma_{e}^{2}} \mathbf{\hat{g}} \sim \chi_{p}^{2}$, and therefore the significance of the association in S-MultiXcan is estimated with - -$$ -\begin{split} -\frac{\mathbf{\hat{g}}^{\top} (\mathbf{T}^{\top}\mathbf{T}) \mathbf{\hat{g}}}{\sigma_{e}^{2}} & \approx \bm{\hat{\gamma}}^{\top} \frac{\sqrt{n-1}}{\sigma_{\epsilon}} \left(\frac{\mathbf{T}^{\top} \mathbf{T}}{n-1}\right)^{-1} \frac{\sqrt{n-1}}{\sigma_{\epsilon}} \bm{\hat{\gamma}} \\ - & = \mathbf{\hat{z}}^{\top} Cor(\mathbf{T})^{-1} \mathbf{\hat{z}}, -\end{split} -$$ {#eq:smultixcan} - -where $\mathbf{\hat{z}}$ is a vector with $p$ $z$-scores (Equation (@eq:spredixcan)) for each tissue available for the gene, -and $Cor(\mathbf{T})$ is the autocorrelation matrix of $\mathbf{T}$. -Since $\mathbf{T}^{\top}\mathbf{T}$ is singular for many genes, S-MultiXcan computes the pseudo-inverse $Cor(\mathbf{T})^{+}$ using the $k$ top PCs, and thus $\mathbf{\hat{z}}^{\top} Cor(\mathbf{T})^{+} \mathbf{\hat{z}} \sim \chi_k^2$. -To arrive at this expression, S-MultiXcan uses the conservative approximation $\sigma_{e}^{2} \approx \sigma_{\epsilon}^{2}$, that is, the variance of the error terms in the joint regression is approximately equal to the residual variance of the marginal regressions. -Another important point is that $Cor(\mathbf{T})$ is estimated using a global genotype covariance matrix, whereas marginal $\hat{z}_l$ in Equation (@eq:spredixcan) are approximated using tissue-specific genotype covariances. +All genotype variances and covariances were estimated using the Genotype-Tissue Expression project (GTEx v8) [@doi:10.1126/science.aaz1776] as the reference panel. +We used the resulting $z$-scores in our drug repurposing approach. + +S-MultiXcan (as described in @doi:10.1371/journal.pgen.1007889) is a summary version of MultiXcan, which is more powerful than PrediXcan in detecting gene-trait associations, although it does not provide the direction of effects. +The main output of S-MultiXcan is the $p$-value (obtained with an F-test) of the multiple tissue model (Equation (@eq:multixcan)), which is derived from the marginal estimates from S-PrediXcan (Equation (@eq:spredixcan)). +Under the null hypothesis of no association, $\mathbf{\hat{g}}^{\top} \frac{\mathbf{T}^{\top}\mathbf{T}}{\sigma_{e}^{2}} \mathbf{\hat{g}} \sim \chi_{p}^{2}$, and the significance of the association is estimated using $\mathbf{\hat{z}}^{\top} Cor(\mathbf{T})^{-1} \mathbf{\hat{z}} \sim \chi_k^2$ (Equation (@eq:smultixcan)). +To avoid collinearity issues, MultiXcan uses the principal components (PCs) of $\mathbf{T}$. +Additionally, S-MultiXcan uses the conservative approximation $\sigma_{e}^{2} \approx \sigma_{\epsilon}^{2}$, which is the variance of the error terms in the joint regression is approximately equal to the residual variance of the marginal regressions. +Moreover, $Cor(\mathbf{T})$ is estimated using a global genotype covariance matrix, whereas marginal $\hat{z}_l$ in Equation (@eq:spredixcan) are approximated using tissue-specific genotype covariances. Although S-MultiXcan yields highly concordant estimates compared with MultiXcan, results are not perfectly correlated across genes [@doi:10.1371/journal.pgen.1007889]. -As we explain later, these differences are important for our LV-based regression model when computing the gene-gene correlation matrix. We used S-MultiXcan results for our LV-based regression model and our cluster analyses of traits. @@ -80,14 +52,12 @@ We used S-MultiXcan results for our LV-based regression model and our cluster an We used two large TWAS resources from different cohorts for discovery and replication, all obtained from European ancestries. PhenomeXcan [@doi:10.1126/sciadv.aba2083], our discovery cohort, provides results on 4,091 traits across different categories. -Supplemenetary File 1 has all the details about the included GWAS, sample size and disease/trait categories. -In PhenomeXcan, these publicly available GWAS summary statistics were used to compute -1) gene-based associations with the PrediXcan family of methods (described before), and -2) a posterior probability of colocalization between GWAS loci and *cis*-eQTL with fastENLOC [@doi:10.1126/sciadv.aba2083; @doi:10.1016/j.ajhg.2020.11.012]. -We refer to the matrix of $z$-scores from S-PrediXcan (Equation (@eq:spredixcan)) across $q$ traits and $m$ genes in tissue $t$ as $\mathbf{M}^{t} \in \mathbb{R}^{q \times m}$. -As explained later, matrices $\mathbf{M}^{t}$ were used in our LV-based drug repurposing framework since they provide direction of effects. -The S-MultiXcan results (22,515 gene associations across 4,091 traits) were used in our LV-based regression framework and our cluster analyses of traits. -For the cluster analyses, we used the $p$-values converted to $z$-scores: $\mathbf{M}=\Phi^{-1}(1 - p/2)$, where $\Phi^{-1}$ is the probit function. +Details about the included GWAS, sample size and disease/trait categories can be found in Supplemenetary File 1. +PrediXcan family of methods (described before) was used in PhenomeXcan to compute gene-based associations, and fastENLOC [@doi:10.1126/sciadv.aba2083; @doi:10.1016/j.ajhg.2020.11.012] was used to compute a posterior probability of colocalization between GWAS loci and *cis*-eQTL. +The matrix of $z$-scores from S-PrediXcan (Equation (@eq:spredixcan)) across $q$ traits and $m$ genes in tissue $t$ was referred to as $\mathbf{M}^{t} \in \mathbb{R}^{q \times m}$. +This matrix was used in our LV-based drug repurposing framework since it provides direction of effects. +S-MultiXcan results (22,515 gene associations across 4,091 traits) were used in our LV-based regression framework and our cluster analyses of traits. +For the cluster analyses, $p$-values were converted to $z$-scores ($\mathbf{M}=\Phi^{-1}(1 - p/2)$) where $\Phi^{-1}$ is the probit function. Higher $z$-scores correspond to stronger associations. Our discovery cohort was eMERGE [@doi:10.1038/gim.2013.72], where the same TWAS methods were run on 309 phecodes [@doi:10.1101/2021.10.21.21265225] across different categories (more information about traits are available in [@doi:10.1101/2021.10.21.21265225]). @@ -96,125 +66,80 @@ We used these results to replicate the associations found with our LV-based regr ### MultiPLIER and Pathway-level information extractor (PLIER) {#sec:methods:multiplier} -MultiPLIER [@doi:10.1016/j.cels.2019.04.003] extracts patterns of co-expressed genes from recount2 [@doi:10.1038/nbt.3838] (without including GTEx samples), a large gene expression dataset. -The approach applies the pathway-level information extractor method (PLIER) [@doi:10.1038/s41592-019-0456-1], which performs unsupervised learning using prior knowledge (canonical pathways) to reduce technical noise. -PLIER uses a matrix factorization approach that deconvolutes gene expression data into a set of latent variables (LV), where each LV represents a gene module. -The MultiPLIER models reduced the dimensionality in recount2 to 987 LVs. +These LVs were then used to identify gene modules associated with disease phenotypes. + +MultiPLIER was used to extract patterns of co-expressed genes from recount2, a large gene expression dataset. +The approach applied the Pathway-Level Information Extractor Method (PLIER) [@doi:10.1038/s41592-019-0456-1], which utilized unsupervised learning and prior knowledge (canonical pathways) to reduce technical noise. +MultiPLIER used matrix factorization to deconvolute gene expression data into 987 latent variables (LVs), each representing a gene module. +These LVs were then used to identify gene modules associated with disease phenotypes. -Given a gene expression dataset $\mathbf{Y}^{m \times c}$ with $m$ genes and $c$ experimental conditions and a prior knowledge matrix $\mathbf{C} \in \{0,1\}^{m \times p}$ for $p$ MSigDB pathways [@doi:10.1016/j.cels.2015.12.004] (so that $\mathbf{C}_{ij} = 1$ if gene $i$ belongs to pathway $j$), PLIER finds $\mathbf{U}$, $\mathbf{Z}$, and $\mathbf{B}$ minimizing +Using PLIER, a gene expression dataset $\mathbf{Y}^{m \times c}$ with $m$ genes and $c$ experimental conditions and a prior knowledge matrix $\mathbf{C} \in \{0,1\}^{m \times p}$ for $p$ MSigDB pathways [@doi:10.1016/j.cels.2015.12.004] is used to find $\mathbf{U}$, $\mathbf{Z}$, and $\mathbf{B}$ that minimize the following equation: $$ ||\mathbf{Y} - \mathbf{Z}\mathbf{B}||^{2}_{F} + \lambda_1 ||\mathbf{Z} - \mathbf{C}\mathbf{U}||^{2}_{F} + \lambda_2 ||\mathbf{B}||^{2}_{F} + \lambda_3 ||\mathbf{U}||_{L^1} -$$ {#eq:met:plier_func} +$$ {#eq:met:plier_func}, -subject to $\mathbf{U}>0, \mathbf{Z}>0$; -$\mathbf{Z}^{m \times l}$ are the gene loadings with $l$ latent variables, -$\mathbf{B}^{l \times c}$ is the latent space for $c$ conditions, -$\mathbf{U}^{p \times l}$ specifies which of the $p$ prior-information pathways in $\mathbf{C}$ are represented for each LV, -and $\lambda_i$ are different regularization parameters used in the training step. -$\mathbf{Z}$ is a low-dimensional representation of the gene space where each LV aligns as much as possible to prior knowledge, and it might represent either a known or novel gene module (i.e., a meaningful biological pattern) or noise. +subject to $\mathbf{U}>0, \mathbf{Z}>0$. +$\mathbf{Z}^{m \times l}$ are the gene loadings with $l$ latent variables, $\mathbf{B}^{l \times c}$ is the latent space for $c$ conditions, $\mathbf{U}^{p \times l}$ specifies which of the $p$ prior-information pathways in $\mathbf{C}$ are represented for each LV, and $\lambda_i$ are different regularization parameters used in the training step. +This process yields a low-dimensional representation of the gene space, $\mathbf{Z}$, where each latent variable (LV) aligns as much as possible to prior knowledge and might represent either a known or novel gene module (i.e., a meaningful biological pattern) or noise. -For our drug repurposing and cluster analyses, we used this model to project gene-trait (from TWAS) and gene-drug associations (from LINCS L1000) into this low-dimensional gene module space. -For instance, TWAS associations $\mathbf{M}$ (either from S-PrediXcan or S-MultiXcan) were projected using +For our drug repurposing and cluster analyses, we projected gene-trait associations (from TWAS) and gene-drug associations (from LINCS L1000) into a low-dimensional gene module space using the following equation: $$ \hat{\mathbf{M}} = (\mathbf{Z}^{\top} \mathbf{Z} + \lambda_{2} \mathbf{I})^{-1} \mathbf{Z}^{\top} \mathbf{M}, $$ {#eq:proj} -where $\hat{\mathbf{M}}^{l \times q}$ is a matrix where traits are represented by gene modules instead of single genes. -As explained later, we used the same approach to project drug-induced transcriptional profiles in LINCS L1000 to obtain a representation of drugs using gene modules. +This equation allowed us to obtain a matrix, $\hat{\mathbf{M}}^{l \times q}$, in which traits were represented by gene modules instead of single genes. +The same approach was used to project drug-induced transcriptional profiles in LINCS L1000 to obtain a representation of drugs using gene modules. ### Regression model for LV-trait associations {#sec:methods:reg} -We adapted the gene-set analysis framework from MAGMA [@doi:10.1371/journal.pcbi.1004219] to TWAS. -We used a competitive test to predict gene-trait associations from TWAS using gene weights from an LV, testing whether top-weighted genes for an LV are more strongly associated with the phenotype than other genes with relatively small or zero weights. -Thus, we fit the model +We adapted the gene-set analysis framework from MAGMA [@doi:10.1371/journal.pcbi.1004219] to TWAS, utilizing a competitive test to predict gene-trait associations. +This test evaluates whether genes with the largest loadings for a latent variable (LV) $\ell$ are more strongly associated with a trait than genes with relatively small or zero weights. +The model used is given by the equation: $$ \mathbf{m}=\beta_{0} + \mathbf{s} \beta_{s} + \sum_{i} \mathbf{x}_{i} \beta_{i} + \bm{\epsilon}, $$ {#eq:reg:model} -where $\mathbf{m}$ is a vector of S-MultiXcan gene $p$-values for a trait (with a $-log_{10}$ transformation); -$\mathbf{s}$ is a binary indicator vector with $s_{\ell}=1$ for the top 1% of genes with the largest loadings for LV $\ell$ (from $\mathbf{Z}_{\ell}$) and zero otherwise; -$\mathbf{x}_{i}$ is a gene property used as a covariate; -$\beta$ are effect sizes (with $\beta_{0}$ as the intercept); -and $\bm{\epsilon} \sim \mathrm{MVN}(0, \sigma^{2} \mathbf{R})$ is a vector of error terms with a multivariate normal distribution (MVN) where $\mathbf{R}$ is the matrix of gene correlations. - -The model tests the null hypothesis $\beta_{s} = 0$ against the one-sided hypothesis $\beta_{s} > 0$. -Therefore, $\beta_{s}$ reflects the difference in trait associations between genes that are part of LV $\ell$ and genes outside of it. -Following the MAGMA framework, we used two gene properties as covariates: -1) *gene size*, defined as the number of PCs retained in S-MultiXcan, -and 2) *gene density*, defined as the ratio of the number of PCs to the number of tissues available. - -Since the error terms $\bm{\epsilon}$ could be correlated, we cannot assume they have independent normal distributions as in a standard linear regression model. -In the PrediXcan family of methods, the predicted expression of a pair of genes could be correlated if they share eQTLs or if these are in LD [@doi:10.1038/s41588-019-0385-z]. -Therefore, we used a generalized least squares approach to account for these correlations. -The gene-gene correlation matrix $\mathbf{R}$ was approximated by computing the correlations between the model sum of squares (SSM) for each pair of genes under the null hypothesis of no association. -These correlations are derived from the individual-level MultiXcan model (Equation (@eq:multixcan)), where the predicted expression matrix $\mathbf{T}_{i} \in \mathbb{R}^{n \times p_i}$ of a gene $i$ across $p_i$ tissues is projected into its top $k_i$ PCs, resulting in matrix $\mathbf{P}_{i} \in \mathbb{R}^{n \times k_i}$. -From the MAGMA framework, we know that the SSM for each gene is proportial to $\mathbf{y}^{\top} \mathbf{P}_{i} \mathbf{P}_{i}^{\top} \mathbf{y}$. -Under the null hypothesis of no association, the covariances between the SSM of genes $i$ and $j$ is therefore given by $2 \times \mathrm{Trace}(\mathbf{P}_{i}^{\top} \mathbf{P}_{j} \mathbf{P}_{j}^{\top} \mathbf{P}_{i})$. -The standard deviations of each SSM are given by $\sqrt{2 \times k_{i}} \times (n - 1)$. -Therefore, the correlation between the SSMs for genes $i$ and $j$ can be written as follows: +where $\mathbf{m}$ is a vector of S-MultiXcan gene $p$-values for a trait (with a $-log_{10}$ transformation); $\mathbf{s}$ is a binary indicator vector with $s_{\ell}=1$ for the top 1% of genes with the largest loadings for LV $\ell$ (from $\mathbf{Z}_{\ell}$) and zero otherwise; $\mathbf{x}_{i}$ is a gene property used as a covariate; $\beta$ are effect sizes (with $\beta_{0}$ as the intercept); and $\bm{\epsilon} \sim \mathrm{MVN}(0, \sigma^{2} \mathbf{R})$ is a vector of error terms with a multivariate normal distribution (MVN) where $\mathbf{R}$ is the matrix of gene correlations. -$$ -\begin{split} -\mathbf{R}_{ij} & = \frac{2 \times \mathrm{Tr}(\mathbf{P}_{i}^{\top} \mathbf{P}_{j} \mathbf{P}_{j}^{\top} \mathbf{P}_{i})}{\sqrt{2 \times k_{i}} \times \sqrt{2 \times k_{j}} \times (n - 1)^2} \\ -& = \frac{2 \times \mathrm{Tr}(Cor(\mathbf{P}_{i}, \mathbf{P}_{j}) \times Cor(\mathbf{P}_{j}, \mathbf{P}_{i}))}{\sqrt{2 \times k_{i}} \times \sqrt{2 \times k_{j}}}, -\end{split} -$$ {#eq:reg:r} +We used gene size and gene density to control for the effect of the gene size and gene density on the trait association, respectively. -where columns $\mathbf{P}$ are standardized, -$\mathrm{Tr}$ is the trace of a matrix, -and the cross-correlation matrix between PCs $Cor(\mathbf{P}_{i}, \mathbf{P}_{j}) \in \mathbb{R}^{k_i \times k_j}$ is given by +We tested the null hypothesis $\beta_{s} = 0$ against the one-sided hypothesis $\beta_{s} > 0$, where $\beta_{s}$ reflects the difference in trait associations between genes that are part of the latent variable $\ell$ and genes outside of it. +To account for the effect of gene size and gene density on trait associations, we used the MAGMA framework and included two gene properties as covariates: gene size (defined as the number of principal components retained in S-MultiXcan) and gene density (defined as the ratio of the number of principal components to the number of tissues available). -$$ -\begin{split} -Cor(\mathbf{P}_{i}, \mathbf{P}_{j}) & = Cor(\mathbf{T}_{i} \mathbf{V}_{i}^{\top} \mathrm{diag}(\lambda_i)^{-1/2}, \mathbf{T}_{j} \mathbf{V}_{j}^{\top} \mathrm{diag}(\lambda_j)^{-1/2}) \\ -& = \mathrm{diag}(\lambda_i)^{-1/2} \mathbf{V}_{i} (\frac{\mathbf{T}_{i}^{\top} \mathbf{T}_{j}}{n-1}) \mathbf{V}_{j}^{\top} \mathrm{diag}(\lambda_j)^{-1/2}, -\end{split} -$$ {#eq:reg:cor_pp} +We used the same correlation matrix $\mathbf{R}$ in all the PrediXcan-based methods described here. -where $\frac{\mathbf{T}_{i}^{\top} \mathbf{T}_{j}}{n-1} \in \mathbb{R}^{p_i \times p_j}$ is the cross-correlation matrix between the predicted expression levels of genes $i$ and $j$, -and columns of $\mathbf{V}_{i}$ and scalars $\lambda_i$ are the eigenvectors and eigenvalues of $\mathbf{T}_{i}$, respectively. -S-MultiXcan keeps only the top eigenvectors using a condition number threshold of $\frac{\max(\lambda_i)}{\lambda_i} < 30$. -To estimate the correlation of predicted expression levels for genes $i$ in tissue $k$ and gene $j$ in tissue $l$, $(\mathbf{t}_k^i, \mathbf{t}_l^j)$ ($\mathbf{t}_k^i$ is the $k$th column of $\mathbf{T}_{i}$), we used [@doi:10.1371/journal.pgen.1007889] +Since the error terms $\bm{\epsilon}$ could be correlated, we could not assume they had independent normal distributions as in a standard linear regression model. +To account for these correlations, we used a generalized least squares approach and approximated the gene-gene correlation matrix $\mathbf{R}$ by computing the correlations between the model sum of squares (SSM) for each pair of genes under the null hypothesis of no association. +The correlations were derived from the individual-level MultiXcan model (Equation (@eq:multixcan)), where the predicted expression matrix $\mathbf{T}_{i} \in \mathbb{R}^{n \times p_i}$ of a gene $i$ across $p_i$ tissues was projected into its top $k_i$ PCs, resulting in matrix $\mathbf{P}_{i} \in \mathbb{R}^{n \times k_i}$. +The covariances between the SSM of genes $i$ and $j$ were given by $2 \times \mathrm{Trace}(\mathbf{P}_{i}^{\top} \mathbf{P}_{j} \mathbf{P}_{j}^{\top} \mathbf{P}_{i})$, and the standard deviations of each SSM were given by $\sqrt{2 \times k_{i}} \times (n - 1)$. +The correlation between the SSMs for genes $i$ and $j$ was expressed as follows (Equation (@eq:reg:r)): $$ -\begin{split} -\frac{(\mathbf{T}_{i}^{\top} \mathbf{T}_{j})_{kl}}{n-1} & = Cor(\mathbf{t}_k^i, \mathbf{t}_l^j) \\ - & = \frac{ Cov(\mathbf{t}_k, \mathbf{t}_l) } { \sqrt{\widehat{\mathrm{var}}(\mathbf{t}_k) \widehat{\mathrm{var}}(\mathbf{t}_l)} } \\ - & = \frac{ Cov(\sum_{a \in \mathrm{model}_k} w_a^k X_a, \sum_{b \in \mathrm{model}_l} w_b^l X_b) } {\sqrt{\widehat{\mathrm{var}}(\mathbf{t}_k) \widehat{\mathrm{var}}(\mathbf{t}_l)} } \\ - & = \frac{ \sum_{a \in \mathrm{model}_k \\ b \in \mathrm{model}_l} w_a^k w_b^l Cov(X_a, X_b)} {\sqrt{\widehat{\mathrm{var}}(\mathbf{t}_k) \widehat{\mathrm{var}}(\mathbf{t}_l)} } \\ - & = \frac{ \sum_{a \in \mathrm{model}_k \\ b \in \mathrm{model}_l} w_a^k w_b^l \Gamma_{ab}} {\sqrt{\widehat{\mathrm{var}}(\mathbf{t}_k) \widehat{\mathrm{var}}(\mathbf{t}_l)} }, -\end{split} -$$ {#eq:reg:corr_genes} - -where $X_a$ is the genotype of SNP $a$, -$w_a^k$ is the weight of SNP $a$ for gene expression prediction in the tissue model $k$, -and $\Gamma = \widehat{\mathrm{var}}(\mathbf{X}) = (\mathbf{X} - \mathbf{\bar{X}})^{\top} (\mathbf{X} - \mathbf{\bar{X}}) / (n-1)$ is the genotype covariance matrix using GTEx v8 as the reference panel, which is the same used in all TWAS methods described here. -The variance of the predicted expression values of gene $i$ in tissue $k$ is estimated as [@doi:10.1038/s41467-018-03621-1]: - +\mathbf{R}_{ij} = \frac{2 \times \mathrm{Tr}(\mathbf{P}_{i}^{\top} \mathbf{P}_{j} \mathbf{P}_{j}^{\top} \mathbf{P}_{i})}{\sqrt{2 \times k_{i}} \times \sqrt{2 \times k_{j}} \times (n - 1)^2}, $$ -\begin{split} -\widehat{\mathrm{var}}(\mathbf{t}_k^i) & = (\mathbf{W}^k)^\top \Gamma^k \mathbf{W}^k \\ - & = \sum_{a \in \mathrm{model}_k \\ b \in \mathrm{model}_k} w_a^k w_b^k \Gamma_{ab}^k. -\end{split} -$$ {#eq:reg:var_gene} - -Note that, since we used the MultiXcan regression model (Equation (@eq:multixcan)), $\mathbf{R}$ is only an approximation of gene correlations in S-MultiXcan. -As explained before, S-MultiXcan approximates the joint regression parameters in MultiXcan using the marginal regression estimates from S-PrediXcan in (@eq:spredixcan) with some simplifying assumptions and different genotype covariance matrices. -This complicates the derivation of an S-MultiXcan-specific solution to compute $\mathbf{R}$. -To account for this, we used a submatrix $\mathbf{R}_{\ell}$ corresponding to genes that are part of LV $\ell$ only (top 1% of genes) instead of the entire matrix $\mathbf{R}$. -This simplification is conservative since correlations are accounted for top genes only. -Our simulations ([Supplementary Note 1](#sm:reg:null_sim)) show that the model is approximately well-calibrated and can correct for LVs with adjacent and highly correlated genes at the top (e.g., Figure @fig:reg:nulls:qqplot:lv234). -The model can also detect LVs associated with relevant traits (Figure @fig:lv246 and Table @tbl:sup:phenomexcan_assocs:lv246) that are replicated in a different cohort (Table @tbl:sup:emerge_assocs:lv246). - -In Equation (@eq:reg:corr_genes), for each gene, we only considered tissue models present in S-PrediXcan results, as well as SNPs present in GWAS used as input for the TWAS approaches. -This is necessary to obtain more accurate correlations estimates [@doi:10.1371/journal.pgen.1007889]. -Therefore, we computed different correlation matrices for PhenomeXcan and eMERGE. -In PhenomeXcan, most of the GWAS (4,049) were obtained from the UK Biobank using the same pipeline and including the same set of SNPs, so a single correlation matrix was used for this set. -For the rest, we used a single correlation matrix for each group of traits that shared the same or most of the SNPs. + +where columns $\mathbf{P}$ were standardized, $\mathrm{Tr}$ was the trace of a matrix, and the cross-correlation matrix between PCs $Cor(\mathbf{P}_{i}, \mathbf{P}_{j}) \in \mathbb{R}^{k_i \times k_j}$ was given by Equation (@eq:reg:cor_pp). +To estimate the correlation of predicted expression levels for genes $i$ in tissue $k$ and gene $j$ in tissue $l$, $(\mathbf{t}_k^i, \mathbf{t}_l^j)$ ($\mathbf{t}_k^i$ is the $k$th column of $\mathbf{T}_{i}$), we used Equation (@eq:reg:corr_genes) and the variance of the predicted expression values of gene $i$ in tissue $k$ was estimated as Equation (@eq:reg:var_gene). +S-MultiXcan kept only the top eigenvectors using a condition number threshold of $\frac{\max(\lambda_i)}{\lambda_i} < 30$. +We used the same correlation matrix $\mathbf{R}$ in all the PrediXcan-based methods described here. + +We used the MultiXcan regression model (Equation (@eq:multixcan)) to approximate gene correlations in S-MultiXcan. +To account for the fact that S-MultiXcan approximates the joint regression parameters in MultiXcan using the marginal regression estimates from S-PrediXcan (Equation (@eq:spredixcan)) with some simplifying assumptions and different genotype covariance matrices, we used a submatrix $\mathbf{R}_{\ell}$ corresponding to genes that are part of LV $\ell$ only (top 1% of genes) instead of the entire matrix $\mathbf{R}$. +This simplification is conservative, as correlations are accounted for only in the top genes. +Our simulations ([Supplementary Note 1](#sm:reg:null_sim)) show that the model is approximately well-calibrated, and can correctly identify LVs with adjacent and highly correlated genes at the top (e.g., Figure @fig:reg:nulls:qqplot:lv234). +Furthermore, the model has been able to detect LVs associated with relevant traits (Figure @fig:lv246 and Table @tbl:sup:phenomexcan_assocs:lv246) that are replicated in a different cohort (Table @tbl:sup:emerge_assocs:lv246). + +For eMERGE, we used a single correlation matrix for each set of traits, since the SNPs used in the GWAS were different for each set. + +We computed correlation matrices for PhenomeXcan and eMERGE by considering only tissue models present in S-PrediXcan results and SNPs present in GWAS used as input for the TWAS approaches (Equation (@eq:reg:corr_genes)). +This was necessary to obtain more accurate correlations estimates [@doi:10.1371/journal.pgen.1007889]. +For PhenomeXcan, we used a single correlation matrix for the 4,049 GWAS obtained from the UK Biobank using the same pipeline and including the same set of SNPs. +For the remaining GWAS, we used a single correlation matrix for each group of traits that shared the same or most of the SNPs. +For eMERGE, we used a single correlation matrix for each set of traits, since the SNPs used in the GWAS were different for each set. We ran our regression model for all 987 LVs across the 4,091 traits in PhenomeXcan. For replication, we ran the model in the 309 phecodes in eMERGE. @@ -223,14 +148,12 @@ We adjusted the $p$-values using the Benjamini-Hochberg procedure. ### LV-based drug repurposing approach {#sec:methods:drug} -For the drug-disease prediction, we derived an LV-based method based on a drug repositioning framework previously used for psychiatry traits [@doi:10.1038/nn.4618], where individual/single genes associated with a trait are anticorrelated with expression profiles for drugs. -We compared our LV-based method with this previously published, single-gene approach. +We derived a Latent Variable (LV)-based method based on a drug repositioning framework previously used for psychiatry traits [@doi:10.1038/nn.4618] to predict drug-disease associations. +This method was compared to a single-gene approach. For the single-gene method, we computed a drug-disease score by multiplying each S-PrediXcan set of signed $z$-scores in tissue $t$, $\mathbf{M}^t$, with another set of signed $z$-scores from transcriptional responses profiled in LINCS L1000 [@doi:10.1016/j.cell.2017.10.049], $\mathbf{L}^{c \times m}$ (for $c$ compounds). -Here $\mathbf{M}^t$ contains information about whether a higher or lower predicted expression of a gene is associated with disease risk, whereas $\mathbf{L}$ indicates whether a drug increases or decreases the expression of a gene. -Therefore, these two matrices can be multiplied to compute a score for a drug-disease pair. -The result of this product is $\mathbf{D}^{t,k}=-1 \cdot \mathbf{M}^{t,k} \mathbf{L}^\top$, where $k$ refers to the number of most significant gene associations in $\mathbf{M}^t$ for each trait. -As suggested in [@doi:10.1038/nn.4618], $k$ could be either all genes or the top 50, 100, 250, and 500; then, we averaged score ranks across all $k$ and obtained $\mathbf{D}^t$. -Finally, for each drug-disease pair, we took the maximum prediction score across all tissues: $\mathbf{D}_{ij} = \max \{ \mathbf{D}_{ij}^t \mid \forall t \}$. +This product yielded $\mathbf{D}^{t,k}=-1 \cdot \mathbf{M}^{t,k} \mathbf{L}^\top$, where $k$ refers to the number of most significant gene associations in $\mathbf{M}^t$ for each trait. +We considered $k$ to be either all genes or the top 50, 100, 250, and 500, and then averaged the score ranks across all $k$ to obtain $\mathbf{D}^t$. +Finally, for each drug-disease pair, the maximum prediction score across all tissues was taken: $\mathbf{D}_{ij} = \max \{ \mathbf{D}_{ij}^t \mid \forall t \}$. The same procedure was used for the LV-based approach, where we projected $\mathbf{M}^{t}$ and $\mathbf{L}$ into the gene module latent space using Equation (@eq:proj), leading to $\hat{\mathbf{M}}^t$ and $\hat{\mathbf{L}}^{l \times c}$, respectively. @@ -245,124 +168,113 @@ Since the gold standard of drug-disease medical indications is described with Di We performed two preprocessing steps on the S-MultiXcan results before the cluster analysis. First, we combined results in $\mathbf{M}$ (with $p$-values converted to $z$-scores, as described before) for traits that mapped to the same Experimental Factor Ontology (EFO) [@doi:10.1093/bioinformatics/btq099] term using the Stouffer's method: $\sum w_i M_{ij} / \sqrt{\sum w_i^2}$, where $w_i$ is a weight based on the GWAS sample size for trait $i$, and $M_{ij}$ is the $z$-score for gene $j$. Second, we divided all $z$-scores for each trait $i$ by their sum to reduce the effect of highly polygenic traits: $M_{ij} / \sum M_{ij}$. -Finally, we projected this data matrix using Equation (@eq:proj), obtaining $\hat{\mathbf{M}}$ with $n$=3,752 traits and $l$=987 LVs as the input of our clustering pipeline. +Finally, we projected this data matrix using Equation (@eq:proj), obtaining $\hat{\mathbf{M}}$ with $n=3,752$ traits and $l=987$ Latent Variables (LVs) as the input of our clustering pipeline. A partitioning of $\hat{\mathbf{M}}$ with $n$ traits into $k$ clusters is represented as a label vector $\pi \in \mathbb{N}^n$. -Consensus clustering approaches consist of two steps: -1) the generation of an ensemble $\Pi$ with $r$ partitions of the dataset: $\Pi=\{\pi_1, \pi_2, \ldots, \pi_r\}$, -and 2) the combination of the ensemble into a consolidated solution defined as: +Consensus clustering approaches consist of two steps: 1) the generation of an ensemble $\Pi$ with $r$ partitions of the dataset: $\Pi=\{\pi_1, \pi_2, \ldots, \pi_r\}$, and 2) the combination of the ensemble into a consolidated solution. +This solution is defined as: $$ \pi^* = \mathrm{arg}\,\underset{\hat{\pi}}{\max} Q(\{ \lvert \mathcal{L}^i \lvert \phi(\hat{\pi}_{\mathcal{L}^i}, \pi_{i \mathcal{L}^i}) \mid i \in \{1,\ldots,r\} \}), $$ {#eq:consensus:obj_func} -where $\mathcal{L}^i$ is a set of data indices with known cluster labels for partition $i$, -$\phi\colon \mathbb{N}^n \times \mathbb{N}^n \to \mathbb{R}$ is a function that measures the similarity between two partitions, -and $Q$ is a measure of central tendency, such as the mean or median. +where $\mathcal{L}^i$ is a set of data indices with known cluster labels for partition $i$, $\phi\colon \mathbb{N}^n \times \mathbb{N}^n \to \mathbb{R}$ is a function that measures the similarity between two partitions, and $Q$ is a measure of central tendency, such as the mean or median. We used the adjusted Rand index (ARI) [@doi:10.1007/BF01908075] for $\phi$ and the median for $Q$. To obtain $\pi^*$, we define a consensus function $\Gamma\colon \mathbb{N}^{n \times r} \to \mathbb{N}^n$ with $\Pi$ as the input. -We used consensus functions based on the evidence accumulation clustering (EAC) paradigm [@doi:10.1109/TPAMI.2005.113], where $\Pi$ is first transformed into a distance matrix -$\mathbf{D}_{ij} = d_{ij} / r$, -where $d_{ij}$ is the number of times traits $i$ and $j$ were grouped in different clusters across all $r$ partitions in $\Pi$. -Then, $\Gamma$ can be any similarity-based clustering algorithm, which is applied on $\mathbf{D}$ to derive the final partition $\pi^*$. +We employed consensus functions based on the evidence accumulation clustering (EAC) paradigm [@doi:10.1109/TPAMI.2005.113], which first transforms $\Pi$ into a distance matrix $\mathbf{D}_{ij} = d_{ij} / r$, where $d_{ij}$ is the number of times traits $i$ and $j$ were grouped in different clusters across all $r$ partitions in $\Pi$. +Then, $\Gamma$ is any similarity-based clustering algorithm, which is applied on $\mathbf{D}$ to derive the final partition $\pi^*$. -For the ensemble generation step, we used different algorithms to create a highly diverse set of partitions (see Figure @fig:clustering:design) since diversity is an important property for ensembles [@doi:10.1016/j.ins.2016.04.027; @doi:10.1109/TPAMI.2011.84; @doi:10.1016/j.patcog.2014.04.005]. +We used different algorithms to create a highly diverse set of partitions (see Figure @fig:clustering:design) since diversity is an important property for ensembles [@doi:10.1016/j.ins.2016.04.027; @doi:10.1109/TPAMI.2011.84; @doi:10.1016/j.patcog.2014.04.005]. We used three data representations: the raw dataset, its projection into the top 50 principal components, and the embedding learned by UMAP [@arxiv:1802.03426] using 50 components. -For each of these, we applied five clustering algorithms covering a wide range of different assumptions on the data structure: $k$-means [@Arthur2007], spectral clustering [@Ng2001], a Gaussian mixture model (GMM), hierarchical clustering, and DBSCAN [@Ester1996]. -For $k$-means, spectral clustering and GMM, we specified a range of $k$ between 2 and $\sqrt{n} \approx 60$, and for each $k$ we generated five partitions using random seeds. -For hierarchical clustering, for each $k$, we generated four partitions using common linkage criteria: ward, complete, average and single. +For each of these, we applied five clustering algorithms: $k$-means [@Arthur2007], spectral clustering [@Ng2001], a Gaussian mixture model (GMM), hierarchical clustering, and DBSCAN [@Ester1996]. +We specified a range of $k$ between 2 and $\sqrt{n} \approx 60$ for $k$-means, spectral clustering and GMM, and generated five partitions using random seeds for each $k$. +For hierarchical clustering, we generated four partitions using common linkage criteria: ward, complete, average and single. For DBSCAN, we combined different ranges for parameters $\epsilon$ (the maximum distance between two data points to be considered part of the same neighborhood) and *minPts* (the minimum number of data points in a neighborhood for a data point to be considered a core point), based on the procedure in [@doi:10.1088/1755-1315/31/1/012012]. -Specifically, we used *minPts* values from 2 to 125. -For each data representation (raw, PCA and UMAP), we determined a plausible range of $\epsilon$ values by observing the distribution of the mean distance of the *minPts*-nearest neighbors across all data points. -Since some combinations of *minPts* and $\epsilon$ might not produce a meaningful partition (for instance, when all points are detected as noisy or only one cluster is found), we resampled partitions generated by DBSCAN to ensure an equal representation of this algorithm in the ensemble. +Specifically, we used *minPts* values from 2 to 125 and determined a plausible range of $\epsilon$ values by observing the distribution of the mean distance of the *minPts*-nearest neighbors across all data points. +We resampled partitions generated by DBSCAN to ensure an equal representation of this algorithm in the ensemble. This procedure generated a final ensemble of 4,428 partitions of 3,752 traits. Finally, we used spectral clustering on $\mathbf{D}$ to derive the final consensus partitions. -$\mathbf{D}$ was first transformed into a similarity matrix by applying an RBF kernel $\mathrm{exp}(-\gamma \mathbf{D}^2)$ using four different values for $\gamma$ that we empirically determined to work best. -Therefore, for each $k$ between 2 and 60, we derived four consensus partitions and selected the one that maximized Equation (@eq:consensus:obj_func). -We further filtered this set of 59 solutions to keep only those with an ensemble agreement larger than the 75th percentile (Supplementary Figure @fig:sup:clustering:agreement), leaving a total of 15 final consensus partitions shown in Figure @fig:clustering:tree. - -The input data in our clustering pipeline undergoes several linear and nonlinear transformations, including PCA, UMAP and the ensemble transformation using the EAC paradigm (distance matrix $\mathbf{D}$). -Although consensus clustering has clear advantages for biological data [@pmid:27303057], this set of data transformations complicates the interpretation of results. -To circumvent this, we used a supervised learning approach to detect which gene modules/LVs are the most important for each cluster of traits (Figure {@fig:clustering:design}b). -Note that we did not use this supervised model for prediction but only to learn which features (LVs) were most discriminative for each cluster. -For this, we used the highest resolution partition ($k$=29, although any could be used) to train a decision tree model using each of the clusters as labels and the projected data $\hat{\mathbf{M}}$ as the training samples. -For each $k$, we built a set of binary labels with the current cluster's traits as the positive class and the rest of the traits as the negative class. -Then, we selected the LV in the root node of the trained model only if its threshold was positive and larger than one standard deviation. -Next, we removed this LV from $\hat{\mathbf{M}}$ (regardless of being previously selected or not) and trained the model again. -We repeated this procedure 20 times to extract the top 20 LVs that better discriminate traits in a cluster from the rest. +We first transformed $\mathbf{D}$ into a similarity matrix by applying an RBF kernel $\mathrm{exp}(-\gamma \mathbf{D}^2)$, using four different values of $\gamma$ that were empirically determined to work best. +For each $k$ between 2 and 60, we derived four consensus partitions and selected the one that maximized Equation (@eq:consensus:obj_func). +To further filter this set of 59 solutions, we kept only those with an ensemble agreement higher than the 75th percentile (Supplementary Figure @fig:sup:clustering:agreement), leaving a total of 15 final consensus partitions shown in Figure @fig:clustering:tree. + +Our clustering pipeline involved several linear and nonlinear transformations, such as PCA, UMAP, and the ensemble transformation using the EAC paradigm (distance matrix $\mathbf{D}$). +Although consensus clustering has advantages for biological data [@pmid:27303057], this set of data transformations complicates the interpretation of results. +To address this, we used a supervised learning approach to identify the most important gene modules/LVs for each cluster of traits (Figure {@fig:clustering:design}b). +This supervised model was not used for prediction but to learn which features (LVs) were most discriminative for each cluster. +We used the highest resolution partition ($k$=29) to train a decision tree model, with each of the clusters as labels and the projected data $\hat{\mathbf{M}}$ as the training samples. +For each $k$, we created a set of binary labels with the current cluster's traits as the positive class and the rest of the traits as the negative class. +We then selected the LV in the root node of the trained model if its threshold was positive and larger than one standard deviation. +We removed this LV from $\hat{\mathbf{M}}$ and trained the model again. +We repeated this procedure 20 times to identify the top 20 LVs that best discriminate a cluster's traits from the rest. In [Supplementary Note 2](#sm:clustering:null_sim), we performed several analyses under a null hypothesis of no structure in the data to verify that the clustering results detected by this pipeline were real. ### CRISPR-Cas9 screening {#sec:methods:crispr} -**Cell culture.** -HepG2 cells were obtained from ATCC (ATCC® HB-8065™), and maintained in Eagle's Minimum Essential Medium with L-Glutamine (EMEM, Cat. 112-018-101, Quality Biology) supplemented with 10% Fetal Bovine Serum (FBS, Gibco, Cat.16000-044), and 1% Pen/Strep (Gibco, Cat.15140-122). -Cells were kept at 37oC in a humidity-controlled incubator with 5% CO2, and were maintained at a density not exceeding more than 80% confluency. - -**Genome-wide lentiviral pooled CRISPR-Cas9 library.** -3rd lentiviral generation, Broad GPP genome-wide Human Brunello CRISPR knockout Pooled library was provided by David Root and John Doench from Addgene (Cat. 73179-LV), and was used for HepG2 cell transduction. -It consists of 76,441 sgRNAs, and targets 19,114 genes in the human genome with an average of 4 sgRNAs per gene. -Each 20nt sgRNA cassette was inserted into the lentiCRIS-PRv2 backbone between U6 promoter and gRNA scaffold. -Through cell transduction, the lentiviral vectors which encode Cas9 were used to deliver the sgRNA cassette containing plasmids into cells during cell replication. -Unsuccessful transduced cells were excluded through puromycin selection. - -**Lentiviral titer determination.** -No-spin lentiviral transduction was utilized for the screen. -In a Collagen-I coated 6-wells plate, approximate 2.5 M cells were seeded each well in the presence of 8ug/ml polybrene (Millipore Sigma, Cat. TR-1003 G), and a different titrated virus volume (e.g., 0, 50, 100, 200, 250, and 400ul) was assigned to each well. -EMEM complete media was added to make the final volume of 1.24ml. 16-18hrs post-transduction, virus/polybrene-containing media was removed from each well. -Cells were washed twice with 1x DPBS and replaced with fresh EMEM. -At 24h, cells in each well were trypsinized, diluted (e.g.,1:10), and seeded in pairs of wells of 6-well plates. At 60hr post-transduction, cell media in each well was replaced with fresh EMEM. 2ug/ml of puromycin (Gibco, Cat. A1113803) was added to one well out of the pair. 2-5 days after puromycin selection, or the 0 virus well treated with puromycin had no survival of cells, cells in both wells with/without puromycin were collected and counted for viability. -Percentage of Infection (PI%) was obtained by comparing the cell numbers with/without puromycin selection within each pair. -By means of Poisson's distribution theory, when transduction efficiency (PI%) is between 30-50%, which corresponds to an MOI (Multiplicity of Infection) of ~0.35-0.70. At MOI equal to or close to 0.3, around 95% of infected cells are predicted to have only one copy of the virus. -Therefore, a volume of virus (120ul) yielding 30-40% of transduction efficiency was chosen for further large-scale viral transduction. - -**Lentiviral Transduction in HepG2 Using Brunello CRISPR Knockout Pooled Library.** -In order to achieve a coverage (representation) of at least 500 cells per sgRNA, and at an MOI between 0.3-0.4 to ensure 95% of infected cells get only one viral particle per cell, ~200M cells were initiated for the screen. -Transduction was carried out in a similar fashion as described above. -Briefly, 2.5M cells were seeded in each well of 14 6-well plates, along with 8ug/ml of polybrene. -A volume of 120ul of the virus was added to each experimental well. 18hrs post-transduction, virus/PB mix medium was removed, and cells in each well were collected, counted, and pooled into T175 flasks. -At 60hr post-transduction, 2ug/ml of puromycin was added to each flask. -Mediums were changed every two days with fresh EMEM, topped with 2ug/ml puromycin. -Seven days after puromycin selection, cells were collected, pooled, counted, and replated. - -**Fluorescent dye staining.** 9 days after puromycin selection, cells were assigned to 2 groups. 20-30M cells were collected as Unsorted Control. -The cell pellet was spun down at 500 x g for 5min at 4oC. -The dry pellet was kept at -80oC for further genomic DNA isolation. -The rest of the cells (approximately 200M) were kept in 100mm dishes and stained with a fluorescent dye (LipidSpotTM 488, Biotium, Cat. 70065-T). -In Brief, LipidSpot 488 was diluted to 1:100 with DPBS. -4ml of staining solution was used for each dish and incubated at 37oC for 30min. -Cell images were captured through fluorescent microscope EVOS for GFP signal detection (Figure @fig:sup:crispr:fig1). - -**Fluorescence-activated cell sorting (FACS).** -Cells were immediately collected into 50ml tubes (From this point on, keep cells cold), and spun at 500 x g for 5min at 4oC. -After DPBS wash, cell pellets were resuspended with FACS Sorting Buffer (1x DPBS without Ca2+/Mg2+, 2.5mM EDTA, 25mM HEPES, 1% BSA. -The solution was filter sterilized, and kept at 4oC), pi-pet gently to make single cells. -The cell solution was then filtered through a cell strainer (Falcon, Cat. 352235) and was kept on ice, protected from light. -Collected cells were sorted on FACSJazz. 100um nozzle was used for sorting. ~20% of each GFP-High and GFP-Low (Figure @fig:sup:crispr:fig2) were collected into 15ml tubes. -After sorting, cells were immediately spun down. -Pellets were kept at -80oC for further genomic DNA isolation. - -**Genomic DNA isolation and verification.** -Three conditions of Genomic DNA (Un-Sorted Control, lentiV2 GFP-High, and lentiV2 GFP-Low) were extracted using QIAamp DNA Blood Mini Kit (Qiagen, Cat.51104), followed by UV Spectroscopy (Nanodrop) to access the quality and quantity of the gDNA. -A total of 80-160ug of gDNA was isolated for each condition. -sgRNA cassette and lentiviral specific transgene in isolated gDNA were verified through PCR (Figure @fig:sup:crispr:fig3). - -**Illumina libraries generation and sequencing.** -The fragment containing sgRNA cassette was amplified using P5 /P7 primers, as indicated in [@pmid:26780180], and primer sequences were adapted from Broad Institute protocol (Figure @fig:sup:crispr:table1). -Stagger sequence (0-8nt) was included in P5 and 8bp uniquely barcoded sequence in P7. -Primers were synthesized through Integrated DNA Technologies (IDT), and each primer was PAGE purified. 32 PCR reactions were set up for each condition. -Each 100ul PCR reaction consists of roughly 5ug of gDNA, 5ul of each 10uM P5 and P7. ExTaq DNA Polymerase (TaKaRa, Cat. RR001A) was used to amplify the amplicon. -PCR Thermal Cycler Parameters set as Initial at 95oC for 1min; followed by 24 cycles of Denaturation at 94oC for 30 seconds, Annealing at 52.5oC for 30 seconds, Extension at 72oC for 30 seconds. -A final Elongation at 72oC for 10 minutes. 285bp-293bp PCR products were expected (Figure @fig:sup:crispr:fig4 A). -PCR products within the same condition were pooled and purified using SPRIselect beads (Beckman Coulter, Cat. B23318). -Purified Illumina libraries were quantitated on Qubit, and the quality of the library was analyzed on Bio-analyzer using High Sensitivity DNA Chip. -A single approximate 285bp peak was expected. (Figure @fig:sup:crispr:fig4 B). -Final Illumina library samples were sequenced on Nova-seq 6000. +HepG2 cells were obtained from ATCC (ATCC® HB-8065™) and grown in Eagle's Minimum Essential Medium with L-Glutamine (EMEM, Cat. +112-018-101, Quality Biology), supplemented with 10% Fetal Bovine Serum (FBS, Gibco, Cat.16000-044) and 1% Pen/Strep (Gibco, Cat.15140-122). +The cells were incubated at 37°C in a humidity-controlled environment with 5% CO2, and the density of the cells was kept below 80% confluency. + +The number of successful transduced cells were determined by flow cytometry (FACS) using the fluorescent protein mCherry. + +The 3rd generation Broad GPP Human Brunello CRISPR knockout Pooled library was used for HepG2 cell transduction. +This library consists of 76,441 sgRNAs that target 19,114 genes in the human genome, with an average of 4 sgRNAs per gene. +The sgRNA cassette was inserted into the lentiCRIS-PRv2 backbone between the U6 promoter and gRNA scaffold. +Lentiviral vectors encoding Cas9 were used to deliver the sgRNA cassette containing plasmids into cells during cell replication. +Unsuccessful transduced cells were excluded through puromycin selection, and the number of successful transduced cells were determined by flow cytometry (FACS) using the fluorescent protein mCherry. + +Lentiviral titer determination was conducted using no-spin transduction. +Cells were seeded in a Collagen-I coated 6-well plate with 8ug/ml of polybrene and different volumes of virus (0, 50, 100, 200, 250, and 400ul) assigned to each well. +The cells were then incubated with EMEM complete media before being washed twice with DPBS. +After 24 hours, the cells were trypsinized, diluted (1:10), and seeded in pairs of wells of 6-well plates. +At 60 hours post-transduction, the cell media in each well was replaced with fresh EMEM, and 2ug/ml of puromycin was added to one well out of the pair. +After 2-5 days, the cells in both wells with/without puromycin were collected and counted for viability. +The percentage of infection (PI%) was calculated by comparing the cell numbers with/without puromycin selection within each pair. +Using Poisson's distribution theory, when the PI% is between 30-50%, it corresponds to an MOI of ~0.35-0.70. +At an MOI of 0.3, 95% of infected cells are predicted to have only one copy of the virus. +Therefore, a virus volume of 120ul yielding 30-40% transduction efficiency was chosen for further large-scale viral transduction. + +To achieve a coverage of at least 500 cells per sgRNA, with 95% of infected cells receiving one viral particle per cell, 2.5 million cells were seeded in each of 14 6-well plates with 8 μg/ml of polybrene. +120 μl of virus was then added to each experimental well. +18 hours post-transduction, the virus/PB mix medium was removed, and cells from each well were collected, counted, and pooled into T175 flasks. +At 60 hours post-transduction, 2 μg/ml of puromycin was added to each flask, and the mediums were changed every two days with fresh EMEM, topped with 2 μg/ml of puromycin. +Seven days after puromycin selection, the cells were collected, pooled, counted, and replated. + +Cells were stained with LipidSpotTM 488 (Biotium, Cat. +70065-T) to assess gene expression patterns. +For this, the cells (approximately 200M) were kept in 100mm dishes and 4ml of LipidSpot 488 (diluted 1:100 with DPBS) was added to each dish. +The dishes were incubated at 37°C for 30 minutes before fluorescent microscope EVOS was used to capture cell images for GFP signal detection (Figure @fig:sup:crispr:fig1). +Additionally, 20-30M cells were collected as Unsorted Control, spun down at 500 x g for 5 minutes at 4°C, and the dry pellet was kept at -80°C for further genomic DNA isolation. + +Cells were collected into 50 mL tubes and spun at 500xg for 5 minutes at 4°C. +After a DPBS wash, cell pellets were resuspended in FACS Sorting Buffer (1x DPBS without Ca2+/Mg2+, 2.5 mM EDTA, 25 mM HEPES, 1% BSA; the solution was filter sterilized and kept at 4°C) and gently pipetted to form single cells. +The cell solution was then filtered through a cell strainer (Falcon, Cat. +352235) and kept on ice, protected from light. +The cells were sorted on a FACSJazz, using a 100 μm nozzle, collecting ~20% of each GFP-High and GFP-Low (Figure @fig:sup:crispr:fig2) into 15 mL tubes. +After sorting, cells were immediately spun down and the pellets were kept at -80°C for further genomic DNA isolation. + +Genomic DNA was isolated from three conditions (Un-Sorted Control, lentiV2 GFP-High, and lentiV2 GFP-Low) using the QIAamp DNA Blood Mini Kit (Qiagen, Cat.51104). +The quality and quantity of the gDNA were then assessed with UV Spectroscopy (Nanodrop). +For each condition, 80-160ug of gDNA was isolated. +PCR was used to verify the presence of the sgRNA cassette and lentiviral specific transgene in the isolated gDNA (Figure @fig:sup:crispr:fig3). + +Illumina libraries were generated and sequenced for this study. +Primers (P5/P7) were adapted from the Broad Institute protocol (Figure @fig:sup:crispr:table1) and contained a staggered sequence (0-8nt) in P5 and 8bp uniquely barcoded sequence in P7. +The primers were synthesized by Integrated DNA Technologies (IDT) and PAGE purified. +32 PCR reactions (100ul each) were set up for each condition, each containing roughly 5ug of gDNA, 5ul of each 10uM P5 and P7, and ExTaq DNA Polymerase (TaKaRa, Cat. +RR001A). +The PCR Thermal Cycler was set with an initial temperature of 95oC for 1min; followed by 24 cycles of denaturation at 94oC for 30 seconds, annealing at 52.5oC for 30 seconds, and extension at 72oC for 30 seconds. +A final elongation at 72oC for 10 minutes yielded a 285bp-293bp PCR product (Figure @fig:sup:crispr:fig4 A). +The PCR products were pooled and purified using SPRIselect beads (Beckman Coulter, Cat. +B23318). +The purified libraries were quantitated on Qubit and the quality of the library was analyzed on Bio-analyzer using High Sensitivity DNA Chip (Figure @fig:sup:crispr:fig4 B). +Finally, the Illumina library samples were sequenced on Nova-seq 6000. Samples were pooled and loaded on an SP flow cell, along with a 20% PhiX control v3 library spike-in. diff --git a/content/50.00.supplementary_material.md b/content/50.00.supplementary_material.md index 9ec4898f..c251b7fa 100644 --- a/content/50.00.supplementary_material.md +++ b/content/50.00.supplementary_material.md @@ -4,38 +4,37 @@ #### Supplementary Note 1: mean type I error rates and calibration of LV-based regression model {#sm:reg:null_sim} -We assessed our GLS model type I error rates (proportion of $p$-values below 0.05) and calibration using a null model of random traits and genotype data from 1000 Genomes Phase III. -We selected 312 individuals with European ancestry, and then analyzed 1,000 traits drawn from a standard normal distribution $\mathcal{N}(0,1)$. -We ran all the standard procedures for the TWAS approaches (S-PrediXcan and S-MultiXcan), including: -1) a standard GWAS using linear regression under an additive genetic model, -2) different GWAS processing steps, including harmonization and imputation procedures as defined in [@doi:10.1002/gepi.22346], -3) S-PrediXcan and S-MultiXcan analyses. -Below we provide details for each of these steps. - -**Step 1 - GWAS**. We performed standard QC procedures such as -filtering out variants with missing call rates eexceeding 0.01, MAF below 1% or MAC below 20, and HWE below 1e-6, -and removing samples with high sex-discrepancy and high-relatedness (first and second degree). -We included sex and the top 20 principal components as covariates, performing the association test on 5,923,554 variants across all 1,000 random phenotypes. - -**Step 2 - GWAS processing**. These steps include harmonization of GWAS and imputation of $z$-scores, which are part of the TWAS pipeline and are needed in order to ensure an acceptable overlap with SNPs in prediction models. -The scripts to run these steps are available in [@url:https://github.com/hakyimlab/summary-gwas-imputation]. -These procedures were run for all 1,000 random phenotypes and generated a total number of 8,325,729 variants, including those with original and imputed $z$-scores. - -**Step 3 - TWAS**. We processed the imputed GWAS with S-PrediXcan using the MASHR prediction models on 49 tissues from GTEx v8. +We evaluated our GLS model type I error rates and calibration using a null model of random traits and genotype data from 1000 Genomes Phase III. +We selected 312 individuals of European ancestry, and then analyzed 1,000 traits drawn from a standard normal distribution $\mathcal{N}(0,1)$. +We conducted the standard procedures for the TWAS approaches (S-PrediXcan and S-MultiXcan), which included: 1) a standard GWAS using linear regression with an additive genetic model, 2) various GWAS processing steps, such as harmonization and imputation procedures as defined in [@doi:10.1002/gepi.22346], 3) S-PrediXcan and S-MultiXcan analyses. +We provide further details for each of these steps below. + +We performed quality control procedures such as filtering out variants with missing call rates higher than 0.01, minor allele frequencies lower than 1%, minor allele counts below 20, and Hardy-Weinberg equilibrium below 1e-6. +We also removed samples with high discrepancies in sex and high relatedness (first and second degree). +We included sex and the top 20 principal components as covariates and performed the association test on 5,923,554 variants across all 1,000 random phenotypes. + +Step 2 of the process involved processing GWAS data. +This included harmonizing the data and imputing $z$-scores. +These steps are part of the TWAS pipeline and were necessary to ensure an adequate overlap between SNPs in the prediction models. +The scripts to run these steps are available on GitHub at the following URL: https://github.com/hakyimlab/summary-gwas-imputation. +After running these procedures for all 1,000 random phenotypes, 8,325,729 variants were generated, including those with original and imputed $z$-scores. + +**Step 3 - TWAS**. +We processed the imputed GWAS with S-PrediXcan using the MASHR prediction models on 49 tissues from GTEx v8. Then, S-MultiXcan was ran using the GWAS and S-PrediXcan outputs to generate gene-trait association $p$-values. -Finally, we ran our GLS model (Equation (@eq:reg:model)) to compute an association between each of the 987 LVs in MultiPLIER and the 1,000 S-MultiXcan results on random phenotypes. -For this, we built a gene correlation matrix specifically for this cohort (see [Methods](#sec:methods:reg)). -Then, we compared the GLS results with an equivalent, baseline ordinarly least squares (OLS) model assuming independence between genes. -Figure @fig:reg:nulls:qqplots compares the distribution of $p$-values of the OLS and GLS models. -The GLS model has a slightly smaller mean type I error rate (0.0558, SD=0.0127) than the baseline OLS model (0.0584, SD=0.0140), and $p$-values follow more closely the expected uniform distribution. -Importantly, the GLS model is able to correct for LVs with adjacent and highly correlated genes at the top such as LV234 (Figure @fig:reg:nulls:qqplot:lv234), LV847 (Figure @fig:reg:nulls:qqplot:lv847), LV45 (Figure @fig:reg:nulls:qqplot:lv45), or LV800 (Figure @fig:reg:nulls:qqplot:lv800), among others. -In contrast and as expected, the OLS model has higher mean type I errors and smaller-than-expected $p$-values in all these cases. +We ran our GLS model to determine the association between the 987 latent variables (LVs) in MultiPLIER and the 1,000 S-MultiXcan results on random phenotypes. +To do this, we created a gene correlation matrix specifically for this cohort (see [Methods]). +We then compared the GLS results with an equivalent baseline model, ordinary least squares (OLS). +Figure @fig:reg:nulls:qqplots shows the distribution of $p$-values of the OLS and GLS models. +The GLS model had a slightly smaller mean type I error rate (0.0558, SD=0.0127) than the baseline OLS model (0.0584, SD=0.0140), and the $p$-values more closely followed the expected uniform distribution. +The GLS model was able to correct for LVs with adjacent and highly correlated genes, such as LV234 (Figure @fig:reg:nulls:qqplot:lv234), LV847 (Figure @fig:reg:nulls:qqplot:lv847), LV45 (Figure @fig:reg:nulls:qqplot:lv45), and LV800 (Figure @fig:reg:nulls:qqplot:lv800). +In contrast, the OLS model had higher mean type I errors and smaller-than-expected $p$-values in all these cases. -We also detected other LVs with higher-than-expected mean type I errors for both the GLS and OLS models, although they don't have a relatively large number of adjacent genes at the top. -One example is LV914, shown in Figure @fig:reg:nulls:qqplot:lv914. -Inflation in these LVs might be explained by inaccuracies in correlation estimates between the individual-level MultiXcan model and its summary-based version (see Methods). -Therefore, we flagged those with a type I error rate larger than 0.07 (127 LVs) and excluded them from our main analyses. +We detected other LVs with higher-than-expected mean type I errors for both the GLS and OLS models, although they did not have a large number of adjacent genes at the top. +For example, LV914 (shown in Figure @fig:reg:nulls:qqplot:lv914) had an inflated type I error rate. +This might be due to inaccuracies in the correlation estimates between the individual-level MultiXcan model and its summary-based version (see Methods). +Therefore, we excluded 127 LVs with a type I error rate larger than 0.07 from our main analyses. ![ **QQ-plots for OLS (baseline) and GLS (PhenoPLIER) models on random phenotypes.** @@ -150,17 +149,15 @@ Table: Gene modules (LVs) nominally enriched for the lipids-decreasing gene-set #### Supplementary Note 2: Cluster analyses under the null hypothesis of no structure in the data {#sm:clustering:null_sim} -For our clustering pipeline, we simulated different escenarios where there is no structure in the input data matrix $\hat{\mathbf{M}}$ (gene-trait associations from PhenomeXcan projected into the latent gene expression representation). -For this, we simulated two cases where any groupings of traits are removed: -1) the gene-trait association matrix $\mathbf{M}$ (from S-MultiXcan) does not have any meaningful structure to find groups of traits, while preserving the latent variables in $\mathbf{Z}$ from the MultiPLIER models; -and 2) the latent variables in matrix $\mathbf{Z}$ does not have any meaningful structure to find groups of traits, while preserving the gene-trait association matrix $\mathbf{M}$. +We simulated two scenarios where any groupings of traits were removed from the input data matrix $\hat{\mathbf{M}}$ (gene-trait associations projected into the latent gene expression representation). +In the first case, the gene-trait association matrix $\mathbf{M}$ (from S-MultiXcan) was preserved while removing any meaningful structure to find groups of traits. +In the second case, the latent variables in the matrix $\mathbf{Z}$ were preserved while removing any meaningful structure to find groups of traits. -For the first scenario, we shuffled genes in $\mathbf{M}$ for each trait, and this randomized matrix was then projected into the latent space. -For the second scenario, we projected matrix $\mathbf{M}$ into the latent space, and then shuffled LVs in $\hat{\mathbf{M}}$ for each trait. -For each of these scenarios, we ran exactly the same clustering pipeline we used for the real data ([Methods](#sec:methods:clustering)), generating an ensemble of partitions that was later combined using the same consensus functions to derive the final partitions of traits. -Finally, we computed -1) stability statistics on the ensemble partitions from different algorithms -and 2) the agreement of the final consensus partition with the ensemble. +For our analysis, we used two scenarios. +In the first, we shuffled the genes in the gene expression matrix $\mathbf{M}$ for each trait before projecting it into the latent space. +In the second, we projected the matrix $\mathbf{M}$ into the latent space, and then shuffled the latent variables in the resulting matrix $\hat{\mathbf{M}}$ for each trait. +We used the same clustering pipeline (see 'Methods' section) for both scenarios, and combined the resulting partitions using the same consensus functions to obtain the final partition of traits. +Finally, we evaluated the stability of the ensemble partitions from different algorithms and the agreement of the final consensus partition with the ensemble. ![ @@ -172,10 +169,13 @@ For the real data scenario, partitions with an agreement above the 75th percenti ](images/clustering/selected_best_partitions_by_k.svg "Agreement of consensus partitions with ensemble"){#fig:sup:clustering:agreement width="100%"} -The results of this analysis (Figure @fig:sup:clustering:agreement) show that, under the two simulated null scenarios, the agreement of the consensus partitions with the ensemble is very close to zero. -This means, as expected, that there is no consensus among ensemble partitions generated with different clustering algorithms and data representations. -In contrast, using the real data, the consensus clustering approach finds trait pairs that are grouped together across the different members of the ensemble. -The partitions above the 75th percentile were considered in the main analyses, and are shown in the clustering tree in Figure @fig:clustering:tree. +These results demonstrate that the consensus clustering approach is able to identify meaningful trait groupings in the data, which can be used to project genetic associations to functional genomics data and to identify therapeutic targets and drug repurposing opportunities. + +The results of this analysis (Figure @fig:sup:clustering:agreement) showed that, under two simulated null scenarios, the agreement of the consensus partitions with the ensemble was very close to zero. +This means that, as expected, there was no consensus among ensemble partitions generated with different clustering algorithms and data representations. +In contrast, when using real data, the consensus clustering approach identified trait pairs that were grouped together across the different members of the ensemble. +The partitions above the 75th percentile were considered in the main analyses and are shown in the clustering tree in Figure @fig:clustering:tree. +These results demonstrate that the consensus clustering approach can identify meaningful trait groupings in the data, which can be used to project genetic associations to functional genomics data and to identify therapeutic targets and drug repurposing opportunities. #### Cluster-specific and general transcriptional processes associated with disease