Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms

Milton Pividori; Sumei Lu; Binglan Li; Chun Su; Matthew E. Johnson; Wei-Qi Wei; Qiping Feng; Bahram Namjou; Krzysztof Kiryluk; Iftikhar Kullo; Yuan Luo; Blair D. Sullivan; Benjamin F. Voight; Carsten Skarke; Marylyn D. Ritchie; Struan F.A. Grant; Casey S. Greene

Understanding how dysregulated transcriptional processes result in tissue-specific pathology requires a mechanistic interpretation of expression regulation across different cell types. It has been shown that this insight is key for the development of new therapies. These mechanisms can be identified with transcriptome-wide association studies (TWAS), which have represented a significant step forward to test the mediating role of gene expression in GWAS associations. However, it is hard to disentangle causal cell types using eQTL data alone, and other methods generally do not use the large amounts of publicly available RNA-seq data. Here we introduce PhenoPLIER, a polygenic approach that maps both 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 tissues. 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 phenotype-associated modules by PhenoPLIER. By incorporating groups of co-expressed genes, PhenoPLIER can contextualize genetic associations and reveal potential targets within associated processes that are missed by single-gene strategies.

Introduction

Human diseases have tissue-specific etiologies and manifestations [1,2,3]. In this context, determining how genes influence these complex phenotypes requires mechanistically understanding expression regulation across different cell types [4,5,6], which in turn should lead to improved treatments [7,8]. Previous studies have described regulatory DNA elements, including chromatin-state annotations [9,10], high-resolution enhancers [11,12], DNase I hypersensitivity maps [5], and genetic effects on gene expression across different tissues [4]. Integrating functional genomics data and GWAS data [13] has improved the identification of these transcriptional mechanisms that, when dysregulated, commonly result in tissue- and cell lineage-specific pathology.

Given the availability of gene expression data across several tissues [4,14,15,16], a popular 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 [17,18,19,20]. However, TWAS has not reliably detected tissue-specific effects because eQTLs are commonly shared across tissues [21,22]. This sharing makes it challenging to identify the tissue or tissues that are etiologically relevant. Existing methods that connect GWAS findings with gene expression data can infer disease-relevant tissues and cell types [22,23,24,25,26,27], but they generally rely on small sets of expression data compared with the total number of RNA-seq samples that are increasingly available [14,15]. Moreover, widespread gene pleiotropy across complex traits reveals the highly interconnected nature of transcriptional networks [28,29], where potentially all genes expressed in disease-relevant cell types have a non-zero effect [30,31]. Consequently, this complicates the interpretation of genetic effects and hampers translational efforts.

We propose PhenoPLIER, a polygenic approach that infers how groups of functionally-related genes influence complex traits, and how pharmacological perturbations affect these genes’ activity to exert their effects. The approach maps both gene-trait associations and drug-induced transcriptional responses into a common representation for a joint analysis. For this, we integrated more than 4,000 gene-trait associations (using TWAS from PhenomeXcan [32]) and transcriptional profiles of drugs (LINCS L1000 [33]) into a low-dimensional space learned from public gene expression data on tens of thousands of RNA-seq samples (recount2 [14,34]). We used a latent representation defined by a computational approach [35] that learns recurrent gene co-expression patterns with certain sparsity constraints and preferences for those that align with prior knowledge (pathways). This low-dimensional space comprised features representing groups of genes (gene modules) with coordinated expression across different tissues and cell types. When mapping gene-trait associations to this reduced expression space, we observed that diseases were significantly associated with gene modules expressed in relevant cell types, such as hypothyroidism with T cells and thyroid, corneal endothelial cells with keratometry measurements, hematological assays on specific blood cell types, plasma lipids with adipose tissue, and neuropsychiatric disorders with different brain cell types. Moreover, since we rely on a large and heterogeneous RNA-seq dataset, we were also able to identify modules associated with cell types under specific stimuli or disease states. We replicated gene module associations with cardiovascular and autoimmune diseases in the Electronic Medical Records and Genomics (eMERGE) network phase III [36]. Furthermore, we performed a CRISPR screen to analyze lipid regulation in HepG2 cells and observed more robust trait associations with modules than with individual genes, even when single genes known to be involved in lipid metabolism did not reach genome-wide significance. Compared to a single-gene approach, our module-based method also better predicted FDA-approved drug-disease links by capturing tissue-specific pathophysiological mechanisms linked with the mechanism of action of drugs (e.g., niacin with cardiovascular traits via a known immune mechanism), suggesting that modules may provide a better means to examine drug-disease relationships than individual genes. Finally, exploring the phenotype-module space also revealed stable trait clusters associated with relevant tissues, including a complex branch involving lipids with cardiovascular, autoimmune, and neuropsychiatric disorders. In summary, instead of considering single genes associated with different complex traits, PhenoPLIER incorporates groups of genes that act together to carry out different functions in specific cell types. This improves robustness to detect and interpret genetic associations, and here we show how it can prioritize alternative and potentially more promising candidate targets when important single gene associations are not detected. The approach represents a conceptual shift in the interpretation of genetic studies, and has the potential to extract mechanistic insight from statistical associations to enhance the understanding of complex diseases and their therapeutic modalities.

Results

PhenoPLIER: an integration framework based on gene co-expression patterns

PhenoPLIER combines TWAS and drug-induced transcriptional responses with gene co-expression patterns by projecting gene-trait and gene-drug associations onto a latent gene expression representation (Figure 1). We used PhenomeXcan [32], a TWAS resource for the UK Biobank [37] and other cohorts with 4,091 different diseases and traits, and the transcriptional responses to small molecule perturbations from LINCS L1000 [33] comprising 1,170 compounds. We obtained a latent gene expression representation from MultiPLIER [34], an unsupervised learning approach applied to recount2 [14] – 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 state, cell types differentiation stages, and various stimuli (Methods). MultiPLIER extracted 987 latent variables (LV) by optimizing data reconstruction but also the alignment of LVs with prior knowledge/pathways (Methods). 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 [38,39], we conjecture that the projection of TWAS and pharmacologic perturbations data into this latent space could provide context for their interpretation. 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. This process links different traits and drugs to LVs representing cell types and tissues, even at specific developmental stages or under distinct stimuli. Examining these LVs is possible because the MultiPLIER’s models link to samples, which may be annotated for experimental conditions (represented by matrix \(\mathbf{B}\) in Figure 1 b, top) in which genes in an LV are expressed. To further analyze LV-trait associations, we adapted the gene-property approach from MAGMA [40] to compute an LV-trait \(p\)-value by fitting a regression model with LV weights as predictors of a trait’s gene associations. Thus 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 [30])? 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 [31]) and thus represent alternative and potentially better candidate targets? Are drugs perturbing these transcriptional mechanisms, and if so, can they suggest potential mechanisms of action?

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 [41]. We analyzed LV603 using PhenoPLIER (Figure 1 c) and found that 1) neutrophil counts and other white blood cell traits were ranked among the top 10 traits out of 4,091 (Figure 1 c, bottom) and significantly associated with this LV (Supplementary Table 3) when using the gene-property approach, and 2) LV603’s genes were expressed in highly relevant cell types (Figure 1 c, top). These results suggested that groups of functionally related and co-expressed genes tend to correspond to groups of trait-associated genes. Thus, the gene expression space (which has no GTEx samples) and the TWAS space (with gene models trained using GTEx v8) contained shared patterns, and the approach can link transcriptional mechanisms from large and diverse dataset collections to complex traits.

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). 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 1).

Next, we analyzed all 987 LVs using Fast Gene Set Enrichment Analysis (FGSEA) [42], and found 15 LVs nominally enriched (unadjusted P < 0.01) with these lipid-altering gene-sets (Supplementary Tables 5 and 6). 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 2 a), which plays a key role in coordinating and regulating lipid metabolism. Using the gene-property analysis method, we found that gene weights for this LV were predictive of gene associations for plasma lipids and high cholesterol (Supplementary Table 8). 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 2 b, in boldface). However, in contrast to other members of this LV, DGAT2 and ACACA were not strongly associated nor colocalized with any of the cardiovascular-related traits and thus would not have been prioritized by TWAS alone; other members of LV246, such as SCD, LPL, FADS2, HMGCR, and LDLR, were instead significantly associated and colocalized with lipid-related traits. This lack of association/colocalization of two high-confidence genes from our CRISPR-screen might be explained from an omnigenic point of view [31]. 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. These results suggested that our LV-based approach can contextualize genetic associations and prioritize alternative therapeutic targets, mainly when TWAS cannot detect them or hits are not druggable.

PhenoPLIER with LVs predicts drug-disease pairs better than single genes

We next determined the extent to which substituting LVs in place of individual genes predicted known treatment-disease relationships. For this, we used the transcriptional responses to small molecule perturbations profiled in LINCS L1000 [33], which were further processed and mapped to DrugBank IDs [43,44,45]. Based on an established drug repurposing strategy that matches reversed transcriptome patterns between genes and drug-induced perturbations [46,47], we adopted a previously described framework that uses imputed transcriptomes from TWAS to prioritize drug candidates [48]. 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 size (see Methods). Therefore, a large score for a drug-disease pair indicated that a higher (lower) predicted expression 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 1 b). We used a manually-curated gold standard set of drug-disease medical indications [44,49] for 322 drugs across 53 diseases to evaluate the prediction performance.

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 3). 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.

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 to prevent cardiovascular disease [50,51,52]. Niacin exerts its effects on multiple tissues, although its mechanisms are not well understood [53,54,55,56]. 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) by modulating triglyceride synthesis in hepatocytes or by inhibiting adipocyte triglyceride lipolysis [53]. 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 were positively contributing to these predictions (i.e., with an opposite direction between niacin and the disease). Notably, LV246 (Figure 2), expressed in adipose tissue and liver and associated with plasma lipids and high cholesterol (Supplementary Table 8), 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).

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 [57,58]. It was initially thought that the antiatherogenic effects of niacin were solely due to inhibition of lipolysis in adipose tissue. However, it has been shown that nicotinic acid can reduce atherosclerosis progression independently of its antidyslipidemic activity through the activation of GPR109A in immune cells [59], thus boosting anti-inflammatory processes [60]. 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 4, Supplementary Figure 13, and Supplementary Tables 10 and 11) were the top two modules for AT, with a strong signature in monocytes, macrophages, neutrophils, dendritic cells, among others. In Figure 4, 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 [61], samples from multiple sclerosis or systemic lupus erythematosus [62,63], or infected with different viruses (such as herpes simplex [64], West Nile virus [65], Salmonella typhimurium [66], 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 1). Given the current controversy around this compound and its effect on cardiovascular disease, our approach may help to better understand different pathophysiological mechanisms linked with the mechanism of action of niacin.

The LV-based method was able to integrate different data types to provide an interpretable approach for drug repositioning research based on genetic studies. Additionally, our approach could also be helpful to understand better the mechanism of pharmacological effect of known or experimental drugs. For example, LV66, one of the top LVs affected by niacin (Supplementary Figure 14) was mainly expressed in ovarian granulosa cells. This compound has been very recently considered as a potential therapeutic for ovarian diseases [67,68], as it was found to promote follicle growth and inhibit granulosa cell apoptosis in animal models. Our LV-based approach could be helpful to generate novel hypotheses to evaluate potential mechanisms of action, or even adverse effects, of different drugs.

LV projections reveal trait clusters with shared transcriptomic properties

The previous results suggested that the compression into \(\hat{\mathbf{M}}\) increases the signal-to-noise ratio. Thus, we analyzed \(\hat{\mathbf{M}}\) to find groups of traits that were affected by the same transcriptional processes. To identify relationships that were robust to the selection of a specific clustering algorithm (each of which makes particular assumptions about the structure of data), we employed a consensus clustering approach. Therefore, we applied different methods with varying sets of parameters and later combined these into a consolidated solution (Methods). Our clustering pipeline generated 15 final consensus clustering solutions with 5 to 29 clusters (Supplementary Figure 15). Instead of selecting a specific number of clusters, we used a clustering tree [69] (Figure 6) 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.

We found that phenotypes were grouped into five clear branches (Figure 6). These were 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 (type 1 diabetes, psoriasis, hyper/hypothyroidism, rheumatoid arthritis, systemic lupus erythematosus, celiac disease), and cardiovascular diseases (hypertension, coronary artery disease, myocardial infarction, hypercholesterolemia, and other cardiovascular-related traits such hand-grip strength [70], and environmental/behavioral factors such as physical activity and diet) (See Supplementary Files 2-6 for clustering results). Within these branches, results were relatively stable. The same traits were often clustered together across different resolutions, even with the consensus algorithm using random initializations at each level. Arrows between different clusters show traits moving from one group to another across different resolutions. This mainly happens between clusters within the “complex” branch, and between clusters from the “large” branch to the “complex” branch. We would expect that continuing to explore higher dimensionalities would result in further subdivisions of these large groupings. This behavior was expected since complex diseases are usually associated with shared genetic and environmental factors and are thus hard to categorize into a single cluster. We would also expect that exploring solutions with a larger number of clusters would result in further subdivisions of these large groupings.

Next, we analyzed which LVs were driving these clusters of traits. We trained decision tree classifiers on the input data (Figure 5) using each cluster at \(k\)=29 (bottom of Figure 6) as labels (see Methods). This yielded for each cluster the top LVs, where several of them were well-aligned to existing pathways (Figure 7), and others were novel and expressed in relevant tissues (Supplementary Figure 16). In Figure 7, it can be seen that some LVs were highly specific to certain types of traits, while others were associated with a wide range of different phenotypes, thus potentially involved in more general biological functions. For example, LVs such as LV928 and LV30, which were well-aligned to early progenitors of the erythrocytes lineage [71] (Supplementary Tables 13 and 16), were predominantly expressed in early differentiation stages of erythropoiesis (Supplementary Figures 17 and 18) and strongly associated with different assays on red blood cells (FDR < 0.05; Supplementary Tables 14, 15, and 18). In contrast, other LVs were highly specific, such as LV730, which is expressed in thrombocytes from different cancer samples (Supplementary Figures 19 and Supplementary Table 19), and strongly associated with hematological assays on platelets (FDR < 2e-4, Supplementary Table 20); or LV598, whose genes were expressed in corneal endothelial cells (Supplementary Figures 20 and Supplementary Table 22) and associated with keratometry measurements (FDR < 4e-05; Supplementary Table 23).

The autoimmune diseases sub-branch also had significant LVs associations expressed in relevant cell types. LV844 was the most strongly associated gene module with autoimmune disorders in both PhenomeXcan (FDR < 7e-16; Supplementary Tables 26) and eMERGE (FDR < 2e-6, 27), and 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 Figures 21 and Supplementary Table 25). LV155 was strongly expressed in the thyroid (Supplementary Figures 22 and Supplementary Table 28) and significantly associated with hypothyroidism both in PhenomeXcan (FDR < 0.05, Supplementary Table 29) and eMERGE (FDR < 0.10, Supplementary Table 30). Other important LVs associated with autoimmunity in both PhenomeXcan and eMERGE were LV57, expressed in T cells (Supplementary Figure 23 and Supplementary Tables 31, 32, 33), and LV54, expressed in different soft tissue tumors, breast, lung, pterygia and epithelial cells (Supplementary Figure 24 and Supplementary Tables 34, 35, 36).

The cardiovascular sub-branch also exhibited significant associations. LV847 (Supplementary Figure 25 and Supplementary Table 37) was strongly associated with hypertension in PhenomeXcan (FDR < 2e-13, Supplementary Tables 38) and several cardiovascular diseases, type 1 diabetes, prostate cancer, and others in eMERGE (FDR < 0.05, Supplementary Table 39). LV847 was expressed in CD19 (B cells) (which are related to preeclampsia [72]), Jurkat cells (T lymphocyte cells), and cervical carcinoma cell lines (the uterus was previously reported to be linked to blood pressure through a potential hormonal pathway [73,74]). LV136 was aligned with known collagen formation and muscle contraction pathways (Supplementary Table 40), and it was associated with coronary artery disease (FDR < 2e-8), myocardial infarction (FDR < 6e-4) and keratometry measurements (FDR < 1e-7) in PhenomeXcan (Supplementary Tables 41), but with no strong associations in eMERGE (FDR < 0.20, Supplementary Table 42). This LV was expressed in a wide range of cell types, including fibroblasts, mesenchymal stem cells, osteoblasts, pancreatic stellate cells, cardiomyocytes, and adipocytes (Supplementary Figure 26). Lipids, clustered with chronotype and Alzheimer’s disease, were significantly associated with several modules expressed mainly in brain cell types, including LV93 (Supplementary Figure 27 and Supplementary Tables 43, 44, 45), LV206 (Supplementary Figure 28 and Supplementary Tables 46, 47, 48), and LV260 (Supplementary Figure 29 and Supplementary Tables 49, 50 and 51). These modules were associated mainly with cardiovascular traits in eMERGE.

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 [75,76,77,78] and share several risk factors, including hypertension, high cholesterol, obesity, smoking, among others [79,80]. In our results, however, these diseases were grouped by potentially shared transcriptional processes expressed in specific tissues/cell types. Alzheimer’s disease, for example, was significantly associated with LV21 in PhenomeXcan (FDR < 2e-19, Supplementary Table 53) and with LV5 (FDR < 3e-3, Supplementary Table 56). LV21 was strongly expressed in a variety of soft tissue sarcomas, monocytes/macrophages (including microglia from cortex samples), and aortic valves (Supplementary Figure 30 and Supplementary Table 52). This LV was also associated with lipids: LDL cholesterol (FDR < 1e-4) and triglycerides (FDR < 0.02). As discussed previously, macrophages play a key role in the reverse cholesterol transport and thus atherogenesis [81], and lipid metabolism in microglia has been recently identified as an important factor in the development of neurodegenerative diseases [82]. On the other hand, LV5 was expressed in breast cancer and brain glioma samples, microglia (cortex), liver, and kidney, among other cell types (Supplementary Figure 31 and Supplementary Table 55). In addition to lipids, LV5 was also associated with depression traits from the UK Biobank (FDR < 0.05, Supplementary Table 56). Since Alzheimer’s disease was not present in eMERGE, we could not replicate this association. ADHD was the only significantly associated trait for LV434 (FDR < 6e-3) (Supplementary Table 59), which was expressed in breast cancer and glioma cells, cerebral organoids, and several different cell populations from the brain: fetal neurons (replicating and quiescence), microglia, and astrocytes (Supplementary Figure 32 and Supplementary Table 58). Schizophrenia was not significantly associated (FDR < 0.05) with any gene module tested in our analysis. None of these LVs were significantly aligned to prior pathways, which might represent potentially novel transcriptional processes affecting the cardiovascular and central nervous systems.

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 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. We found that 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. Using our gene module perspective, we also integrated drug-induced transcriptional profiles, which allowed us to connect diseases, drugs, and cell types. Furthermore, and beyond statistical prediction, we focused on a particular drug (niacin) and set of traits (cardiovascular diseases) to show that the approach connects disease-relevant transcriptional processes with known mechanisms of action. This suggests that the conceptual approach may reveal the mechanisms of pharmacological effect of known or experimental drugs. Finally, 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.

In some cases, the features/LVs linked to phenotypes appear 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, thyroid in LV155, or ovary in LV66). Others appeared to be expressed in many contexts, and these may capture pathways associated with a set of related complex diseases. For example, LV136 is associated with cardiovascular disease and measures of corneal biomechanics, and expressed in fibroblasts, osteoblasts, pancreas, liver, and cardiomyocytes, among others. Another example is LV844, expressed in whole blood samples and strongly associated with a range of autoimmune diseases. 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 identify cell type and pathway effects on complex phenotypes that is computationally simple to implement.

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 [15,83]. 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, and other effects such as coding variants disrupting protein-protein interactions are not captured. Additionally, TWAS has several limitations that can lead to false positives [84,85]. Like GWAS, which generally detects groups of associated variants in linkage disequilibrium (LD), TWAS usually identifies several genes within the same locus [20,86]. 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 [84]. Larger datasets and methods designed to learn representations with this application in mind could further refine the approach and are a promising avenue for future research.

Our findings are concordant with previous studies showing that drugs with genetic support are more likely to succeed through the drug development pipeline [7,48]. 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 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. 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. In this portion, we used S-MultiXcan associations, which only provide the association strength between a gene and a trait, but with no direction of effect. This does mean that traits are grouped based on associated genes, but genes could have opposite effects on traits within the same cluster. Considering groups of related diseases was previously shown to be more powerful to detect shared genetic etiology [87,88], and clustering trees provide a way to explore such relationships in the context of latent variables. Finally, 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 cluster analysis of traits, data preprocessing steps are always challenging and might not avoid bias altogether.

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 [89]. 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.

Methods

PhenomeXcan: gene-based associations on 4,091 traits

We used TWAS results from PhenomeXcan [32] on 4,091 traits for 22,515 genes. PhenomeXcan was built using publicly available GWAS summary statistics to compute 1) gene-based associations with the PrediXcan family of methods [19,20,90], and 2) a posterior probability of colocalization between GWAS loci and cis-eQTL with fastENLOC [32,91]. The PrediXcan family of methods first builds prediction models using data from the Genotype-Tissue Expression project (GTEx v8) [4] for gene expression imputation and then correlate this predicted expression with the phenotype of interest. This family is comprised of S-PrediXcan [90] (which computes a gene-tissue-trait association using GWAS as input) and S-MultiXcan [19] (which computes a gene-trait association by aggregating evidence of associations across all tissues).

We refer to the standardized effect sizes (\(z\)-scores) of S-PrediXcan across \(n\) traits and \(m\) genes in tissue \(t\) as \(\mathbf{M}^{t} \in \mathbb{R}^{n \times m}\). For S-MultiXcan, we do not have the direction of effect, and we used the \(p\)-values 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.

MultiPLIER and Pathway-level information extractor (PLIER)

MultiPLIER [34] extracts patterns of co-expressed genes from recount2 [14], a large gene expression dataset. The approach applies the pathway-level information extractor method (PLIER) [35], which performs unsupervised learning using prior knowledge (canonical pathways) to reduce technical noise. Via a matrix factorization approach, PLIER deconvolutes the gene expression data into a set of latent variables (LV), where each represents a gene module. This reduced the data dimensionality into 987 latent variables or gene modules.

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 [92] (so that \(\mathbf{C}_{ij} = 1\) if gene \(i\) belongs to pathway \(j\)), PLIER finds \(\mathbf{U}\), \(\mathbf{Z}\), and \(\mathbf{B}\) minimizing

\[ ||\mathbf{Y} - \mathbf{Z}\mathbf{B}||^{2}_{F} + \lambda_1 ||\mathbf{Z} - \mathbf{C}\mathbf{U}||^{2}_{F} + \lambda_2 ||\mathbf{B}||^{2}_{F} + \lambda_3 ||\mathbf{U}||_{L^1} \](1)

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.

We projected \(\mathbf{M}\) (either from S-PrediXcan across each tissue, or S-MultiXcan) into the low-dimensional gene module space learned by MultiPLIER using

\[ \hat{\mathbf{M}} = (\mathbf{Z}^{\top} \mathbf{Z} + \lambda_{2} \mathbf{I})^{-1} \mathbf{Z}^{\top} \mathbf{M}, \](2)

where in \(\hat{\mathbf{M}}^{l \times n}\) all traits in PhenomeXcan are now described by gene modules.

LV-trait associations via gene-property analysis

To compute an association between a gene module and a trait, we used an approach similar to the gene-property analysis in MAGMA [40], which is essentially a competitive test using gene weights from \(\mathbf{Z}\) to predict gene \(z\)-scores from \(\mathbf{M}\). Thus, the regression model uses genes as data points by fitting \(\mathbf{m}=\beta_0 + \mathbf{z} \beta_z + \epsilon\), where \(\epsilon \sim \mathrm{MVN}(0, \hat{\Sigma})\), \(\mathbf{m}\) are gene \(p\)-values (for a trait) from S-MultiXcan that we transformed to \(z\)-scores as mentioned before. Since we are only interested in whether genes with a stronger membership to a module (highest weights) are more associated with the phenotype, we performed a one-sided test on the coefficient \(\beta_z\) with the null hypothesis of \(\beta_z = 0\) against the alternative \(\beta_z>0\). Since the error terms \(\epsilon\) could be correlated due to correlation between predicted expression, we used a generalized least squares approach instead of standard linear regression. To calculate \(\hat\Sigma\), we first estimated the correlation of predicted expression for each gene pair \((\mathbf{t}_i, \mathbf{t}_j)\) in tissue \(t\) using equations from [19,90]:

\[ \begin{split} \hat{\Sigma}_{ij}^{t} & = Cor(\mathbf{t}_i, \mathbf{t}_j) \\ & = \frac{ Cov(\mathbf{t}_i, \mathbf{t}_j) } { \sqrt{\widehat{\mathrm{var}}(\mathbf{t}_i) \widehat{\mathrm{var}}(\mathbf{t}_j)} } \\ & = \frac{ Cov(\sum_{a \in \mathrm{model}_i} w_a^i X_a, \sum_{b \in \mathrm{model}_j} w_b^j X_b) } {\sqrt{\widehat{\mathrm{var}}(\mathbf{t}_i) \widehat{\mathrm{var}}(\mathbf{t}_j)} } \\ & = \frac{ \sum_{a \in \mathrm{model}_i \\ b \in \mathrm{model}_j} w_a^i w_b^j Cov(X_a, X_b)} {\sqrt{\widehat{\mathrm{var}}(\mathbf{t}_i) \widehat{\mathrm{var}}(\mathbf{t}_j)} } \\ & = \frac{ \sum_{a \in \mathrm{model}_i \\ b \in \mathrm{model}_j} w_a^i w_b^j \Gamma_{ab}} {\sqrt{\widehat{\mathrm{var}}(\mathbf{t}_i) \widehat{\mathrm{var}}(\mathbf{t}_j)} }, \end{split} \](3)

where \(\Gamma = \widehat{\mathrm{var}}(\mathbf{X}) = (\mathbf{X} - \mathbf{\bar{X}})^{\top} (\mathbf{X} - \mathbf{\bar{X}}) / (m-1)\) is the genotype covariance matrix using 1000 Genomes Project data [93,94]. The variances for predicted gene expression of gene \(i\) is estimated as:

\[ \begin{split} \widehat{\mathrm{var}}(\mathbf{t}_i) & = (\mathbf{W}^i)^\top \Gamma^i \mathbf{W}^i \\ & = \sum_{a \in \mathrm{model}_i \\ b \in \mathrm{model}_i} w_a^i w_b^i \Gamma_{ab}^i. \end{split} \](4)

Finally, \(\hat{\Sigma} = \sum_t \hat{\Sigma}^t / |t|\) where \(|t|\)=49 is the number of tissues.

Because of computational reasons, we did not run the gene-property analysis on all possible LV-trait pairs. In PhenomeXcan, we reduced the number of LV-trait pairs by considering only the top discriminative LVs for each cluster (see “Cluster interpretation” section in Methods) and the traits in that cluster, leading to 5,782 LV-trait tests. For replication in eMERGE, we selected all the 25 LVs analyzed in the main text and ran the gene-property analysis against all 309 traits in this cohort, leading to 7,725 tests. We adjusted the \(p\)-values using the Benjamini-Hochberg procedure.

Drug-disease prediction

For the drug-disease prediction, we used a method based on a drug repositioning framework previously used for psychiatry traits [48] where gene-trait associations are anticorrelated with expression profiles for drugs. For the single-gene approach, we computed a drug-disease score by multiplying each S-PrediXcan set of results in tissue \(t\), \(\mathbf{M}^t\), with the transcriptional responses profiled in LINCS L1000 [33], \(\mathbf{L}^{c \times m}\) (for \(c\) compounds): \(\mathbf{D}^{t,k}=-1 \cdot \mathbf{M}^{t,k} \mathbf{L}^\top\), where \(k\) refers to the number of most significant gene associations in \(\mathbf{M}^t\) for each trait. As suggested in [48], \(k\) could be either all genes or the top 50, 100, 250, and 500; then we average score ranks across all \(k\) and obtain \(\mathbf{D}^t\). Finally, for each drug-disease pair, we took the maximum prediction score across all tissues: \(\mathbf{D}_{ij} = \max \{ \mathbf{D}_{ij}^t \mid \forall t \}\).

The same procedure was used for the gene module-based approach, where we projected S-PrediXcan results into our latent representation, leading to \(\hat{\mathbf{M}}^t\); and also \(\mathbf{L}\), leading to \(\hat{\mathbf{L}}^{l \times c}\). Finally, \(\mathbf{D}^{t,k}=-1 \cdot \hat{\mathbf{M}}^{t,k} \hat{\mathbf{L}}^\top\), where in this case \(k\) could be all LVs or the top 5, 10, 25 and 50 (since we have an order of magnitude less LVs than genes).

Since the gold standard of drug-disease medical indications used contained Disease Ontology IDs (DOID) [95], we mapped PhenomeXcan traits to the Experimental Factor Ontology [96] using [97], and then to DOID.

Consensus clustering of traits

We performed two preprocessing steps on the S-MultiXcan results before the cluster analysis procedure. First, we combined results in \(\mathbf{M}\) (S-MultiXcan) for traits that mapped to the same Experimental Factor Ontology (EFO) [96] 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 standardized 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 2, obtaining \(\hat{\mathbf{M}}\) with \(n\)=3,752 traits and \(l\)=987 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:

\[ \pi^* = \mathrm{arg}\,\underset{\hat{\pi}}{\max} Q(\{ \lvert \mathcal{L}^i \lvert \phi(\hat{\pi}_{\mathcal{L}^i}, \pi_{i \mathcal{L}^i}) \mid i \in \{1,\ldots,r\} \}), \](5)

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) [98] 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 [99], 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 \(\pi^*\).

For the ensemble generation step, we used different algorithms to create a highly diverse set of partitions (see Figure 5) since diversity is an important property for ensembles [100,101,102]. We used three data representations: the raw dataset, its projection into the top 50 principal components, and the embedding learned by UMAP [103] 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 [104], spectral clustering [105], a Gaussian mixture model (GMM), hierarchical clustering, and DBSCAN [106]. 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 four 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). Specifically, we used minPts values from 2 to 125, and for each data version, 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 in the ensemble. This procedure generated a final ensemble of 4,428 partitions.

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. Thus for each \(k\) between 2 and 60, we derived four consensus partitions and selected the one that maximized Equation 5. We further filtered this set of 59 solutions to keep only those with an ensemble agreement larger than the 75th percentile, leaving a total of 15 final consensus partitions shown in Figure 6.

Cluster interpretation

We used a supervised learning approach to interpret clustering results by detecting which gene modules are the most important for clusters of traits. 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.

CRISPR-Cas9 screening

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 exceed 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, targets 19,114 genes in the human genome with an average of 4 sgRNAs per gene. Each 20nt sgRNA cassette was inserted into 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 corresponding to an MOI (Multiplicity of Infection) of ~0.35-0.70. At MOI equal or close to 0.3, around 95% of infected cells are predicted to have only one copy of 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 the 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. Volume of 120ul of virus was added to each experimental well. 18hrs post transduction, virus/PB mix medium was removed, and cells in each well were collect-ed, counted, and pooled into T175 flasks. At 60hr post transduction, 2ug/ml of puromycin was added to each flask. Mediums were changed every 2 days with fresh EMEM, topped with 2ug/ml puromycin. 7 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. Cell pellet was spun down at 500 x g for 5min at 4oC. 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 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 (Supplementary Figure 8).

Fluorescence-activated cell sorting (FACS). Cells were immediately collected into 50ml tubes (From this point on, keep cells cold), and spin 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. Solution was filter sterilized, and kept at 4oC), pi-pet gently to make single cells. Cell solution then filtered through cell strainer (Falcon, Cat. 352235), and were 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 (Supplementary Figure 9) were collected into 15ml tubes. After sorting, cells were immediately spun down. Pellets were kept in -80oC for further genomic DNA isolation.

Genomic DNA isolation and verification. 3 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. Total 80-160ug of gDNA was isolated for each condition. sgRNA cassette and lentiviral specific transgene in isolated gDNA were verified through PCR (Supplementary Figure 10).

Illumina libraries generation and sequencing. Fragment containing sgRNA cassette was amplified using P5 /P7 primers, as indicated in [107] and primer sequences were adapted from Broad Institute protocol (Supplementary Figure 11). Stagger sequence (0-8nt) was included in P5, and 8bp uniquely barcoded sequence in P7. Primers were synthesized through Integrated DNA Technologies (IDT), 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 (Supplementary Figure 12 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 were analyzed on Bio-analyzer using High Sensitivity DNA Chip. A single approximate 285bp peak was expected. (Supplementary Figure 12 B). Final illumina library samples were sequenced on Nova-seq 6000. Samples were pooled and loaded on a SP flow cell, along with 20% PhiX control v3 library spike-in.

Code and data availability

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Natthakan Iam-On, Tossapon Boongoen, Simon Garrett, Chris Price
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108. Homo sapiens (ID 232177) - BioProject - NCBI https://www.ncbi.nlm.nih.gov/bioproject/PRJNA232177

LV	Cell type	Disease
LV116	Immune cells, skin	Atherosclerosis (ICD10 I70)
		Chronic ischaemic heart disease (ICD10 I25)
		Heart attack, angina, stroke or hypertension
		Ischaemic heart disease (wide definition)
LV931	Immune cells	Atherosclerosis (ICD10 I70)
		Heart attack, angina, stroke or hypertension
		Ischaemic heart disease (wide definition)
LV246	Adipose tissue, liver	Atherosclerosis (ICD10 I70)
		High cholesterol (self-reported)
		Ischaemic heart disease (wide definition)

Pathway	AUC	FDR
IRIS Neutrophil-Resting	0.91	4.51e-35
SVM Neutrophils	0.98	1.43e-09
PID IL8CXCR2 PATHWAY	0.81	7.04e-03
SIG PIP3 SIGNALING IN B LYMPHOCYTES	0.77	1.95e-02

Trait description	Sample size	FDR
Myeloid White Cell Count	173,480	3.63e‑07
Sum Basophil Neutrophil Count	173,480	4.07e‑07
Neutrophil Count	173,480	4.17e‑07
Sum Neutrophil Eosinophil Count	173,480	4.72e‑07
Granulocyte Count	173,480	5.16e‑07
White Blood Cell Count	173,480	2.67e‑06
Neutrophill count	349,856	1.58e‑05
White blood cell (leukocyte) count	350,470	2.06e‑05

Gene module	Lipids gene-set	Leading edge	p-value
LV246	increase	DGAT2, ACACA	0.0035
LV702	increase	ACACA, DGAT2	0.0046
LV607	increase	ACACA, DGAT2	0.0058
LV890	increase	ACACA, DGAT2	0.0067
LV74	increase	MBTPS1, DGAT2	0.0078
LV865	increase	ACACA, DGAT2	0.0092
LV841	increase	ACACA, DGAT2	0.0096

Gene module	Lipids gene-set	Leading edge	p-value
LV520	decrease	FBXW7, TCF7L2	0.0006
LV801	decrease	UBE2J2, TCF7L2	0.0022
LV512	decrease	FBXW7, TCF7L2	0.0025
LV612	decrease	PTEN, FBXW7	0.0036
LV41	decrease	PCYT2, TCF7L2	0.0041
LV838	decrease	UBE2J2, TCF7L2	0.0070
LV302	decrease	TCF7L2, PTEN	0.0083
LV959	decrease	TCF7L2, PTEN	0.0092

Pathway	AUC	FDR
REACTOME FATTY ACID TRIACYLGLYCEROL AND KETONE BODY METABOLISM	0.89	3.97e-16
REACTOME METABOLISM OF LIPIDS AND LIPOPROTEINS	0.67	1.14e-08
REACTOME TRIGLYCERIDE BIOSYNTHESIS	0.86	6.52e-04
KEGG PYRUVATE METABOLISM	0.82	2.66e-03
KEGG PROPANOATE METABOLISM	0.83	4.27e-03

Trait description	Sample size	Cases	FDR
CH2DB NMR	24,154		1.05e‑12
High cholesterol (self-reported)	361,141	43,957	7.78e‑07
Ankle spacing width	206,589		2.06e‑05
Ankle spacing width (right)	114,614		9.03e‑05
HDL Cholesterol NMR	19,270		2.46e‑04
Cholesterol lowering medication	193,148	24,247	2.54e‑04
Ankle spacing width (left)	114,630		5.39e‑04
Fasting Glucose	46,186		1.90e‑03
Cholesterol lowering medication	165,340	38,057	6.74e‑03
Pulse rate, automated reading	340,162		8.61e‑03
Recent easy annoyance or irritability	117,483		1.04e‑02
Treatment/medication code: simvastatin	361,141	40,921	1.11e‑02
Pulse rate	118,850		1.19e‑02
Mean platelet (thrombocyte) volume	350,470		1.37e‑02
Duration of moderate activity	268,826		1.69e‑02
LDL Cholesterol NMR	13,527		1.94e‑02
Red blood cell (erythrocyte) distribution width	350,473		1.99e‑02
Intra-ocular pressure, corneal-compensated (right)	76,630		2.63e‑02
Heel bone mineral density (BMD) (right)	114,552		2.74e‑02
Triglycerides NMR	21,559		3.47e‑02
Heel bone mineral density T-score, automated (right)	114,614		3.75e‑02
Heel quantitative ultrasound index, direct entry (right)	114,614		3.75e‑02
Days/week of moderate physical activity 10+ minutes	343,943		4.85e‑02
Ischaemic heart disease (wide definition)	361,194	20,857	4.97e‑02

Phecode	Trait description	Sample size	Cases	FDR
276.41	Acidosis	41,838	1,526	2.40e‑03
276.4	Acid-base balance disorder	42,005	1,693	2.97e‑03

Pathway	AUC	FDR
REACTOME INTERFERON SIGNALING	0.84	3.48e-09
SVM Macrophages M1	0.92	2.09e-05
REACTOME INTERFERON ALPHA BETA SIGNALING	0.94	3.36e-05
REACTOME CYTOKINE SIGNALING IN IMMUNE SYSTEM	0.67	1.53e-04
IRIS DendriticCell-LPSstimulated	0.65	1.09e-03
KEGG CYTOSOLIC DNA SENSING PATHWAY	0.84	3.22e-03
REACTOME NEGATIVE REGULATORS OF RIG I MDA5 SIGNALING	0.81	1.61e-02

Trait description	Sample size	Partition / cluster	FDR
Mean sphered cell volume	344,729	29 / 2	2.18e‑46
Mean reticulocyte volume	344,728	29 / 2	4.08e‑44
Mean corpuscular volume	350,473	29 / 2	5.57e‑33
Red blood cell (erythrocyte) distribution width	350,473	29 / 23	7.62e‑31
Reticulocyte percentage	344,728	29 / 4	2.12e‑29
Reticulocyte count	344,729	29 / 4	6.88e‑28
Reticulocyte Count	173,480	29 / 4	1.06e‑23
Mean corpuscular haemoglobin	350,472	29 / 2	7.89e‑23
High light scatter reticulocyte count	344,729	29 / 4	3.05e‑21
Immature reticulocyte fraction	344,728	29 / 4	5.04e‑21
High light scatter reticulocyte percentage	344,729	29 / 4	1.57e‑16
Red blood cell (erythrocyte) count	350,475	29 / 5	2.53e‑16
Red Blood Cell Count	173,480	29 / 5	2.62e‑09
Haemoglobin concentration	350,474	29 / 5	1.25e‑03
Haematocrit percentage	350,475	29 / 5	3.77e‑03

Phecode	Trait description	Sample size	Cases	FDR
250.24	Type 2 diabetes with neurological manifestations	43,236	2,963	1.44e‑02
195	Cancer, suspected or other	50,040	2,250	2.42e‑02
514.2	Solitary pulmonary nodule	50,389	2,270	4.65e‑02

Pathway	AUC	FDR
DMAP ERY3	0.95	5.62e-52
DMAP ERY4	0.98	5.28e-51
DMAP ERY5	0.98	1.96e-49

Trait description	Sample size	Partition / cluster	FDR
Platelet distribution width	350,470	29 / 1	2.88e‑13
Mean platelet (thrombocyte) volume	350,470	29 / 1	7.26e‑13
Platelet count	350,474	29 / 1	1.24e‑07
Platelet Count	173,480	29 / 1	1.21e‑06
Platelet crit	350,471	29 / 1	1.38e‑04

Pathway	AUC	FDR
PID SYNDECAN 1 PATHWAY	0.81	1.20e-02
REACTOME COLLAGEN FORMATION	0.77	1.89e-02

Trait description	Sample size	Partition / cluster	FDR
6mm strong meridian (right)	66,256	29 / 10	3.21e‑07
6mm weak meridian (right)	66,256	29 / 10	2.04e‑06
6mm strong meridian (left)	65,551	29 / 10	2.44e‑06
3mm strong meridian (left)	75,398	29 / 10	2.53e‑06
6mm weak meridian (left)	65,551	29 / 10	1.20e‑05
3mm weak meridian (left)	75,398	29 / 10	1.58e‑05
3mm strong meridian (right)	75,410	29 / 10	2.95e‑05
3mm weak meridian (right)	75,410	29 / 10	3.86e‑05

Table 11: Pathways aligned to LV931 from the MultiPLIER models.
Pathway	AUC	FDR
MIPS SPLICEOSOME	0.63	3.13e-02
PID TGFBRPATHWAY	0.71	3.99e-02

Table 26: Significant trait associations of LV844 in PhenomeXcan.
Trait description	Sample size	Cases	Partition / cluster	FDR
Rheumatoid Arthritis	80,799	19,234	29 / 26	1.20e‑57
malabsorption/coeliac disease (self-reported)	361,141	1,587	29 / 8	1.25e‑43
Coeliac disease	361,194	842	29 / 8	1.52e‑41
Intestinal malabsorption (ICD10 K90)	361,194	922	29 / 8	5.28e‑40
Started insulin within one year diagnosis of diabetes	16,415	1,999	29 / 13	7.77e‑38
Systemic Lupus Erythematosus	23,210	7,219	29 / 26	6.45e‑35
Age diabetes diagnosed	16,166		29 / 13	1.92e‑34
Never eat: Wheat products	359,777	9,573	29 / 13	1.37e‑31
hyperthyroidism (self-reported)	361,141	2,730	29 / 13	3.41e‑30
Medication: insulin product	361,141	3,545	29 / 13	1.78e‑25
Insulin medication (females)	193,148	1,476	29 / 13	2.79e‑23
Insulin medication (males)	165,340	2,248	29 / 13	1.27e‑20
hypothyroidism (self-reported)	361,141	17,574	29 / 13	3.35e‑20
Medication: levothyroxine sodium	361,141	14,689	29 / 13	2.76e‑19
psoriasis (self-reported)	361,141	4,192	29 / 13	6.74e‑16

Table 27: Significant trait associations of LV844 in eMERGE.
Phecode	Trait description	Sample size	Cases	FDR
714.1	Rheumatoid arthritis	49,453	2,541	1.53e‑08
250.1	Type 1 diabetes	42,723	2,450	3.85e‑08
714	Rheumatoid arthritis and other inflammatory polyarthropathies	50,215	3,303	1.02e‑06
440	Atherosclerosis	47,471	4,993	6.73e‑03
578.8	Hemorrhage of rectum and anus	47,545	1,991	6.73e‑03
585.32	End stage renal disease	43,309	1,842	9.01e‑03
440.2	Atherosclerosis of the extremities	45,524	3,046	1.09e‑02
514.2	Solitary pulmonary nodule	50,389	2,270	1.39e‑02
444	Arterial embolism and thrombosis	43,378	900	2.97e‑02
440.22	Atherosclerosis of native arteries of the extremities with intermittent claudication	44,639	2,161	3.61e‑02

Table 31: Pathways aligned to LV57 from the MultiPLIER models.
Pathway	AUC	FDR
KEGG T CELL RECEPTOR SIGNALING PATHWAY	0.70	1.26e-03
SVM T cells CD4 memory activated	0.79	2.59e-03
IRIS CD4Tcell-Th2-restimulated12hour	0.78	7.57e-03
KEGG ALLOGRAFT REJECTION	1.00	1.09e-02
Custom Treg	0.98	1.37e-02
PID NFAT TFPATHWAY	0.74	1.52e-02
IRIS MemoryTcell-RO-activated	0.70	2.87e-02

Phecode	Trait description	Sample size	Cases	FDR
244	Hypothyroidism	54,404	9,720	7.66e‑09
244.4	Hypothyroidism NOS	53,968	9,284	7.66e‑09
279	Disorders involving the immune mechanism	56,771	3,309	1.07e‑02
514.2	Solitary pulmonary nodule	50,389	2,270	2.62e‑02
714	Rheumatoid arthritis and other inflammatory polyarthropathies	50,215	3,303	3.77e‑02

Pathway	AUC	FDR
KEGG SYSTEMIC LUPUS ERYTHEMATOSUS	0.74	1.86e-04
REACTOME MEIOTIC RECOMBINATION	0.78	3.60e-04
REACTOME RNA POL I TRANSCRIPTION	0.75	5.56e-04
REACTOME AMYLOIDS	0.76	2.17e-03

Pathway	AUC	FDR
PID INTEGRIN1 PATHWAY	0.88	9.35e-06
KEGG ECM RECEPTOR INTERACTION	0.80	7.29e-05
REACTOME COLLAGEN FORMATION	0.87	2.00e-04
REACTOME MUSCLE CONTRACTION	0.75	1.49e-02

Phecode	Trait description	Sample size	Cases	FDR
747.1	Cardiac congenital anomalies	59,198	1,871	1.57e‑01
411.4	Coronary atherosclerosis	52,836	13,715	1.60e‑01

Trait description	Sample size	Partition / cluster	FDR
CH2DB NMR	24,154	29 / 16	5.91e‑24
Reticulocyte Count	173,480	29 / 4	1.33e‑14
High light scatter reticulocyte count	344,729	29 / 4	2.52e‑13
Reticulocyte count	344,729	29 / 4	1.35e‑12
Reticulocyte percentage	344,728	29 / 4	4.93e‑11
High light scatter reticulocyte percentage	344,729	29 / 4	2.18e‑10
Immature reticulocyte fraction	344,728	29 / 4	2.31e‑08
Chronotype	128,266	29 / 16	9.73e‑04
HDL Cholesterol NMR	19,270	29 / 16	2.41e‑03

Phecode	Trait description	Sample size	Cases	FDR
208	Benign neoplasm of colon	55,694	8,597	1.39e‑02
440.2	Atherosclerosis of the extremities	45,524	3,046	2.87e‑02

Phecode	Trait description	Sample size	Cases	FDR
458	Hypotension	51,341	4,432	3.06e‑02
286.9	Abnormal coagulation profile	48,006	800	3.39e‑02
458.9	Hypotension NOS	50,150	3,241	3.58e‑02
428.2	Heart failure NOS	48,178	3,584	3.71e‑02

Trait description	Sample size	Cases	Partition / cluster	FDR
Alzheimers Disease	54,162	17,008	29 / 16	1.11e‑19
LDL Cholesterol NMR	13,527		29 / 16	9.53e‑05
Triglycerides NMR	21,559		29 / 16	1.74e‑02

Phecode	Trait description	Sample size	Cases	FDR
573	Other disorders of liver	47,826	2,524	2.99e‑02
577	Diseases of pancreas	60,538	1,795	5.51e‑02

Phecode	Trait description	Sample size	Cases	FDR
241	Nontoxic nodular goiter	47,842	3,158	1.92e‑02
241.1	Nontoxic uninodular goiter	47,125	2,441	6.84e‑02

Authors

Abstract

Introduction

Results

PhenoPLIER: an integration framework based on gene co-expression patterns

LVs link genes that alter lipid accumulation with relevant traits and tissues

PhenoPLIER with LVs predicts drug-disease pairs better than single genes

LV projections reveal trait clusters with shared transcriptomic properties

Discussion

Methods

PhenomeXcan: gene-based associations on 4,091 traits

MultiPLIER and Pathway-level information extractor (PLIER)

LV-trait associations via gene-property analysis

Drug-disease prediction

Consensus clustering of traits

Cluster interpretation

CRISPR-Cas9 screening

Code and data availability

References

Acknowledgements

Supplementary material

CRISPR-screen

LV603

Gene modules enrichment for lipids gene-sets

LV246

LV116

LV931

LV66

Agreement of consensus clustering partitions with the ensemble by number of clusters

Cluster-specific and general transcriptional processes associated with disease

LV928

LV30

LV730

LV598

LV844

LV155

LV57

LV54

LV847

LV136

LV93

LV206

LV260

LV21

LV5

LV434

Phecode	Trait description	Sample size	Cases	FDR
722	Intervertebral disc disorders	47,659	7,458	1.44e‑02
721	Spondylosis and allied disorders	47,517	7,316	1.64e‑02
250.4	Abnormal glucose	45,220	4,947	2.23e‑02
721.1	Spondylosis without myelopathy	47,315	7,114	2.66e‑02
720	Spinal stenosis	44,807	4,606	3.99e‑02