Myeloid-Specific JAK2 Contributes to Inflammation and Salt Sensitivity of Blood Pressure.

BACKGROUND: Salt sensitivity of blood pressure (SSBP), characterized by acute changes in blood pressure with changes in dietary sodium intake, is an independent risk factor for cardiovascular disease and mortality in people with and without hypertension. We previously found that elevated sodium concentration activates antigen-presenting cells (APCs), resulting in high blood pressure, but the mechanisms are unknown. Here, we hypothesized that APC-specific JAK2 (Janus kinase 2) through STAT3 (signal transducer and activator of transcription 3) and SMAD3 (small mothers against decapentaplegic homolog 3) contributes to SSBP. METHOD: We performed bulk or single-cell transcriptomic analyses following in vitro monocytes exposed to high salt and in vivo high sodium treatment in humans using a rigorous salt-loading/depletion protocol to phenotype SSBP. We also used a myeloid cell-specific CD11c+ JAK2 knockout mouse model and measured blood pressure with radiotelemetry after N-omega-nitro-L-arginine-methyl ester and a high salt diet treatment. We used flow cytometry for immunophenotyping and measuring cytokine levels. Fluorescence in situ hybridization and immunohistochemistry were performed to spatially visualize the kidney's immune cells and cytokine levels. Echocardiography was performed to assess cardiac function. RESULTS: We found that high salt treatment upregulates gene expression of the JAK/STAT/SMAD pathway while downregulating inhibitors of this pathway, such as suppression of cytokine signaling and cytokine-inducible SH2, in human monocytes. Expression of the JAK2 pathway genes mirrored changes in blood pressure after salt loading and depletion in salt-sensitive but not salt-resistant humans. Ablation of JAK2, specifically in CD11c+ APCs, attenuated salt-induced hypertension in mice with SSBP. Mechanistically, we found that SMAD3 acted downstream of JAK2 and STAT3, leading to increased production of highly reactive isolevuglandins and proinflammatory cytokine IL (interleukin)-6 in renal APCs, which activate T cells and increase production of IL-17A, IL-6, and TNF-α (tumor necrosis factor-alpha). CONCLUSIONS: Our findings reveal the APC JAK2 signaling pathway as a potential target for the diagnosis and treatment of SSBP in humans.

Integration of estimated regional gene expression with neuroimaging and clinical phenotypes at biobank scale.

An understanding of human brain individuality requires the integration of data on brain organization across people and brain regions, molecular and systems scales, as well as healthy and clinical states. Here, we help advance this understanding by leveraging methods from computational genomics to integrate large-scale genomic, transcriptomic, neuroimaging, and electronic-health record data sets. We estimated genetically regulated gene expression (gr-expression) of 18,647 genes, across 10 cortical and subcortical regions of 45,549 people from the UK Biobank. First, we showed that patterns of estimated gr-expression reflect known genetic-ancestry relationships, regional identities, as well as inter-regional correlation structure of directly assayed gene expression. Second, we performed transcriptome-wide association studies (TWAS) to discover 1,065 associations between individual variation in gr-expression and gray-matter volumes across people and brain regions. We benchmarked these associations against results from genome-wide association studies (GWAS) of the same sample and found hundreds of novel associations relative to these GWAS. Third, we integrated our results with clinical associations of gr-expression from the Vanderbilt Biobank. This integration allowed us to link genes, via gr-expression, to neuroimaging and clinical phenotypes. Fourth, we identified associations of polygenic gr-expression with structural and functional MRI phenotypes in the Human Connectome Project (HCP), a small neuroimaging-genomic data set with high-quality functional imaging data. Finally, we showed that estimates of gr-expression and magnitudes of TWAS were generally replicable and that the p-values of TWAS were replicable in large samples. Collectively, our results provide a powerful new resource for integrating gr-expression with population genetics of brain organization and disease.

Transcriptome-Wide Root Causal Inference.

Root causal genes correspond to the first gene expression levels perturbed during pathogenesis by genetic or non-genetic factors. Targeting root causal genes has the potential to alleviate disease entirely by eliminating pathology near its onset. No existing algorithm discovers root causal genes from observational data alone. We therefore propose the Transcriptome-Wide Root Causal Inference (TWRCI) algorithm that identifies root causal genes and their causal graph using a combination of genetic variant and unperturbed bulk RNA sequencing data. TWRCI uses a novel competitive regression procedure to annotate cis and trans-genetic variants to the gene expression levels they directly cause. The algorithm simultaneously recovers a causal ordering of the expression levels to pinpoint the underlying causal graph and estimate root causal effects. TWRCI outperforms alternative approaches across a diverse group of metrics by directly targeting root causal genes while accounting for distal relations, linkage disequilibrium, patient heterogeneity and widespread pleiotropy. We demonstrate the algorithm by uncovering the root causal mechanisms of two complex diseases, which we confirm by replication using independent genome-wide summary statistics.

A multi-modal framework improves prediction of tissue-specific gene expression from a surrogate tissue.

BACKGROUND: Tissue-specific analysis of the transcriptome is critical to elucidating the molecular basis of complex traits, but central tissues are often not accessible. We propose a methodology, Multi-mOdal-based framework to bridge the Transcriptome between PEripheral and Central tissues (MOTPEC). METHODS: Multi-modal regulatory elements in peripheral blood are incorporated as features for gene expression prediction in 48 central tissues. To demonstrate the utility, we apply it to the identification of BMI-associated genes and compare the tissue-specific results with those derived directly from surrogate blood. FINDINGS: MOTPEC models demonstrate superior performance compared with both baseline models in blood and existing models across the 48 central tissues. We identify a set of BMI-associated genes using the central tissue MOTPEC-predicted transcriptome data. The MOTPEC-based differential gene expression (DGE) analysis of BMI in the central tissues (including brain caudate basal ganglia and visceral omentum adipose tissue) identifies 378 genes overlapping the results from a TWAS of BMI, while only 162 overlapping genes are identified using gene expression in blood. Cellular perturbation analysis further supports the utility of MOTPEC for identifying trait-associated gene sets and narrowing the effect size divergence between peripheral blood and central tissues. INTERPRETATION: The MOTPEC framework improves the gene expression prediction accuracy for central tissues and enhances the identification of tissue-specific trait-associated genes. FUNDING: This research is supported by the National Natural Science Foundation of China 82204118 (D.Z.), the seed funding of the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province (2020E10004), the National Institutes of Health (NIH) Genomic Innovator Award R35HG010718 (E.R.G.), NIH/NHGRI R01HG011138 (E.R.G.), NIH/NIA R56AG068026 (E.R.G.), NIH Office of the Director U24OD035523 (E.R.G.), and NIH/NIGMS R01GM140287 (E.R.G.).

Transcriptome-wide association study and Mendelian randomization in pancreatic cancer identifies susceptibility genes and causal relationships with type 2 diabetes and venous thromboembolism.

BACKGROUND: Two important questions regarding the genetics of pancreatic adenocarcinoma (PDAC) are 1. Which germline genetic variants influence the incidence of this cancer; and 2. Whether PDAC causally predisposes to associated non-malignant phenotypes, such as type 2 diabetes (T2D) and venous thromboembolism (VTE). METHODS: In this study of 8803 patients with PDAC and 67,523 controls, we first performed a large-scale transcriptome-wide association study to investigate the association between genetically determined gene expression in normal pancreas tissue and PDAC risk. Secondly, we used Mendelian Randomization (MR) to analyse the causal relationships among PDAC, T2D (74,124 cases and 824,006 controls) and VTE (30,234 cases and 172,122 controls). FINDINGS: Sixteen genes showed an association with PDAC risk (FDR <0.10), including six genes not yet reported for PDAC risk (PPIP5K2, TFR2, HNF4G, LRRC10B, PRC1 and FBXL20) and ten previously reported genes (INHBA, SMC2, ABO, PDX1, MTMR6, ACOT2, PGAP3, STARD3, GSDMB, ADAM33). MR provided support for a causal effect of PDAC on T2D using genetic instruments in the HNF4G and PDX1 loci, and unidirectional causality of VTE on PDAC involving the ABO locus (OR 2.12, P < 1e-7). No evidence of a causal effect of PDAC on VTE was found. INTERPRETATION: These analyses identified candidate susceptibility genes and disease relationships for PDAC that warrant further investigation. HNF4G and PDX1 may induce PDAC-associated diabetes, whereas ABO may induce the causative effect of VTE on PDAC. FUNDING: National Institutes of Health (USA).

Metabolic gene function discovery platform GeneMAP identifies SLC25A48 as necessary for mitochondrial choline import.

Organisms maintain metabolic homeostasis through the combined functions of small-molecule transporters and enzymes. While many metabolic components have been well established, a substantial number remains without identified physiological substrates. To bridge this gap, we have leveraged large-scale plasma metabolome genome-wide association studies (GWAS) to develop a multiomic Gene-Metabolite Association Prediction (GeneMAP) discovery platform. GeneMAP can generate accurate predictions and even pinpoint genes that are distant from the variants implicated by GWAS. In particular, our analysis identified solute carrier family 25 member 48 (SLC25A48) as a genetic determinant of plasma choline levels. Mechanistically, SLC25A48 loss strongly impairs mitochondrial choline import and synthesis of its downstream metabolite betaine. Integrative rare variant and polygenic score analyses in UK Biobank provide strong evidence that the SLC25A48 causal effects on human disease may in part be mediated by the effects of choline. Altogether, our study provides a discovery platform for metabolic gene function and proposes SLC25A48 as a mitochondrial choline transporter.

Molecular and clinical characterization of a founder mutation causing G6PC3 deficiency.

G6PC3 deficiency is a monogenic immunometabolic disorder that causes syndromic congenital neutropenia. Patients display heterogeneous extra-hematological manifestations, contributing to delayed diagnosis. Here, we investigated the origin and functional consequence of the G6PC3 c.210delC variant found in patients of Mexican origin. Based on the shared haplotypes amongst carriers of the c.210delC mutation, we estimated that this variant originated from a founder effect in a common ancestor. Furthermore, by ancestry analysis, we concluded that it originated in the indigenous Mexican population. At the protein level, we showed that this frameshift mutation leads to an aberrant protein expression in overexpression and patient-derived cells. G6PC3 pathology is driven by the intracellular accumulation of the metabolite 1,5-anhydroglucitol-6-phosphate (1,5-AG6P) that inhibits glycolysis. We characterized how the variant c.210delC impacts glycolysis by performing extracellular flux assays on patient-derived cells. When treated with 1,5-anhydroglucitol (1,5-AG), the precursor to 1,5-AG6P, patient-derived cells exhibited markedly reduced engagement of glycolysis. Finally, we compared the clinical presentation of patients with the mutation c.210delC and all other G6PC3 deficient patients reported in the literature to date, and we found that c.210delC carriers display all prominent clinical features observed in prior G6PC3 deficient patients. In conclusion, G6PC3 c.210delC is a loss-of-function mutation that arose from a founder effect in the indigenous Mexican population. These findings may facilitate the diagnosis of additional patients in this geographical area. Moreover, the in vitro 1,5-AG-dependent functional assay used in our study could be employed to assess the pathogenicity of additional G6PC3 variants.

A transcriptomic atlas of the human brain reveals genetically determined aspects of neuropsychiatric health.

Regulation of gene expression is a vital component of neurological homeostasis. Cataloging the consequences of endogenous gene expression on the physical structure and connectivity of the brain offers a means of unifying trait-associated genetic variation with trait-associated neurological features. We perform tissue-specific transcriptome-wide association studies (TWASs) on over 3,400 neuroimaging phenotypes in the UK Biobank (N = 33,224) using our joint-tissue imputation (JTI)-TWAS method. We identify highly significant associations between predicted expression for 7,192 genes and a wide variety of measures of the brain derived from magnetic resonance imaging (MRI). Our approach generates reproducible results in internal and external replication datasets. Genetically determined expression alone is sufficient for high-fidelity reconstruction of brain structure and organization. We demonstrate complementary benefits of cross-tissue and single-tissue analyses toward an integrated neurobiology and provide evidence that gene expression outside the central nervous system provides unique insights into brain health. As an application, we provide evidence suggesting that the genetically regulated expression of schizophrenia risk genes causally affects over 73% of neurological phenotypes that are altered in individuals with schizophrenia (as identified by neuroimaging studies). Imaging features associated with neuropsychiatric traits can provide valuable insights into underlying pathophysiology. By linking neuroimaging-derived phenotypes with expression levels of specific genes, this resource represents a powerful gene prioritization schema that can improve our understanding of brain function, development, and disease. The use of multiple different cortical and subcortical atlases in the resource facilitates direct integration of these data with findings from a diverse range of clinical neuroimaging studies.

Molecular and clinical characterization of a founder mutation causing G6PC3 deficiency.

Background: G6PC3 deficiency is a rare genetic disorder that causes syndromic congenital neutropenia. It is driven by the intracellular accumulation of a metabolite named 1,5-anhydroglucitol-6-phosphate (1,5-AG6P) that inhibits glycolysis. Patients display heterogeneous extra-hematological manifestations, contributing to delayed diagnosis. Objective: The G6PC3 c.210delC variant has been identified in patients of Mexican origin. We set out to study the origin and functional consequence of this mutation. Furthermore, we sought to characterize the clinical phenotypes caused by it. Methods: Using whole-genome sequencing data, we conducted haplotype analysis to estimate the age of this allele and traced its ancestral origin. We examined how this mutation affected G6PC3 protein expression and performed extracellular flux assays on patient-derived cells to characterize how this mutation impacts glycolysis. Finally, we compared the clinical presentations of patients with the c.210delC mutation relative to other G6PC3 deficient patients published to date. Results: Based on the length of haplotypes shared amongst ten carriers of the G6PC3 c.210delC mutation, we estimated that this variant originated in a common ancestor of indigenous American origin. The mutation causes a frameshift that introduces a premature stop codon, leading to a complete loss of G6PC3 protein expression. When treated with 1,5-anhydroglucitol (1,5-AG), the precursor to 1,5-AG6P, patient-derived cells exhibited markedly reduced engagement of glycolysis. Clinically, c.210delC carriers display all the clinical features of syndromic severe congenital neutropenia type 4 observed in prior reports of G6PC3 deficiency. Conclusion: The G6PC3 c.210delC is a loss-of-function mutation that arose from a founder effect in the indigenous Mexican population. These findings may facilitate the diagnosis of additional patients in this geographical area. Moreover, the in vitro 1,5-AG-dependent functional assay used in our study could be employed to assess the pathogenicity of additional G6PC3 variants.

Transcriptome analysis of cardiac endothelial cells after myocardial infarction reveals temporal changes and long-term deficits.

Endothelial cells (ECs) have essential roles in cardiac tissue repair after myocardial infarction (MI). To establish stage-specific and long-term effects of the ischemic injury on cardiac ECs, we analyzed their transcriptome at landmark time points after MI in mice. We found that early EC response at Day 2 post-MI centered on metabolic changes, acquisition of proinflammatory phenotypes, initiation of the S phase of cell cycle, and activation of stress-response pathways, followed by progression to mitosis (M/G2 phase) and acquisition of proangiogenic and mesenchymal properties during scar formation at Day 7. In contrast, genes involved in vascular physiology and maintenance of vascular tone were suppressed. Importantly, ECs did not return to pre-injury phenotypes after repair has been completed but maintained inflammatory, fibrotic and thrombotic characteristics and lost circadian rhythmicity. We discovered that the highest induced transcript is the mammalian-specific Sh2d5 gene that promoted migration and invasion of ECs through Rac1 GTPase. Our results revealed a synchronized, temporal activation of disease phenotypes, metabolic pathways, and proliferation in quiescent ECs after MI, indicating that precisely-timed interventions are necessary to optimize cardiac tissue repair and improve outcomes. Furthermore, long-term effects of acute ischemic injury on ECs may contribute to vascular dysfunction and development of heart failure.

The broad impact of cell death genes on the human disease phenome.

Cell death mediated by genetically defined signaling pathways influences the health and dynamics of all tissues, however the tissue specificity of cell death pathways and the relationships between these pathways and human disease are not well understood. We analyzed the expression profiles of an array of 44 cell death genes involved in apoptosis, necroptosis, and pyroptosis cell death pathways across 49 human tissues from GTEx, to elucidate the landscape of cell death gene expression across human tissues, and the relationship between tissue-specific genetically determined expression and the human phenome. We uncovered unique cell death gene expression profiles across tissue types, suggesting there are physiologically distinct cell death programs in different tissues. Using summary statistics-based transcriptome wide association studies (TWAS) on human traits in the UK Biobank (n ~ 500,000), we evaluated 513 traits encompassing ICD-10 defined diagnoses and laboratory-derived traits. Our analysis revealed hundreds of significant (FDR < 0.05) associations between genetically regulated cell death gene expression and an array of human phenotypes encompassing both clinical diagnoses and hematologic parameters, which were independently validated in another large-scale DNA biobank (BioVU) at Vanderbilt University Medical Center (n = 94,474) with matching phenotypes. Cell death genes were highly enriched for significant associations with blood traits versus non-cell-death genes, with apoptosis-associated genes enriched for leukocyte and platelet traits. Our findings are also concordant with independently published studies (e.g. associations between BCL2L11/BIM expression and platelet & lymphocyte counts). Overall, these results suggest that cell death genes play distinct roles in their contribution to human phenotypes, and that cell death genes influence a diverse array of human traits.

Genetic correlation and causal associations between psychiatric disorders and lung cancer risk.

BACKGROUND: Patients with certain psychiatric disorders have increased lung cancer incidence. However, establishing a causal relationship through traditional epidemiological methods poses challenges. METHODS: Available summary statistics of genome-wide association studies of cigarette smoking, lung cancer, and eight psychiatric disorders, including attention deficit/hyperactivity disorder (ADHD), autism, depression, major depressive disorder, bipolar disorder, insomnia, neuroticism, and schizophrenia (range N: 46,350-1,331,010) were leveraged to estimate genetic correlations using Linkage Disequilibrium Score Regression and assess causal effect of each psychiatric disorder on lung cancer using two-sample Mendelian randomization (MR) models, comprising inverse-variance weighted (IVW), weighted median, MR-Egger, pleiotropy residual sum and outlier testing (MR-PRESSO), and a constrained maximum likelihood approach (cML-MR). RESULTS: Significant positive correlations were observed between each psychiatric disorder and both smoking and lung cancer (all FDR < 0.05), except for the correlation between autism and lung cancer. Both univariable and the cML-MA MR analyses demonstrated that liability to schizophrenia, depression, ADHD, or insomnia was associated with an increased risk of overall lung cancer. Genetic liability to insomnia was linked specifically to squamous cell carcinoma (SCC), while genetic liability to ADHD was associated with an elevated risk of both SCC and small cell lung cancer (all P < 0.05). The later was further supported by multivariable MR analyses, which accounted for smoking. LIMITATIONS: Participants were constrained to European ancestry populations. Causal estimates from binary psychiatric disorders may be biased. CONCLUSION: Our findings suggest appropriate management of several psychiatric disorders, particularly ADHD, may potentially reduce the risk of developing lung cancer.

Transcriptome-Wide Association Studies (TWAS): Methodologies, Applications, and Challenges.

Transcriptome-wide association study (TWAS) methodologies aim to identify genetic effects on phenotypes through the mediation of gene transcription. In TWAS, in silico models of gene expression are trained as functions of genetic variants and then applied to genome-wide association study (GWAS) data. This post-GWAS analysis identifies gene-trait associations with high interpretability, enabling follow-up functional genomics studies and the development of genetics-anchored resources. We provide an overview of commonly used TWAS approaches, their advantages and limitations, and some widely used applications. © 2024 Wiley Periodicals LLC.

Microvascular insulin resistance associates with enhanced muscle glucose disposal in CD36 deficiency.

Dysfunction of endothelial insulin delivery to muscle associates with insulin resistance. CD36, a fatty acid transporter and modulator of insulin signaling is abundant in endothelial cells, especially in capillaries. Humans with inherited 50% reduction in CD36 expression have endothelial dysfunction but whether it is associated with insulin resistance is unclear. Using hyperinsulinemic/euglycemic clamps in Cd36-/- and wildtype mice, and in 50% CD36 deficient humans and matched controls we found that Cd36-/- mice have enhanced systemic glucose disposal despite unaltered transendothelial insulin transfer and reductions in microvascular perfusion and blood vessel compliance. Partially CD36 deficient humans also have better glucose disposal than controls with no capillary recruitment by insulin. CD36 knockdown in primary human-derived microvascular cells impairs insulin action on AKT, endothelial nitric oxide synthase, and nitric oxide release. Thus, insulin resistance of microvascular function in CD36 deficiency paradoxically associates with increased glucose utilization, likely through a remodeling of muscle gene expression.

Alternative polyadenylation quantitative trait methylation mapping in human cancers provides clues into the molecular mechanisms of APA.

Genetic variants are involved in the orchestration of alternative polyadenylation (APA) events, while the role of DNA methylation in regulating APA remains unclear. We generated a comprehensive atlas of APA quantitative trait methylation sites (apaQTMs) across 21 different types of cancer (1,612 to 60,219 acting in cis and 4,448 to 142,349 in trans). Potential causal apaQTMs in non-cancer samples were also identified. Mechanistically, we observed a strong enrichment of cis-apaQTMs near polyadenylation sites (PASs) and both cis- and trans-apaQTMs in proximity to transcription factor (TF) binding regions. Through the integration of ChIP-signals and RNA-seq data from cell lines, we have identified several regulators of APA events, acting either directly or indirectly, implicating novel functions of some important genes, such as TCF7L2, which is known for its involvement in type 2 diabetes and cancers. Furthermore, we have identified a vast number of QTMs that share the same putative causal CpG sites with five different cancer types, underscoring the roles of QTMs, including apaQTMs, in the process of tumorigenesis. DNA methylation is extensively involved in the regulation of APA events in human cancers. In an attempt to elucidate the potential underlying molecular mechanisms of APA by DNA methylation, our study paves the way for subsequent experimental validations into the intricate biological functions of DNA methylation in APA regulation and the pathogenesis of human cancers. To present a comprehensive catalog of apaQTM patterns, we introduce the Pancan-apaQTM database, available at https://pancan-apaqtm-zju.shinyapps.io/pancanaQTM/.

Brain eQTLs of European, African American, and Asian ancestry improve interpretation of schizophrenia GWAS.

Research on brain expression quantitative trait loci (eQTLs) has illuminated the genetic underpinnings of schizophrenia (SCZ). Yet, the majority of these studies have been centered on European populations, leading to a constrained understanding of population diversities and disease risks. To address this gap, we examined genotype and RNA-seq data from African Americans (AA, n=158), Europeans (EUR, n=408), and East Asians (EAS, n=217). When comparing eQTLs between EUR and non-EUR populations, we observed concordant patterns of genetic regulatory effect, particularly in terms of the effect sizes of the eQTLs. However, 343,737 cis-eQTLs (representing ∼17% of all eQTLs pairs) linked to 1,276 genes (about 10% of all eGenes) and 198,769 SNPs (approximately 16% of all eSNPs) were identified only in the non-EUR populations. Over 90% of observed population differences in eQTLs could be traced back to differences in allele frequency. Furthermore, 35% of these eQTLs were notably rare (MAF < 0.05) in the EUR population. Integrating brain eQTLs with SCZ signals from diverse populations, we observed a higher disease heritability enrichment of brain eQTLs in matched populations compared to mismatched ones. Prioritization analysis identified seven new risk genes ( SFXN2 , RP11-282018.3 , CYP17A1 , VPS37B , DENR , FTCDNL1 , and NT5DC2 ), and three potential novel regulatory variants in known risk genes ( CNNM2 , C12orf65 , and MPHOSPH9 ) that were missed in the EUR dataset. Our findings underscore that increasing genetic ancestral diversity is more efficient for power improvement than merely increasing the sample size within single-ancestry eQTLs datasets. Such a strategy will not only improve our understanding of the biological underpinnings of population structures but also pave the way for the identification of novel risk genes in SCZ.

Novel ancestry-specific primary open-angle glaucoma loci and shared biology with vascular mechanisms and cell proliferation.

Primary open-angle glaucoma (POAG), a leading cause of irreversible blindness globally, shows disparity in prevalence and manifestations across ancestries. We perform meta-analysis across 15 biobanks (of the Global Biobank Meta-analysis Initiative) (n = 1,487,441: cases = 26,848) and merge with previous multi-ancestry studies, with the combined dataset representing the largest and most diverse POAG study to date (n = 1,478,037: cases = 46,325) and identify 17 novel significant loci, 5 of which were ancestry specific. Gene-enrichment and transcriptome-wide association analyses implicate vascular and cancer genes, a fifth of which are primary ciliary related. We perform an extensive statistical analysis of SIX6 and CDKN2B-AS1 loci in human GTEx data and across large electronic health records showing interaction between SIX6 gene and causal variants in the chr9p21.3 locus, with expression effect on CDKN2A/B. Our results suggest that some POAG risk variants may be ancestry specific, sex specific, or both, and support the contribution of genes involved in programmed cell death in POAG pathogenesis.

Mitigating pathogenesis for target discovery and disease subtyping.

Treatments ideally mitigate pathogenesis, or the detrimental effects of the root causes of disease. However, existing definitions of treatment effect fail to account for pathogenic mechanism. We therefore introduce the Treated Root causal Effects (TRE) metric which measures the ability of a treatment to modify root causal effects. We leverage TREs to automatically identify treatment targets and cluster patients who respond similarly to treatment. The proposed algorithm learns a partially linear causal model to extract the root causal effects of each variable and then estimates TREs for target discovery and downstream subtyping. We maintain interpretability even without assuming an invertible structural equation model. Experiments across a range of datasets corroborate the generality of the proposed approach.

Multiplexed Assays of Variant Effect and Automated Patch-clamping Improve KCNH2-LQTS Variant Classification and Cardiac Event Risk Stratification.

Background: Long QT syndrome (LQTS) is a lethal arrhythmia syndrome, frequently caused by rare loss-of-function variants in the potassium channel encoded by KCNH2. Variant classification is difficult, often owing to lack of functional data. Moreover, variant-based risk stratification is also complicated by heterogenous clinical data and incomplete penetrance. Here, we sought to test whether variant-specific information, primarily from high-throughput functional assays, could improve both classification and cardiac event risk stratification in a large, harmonized cohort of KCNH2 missense variant heterozygotes. Methods: We quantified cell-surface trafficking of 18,796 variants in KCNH2 using a Multiplexed Assay of Variant Effect (MAVE). We recorded KCNH2 current density for 533 variants by automated patch clamping (APC). We calibrated the strength of evidence of MAVE data according to ClinGen guidelines. We deeply phenotyped 1,458 patients with KCNH2 missense variants, including QTc, cardiac event history, and mortality. We correlated variant functional data and Bayesian LQTS penetrance estimates with cohort phenotypes and assessed hazard ratios for cardiac events. Results: Variant MAVE trafficking scores and APC peak tail currents were highly correlated (Spearman Rank-order ρ = 0.69). The MAVE data were found to provide up to pathogenic very strong evidence for severe loss-of-function variants. In the cohort, both functional assays and Bayesian LQTS penetrance estimates were significantly predictive of cardiac events when independently modeled with patient sex and adjusted QT interval (QTc); however, MAVE data became non-significant when peak-tail current and penetrance estimates were also available. The area under the ROC for 20-year event outcomes based on patient-specific sex and QTc (AUC 0.80 [0.76-0.83]) was improved with prospectively available penetrance scores conditioned on MAVE (AUC 0.86 [0.83-0.89]) or attainable APC peak tail current data (AUC 0.84 [0.81-0.88]). Conclusion: High throughput KCNH2 variant MAVE data meaningfully contribute to variant classification at scale while LQTS penetrance estimates and APC peak tail current measurements meaningfully contribute to risk stratification of cardiac events in patients with heterozygous KCNH2 missense variants.

Discovering Root Causal Genes with High Throughput Perturbations.

Root causal gene expression levels - or root causal genes for short - correspond to the initial changes to gene expression that generate patient symptoms as a downstream effect. Identifying root causal genes is critical towards developing treatments that modify disease near its onset, but no existing algorithms attempt to identify root causal genes from data. RNA-sequencing (RNA-seq) data introduces challenges such as measurement error, high dimensionality and non-linearity that compromise accurate estimation of root causal effects even with state-of-the-art approaches. We therefore instead leverage Perturb-seq, or high throughput perturbations with single cell RNA-seq readout, to learn the causal order between the genes. We then transfer the causal order to bulk RNA-seq and identify root causal genes specific to a given patient for the first time using a novel statistic. Experiments demonstrate large improvements in performance. Applications to macular degeneration and multiple sclerosis also reveal root causal genes that lie on known pathogenic pathways, delineate patient subgroups and implicate a newly defined omnigenic root causal model.