Phenome-wide identification of therapeutic genetic targets, leveraging knowledge graphs, graph neural networks, and UK Biobank data.

The ongoing expansion of human genomic datasets propels therapeutic target identification; however, extracting gene-disease associations from gene annotations remains challenging. Here, we introduce Mantis-ML 2.0, a framework integrating AstraZeneca's Biological Insights Knowledge Graph and numerous tabular datasets, to assess gene-disease probabilities throughout the phenome. We use graph neural networks, capturing the graph's holistic structure, and train them on hundreds of balanced datasets via a robust semi-supervised learning framework to provide gene-disease probabilities across the human exome. Mantis-ML 2.0 incorporates natural language processing to automate disease-relevant feature selection for thousands of diseases. The enhanced models demonstrate a 6.9% average classification power boost, achieving a median receiver operating characteristic (ROC) area under curve (AUC) score of 0.90 across 5220 diseases from Human Phenotype Ontology, OpenTargets, and Genomics England. Notably, Mantis-ML 2.0 prioritizes associations from an independent UK Biobank phenome-wide association study (PheWAS), providing a stronger form of triaging and mitigating against underpowered PheWAS associations. Results are exposed through an interactive web resource.

Rare variant associations with plasma protein levels in the UK Biobank.

Integrating human genomics and proteomics can help elucidate disease mechanisms, identify clinical biomarkers and discover drug targets1-4. Because previous proteogenomic studies have focused on common variation via genome-wide association studies, the contribution of rare variants to the plasma proteome remains largely unknown. Here we identify associations between rare protein-coding variants and 2,923 plasma protein abundances measured in 49,736 UK Biobank individuals. Our variant-level exome-wide association study identified 5,433 rare genotype-protein associations, of which 81% were undetected in a previous genome-wide association study of the same cohort5. We then looked at aggregate signals using gene-level collapsing analysis, which revealed 1,962 gene-protein associations. Of the 691 gene-level signals from protein-truncating variants, 99.4% were associated with decreased protein levels. STAB1 and STAB2, encoding scavenger receptors involved in plasma protein clearance, emerged as pleiotropic loci, with 77 and 41 protein associations, respectively. We demonstrate the utility of our publicly accessible resource through several applications. These include detailing an allelic series in NLRC4, identifying potential biomarkers for a fatty liver disease-associated variant in HSD17B13 and bolstering phenome-wide association studies by integrating protein quantitative trait loci with protein-truncating variants in collapsing analyses. Finally, we uncover distinct proteomic consequences of clonal haematopoiesis (CH), including an association between TET2-CH and increased FLT3 levels. Our results highlight a considerable role for rare variation in plasma protein abundance and the value of proteogenomics in therapeutic discovery.

Neurodevelopmental deficits and cell-type-specific transcriptomic perturbations in a mouse model of HNRNPU haploinsufficiency.

Heterozygous de novo loss-of-function mutations in the gene expression regulator HNRNPU cause an early-onset developmental and epileptic encephalopathy. To gain insight into pathological mechanisms and lay the potential groundwork for developing targeted therapies, we characterized the neurophysiologic and cell-type-specific transcriptomic consequences of a mouse model of HNRNPU haploinsufficiency. Heterozygous mutants demonstrated global developmental delay, impaired ultrasonic vocalizations, cognitive dysfunction and increased seizure susceptibility, thus modeling aspects of the human disease. Single-cell RNA-sequencing of hippocampal and neocortical cells revealed widespread, yet modest, dysregulation of gene expression across mutant neuronal subtypes. We observed an increased burden of differentially-expressed genes in mutant excitatory neurons of the subiculum-a region of the hippocampus implicated in temporal lobe epilepsy. Evaluation of transcriptomic signature reversal as a therapeutic strategy highlights the potential importance of generating cell-type-specific signatures. Overall, this work provides insight into HNRNPU-mediated disease mechanisms and provides a framework for using single-cell RNA-sequencing to study transcriptional regulators implicated in disease.

Literature-based predictions of Mendelian disease therapies.

In the effort to treat Mendelian disorders, correcting the underlying molecular imbalance may be more effective than symptomatic treatment. Identifying treatments that might accomplish this goal requires extensive and up-to-date knowledge of molecular pathways-including drug-gene and gene-gene relationships. To address this challenge, we present "parsing modifiers via article annotations" (PARMESAN), a computational tool that searches PubMed and PubMed Central for information to assemble these relationships into a central knowledge base. PARMESAN then predicts putatively novel drug-gene relationships, assigning an evidence-based score to each prediction. We compare PARMESAN's drug-gene predictions to all of the drug-gene relationships displayed by the Drug-Gene Interaction Database (DGIdb) and show that higher-scoring relationship predictions are more likely to match the directionality (up- versus down-regulation) indicated by this database. PARMESAN had more than 200,000 drug predictions scoring above 8 (as one example cutoff), for more than 3,700 genes. Among these predicted relationships, 210 were registered in DGIdb and 201 (96%) had matching directionality. This publicly available tool provides an automated way to prioritize drug screens to target the most-promising drugs to test, thereby saving time and resources in the development of therapeutics for genetic disorders.

Atoh1 drives the heterogeneity of the pontine nuclei neurons and promotes their differentiation.

Pontine nuclei (PN) neurons mediate the communication between the cerebral cortex andthe cerebellum to refine skilled motor functions. Prior studies showed that PN neurons fall into two subtypes based on their anatomic location and region-specific connectivity, but the extent of their heterogeneity and its molecular drivers remain unknown. Atoh1 encodes a transcription factor that is expressed in the PN precursors. We previously showed that partial loss of Atoh1 function in mice results in delayed PN development and impaired motor learning. In this study, we performed single-cell RNA sequencing to elucidate the cell state-specific functions of Atoh1 during PN development and found that Atoh1 regulates cell cycle exit, differentiation, migration, and survival of PN neurons. Our data revealed six previously not known PN subtypes that are molecularly and spatially distinct. We found that the PN subtypes exhibit differential vulnerability to partial loss of Atoh1 function, providing insights into the prominence of PN phenotypes in patients with ATOH1 missense mutations.

Epilepsy in a mouse model of GNB1 encephalopathy arises from altered potassium (GIRK) channel signaling and is alleviated by a GIRK inhibitor.

De novo mutations in GNB1, encoding the Gß1 subunit of G proteins, cause a neurodevelopmental disorder with global developmental delay and epilepsy, GNB1 encephalopathy. Here, we show that mice carrying a pathogenic mutation, K78R, recapitulate aspects of the disorder, including developmental delay and generalized seizures. Cultured mutant cortical neurons also display aberrant bursting activity on multi-electrode arrays. Strikingly, the antiepileptic drug ethosuximide (ETX) restores normal neuronal network behavior in vitro and suppresses spike-and-wave discharges (SWD) in vivo. ETX is a known blocker of T-type voltage-gated Ca2+ channels and G protein-coupled potassium (GIRK) channels. Accordingly, we present evidence that K78R results in a gain-of-function (GoF) effect by increasing the activation of GIRK channels in cultured neurons and a heterologous model (Xenopus oocytes)-an effect we show can be potently inhibited by ETX. This work implicates a GoF mechanism for GIRK channels in epilepsy, identifies a new mechanism of action for ETX in preventing seizures, and establishes this mouse model as a pre-clinical tool for translational research with predicative value for GNB1 encephalopathy.

Effects of protein-coding variants on blood metabolite measurements and clinical biomarkers in the UK Biobank.

Genome-wide association studies (GWASs) have established the contribution of common and low-frequency variants to metabolic blood measurements in the UK Biobank (UKB). To complement existing GWAS findings, we assessed the contribution of rare protein-coding variants in relation to 355 metabolic blood measurements-including 325 predominantly lipid-related nuclear magnetic resonance (NMR)-derived blood metabolite measurements (Nightingale Health Plc) and 30 clinical blood biomarkers-using 412,393 exome sequences from four genetically diverse ancestries in the UKB. Gene-level collapsing analyses were conducted to evaluate a diverse range of rare-variant architectures for the metabolic blood measurements. Altogether, we identified significant associations (p < 1 × 10-8) for 205 distinct genes that involved 1,968 significant relationships for the Nightingale blood metabolite measurements and 331 for the clinical blood biomarkers. These include associations for rare non-synonymous variants in PLIN1 and CREB3L3 with lipid metabolite measurements and SYT7 with creatinine, among others, which may not only provide insights into novel biology but also deepen our understanding of established disease mechanisms. Of the study-wide significant clinical biomarker associations, 40% were not previously detected on analyzing coding variants in a GWAS in the same cohort, reinforcing the importance of studying rare variation to fully understand the genetic architecture of metabolic blood measurements.

A minimal role for synonymous variation in human disease.

Synonymous mutations change the DNA sequence of a gene without affecting the amino acid sequence of the encoded protein. Although some synonymous mutations can affect RNA splicing, translational efficiency, and mRNA stability, studies in human genetics, mutagenesis screens, and other experiments and evolutionary analyses have repeatedly shown that most synonymous variants are neutral or only weakly deleterious, with some notable exceptions. Based on a recent study in yeast, there have been claims that synonymous mutations could be as important as nonsynonymous mutations in causing disease, assuming the yeast findings hold up and translate to humans. Here, we argue that there is insufficient evidence to overturn the large, coherent body of knowledge establishing the predominant neutrality of synonymous variants in the human genome.

Human genetics uncovers MAP3K15 as an obesity-independent therapeutic target for diabetes.

We performed collapsing analyses on 454,796 UK Biobank (UKB) exomes to detect gene-level associations with diabetes. Recessive carriers of nonsynonymous variants in MAP3K15 were 30% less likely to develop diabetes (P = 5.7 × 10-10) and had lower glycosylated hemoglobin (ß = -0.14 SD units, P = 1.1 × 10-24). These associations were independent of body mass index, suggesting protection against insulin resistance even in the setting of obesity. We replicated these findings in 96,811 Admixed Americans in the Mexico City Prospective Study (P < 0.05)Moreover, the protective effect of MAP3K15 variants was stronger in individuals who did not carry the Latino-enriched SLC16A11 risk haplotype (P = 6.0 × 10-4). Separately, we identified a Finnish-enriched MAP3K15 protein-truncating variant associated with decreased odds of both type 1 and type 2 diabetes (P < 0.05) in FinnGen. No adverse phenotypes were associated with protein-truncating MAP3K15 variants in the UKB, supporting this gene as a therapeutic target for diabetes.

DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets.

The druggability of targets is a crucial consideration in drug target selection. Here, we adopt a stochastic semi-supervised ML framework to develop DrugnomeAI, which estimates the druggability likelihood for every protein-coding gene in the human exome. DrugnomeAI integrates gene-level properties from 15 sources resulting in 324 features. The tool generates exome-wide predictions based on labelled sets of known drug targets (median AUC: 0.97), highlighting features from protein-protein interaction networks as top predictors. DrugnomeAI provides generic as well as specialised models stratified by disease type or drug therapeutic modality. The top-ranking DrugnomeAI genes were significantly enriched for genes previously selected for clinical development programs (p value < 1 × 10-308) and for genes achieving genome-wide significance in phenome-wide association studies of 450 K UK Biobank exomes for binary (p value = 1.7 × 10-5) and quantitative traits (p value = 1.6 × 10-7). We accompany our method with a web application ( http://drugnomeai.public.cgr.astrazeneca.com ) to visualise the druggability predictions and the key features that define gene druggability, per disease type and modality.

Identification of the NRF2 transcriptional network as a therapeutic target for trigeminal neuropathic pain.

Trigeminal neuralgia, historically dubbed the "suicide disease," is an exceedingly painful neurologic condition characterized by sudden episodes of intense facial pain. Unfortunately, the only U.S. Food and Drug Administration (FDA)-approved medication for trigeminal neuralgia carries substantial side effects, with many patients requiring surgery. Here, we identify the NRF2 transcriptional network as a potential therapeutic target. We report that cerebrospinal fluid from patients with trigeminal neuralgia accumulates reactive oxygen species, several of which directly activate the pain-transducing channel TRPA1. Similar to our patient cohort, a mouse model of trigeminal neuropathic pain also exhibits notable oxidative stress. We discover that stimulating the NRF2 antioxidant transcriptional network is as analgesic as inhibiting TRPA1, in part by reversing the underlying oxidative stress. Using a transcriptome-guided drug discovery strategy, we identify two NRF2 network modulators as potential treatments. One of these candidates, exemestane, is already FDA-approved and may thus be a promising alternative treatment for trigeminal neuropathic pain.

Cancer-driving mutations are enriched in genic regions intolerant to germline variation.

Large reference datasets of protein-coding variation in human populations have allowed us to determine which genes and genic subregions are intolerant to germline genetic variation. There is also a growing number of genes implicated in severe Mendelian diseases that overlap with genes implicated in cancer. We hypothesized that cancer-driving mutations might be enriched in genic subregions that are depleted of germline variation relative to somatic variation. We introduce a new metric, OncMTR (oncology missense tolerance ratio), which uses 125,748 exomes in the Genome Aggregation Database (gnomAD) to identify these genic subregions. We demonstrate that OncMTR can significantly predict driver mutations implicated in hematologic malignancies. Divergent OncMTR regions were enriched for cancer-relevant protein domains, and overlaying OncMTR scores on protein structures identified functionally important protein residues. Last, we performed a rare variant, gene-based collapsing analysis on an independent set of 394,694 exomes from the UK Biobank and find that OncMTR markedly improves genetic signals for hematologic malignancies.

Biliverdin reductase bridges focal adhesion kinase to Src to modulate synaptic signaling.

Synapses connect discrete neurons into vast networks that send, receive, and encode diverse forms of information. Synaptic function and plasticity, the neuronal process of adapting to diverse and variable inputs, depend on the dynamic nature of synaptic molecular components, which is mediated in part by cell adhesion signaling pathways. Here, we found that the enzyme biliverdin reductase (BVR) physically links together key focal adhesion signaling molecules at the synapse. BVR-null (BVR-/-) mice exhibited substantial deficits in learning and memory on neurocognitive tests, and hippocampal slices in which BVR was postsynaptically depleted showed deficits in electrophysiological responses to stimuli. RNA sequencing, biochemistry, and pathway analyses suggested that these deficits were mediated through the loss of focal adhesion signaling at both the transcriptional and biochemical level in the hippocampus. Independently of its catalytic function, BVR acted as a bridge between the primary focal adhesion signaling kinases FAK and Pyk2 and the effector kinase Src. Without BVR, FAK and Pyk2 did not bind to and stimulate Src, which then did not phosphorylate the N-methyl-d-aspartate (NMDA) receptor, a critical posttranslational modification for synaptic plasticity. Src itself is a molecular hub on which many signaling pathways converge to stimulate NMDAR-mediated neurotransmission, thus positioning BVR at a prominent intersection of synaptic signaling.

Association of ultra-rare coding variants with genetic generalized epilepsy: A case-control whole exome sequencing study.

OBJECTIVE: We aimed to identify genes associated with genetic generalized epilepsy (GGE) by combining large cohorts enriched with individuals with a positive family history. Secondarily, we set out to compare the association of genes independently with familial and sporadic GGE. METHODS: We performed a case-control whole exome sequencing study in unrelated individuals of European descent diagnosed with GGE (previously recruited and sequenced through multiple international collaborations) and ancestry-matched controls. The association of ultra-rare variants (URVs; in 18 834 protein-coding genes) with epilepsy was examined in 1928 individuals with GGE (vs. 8578 controls), then separately in 945 individuals with familial GGE (vs. 8626 controls), and finally in 1005 individuals with sporadic GGE (vs. 8621 controls). We additionally examined the association of URVs with familial and sporadic GGE in two gene sets important for inhibitory signaling (19 genes encoding γ-aminobutyric acid type A [GABAA ] receptors, 113 genes representing the GABAergic pathway). RESULTS: GABRG2 was associated with GGE (p = 1.8 × 10-5 ), approaching study-wide significance in familial GGE (p = 3.0 × 10-6 ), whereas no gene approached a significant association with sporadic GGE. Deleterious URVs in the most intolerant subgenic regions in genes encoding GABAA receptors were associated with familial GGE (odds ratio [OR] = 3.9, 95% confidence interval [CI] = 1.9-7.8, false discovery rate [FDR]-adjusted p = .0024), whereas their association with sporadic GGE had marginally lower odds (OR = 3.1, 95% CI = 1.3-6.7, FDR-adjusted p = .022). URVs in GABAergic pathway genes were associated with familial GGE (OR = 1.8, 95% CI = 1.3-2.5, FDR-adjusted p = .0024) but not with sporadic GGE (OR = 1.3, 95% CI = .9-1.9, FDR-adjusted p = .19). SIGNIFICANCE: URVs in GABRG2 are likely an important risk factor for familial GGE. The association of gene sets of GABAergic signaling with familial GGE is more prominent than with sporadic GGE.

High-impact rare genetic variants in severe schizophrenia.

Extreme phenotype sequencing has led to the identification of high-impact rare genetic variants for many complex disorders but has not been applied to studies of severe schizophrenia. We sequenced 112 individuals with severe, extremely treatment-resistant schizophrenia, 218 individuals with typical schizophrenia, and 4,929 controls. We compared the burden of rare, damaging missense and loss-of-function variants between severe, extremely treatment-resistant schizophrenia, typical schizophrenia, and controls across mutation intolerant genes. Individuals with severe, extremely treatment-resistant schizophrenia had a high burden of rare loss-of-function (odds ratio, 1.91; 95% CI, 1.39 to 2.63; P = 7.8 × 10-5) and damaging missense variants in intolerant genes (odds ratio, 2.90; 95% CI, 2.02 to 4.15; P = 3.2 × 10-9). A total of 48.2% of individuals with severe, extremely treatment-resistant schizophrenia carried at least one rare, damaging missense or loss-of-function variant in intolerant genes compared to 29.8% of typical schizophrenia individuals (odds ratio, 2.18; 95% CI, 1.33 to 3.60; P = 1.6 × 10-3) and 25.4% of controls (odds ratio, 2.74; 95% CI, 1.85 to 4.06; P = 2.9 × 10-7). Restricting to genes previously associated with schizophrenia risk strengthened the enrichment with 8.9% of individuals with severe, extremely treatment-resistant schizophrenia carrying a damaging missense or loss-of-function variant compared to 2.3% of typical schizophrenia (odds ratio, 5.48; 95% CI, 1.52 to 19.74; P = 0.02) and 1.6% of controls (odds ratio, 5.82; 95% CI, 3.00 to 11.28; P = 2.6 × 10-8). These results demonstrate the power of extreme phenotype case selection in psychiatric genetics and an approach to augment schizophrenia gene discovery efforts.

Rare variant contribution to human disease in 281,104 UK Biobank exomes.

Genome-wide association studies have uncovered thousands of common variants associated with human disease, but the contribution of rare variants to common disease remains relatively unexplored. The UK Biobank contains detailed phenotypic data linked to medical records for approximately 500,000 participants, offering an unprecedented opportunity to evaluate the effect of rare variation on a broad collection of traits1,2. Here we study the relationships between rare protein-coding variants and 17,361 binary and 1,419 quantitative phenotypes using exome sequencing data from 269,171 UK Biobank participants of European ancestry. Gene-based collapsing analyses revealed 1,703 statistically significant gene-phenotype associations for binary traits, with a median odds ratio of 12.4. Furthermore, 83% of these associations were undetectable via single-variant association tests, emphasizing the power of gene-based collapsing analysis in the setting of high allelic heterogeneity. Gene-phenotype associations were also significantly enriched for loss-of-function-mediated traits and approved drug targets. Finally, we performed ancestry-specific and pan-ancestry collapsing analyses using exome sequencing data from 11,933 UK Biobank participants of African, East Asian or South Asian ancestry. Our results highlight a significant contribution of rare variants to common disease. Summary statistics are publicly available through an interactive portal ( http://azphewas.com/ ).