Human T-bet Governs Innate and Innate-like Adaptive IFN-γ Immunity against Mycobacteria.

Inborn errors of human interferon gamma (IFN-γ) immunity underlie mycobacterial disease. We report a patient with mycobacterial disease due to inherited deficiency of the transcription factor T-bet. The patient has extremely low counts of circulating Mycobacterium-reactive natural killer (NK), invariant NKT (iNKT), mucosal-associated invariant T (MAIT), and Vδ2+ γδ T lymphocytes, and of Mycobacterium-non reactive classic TH1 lymphocytes, with the residual populations of these cells also producing abnormally small amounts of IFN-γ. Other lymphocyte subsets develop normally but produce low levels of IFN-γ, with the exception of CD8+ αß T and non-classic CD4+ αß TH1∗ lymphocytes, which produce IFN-γ normally in response to mycobacterial antigens. Human T-bet deficiency thus underlies mycobacterial disease by preventing the development of innate (NK) and innate-like adaptive lymphocytes (iNKT, MAIT, and Vδ2+ γδ T cells) and IFN-γ production by them, with mycobacterium-specific, IFN-γ-producing, purely adaptive CD8+ αß T, and CD4+ αß TH1∗ cells unable to compensate for this deficit.

Neuronal Small RNAs Control Behavior Transgenerationally.

It is unknown whether the activity of the nervous system can be inherited. In Caenorhabditis elegans nematodes, parental responses can transmit heritable small RNAs that regulate gene expression transgenerationally. In this study, we show that a neuronal process can impact the next generations. Neurons-specific synthesis of RDE-4-dependent small RNAs regulates germline amplified endogenous small interfering RNAs (siRNAs) and germline gene expression for multiple generations. Further, the production of small RNAs in neurons controls the chemotaxis behavior of the progeny for at least three generations via the germline Argonaute HRDE-1. Among the targets of these small RNAs, we identified the conserved gene saeg-2, which is transgenerationally downregulated in the germline. Silencing of saeg-2 following neuronal small RNA biogenesis is required for chemotaxis under stress. Thus, we propose a small-RNA-based mechanism for communication of neuronal processes transgenerationally.

Genetic Mapping and Biochemical Basis of Yellow Feather Pigmentation in Budgerigars.

Parrot feathers contain red, orange, and yellow polyene pigments called psittacofulvins. Budgerigars are parrots that have been extensively bred for plumage traits during the last century, but the underlying genes are unknown. Here we use genome-wide association mapping and gene-expression analysis to map the Mendelian blue locus, which abolishes yellow pigmentation in the budgerigar. We find that the blue trait maps to a single amino acid substitution (R644W) in an uncharacterized polyketide synthase (MuPKS). When we expressed MuPKS heterologously in yeast, yellow pigments accumulated. Mass spectrometry confirmed that these yellow pigments match those found in feathers. The R644W substitution abolished MuPKS activity. Furthermore, gene-expression data from feathers of different bird species suggest that parrots acquired their colors through regulatory changes that drive high expression of MuPKS in feather epithelia. Our data also help formulate biochemical models that may explain natural color variation in parrots. VIDEO ABSTRACT.

The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease.

Many common variants have been associated with hematological traits, but identification of causal genes and pathways has proven challenging. We performed a genome-wide association analysis in the UK Biobank and INTERVAL studies, testing 29.5 million genetic variants for association with 36 red cell, white cell, and platelet properties in 173,480 European-ancestry participants. This effort yielded hundreds of low frequency (<5%) and rare (<1%) variants with a strong impact on blood cell phenotypes. Our data highlight general properties of the allelic architecture of complex traits, including the proportion of the heritable component of each blood trait explained by the polygenic signal across different genome regulatory domains. Finally, through Mendelian randomization, we provide evidence of shared genetic pathways linking blood cell indices with complex pathologies, including autoimmune diseases, schizophrenia, and coronary heart disease and evidence suggesting previously reported population associations between blood cell indices and cardiovascular disease may be non-causal.

From Mendel to mycoses: Immuno-genomic warfare at the human-fungus interface.

Fungi are opportunists: They particularly require a defect of immunity to cause severe or disseminated disease. While often secondary to an apparent iatrogenic cause, fungal diseases do occur in the absence of one, albeit infrequently. These rare cases may be due to an underlying genetic immunodeficiency that can present variably in age of onset, severity, or other infections, and in the absence of a family history of disease. They may also be due to anti-cytokine autoantibodies. This review provides a background on how human genetics or autoantibodies underlie cases of susceptibility to severe or disseminated fungal disease. Subsequently, the lessons learned from these inborn errors of immunity marked by fungal disease (IEI-FD) provide a framework to begin to mechanistically decipher fungal syndromes, potentially paving the way for precision therapy of the mycoses.

omicSynth: An open multi-omic community resource for identifying druggable targets across neurodegenerative diseases.

Treatments for neurodegenerative disorders remain rare, but recent FDA approvals, such as lecanemab and aducanumab for Alzheimer disease (MIM: 607822), highlight the importance of the underlying biological mechanisms in driving discovery and creating disease modifying therapies. The global population is aging, driving an urgent need for therapeutics that stop disease progression and eliminate symptoms. In this study, we create an open framework and resource for evidence-based identification of therapeutic targets for neurodegenerative disease. We use summary-data-based Mendelian randomization to identify genetic targets for drug discovery and repurposing. In parallel, we provide mechanistic insights into disease processes and potential network-level consequences of gene-based therapeutics. We identify 116 Alzheimer disease, 3 amyotrophic lateral sclerosis (MIM: 105400), 5 Lewy body dementia (MIM: 127750), 46 Parkinson disease (MIM: 605909), and 9 progressive supranuclear palsy (MIM: 601104) target genes passing multiple test corrections (pSMR_multi < 2.95 × 10-6 and pHEIDI > 0.01). We created a therapeutic scheme to classify our identified target genes into strata based on druggability and approved therapeutics, classifying 41 novel targets, 3 known targets, and 115 difficult targets (of these, 69.8% are expressed in the disease-relevant cell type from single-nucleus experiments). Our novel class of genes provides a springboard for new opportunities in drug discovery, development, and repurposing in the pre-competitive space. In addition, looking at drug-gene interaction networks, we identify previous trials that may require further follow-up such as riluzole in Alzheimer disease. We also provide a user-friendly web platform to help users explore potential therapeutic targets for neurodegenerative diseases, decreasing activation energy for the community.

A Bayesian approach to Mendelian randomization using summary statistics in the univariable and multivariable settings with correlated pleiotropy.

Mendelian randomization uses genetic variants as instrumental variables to make causal inferences on the effect of an exposure on an outcome. Due to the recent abundance of high-powered genome-wide association studies, many putative causal exposures of interest have large numbers of independent genetic variants with which they associate, each representing a potential instrument for use in a Mendelian randomization analysis. Such polygenic analyses increase the power of the study design to detect causal effects; however, they also increase the potential for bias due to instrument invalidity. Recent attention has been given to dealing with bias caused by correlated pleiotropy, which results from violation of the "instrument strength independent of direct effect" assumption. Although methods have been proposed that can account for this bias, a number of restrictive conditions remain in many commonly used techniques. In this paper, we propose a Bayesian framework for Mendelian randomization that provides valid causal inference under very general settings. We propose the methods MR-Horse and MVMR-Horse, which can be performed without access to individual-level data, using only summary statistics of the type commonly published by genome-wide association studies, and can account for both correlated and uncorrelated pleiotropy. In simulation studies, we show that the approach retains type I error rates below nominal levels even in high-pleiotropy scenarios. We demonstrate the proposed approaches in applied examples in both univariable and multivariable settings, some with very weak instruments.

Inferring causal direction between two traits using R2 with application to transcriptome-wide association studies.

In Mendelian randomization, two single SNP-trait correlation-based methods have been developed to infer the causal direction between an exposure (e.g., a gene) and an outcome (e.g., a trait), called MR Steiger's method and its recent extension called Causal Direction-Ratio (CD-Ratio). Here we propose an approach based on R2, the coefficient of determination, to combine information from multiple (possibly correlated) SNPs to simultaneously infer the presence and direction of a causal relationship between an exposure and an outcome. Our proposed method generalizes Steiger's method from using a single SNP to multiple SNPs as IVs. It is especially useful in transcriptome-wide association studies (TWASs) (and similar applications) with typically small sample sizes for gene expression (or another molecular trait) data, providing a more flexible and powerful approach to inferring causal directions. It can be applied to GWAS summary data with a reference panel. We also discuss the influence of invalid IVs and introduce a new approach called R2S to select and remove invalid IVs (if any) to enhance the robustness. We compared the performance of the proposed method with existing methods in simulations to demonstrate its advantages. We applied the methods to identify causal genes for high/low-density lipoprotein cholesterol (HDL/LDL) using the individual-level GTEx gene expression data and UK Biobank GWAS data. The proposed method was able to confirm some well-known causal genes while identifying some novel ones. Additionally, we illustrated an application of the proposed method to GWAS summary to infer causal relationships between HDL/LDL and stroke/coronary artery disease (CAD).

An integrative multi-context Mendelian randomization method for identifying risk genes across human tissues.

Mendelian randomization (MR) provides valuable assessments of the causal effect of exposure on outcome, yet the application of conventional MR methods for mapping risk genes encounters new challenges. One of the issues is the limited availability of expression quantitative trait loci (eQTLs) as instrumental variables (IVs), hampering the estimation of sparse causal effects. Additionally, the often context- or tissue-specific eQTL effects challenge the MR assumption of consistent IV effects across eQTL and GWAS data. To address these challenges, we propose a multi-context multivariable integrative MR framework, mintMR, for mapping expression and molecular traits as joint exposures. It models the effects of molecular exposures across multiple tissues in each gene region, while simultaneously estimating across multiple gene regions. It uses eQTLs with consistent effects across more than one tissue type as IVs, improving IV consistency. A major innovation of mintMR involves employing multi-view learning methods to collectively model latent indicators of disease relevance across multiple tissues, molecular traits, and gene regions. The multi-view learning captures the major patterns of disease relevance and uses these patterns to update the estimated tissue relevance probabilities. The proposed mintMR iterates between performing a multi-tissue MR for each gene region and joint learning the disease-relevant tissue probabilities across gene regions, improving the estimation of sparse effects across genes. We apply mintMR to evaluate the causal effects of gene expression and DNA methylation for 35 complex traits using multi-tissue QTLs as IVs. The proposed mintMR controls genome-wide inflation and offers insights into disease mechanisms.

Benchmarking Mendelian randomization methods for causal inference using genome-wide association study summary statistics.

Mendelian randomization (MR), which utilizes genetic variants as instrumental variables (IVs), has gained popularity as a method for causal inference between phenotypes using genetic data. While efforts have been made to relax IV assumptions and develop new methods for causal inference in the presence of invalid IVs due to confounding, the reliability of MR methods in real-world applications remains uncertain. Instead of using simulated datasets, we conducted a benchmark study evaluating 16 two-sample summary-level MR methods using real-world genetic datasets to provide guidelines for the best practices. Our study focused on the following crucial aspects: type I error control in the presence of various confounding scenarios (e.g., population stratification, pleiotropy, and family-level confounders like assortative mating), the accuracy of causal effect estimates, replicability, and power. By comprehensively evaluating the performance of compared methods over one thousand exposure-outcome trait pairs, our study not only provides valuable insights into the performance and limitations of the compared methods but also offers practical guidance for researchers to choose appropriate MR methods for causal inference.

Expanding the genetic and phenotypic landscape of replication factor C complex-related disorders: RFC4 deficiency is linked to a multisystemic disorder.

The precise regulation of DNA replication is vital for cellular division and genomic integrity. Central to this process is the replication factor C (RFC) complex, encompassing five subunits, which loads proliferating cell nuclear antigen onto DNA to facilitate the recruitment of replication and repair proteins and enhance DNA polymerase processivity. While RFC1's role in cerebellar ataxia, neuropathy, and vestibular areflexia syndrome (CANVAS) is known, the contributions of RFC2-5 subunits on human Mendelian disorders is largely unexplored. Our research links bi-allelic variants in RFC4, encoding a core RFC complex subunit, to an undiagnosed disorder characterized by incoordination and muscle weakness, hearing impairment, and decreased body weight. We discovered across nine affected individuals rare, conserved, predicted pathogenic variants in RFC4, all likely to disrupt the C-terminal domain indispensable for RFC complex formation. Analysis of a previously determined cryo-EM structure of RFC bound to proliferating cell nuclear antigen suggested that the variants disrupt interactions within RFC4 and/or destabilize the RFC complex. Cellular studies using RFC4-deficient HeLa cells and primary fibroblasts demonstrated decreased RFC4 protein, compromised stability of the other RFC complex subunits, and perturbed RFC complex formation. Additionally, functional studies of the RFC4 variants affirmed diminished RFC complex formation, and cell cycle studies suggested perturbation of DNA replication and cell cycle progression. Our integrated approach of combining in silico, structural, cellular, and functional analyses establishes compelling evidence that bi-allelic loss-of-function RFC4 variants contribute to the pathogenesis of this multisystemic disorder. These insights broaden our understanding of the RFC complex and its role in human health and disease.

A syndromic neurodevelopmental disorder caused by rare variants in PPFIA3.

PPFIA3 encodes the protein-tyrosine phosphatase, receptor-type, F-polypeptide-interacting-protein-alpha-3 (PPFIA3), which is a member of the LAR-protein-tyrosine phosphatase-interacting-protein (liprin) family involved in synapse formation and function, synaptic vesicle transport, and presynaptic active zone assembly. The protein structure and function are evolutionarily well conserved, but human diseases related to PPFIA3 dysfunction are not yet reported in OMIM. Here, we report 20 individuals with rare PPFIA3 variants (19 heterozygous and 1 compound heterozygous) presenting with developmental delay, intellectual disability, hypotonia, dysmorphisms, microcephaly or macrocephaly, autistic features, and epilepsy with reduced penetrance. Seventeen unique PPFIA3 variants were detected in 18 families. To determine the pathogenicity of PPFIA3 variants in vivo, we generated transgenic fruit flies producing either human wild-type (WT) PPFIA3 or five missense variants using GAL4-UAS targeted gene expression systems. In the fly overexpression assays, we found that the PPFIA3 variants in the region encoding the N-terminal coiled-coil domain exhibited stronger phenotypes compared to those affecting the C-terminal region. In the loss-of-function fly assay, we show that the homozygous loss of fly Liprin-α leads to embryonic lethality. This lethality is partially rescued by the expression of human PPFIA3 WT, suggesting human PPFIA3 function is partially conserved in the fly. However, two of the tested variants failed to rescue the lethality at the larval stage and one variant failed to rescue lethality at the adult stage. Altogether, the human and fruit fly data reveal that the rare PPFIA3 variants are dominant-negative loss-of-function alleles that perturb multiple developmental processes and synapse formation.

Bi-allelic variants in COQ8B, a gene involved in the biosynthesis of coenzyme Q10, lead to non-syndromic retinitis pigmentosa.

Retinitis pigmentosa (RP) is a Mendelian disease characterized by gradual loss of vision, due to the progressive degeneration of retinal cells. Genetically, it is highly heterogeneous, with pathogenic variants identified in more than 100 genes so far. Following a large-scale sequencing screening, we identified five individuals (four families) with recessive and non-syndromic RP, carrying as well bi-allelic DNA changes in COQ8B, a gene involved in the biosynthesis of coenzyme Q10. Specifically, we detected compound heterozygous assortments of five disease-causing variants (c.187C>T [p.Arg63Trp], c.566G>A [p.Trp189Ter], c.1156G>A [p.Asp386Asn], c.1324G>A [p.Val442Met], and c.1560G>A [p.Trp520Ter]), all segregating with disease according to a recessive pattern of inheritance. Cell-based analysis of recombinant proteins deriving from these genotypes, performed by target engagement assays, showed in all cases a significant decrease in ligand-protein interaction compared to the wild type. Our results indicate that variants in COQ8B lead to recessive non-syndromic RP, possibly by impairing the biosynthesis of coenzyme Q10, a key component of oxidative phosphorylation in the mitochondria.

Systematic analysis of variants escaping nonsense-mediated decay uncovers candidate Mendelian diseases.

Protein-truncating variants (PTVs) near the 3' end of genes may escape nonsense-mediated decay (NMD). PTVs in the NMD-escape region (PTVescs) can cause Mendelian disease but are difficult to interpret given their varying impact on protein function. Previously, PTVesc burden was assessed in an epilepsy cohort, but no large-scale analysis has systematically evaluated these variants in rare disease. We performed a retrospective analysis of 29,031 neurodevelopmental disorder (NDD) parent-offspring trios referred for clinical exome sequencing to identify PTVesc de novo mutations (DNMs). We identified 1,376 PTVesc DNMs and 133 genes that were significantly enriched (binomial p < 0.001). The PTVesc-enriched genes included those with PTVescs previously described to cause dominant Mendelian disease (e.g., SEMA6B, PPM1D, and DAGLA). We annotated ClinVar variants for PTVescs and identified 948 genes with at least one high-confidence pathogenic variant. Twenty-two known Mendelian PTVesc-enriched genes had no prior evidence of PTVesc-associated disease. We found 22 additional PTVesc-enriched genes that are not well established to be associated with Mendelian disease, several of which showed phenotypic similarity between individuals harboring PTVesc variants in the same gene. Four individuals with PTVesc mutations in RAB1A had similar phenotypes including NDD and spasticity. PTVesc mutations in IRF2BP1 were found in two individuals who each had severe immunodeficiency manifesting in NDD. Three individuals with PTVesc mutations in LDB1 all had NDD and multiple congenital anomalies. Using a large-scale, systematic analysis of DNMs, we extend the mutation spectrum for known Mendelian disease-associated genes and identify potentially novel disease-associated genes.

Identification and correction for collider bias in a genome-wide association study of diabetes-related heart failure.

Type 2 diabetes (T2D) is a major risk factor for heart failure (HF) and has elevated incidence among individuals with HF. Since genetics and HF can independently influence T2D, collider bias may occur when T2D (i.e., collider) is controlled for by design or analysis. Thus, we conducted a genome-wide association study (GWAS) of diabetes-related HF with correction for collider bias. We first performed a GWAS of HF to identify genetic instrumental variables (GIVs) for HF and to enable bidirectional Mendelian randomization (MR) analysis between T2D and HF. We identified 61 genomic loci, significantly associated with all-cause HF in 114,275 individuals with HF and over 1.5 million controls of European ancestry. Using a two-sample bidirectional MR approach with 59 and 82 GIVs for HF and T2D, respectively, we estimated that T2D increased HF risk (odds ratio [OR] 1.07, 95% confidence interval [CI] 1.04-1.10), while HF also increased T2D risk (OR 1.60, 95% CI 1.36-1.88). Then we performed a GWAS of diabetes-related HF corrected for collider bias due to the study design of index cases. After removing the spurious association of TCF7L2 locus due to collider bias, we identified two genome-wide significant loci close to PITX2 (chromosome 4) and CDKN2B-AS1 (chromosome 9) associated with diabetes-related HF in the Million Veteran Program and replicated the associations in the UK Biobank. Our MR findings provide strong evidence that HF increases T2D risk. As a result, collider bias leads to spurious genetic associations of diabetes-related HF, which can be effectively corrected to identify true positive loci.

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/.

Standard Deviations: The Biological Bases of Transmission Ratio Distortion.

The rule of Mendelian inheritance is remarkably robust, but deviations from the equal transmission of alternative alleles at a locus [a.k.a. transmission ratio distortion (TRD)] are also commonly observed in genetic mapping populations. Such TRD reveals locus-specific selection acting at some point between the diploid heterozygous parents and progeny genotyping and therefore can provide novel insight into otherwise-hidden genetic and evolutionary processes. Most of the classic selfish genetic elements were discovered through their biasing of transmission, but many unselfish evolutionary and developmental processes can also generate TRD. In this review, we describe methodologies for detecting TRD in mapping populations, detail the arenas and genetic interactions that shape TRD during plant and animal reproduction, and summarize patterns of TRD from across the genetic mapping literature. Finally, we point to new experimental approaches that can accelerate both detection of TRD and characterization of the underlying genetic mechanisms.

Plasma proteometabolome in lung cancer: exploring biomarkers through bidirectional Mendelian randomization and colocalization analysis.

Unlike other cancers with widespread screening (breast, colorectal, cervical, prostate, and skin), lung nodule biopsies for positive screenings have higher morbidity with clinical complications. Development of non-invasive diagnostic biomarkers could thereby significantly enhance lung cancer management for at-risk patients. Here, we leverage Mendelian Randomization (MR) to investigate the plasma proteome and metabolome for potential biomarkers relevant to lung cancer. Utilizing bidirectional MR and co-localization analyses, we identify novel associations, highlighting inverse relationships between plasma proteins SFTPB and KDELC2 in lung adenocarcinoma (LUAD) and positive associations of TCL1A with lung squamous cell carcinoma (LUSC) and CNTN1 with small cell lung cancer (SCLC). Additionally, our work reveals significant negative correlations between metabolites such as theobromine and paraxanthine, along with paraxanthine-related ratios, in both LUAD and LUSC. Conversely, positive correlations are found in caffeine/paraxanthine and arachidonate (20:4n6)/paraxanthine ratios with these cancer types. Through single-cell sequencing data of normal lung tissue, we further explore the role of lung tissue-specific protein SFTPB in carcinogenesis. These findings offer new insights into lung cancer etiology, potentially guiding the development of diagnostic biomarkers and therapeutic approaches.

Genetic correlation between circulating cytokines and risk of three ophthalmic diseases: a bidirectional two-sample Mendelian randomization study.

PURPOSE: Pathogenesis and the associated risk factors of cataracts, glaucoma, and age-related macular degeneration (AMD) remain unclear. We aimed to investigate causal relationships between circulating cytokine levels and the development of these diseases. PATIENTS AND METHODS: Genetic instrumental variables for circulating cytokines were derived from a genome-wide association study of 8293 European participants. Summary-level data for AMD, glaucoma, and senile cataract were obtained from the FinnGen database. The inverse variance weighted (IVW) was the main Mendelian randomization (MR) analysis method. The Cochran's Q, MR-Egger regression, and MR pleiotropy residual sum and outlier test were used for sensitivity analysis. RESULTS: Based on the IVW method, MR analysis demonstrated five circulating cytokines suggestively associated with AMD (SCGF-ß, 1.099 [95%CI, 1.037-1.166], P = 0.002; SCF, 1.155 [95%CI, 1.015-1.315], P = 0.029; MCP-1, 1.103 [95%CI, 1.012-1.202], P = 0.026; IL-10, 1.102 [95%CI, 1.012-1.200], P = 0.025; eotaxin, 1.086 [95%CI, 1.002-1.176], P = 0.044), five suggestively linked with glaucoma (MCP-1, 0.945 [95%CI, 0.894-0.999], P = 0.047; IL1ra, 0.886 [95%CI, 0.809-0.969], P = 0.008; IL-1ß, 0.866 [95%CI, 0.762-0.983], P = 0.027; IL-9, 0.908 [95%CI, 0.841-0.980], P = 0.014; IL2ra, 1.065 [95%CI, 1.004-1.130], P = 0.035), and four suggestively associated with senile cataract (TRAIL, 1.043 [95%CI, 1.009-1.077], P = 0.011; IL-16, 1.032 [95%CI, 1.001-1.064], P = 0.046; IL1ra, 0.942 [95%CI, 0.887-0.999], P = 0.047; FGF-basic, 1.144 [95%CI, 1.052-1.244], P = 0.002). Furthermore, sensitivity analysis results supported the above associations. CONCLUSION: This study highlights the involvement of several circulating cytokines in the development ophthalmic diseases and holds potential as viable pharmacological targets for these diseases.

Harnessing the Power of Electronic Health Records and Genomics for Drug Discovery.

A long-standing recognition that information from human genetics studies has the potential to accelerate drug discovery has led to decades of research on how to leverage genetic and phenotypic information for drug discovery. Established simple and advanced statistical methods that allow the simultaneous analysis of genotype and clinical phenotype data by genome- and phenome-wide analyses, colocalization analyses with quantitative trait loci data from transcriptomics and proteomics data sets from different tissues, and Mendelian randomization are essential tools for drug development in the postgenomic era. Numerous studies have demonstrated how genomic data provide opportunities for the identification of new drug targets, the repurposing of drugs, and drug safety analyses. With an increase in the number of biobanks that enable linking in-depth omics data with rich repositories of phenotypic traits via electronic health records, more powerful ways for the evaluation and validation of drug targets will continue to expand across different disciplines of clinical research.