Significant Risk Genes Research Articles

Proteome-wide association studies using summary pQTL data of three tissues identified 30 risk genes of Alzheimer's disease dementia.

Proteome-wide association study (PWAS) integrating proteomic data with genome-wide association study (GWAS) summary data is a powerful tool for studying Alzheimer's disease (AD) dementia. Existing PWAS analyses of AD often rely on the availability of individual-level proteomic and genetic data of a reference panel. Leveraging summary protein quantitative trait loci (pQTL) reference data of multiple AD-relevant tissues is expected to improve PWAS findings of AD dementia. We conducted PWAS of AD dementia by integrating publicly available summary pQTL data of brain, cerebrospinal fluid (CSF), and plasma tissues, with the latest GWAS summary data of AD dementia. For each target protein per tissue, we employed our recently published OTTERS tool to obtain omnibus PWAS p-value, to test whether the genetically regulated protein abundance in the corresponding tissue is associated with AD dementia. Protein-protein interactions and enriched pathways of identified significant PWAS risk genes were analyzed by STRING. The potential causal effects of these PWAS risk genes were assessed by probabilistic Mendelian randomization analyses. We identified 30 unique significant PWAS risk genes for AD dementia, including 11 for brain, 9 for CSF, and 16 for plasma tissues. Four of these were shared by at least two tissues, and gene MAPK3 was found in all three tissues. We found that 11 of these PWAS risk genes were associated with AD or AD pathological hall marks as shown in GWAS Catalog; 18 of these were detected by transcriptome-wide association studies (TWAS); and 25 of these, including 8 out of 9 novel genes, were interconnected within a protein-protein interaction network involving the well-known AD risk gene APOE. Especially, these PWAS risk genes were enriched in immune response, glial cell proliferation, and high-density lipoprotein particle clearance pathways. Mediated causal effects were validated for 13 PWAS risk genes (43.3%). Our findings provide novel insights into the genetic mechanisms of AD dementia in brain, CSF, and plasma tissues, and targets for developing therapeutic interventions. We also demonstrated the effectiveness of integrating summary pQTL and GWAS data for mapping risk genes of complex human diseases.

Unraveling NEK4 as a Potential Drug Target in Schizophrenia and Bipolar I Disorder: A Proteomic and Genomic Approach.

Investigating the shared brain protein and genetic components of schizophrenia (SCZ) and bipolar I disorder (BD-I) presents a unique opportunity to understand the underlying pathophysiological processes and pinpoint potential drug targets. To identify overlapping susceptibility brain proteins in SCZ and BD-I, we carried out proteome-wide association studies (PWAS) and Mendelian Randomization (MR) by integrating human brain protein quantitative trait loci with large-scale genome-wide association studies for both disorders. We utilized transcriptome-wide association studies (TWAS) to determine the consistency of mRNA-protein dysregulation in both disorders. We applied pleiotropy-informed conditional false discovery rate (pleioFDR) analysis to identify common risk genetic loci for SCZ and BD-I. Additionally, we performed a cell-type-specific analysis in the human brain to detect risk genes notably enriched in distinct brain cell types. The impact of risk gene overexpression on dendritic arborization and axon length in neurons was also examined. Our PWAS identified 42 proteins associated with SCZ and 14 with BD-I, among which NEK4, HARS2, SUGP1, and DUS2 were common to both conditions. TWAS and MR analysis verified the significant risk gene NEK4 for both SCZ and BD-I. PleioFDR analysis further supported genetic risk loci associated with NEK4 for both conditions. The cell-type specificity analysis revealed that NEK4 is expressed on the surface of glutamatergic neurons, and its overexpression enhances dendritic arborization and axon length in cultured primary neurons. These findings underscore a shared genetic origin for SCZ and BD-I, offering novel insights for potential therapeutic target identification.

SR-TWAS: leveraging multiple reference panels to improve transcriptome-wide association study power by ensemble machine learning

Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for transcriptome-wide association studies (TWAS). To leverage expression imputation models (i.e., base models) trained with multiple reference panels, regression methods, and tissues, we develop a Stacked Regression based TWAS (SR-TWAS) tool which can obtain optimal linear combinations of base models for a given validation transcriptomic dataset. Both simulation and real studies show that SR-TWAS improves power, due to increased training sample sizes and borrowed strength across multiple regression methods and tissues. Leveraging base models across multiple reference panels, tissues, and regression methods, our real studies identify 6 independent significant risk genes for Alzheimer’s disease (AD) dementia for supplementary motor area tissue and 9 independent significant risk genes for Parkinson’s disease (PD) for substantia nigra tissue. Relevant biological interpretations are found for these significant risk genes.

Bayesian genome-wide TWAS with reference transcriptomic data of brain and blood tissues identified 141 risk genes for Alzheimer’s disease dementia

BackgroundTranscriptome-wide association study (TWAS) is an influential tool for identifying genes associated with complex diseases whose genetic effects are likely mediated through transcriptome. TWAS utilizes reference genetic and transcriptomic data to estimate effect sizes of genetic variants on gene expression (i.e., effect sizes of a broad sense of expression quantitative trait loci, eQTL). These estimated effect sizes are employed as variant weights in gene-based association tests, facilitating the mapping of risk genes with genome-wide association study (GWAS) data. However, most existing TWAS of Alzheimer's disease (AD) dementia are limited to studying only cis-eQTL proximal to the test gene. To overcome this limitation, we applied the Bayesian Genome-wide TWAS (BGW-TWAS) method to leveraging both cis- and trans- eQTL of brain and blood tissues, in order to enhance mapping risk genes for AD dementia.MethodsWe first applied BGW-TWAS to the Genotype-Tissue Expression (GTEx) V8 dataset to estimate cis- and trans- eQTL effect sizes of the prefrontal cortex, cortex, and whole blood tissues. Estimated eQTL effect sizes were integrated with the summary data of the most recent GWAS of AD dementia to obtain BGW-TWAS (i.e., gene-based association test) p-values of AD dementia per gene per tissue type. Then we used the aggregated Cauchy association test to combine TWAS p-values across three tissues to obtain omnibus TWAS p-values per gene.ResultsWe identified 85 significant genes in prefrontal cortex, 82 in cortex, and 76 in whole blood that were significantly associated with AD dementia. By combining BGW-TWAS p-values across these three tissues, we obtained 141 significant risk genes including 34 genes primarily due to trans-eQTL and 35 mapped risk genes in GWAS Catalog. With these 141 significant risk genes, we detected functional clusters comprised of both known mapped GWAS risk genes of AD in GWAS Catalog and our identified TWAS risk genes by protein-protein interaction network analysis, as well as several enriched phenotypes related to AD.ConclusionWe applied BGW-TWAS and aggregated Cauchy test methods to integrate both cis- and trans- eQTL data of brain and blood tissues with GWAS summary data, identifying 141 TWAS risk genes of AD dementia. These identified risk genes provide novel insights into the underlying biological mechanisms of AD dementia and potential gene targets for therapeutics development.

Abstract 1633: A phenotype risk score to define patients with a 'long-telomeropathy' and at risk for a novel cancer-like syndrome

Abstract Introduction: In human populations, telomere length (TL) displays a large inter-individual variability. At the population level, the most predictive variable for TL is age. Rare loss-of-function mutations in telomere maintenance genes are known to cause telomeropathies, a group of deadly diseases triggered by critically short telomeres. Over the last few decades, GWASs have uncovered many common variants associated with TL. More recently, a small number of PheWASs have suggested short-TL is associated with risk for a wide spectrum of phenotypes whereas long-TL is primarily associated with increased risk for several different neoplasms. We hypothesized that genetic variation contributing to long-TL may cause a unique and heritable telomeropathy, distinguishable from classical telomeropathies, that we refer to as a long-telomeropathy resulting in a cancer-like syndrome. To test this hypothesis, we studied telomere genetics in two large independent cohorts linking genomics with electronic health records data. Methods: Discovery cohort consisted of approximately 70,398 individuals from the All of Us (AoU) Research Program. Marshfield Clinic’s PMRP Biobank of 19,443 individuals served as a replication set. Previously published GWAS results (161 common variant) were utilized to produce a weighted polygenic risk score for TL (TL-PRS). A PheWAS of TL-PRS in AoU identified 19 significant phenotypes that passed a Bonferroni-adjusted p-value threshold (P<2.71E-5). Notably, risk for all 19 phenotypes were associated with long-TL and were categorized as neoplasm or neoplasm-like phenotypes. PheWAS results were used to generate a phenotype risk score for long-telomeropathy (longTL-PheRS), and this model was replicated in PMRP. Results: The validated longTL-PheRS was used to screen rare variants that may contribute to increased risk for a telomere dependent cancer-like syndrome. When focusing on 293 telomere related genes in AoU, SKAT identified ATM (P=8.8E-7) and DCLRE1B (P<1E-10) as statistically significant risk genes. Whereas ATM has been previously implicated in multiple cancer types, DCLRE1B has not. SKAT results were influenced largely by a single missense variant in each gene [P=2.43E-15 (ATM, p.Leu2307Phe) and =1.78E-12 (DCLRE1B, p.Asn510Tyr)]. Neither variant was previously reported as pathogenic. Conclusion: Many decades of research in telomere biology has demonstrated the health consequences associated with telomere shortening. This study builds on a growing body of evidence that long telomeres also have significant negative health implications. Individuals who have a genetic predisposition to long telomeres influenced by both common and rare variants may be at risk for a spectrum of tumors. These findings may lead to new diagnostic or preventative measures for individuals at risk for a long-telomeropathy and a cancer-like syndrome. Citation Format: scott hebbring, Patrick Alaire. A phenotype risk score to define patients with a 'long-telomeropathy' and at risk for a novel cancer-like syndrome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1633.

Bayesian network analysis of BIN1 risk allele and other risk factors and biomarkers of Alzheimer’s disease

AbstractBackgroundThe Bridging Integrator I (BIN1) is a significant genetic risk gene, second only to the established risk gene apolipoprotein E (APOE4), for late‐onset Alzheimer’s disease (LOAD, AD) (Carrasquillo, 2011). BIN1 rs744733 risk allele was shown to affect tau pathology and cognitive decline (Franzmeier, 2021).In this study, we compared BIN1 risk allele rs6733839 with APOE4 on interdependency among AD risk factors and biomarkers by applying Bayesian Network (BN) modeling (Scutari, 2017) to data from Lilly AD clinical trials.MethodData from A05C (n = 142, NCT01565382), A05E (n = 217, NCT02016560), and untreated arms of AZES (n = 206, NCT02245737), LLCF (n = 229, NCT02791191), and LZAX (n = 194, NCT01900665) trial participants were used for the analysis (table 1). We constructed graphs representing the dependencies among the variables, including demographics, genetic risk genes, brain amyloid and tau measured by 18F‐Florbetapir or 18F‐Flortaucipir positron emission tomography (PET) imaging quantified globally and regionally as a standardized uptake value ratio. Longitudinal tau changes from baseline were computed for a sub cohort (n = 234). Conditional Gaussian Bayesian Network (CGBN) was used to model discrete (gender, genotype) and continuous (age, PET quantifications) variables. Directed Acyclic Graphs (DAG) structure was learned using constraint‐based Incremental Association (iamb) algorithm and score‐based Hill‐Climbing (hc) algorithm with one‐sided type I error rate at 10%. The connection strength was estimated by bootstrap resampling and computing the probability of any variable influencing another variable. Model parameters were estimated using the maximum likelihood estimation method.ResultThe result shows that age influences baseline amyloid (Strength s = 1.0) and parietal tau (s = 0.83). APOE4 influences baseline amyloid (s = 0.75) and tau at four lopes with strength ranging from 0.84 to 0.31 in the order of frontal, parietal, occipital, and temporal lobes. BIN1 influences baseline tau at the parietal lobe (s = 0.42) and other lobes with strength below 0.3. We have not observed consistent dependency between variables and change in tau, likely due to the small sample size (n = 234).ConclusionOur study result is consistent with current knowledge of APOE4 influencing amyloid and tau. The finding that BIN1 influences tau supports the observation of BIN1 rs744373, in linkage disequilibrium with rs6733839, in ADNI and BioFINDER studies (Franzmeier, 2021).

177 GENOME-WIDE ASSOCIATION ANALYSIS OF THE FRAILTY PHENOTYPE IN UK BIOBANK HIGHLIGHTS CARDIOMETABOLIC AND AUTOIMMUNE SIGNATURES IN AGEING

Abstract Background Frailty is a state of advanced biological age that is characterized by multisystem physiological decline and advanced risk to adverse health outcomes. One approach to defining frailty is using the Fried frailty phenotype, a five-point list of health-related criteria used to score individuals as non-frail, pre-frail, or frail. The biological basis of frailty is not fully understood, nor why some experience frailty at younger ages. Between 30%-45% of frailty risk is estimated to be genetic. This study aimed to investigate the genetic basis of frailty phenotype risk using data from UK Biobank. Methods UK Biobank is a large-scale biomedical database of community dwelling adults (n = 502,529, age range: 37-73 years, 54% female). UK Biobank has genome-wide genotyping (approx. 93 million variants), health, and demographic data for all participants. A genome-wide association study (GWAS) of the frailty phenotype was performed on British and Irish adults aged 60 to 70 years (n = 157,720) to identify heritable factors which predict frailty progression in later life. Functional mapping was performed to determine genes and biological pathways enriched in GWAS results. Results 95 single nucleotide polymorphisms (SNPs) from three independent genomic risk loci were significantly associated (p < 5e-8) with the frailty phenotype in UK Biobank participants. Three genes, PPP1R3A, LONRF2, and AIF1 were statistically enriched with genetic variants. Tissue-specific expression analysis indicated genes differentially expressed in blood vessels were enriched. Linkage Disequilibrium Score Regression (lambda = 1.2005, intercept = 1.0006) indicated GWAS test statistic inflation resulting from polygenicity. SNP-based heritability was estimated to be 6.34% (SE: 0.36%). Conclusion SNP-based heritability of the frailty phenotype was lower than expected in comparison to previous reports of 30%-45%. Statistically significant genomic risk loci, genes, and tissue-enrichment pointed towards dysregulation of cardiometabolic, cardiovascular, and immunological processes in frailty risk, contrasting to brain pathways enriched in previous genomic analyses of the frailty index.

NAT10 as a potential prognostic biomarker and therapeutic target for HNSCC

BackgroundIncreasing evidence has demonstrated the critical roles of mRNA modification regulators on multiple types of cancers. However, it is still poorly known about the prognostic and therapeutic value of mRNA modification regulators in HNSCC.MethodsThe gene expression profile of 36 mRNA modification regulators and their corresponding clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Stepwise regression in R with both directions was used to construct a model for the prognosis of HNSCC. Univariate Cox regression survival analysis was performed to identify the most significant risk gene. Gene set enrichment analysis (GSEA) was applied to determine the cancer-associated pathways with NAT10. Immunohistochemistry (IHC) staining was performed to evaluate the expression of NAT10 in formalin fixed paraffin-embedded (FFPE) samples of HNSCC. Univariate and multivariate Cox regression survival analysis performed to identify the independent risk factors associated with the OS of patients with HNSCC. HNSCC cell lines (Cal-27, FaDu, and Detroit-562) were transfected with short interfering RNA (siRNA) targeting NAT10 or treated with Remodelin, a small-molecule inhibitor of NAT10. Knockdown efficiency of siRNA was assessed by quantitative real-time PCR (qRT-PCR) and western blotting. In addition, CCK-8 assay, scratch assay and transwell assay were used to examine the proliferation, migration, and invasion abilities of the three HNSCC cell lines after NAT10 was inhibited genetically and pharmaceutically. Cell cycle and cell apoptosis assays were performed by flow cytometry. Finally, the therapeutic value of Remodelin in HNSCC was evaluated via a patient-derived xenograft (PDX) model. The statistical analysis was performed with SPSS 23.0.ResultsA risk prediction model containing 10 mRNA modification regulators was constructed and showed prognostic value in HNSCC. NAT10 was further identified as a key risk gene and independent prognostic factor in TCGA HNSCC dataset. The GSEA analysis suggested that high NAT10 expression was associated with MYC, E2F, G2M checkpoint, mTORC1, DNA repair and oxidative phosphorylation pathways. NAT10 protein expression was significantly up-regulated in tumour cells compared to normal epithelial cells in FFPE samples and increased NAT10 protein expression was correlated with poor overall survival of 267 HNSCC patients. Genetic depletion of NAT10 using siRNA or chemical inhibition of NAT10 using Remodelin resulted in reduced cell proliferation, migration and invasion abilities in Cal-27, FaDu and Detroit-562 cells. Knockdown of NAT10 using siRNA significantly increased cell cycle arrest in S/G2-phase. Remodelin significantly inhibited tumour growth and tumour cell proliferation in the PDX model of HNSCC.ConclusionsNAT10 could be a potential prognostic marker and a therapeutic target for HNSCC.

Transcriptome-wide association study identifies new susceptibility genes and pathways for depression

Depression is the most prevalent mental disorder with substantial morbidity and mortality. Although genome-wide association studies (GWASs) have identified multiple risk variants for depression, due to the complicated gene regulatory mechanisms and complexity of linkage disequilibrium (LD), the biological mechanisms by which the risk variants exert their effects on depression remain largely unknown. Here, we perform a transcriptome-wide association study (TWAS) of depression by integrating GWAS summary statistics from 807,553 individuals (246,363 depression cases and 561,190 controls) and summary-level gene-expression data (from the dorsolateral prefrontal cortex (DLPFC) of 1003 individuals). We identified 53 transcriptome-wide significant (TWS) risk genes for depression, of which 23 genes were not implicated in risk loci of the original GWAS. Seven out of 53 risk genes (B3GALTL, FADS1, TCTEX1D1, XPNPEP3, ZMAT2, ZNF501 and ZNF502) showed TWS associations with depression in two independent brain expression quantitative loci (eQTL) datasets, suggesting that these genes may represent promising candidates. We further conducted conditional analyses and identified the potential risk genes that driven the TWAS association signal in each locus. Finally, pathway enrichment analysis revealed biologically pathways relevant to depression. Our study identified new depression risk genes whose expression dysregulation may play a role in depression. More importantly, we translated the GWAS associations into risk genes and relevant pathways. Further mechanistic study and functional characterization of the TWS depression risk genes will facilitate the diagnostics and therapeutics for depression.

An independent, replicable, functional and significant risk variant block at intron 3 of CACNA1C for schizophrenia.

Genome-wide association studies have identified a significant risk gene, CACNA1C, for schizophrenia. In this study, we comprehensively investigated a large set of CACNA1C single-nucleotide polymorphisms (SNPs) to identify the replicable risk alleles for schizophrenia and explore their biological functions. One Jewish (1044 cases vs 2052 controls), one European (1350 cases vs 1378 controls) and one exploratory African American samples (98 cases vs 20 controls) were analyzed to identify replicable single-nucleotide polymorphism-schizophrenia associations. The regulatory effects of risk alleles on CACNA1C messenger RNA expression were examined. The most robust risk tagSNP (rs1006737) was meta-analyzed on 17 studies (74,122 cases vs 109,062 controls), and associated with the gray matter volumes of seven subcortical structures in 38,258 Europeans, and the surface areas and thickness of 34 cortical regions in 33,992 Europeans and 2944 non-Europeans. Forty-seven replicable risk single-nucleotide polymorphisms, including a 20-single-nucleotide polymorphism haplotype block, were identified in our samples (1.8 × 10-4 ⩽ p ⩽ 0.049). This variant block was consistently associated with schizophrenia across four independent Psychiatric Genomics Consortium cohorts (79,645 cases vs 109,590 controls; 2.5 × 10-17 ⩽ p ⩽ 0.017). This block showed significant expression quantitative trait loci in three independent European brain cohorts (5.1 × 10-12 ⩽ p ⩽ 8.3 × 10-3) and could be tagged by the most significant risk single-nucleotide polymorphism rs1006737. The minor allele A of rs1006737 significantly increased risk for schizophrenia across the Jewish and European samples (p = 0.029 and 0.004, respectively), and this association was highly significant in the meta-analysis (p = 1.62 × 10-42). This allele also significantly altered the CACNA1C messenger RNA expression in five brain regions (5.1 × 10-12 ⩽ p ⩽ 0.05), decreased the gray matter volume of thalamus (p = 0.010), the surface area of isthmus cingulate cortex (p = 0.013) and the thickness of transverse temporal and superior temporal sulcus cortexes (0.005 ⩽ p ⩽ 0.043). We identified an independent, replicable, functional, and significant risk variant block at CACNA1C for schizophrenia, which could be tagged by the most robust risk marker rs1006737, suggesting an important role of CACNA1C in the pathogenesis of schizophrenia.

Novel Variance-Component TWAS method for studying complex human diseases with applications to Alzheimer's dementia.

Transcriptome-wide association studies (TWAS) have been widely used to integrate transcriptomic and genetic data to study complex human diseases. Within a test dataset lacking transcriptomic data, traditional two-stage TWAS methods first impute gene expression by creating a weighted sum that aggregates SNPs with their corresponding cis-eQTL effects on reference transcriptome. Traditional TWAS methods then employ a linear regression model to assess the association between imputed gene expression and test phenotype, thereby assuming the effect of a cis-eQTL SNP on test phenotype is a linear function of the eQTL's estimated effect on reference transcriptome. To increase TWAS robustness to this assumption, we propose a novel Variance-Component TWAS procedure (VC-TWAS) that assumes the effects of cis-eQTL SNPs on phenotype are random (with variance proportional to corresponding reference cis-eQTL effects) rather than fixed. VC-TWAS is applicable to both continuous and dichotomous phenotypes, as well as individual-level and summary-level GWAS data. Using simulated data, we show VC-TWAS is more powerful than traditional TWAS methods based on a two-stage Burden test, especially when eQTL genetic effects on test phenotype are no longer a linear function of their eQTL genetic effects on reference transcriptome. We further applied VC-TWAS to both individual-level (N = ~3.4K) and summary-level (N = ~54K) GWAS data to study Alzheimer's dementia (AD). With the individual-level data, we detected 13 significant risk genes including 6 known GWAS risk genes such as TOMM40 that were missed by traditional TWAS methods. With the summary-level data, we detected 57 significant risk genes considering only cis-SNPs and 71 significant genes considering both cis- and trans- SNPs, which also validated our findings with the individual-level GWAS data. Our VC-TWAS method is implemented in the TIGAR tool for public use.

Studying ABI3 variant immune contributions to Alzheimer’s disease in a transgenic mouse model

AbstractBackgroundAlzheimer’s Disease (AD) is a progressive disorder populating in a world‐wide range, which causes dementia with accumulation of amyloid‐beta (Aβ) and hyperphosphorylated tau. AD destroys memory and disrupts cognition, as well as impairs thinking, behaving and person’s ability to function independently. Numbers of genes now have been confirmed through large scale genetic studies as significant AD risk genes, including ABI3. Abelson interactor (ABI) family is an adaptor protein family commonly participating in regulation of actin cytoskeleton in cell. As a member of ABI family, ABI3 can modulate cell motility process like inhibiting tumor metastasis and migration. Genetic variants of ABI3 have been found to be strongly associated with late onset AD (LOAD) in various human populations, but the molecular mechanisms of how they influence AD pathogenesis remain unknown.MethodIn order to further study the pathological mechanisms of ABI3 in AD in vivo, we created an Abi3 transgenic mouse model on C57BL/6 background carrying a human ABI3 mutation (S209F) by CRISPER/Cas‐mediated genome engineering. ABI3 S209F mutation is encoded by a very rare variant rs616338 (MAF=0.008, OR = 1.43 for AD with P = 4.56 × 10−10). In this transgenic model, a corresponding mouse S212F mutation was introduced into Exon 5 on Chromosome 11 by homology‐directed repair, and homozygous Abi3F/F mice were generated by crossing the F1 heterozygotes. In addition, we also crossed Abi3F/F with the 5xFAD model of AD to determine the influence of this ABI3 mutation on AD pathology and cognitive functions in vivo.ResultWe hypotheses 5xFAD mice who carrying the ABI3 mutation may exhibit exacerbated AD pathologies compared with 5xFAD with wildtype ABI3, including accelerate Aβ aggregation and neuroinflammation. As ABI3 is exclusively expressed in immune cells in the brain and peripheral, this new Abi3F/F model will be an effective method for us to understand the immune contribution in AD pathogenesis and perhaps the crosstalk between peripheral and central in the disease as well.ConclusionWe believe that studying AD with ABI3 Tg mouse models can allow us to have better understanding on the immune contribution to AD pathogenesis and provide valuable strategies on clinical therapy.

Novel Risk Loci in Tinnitus and Causal Inference With Neuropsychiatric Disorders Among Adults of European Ancestry

Tinnitus affects at least 16 million US adults, but its pathophysiology is complicated, and treatment options remain limited. A heritable component has been identified in family and twin studies; however, no large-scale genome-wide association studies (GWAS) have been accomplished. To identify genetic risk loci associated with tinnitus, determine genetic correlations, and infer possible relationships of tinnitus with hearing loss and neuropsychiatric disorders and traits. A GWAS of self-reported tinnitus was performed in the UK Biobank (UKB) cohort using a linear mixed-model method implemented in BOLT-LMM (linear mixed model). Replication of significant findings was sought in the nonoverlapping US Million Veteran Program (MVP) cohort. A total of 172 995 UKB (discovery) and 260 832 MVP (replication) participants of European ancestry with self-report regarding tinnitus and hearing loss underwent genomic analysis. Linkage-disequilibrium score regression and mendelian randomization were performed between tinnitus and hearing loss and neuropsychiatric disorders. Data from the UKB were acquired and analyzed from September 24, 2018, to December 13, 2019. Data acquisition for the MVP cohort was completed July 22, 2019. Data analysis for both cohorts was completed on February 11, 2020. Estimates of single nucleotide variation (SNV)-based heritability for tinnitus, identification of genetic risk loci and genes, functional mapping, and replication were performed. Genetic association and inferred causality of tinnitus compared with hearing loss and neuropsychiatric disorders and traits were analyzed. Of 172 995 UKB participants (53.7% female; mean [SD], 58.0 [8.2] years), 155 395 unrelated participants underwent SNV-based heritability analyses across a range of tinnitus phenotype definitions that explained approximately 6% of the heritability. The GWAS based on the most heritable model in the full UKB cohort identified 6 genome-wide significant loci and 27 genes in gene-based analyses, with replication of 3 of 6 loci and 8 of 27 genes in 260 832 MVP cohort participants (92.8% men; mean [SD] age, 63.8 [13.2] years). Mendelian randomization indicated that major depressive disorder had a permissive effect (β = 0.133; P = .003) and years of education had a protective effect (β = -0.322, P = <.001) on tinnitus, whereas tinnitus and hearing loss inferred a bidirectional association (β = 0.072, P = .001 and β = 1.546, P = <.001, respectively). This large GWAS characterizes the genetic architecture of tinnitus, demonstrating modest but significant heritability and a polygenic profile with multiple significant risk loci and genes. Genetic correlation and inferred causation between tinnitus and major depressive disorder, educational level, and hearing impairment were identified, consistent with clinical and neuroimaging evidence. These findings may guide gene-based diagnostic and therapeutic approaches to this pervasive disorder.

Identification of novel candidate risk genes for myelomeningocele within the glucose homeostasis/oxidative stress and folate/one-carbon metabolism networks.

BackgroundNeural tube defects (NTDs) are the second most common complex birth defect, yet, our understanding of the genetic contribution to their development remains incomplete. Two environmental factors associated with NTDs are Folate and One Carbon Metabolism (FOCM) and Glucose Homeostasis and Oxidative Stress (GHOS). Utilizing next‐generation sequencing of a large patient cohort, we identify novel candidate genes in these two networks to provide insights into NTD mechanisms.MethodsExome sequencing (ES) was performed in 511 patients, born with myelomeningocele, divided between European American and Mexican American ethnicities. Healthy control data from the Genome Aggregation database were ethnically matched and used as controls. Rare, high fidelity, nonsynonymous predicted damaging missense, nonsense, or canonical splice site variants in independently generated candidate gene lists for FOCM and GHOS were identified. We used a gene‐based collapsing approach to quantify mutational burden in case and controls, with the control cohort estimated using cumulative allele frequencies assuming Hardy–Weinberg equilibrium.ResultsWe identified 45 of 837 genes in the FOCM network and 22 of 568 genes in the GHOS network as possible NTD risk genes with p < 0.05. No nominally significant risk genes were shared between ethnicities. Using a novel approach to mutational burden we identify 55 novel NTD risk associations.ConclusionsWe provide a means of utilizing large publicly available sequencing datasets as controls for sequencing projects examining rare disease. This approach confirmed existing risk genes for myelomeningocele and identified possible novel risk genes. Lastly, it suggests possible distinct genetic etiologies for this malformation between different ethnicities.

CACNA1C (rs1006737) may be a susceptibility gene for schizophrenia: An updated meta-analysis.

IntroductionSchizophrenia is a serious mental illness with a genetic predisposition. Genome‐wide association studies (GWAS) have identified the α‐1C subunit of the L‐type voltage‐gated calcium channel (CACNA1C) gene as a significant risk gene for schizophrenia. However, there are inconsistent conclusions in case–control studies.MethodsWe performed a comprehensive meta‐analysis of all available samples from existing studies under four different genetic models (recessive model, dominant model, additive model and allele model) to further confirm whether CACNA1C rs1006737 is an authentic risk single nucleotide polymorphism (SNP) for schizophrenia.ResultsA statistically significant difference under the four models (all p < 0.05) was observed by pooling nine Asian and European studies, including a total of 12,744 cases and 16,460 controls. For European‐decent samples, a significant difference was identified between patients and controls for the four models (all p < 0.05). We observed a significant difference between patients and controls for the recessive model and allele model (GG vs. GA + AA: p < 0.00001; G vs. A: p < 0.00001) using a fixed effect model, but the dominant model (GG + GA vs. AA: OR: p = 0.15) and additive model (GG vs. AA: p = 0.11) showed no significant difference between patients and controls in the Asian samples.ConclusionOur findings provide important evidence for the establishment of CACNA1C as a susceptibility gene for schizophrenia across world populations, but its roles in the pathogenesis of schizophrenia need to be further investigated.

Replicated associations of FADS1, MAD1L1, and a rare variant at 10q26.13 with bipolar disorder in Chinese population

Genetic analyses of psychiatric illnesses, such as bipolar disorder (BPD), have revealed essential information regarding the underlying pathological mechanisms. While such studies in populations of European ancestry have achieved prominent success, understanding the genetic risk factors of these illnesses (especially BPD) in Chinese population remains an urgent task. Given the lack of genome-wide association study (GWAS) of BPD in Chinese population from Mainland China, replicating the previously reported GWAS hits in distinct populations will provide valuable information for future GWAS analysis in Han Chinese. In the present study, we have recruited 1146 BPD cases and 1956 controls from Mainland China for genetic analyses, as well as 65 Han Chinese brain amygdala tissues for mRNA expression analyses. Using this clinical sample, one of the largest Han Chinese BPD samples till now, we have conducted replication analyses of 21 single nucleotide polymorphisms (SNPs) extracted from previous GWAS of distinct populations. Among the 21 tested SNPs, 16 showed the same direction of allelic effects in our samples compared with previous studies; 6 SNPs achieved nominal significance (p < 0.05) at one-tailed test, and 2 additional SNPs showed marginal significance (p < 0.10). Aside from replicating previously reported BPD risk SNPs, we herein also report several intriguing findings: (1) the SNP rs174576 was associated with BPD in our Chinese sample and in the overall global meta-analysis, and was significantly correlated with FADS1 mRNA in diverse public RNA-seq datasets as well as our in house collected Chinese amygdala samples; (2) two (partially) independent SNPs in MAD1L1 were both significantly associated with BPD in our Chinese sample, which was also supported by haplotype analysis; (3) a rare SNP rs78089757 in 10q26.13 region was a genome-wide significant variant for BPD in East Asians, and this SNP was near monomorphic in Europeans. In sum, these results confirmed several significant BPD risk genes. We hope this Chinese BPD case–control sample and the current brain amygdala tissues (with continuous increasing sample size in the near future) will provide helpful resources in elucidating the genetic and molecular basis of BPD in this major world population.

Exome sequencing in large, multiplex bipolar disorder families from Cuba

Bipolar disorder (BD) is a major psychiatric illness affecting around 1% of the global population. BD is characterized by recurrent manic and depressive episodes, and has an estimated heritability of around 70%. Research has identified the first BD susceptibility genes. However, the underlying pathways and regulatory networks remain largely unknown. Research suggests that the cumulative impact of common alleles with small effects explains only around 25–38% of the phenotypic variance for BD. A plausible hypothesis therefore is that rare, high penetrance variants may contribute to BD risk. The present study investigated the role of rare, nonsynonymous, and potentially functional variants via whole exome sequencing in 15 BD cases from two large, multiply affected families from Cuba. The high prevalence of BD in these pedigrees renders them promising in terms of the identification of genetic risk variants with large effect sizes. In addition, SNP array data were used to calculate polygenic risk scores for affected and unaffected family members. After correction for multiple testing, no significant increase in polygenic risk scores for common, BD-associated genetic variants was found in BD cases compared to healthy relatives. Exome sequencing identified a total of 17 rare and potentially damaging variants in 17 genes. The identified variants were shared by all investigated BD cases in the respective pedigree. The most promising variant was located in the gene SERPING1 (p.L349F), which has been reported previously as a genome-wide significant risk gene for schizophrenia. The present data suggest novel candidate genes for BD susceptibility, and may facilitate the discovery of disease-relevant pathways and regulatory networks.

Gene expression microarray analysis reveals prognostic markers of survival in high grade astrocytomas

ABSTRACTObjective: High grade astrocytoma (HGA) as an aggressive brain tumor, is always correlated with poor prognosis. In this paper, we aimed to explore the genetic prognostic biomarkers for HGA.Methods: The genome-wide expression profile of 26 brain tumor samples obtained from 26 patients with HGA was downloaded from Gene Expression Omnibus. The risk genes for prognosis of HGA were identified and verified by the data in TCGA database. Protein–protein interaction (PPI) network of risk factor genes was constructed and significant module was screened. Function and pathway annotations were performed for risk genes and drug target genes were further analyzed.Results: A total of 598 genes were identified as significant risk genes for prognosis, such as checkpoint kinase 1, potassium inwardly-rectifying channel, subfamily J, member 6, leukocyte receptor tyrosine kinase and uncharacterized LOC283887. All risk genes for prognosis of HGA were significantly enriched in cell cycle, mitotic as well as mitotic anaphase. While the genes in the network module mainly participated in functions such as cell cycle, mitotic cell cycle and cell cycle process. Moreover, the genes in the network module mainly participated in the pathways such as cell cycle and cell cycle, mitotic. Drug target analysis showed that seven genes were recorded in Drugbank database, and there were as many as 32 drug records of CHEK1.Conclusion: The prognostic effect of CHEK1 was validated based on the expression profile data of 615 low-grade glioma and glioblastoma samples. We proposed CHEK1 as prognostic biomarker for HGA. Our work might provide the candidate target for HGA therapy.

The impact of psychosis genome-wide associated ZNF804A variation on verbal fluency connectivity

Schizophrenia (SCZ) and bipolar disorder (BD) have high heritability. Genome-wide association studies (GWAS) have identified ZNF804A as a significant risk gene for both illnesses. A validation of this finding at the brain systems-level is imperative as there is still little understanding of how it heightens risk. Based in part on our recent findings of an effect on widespread decreased white matter microstructural fractional anisotropy (putatively a proxy of its integrity), particularly strong in SCZ, we asked whether the risk allele has a detrimental effect on regional brain activation and functional connectivity during a type of cognitive processing which is, together with its neural correlates, impaired in BD and SCZ: verbal fluency. Functional MRI and genotype data was collected from 80 healthy volunteers, and 54 SCZ and 40 BD patients. A standard multifactorial analysis of variance using statistical parametric mapping and significance correction of FWE p < 0.05 was used. We found the GWAS risk allele A was associated with decreased positive functional coupling between the left precentral gyrus/inferior frontal gyrus (i.e. the most highly recruited area for the task) and: 1) the left inferior frontal gyrus, and 2) the left posterior cingulate gyrus, encompassing the precuneus; both as a main effect across controls and psychosis patients. Such association of the risk allele with reduced functional connectivity (with no area where the opposite main effect was detected), converges with findings in other tasks, our previous finding of its widespread impact on brain white matter microstructure, and with the dysconnectivity hypothesis of SCZ.

Identification of shared risk loci and pathways for bipolar disorder and schizophrenia.

Bipolar disorder (BD) is a highly heritable neuropsychiatric disease characterized by recurrent episodes of mania and depression. BD shows substantial clinical and genetic overlap with other psychiatric disorders, in particular schizophrenia (SCZ). The genes underlying this etiological overlap remain largely unknown. A recent SCZ genome wide association study (GWAS) by the Psychiatric Genomics Consortium identified 128 independent genome-wide significant single nucleotide polymorphisms (SNPs). The present study investigated whether these SCZ-associated SNPs also contribute to BD development through the performance of association testing in a large BD GWAS dataset (9747 patients, 14278 controls). After re-imputation and correction for sample overlap, 22 of 107 investigated SCZ SNPs showed nominal association with BD. The number of shared SCZ-BD SNPs was significantly higher than expected (p = 1.46x10-8). This provides further evidence that SCZ-associated loci contribute to the development of BD. Two SNPs remained significant after Bonferroni correction. The most strongly associated SNP was located near TRANK1, which is a reported genome-wide significant risk gene for BD. Pathway analyses for all shared SCZ-BD SNPs revealed 25 nominally enriched gene-sets, which showed partial overlap in terms of the underlying genes. The enriched gene-sets included calcium- and glutamate signaling, neuropathic pain signaling in dorsal horn neurons, and calmodulin binding. The present data provide further insights into shared risk loci and disease-associated pathways for BD and SCZ. This may suggest new research directions for the treatment and prevention of these two major psychiatric disorders.