Candidate Risk Genes Research Articles

Identification of Risk Genes for Attention-Deficit/Hyperactivity Disorder During Early Human Brain Development

ObjectiveAttention-Deficit/Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder with high heritability. Demontis et al. (2023) identified 27 genome-wide significant loci for ADHD through genome-wide association studies (GWASs), but the identification of risk genes that confer susceptibility to ADHD has remained largely unexplored. MethodAs ADHD is a neurodevelopmental disorder, we integrated human brain prenatal gene and transcript expression weight data (n=120) and ADHD GWAS summary statistics (n=225,534; 38,691 cases and 186,843 controls) to perform a transcriptome-wide association study (TWAS) by FUSION (an analytic suite). ResultsOur analysis identified 10 genes, including LSM6, HYAL3, METTL15, RPS26, LRRC37A15P, RP11-142I20.1, ABCB9, AP006621.5, AC000068.5, and PDXDC1, that are significantly associated with ADHD, along with 8 transcripts of 7 genes. We also conducted TWAS analysis using CommonMind Consortium (CMC) adult brain gene and splicing expression weights (n=452), which highlighted several risk genes that showed associations with ADHD in both prenatal and postnatal stages, such as LSM6 and HYAL3. ConclusionOverall, our TWAS of ADHD, by integrating human prenatal brain transcriptome and ADHD GWAS results, uncovered the cis-effects of gene/transcript regulation that are predicted to be associated with ADHD. By combining colocalization and FOCUS fine-mapping analysis, we further unraveled potential causal candidate risk genes. The risk genes/transcripts we identified in this study can serve as a valuable resource for further investigation of the disease mechanisms underlying ADHD.

Bayesian Rare Variant Analysis Identifies Novel Schizophrenia Putative Risk Genes.

The genetics of schizophrenia is so complex that it involves both common variants and rare variants. Rare variant association studies of schizophrenia are challenging because statistical methods for rare variant analysis are under-powered due to the rarity of rare variants. The recent Schizophrenia Exome meta-analysis (SCHEMA) consortium, the largest consortium in this field to date, has successfully identified 10 schizophrenia risk genes from ultra-rare variants by burden test, while more risk genes remain to be discovered by more powerful rare variant association test methods. In this study, we use a recently developed Bayesian rare variant association method that is powerful for detecting sparse rare risk variants that implicates 88 new candidate risk genes associated with schizophrenia from the SCHEMA case-control sample. These newly identified genes are significantly enriched in autism risk genes and GO enrichment analysis indicates that new candidate risk genes are involved in mechanosensory behavior, regulation of cell size, neuron projection morphogenesis, and plasma-membrane-bounded cell projection morphogenesis, that may provide new insights on the etiology of schizophrenia.

A systems biology-based identification and invivo functional screening of Alzheimer's disease risk genes reveal modulators of memory function.

Genome-wide association studies (GWASs) have uncovered over 75 genomic loci associated with risk for late-onset Alzheimer's disease (LOAD), but identification of the underlying causal genes remains challenging. Studies of induced pluripotent stem cell (iPSC)-derived neurons from LOAD patients have demonstrated the existence of neuronal cell-intrinsic functional defects. Here, we searched for genetic contributions to neuronal dysfunction in LOAD using an integrative systems approach that incorporated multi-evidence-based gene mapping and network-analysis-based prioritization. A systematic perturbation screening of candidate risk genes in Caenorhabditis elegans (C.elegans) revealed that neuronal knockdown of the LOAD risk gene orthologs vha-10 (ATP6V1G2), cmd-1 (CALM3), amph-1 (BIN1), ephx-1 (NGEF), and pho-5 (ACP2) alters short-/intermediate-term memory function, the cognitive domain affected earliest during LOAD progression. These results highlight the impact of LOAD risk genes on evolutionarily conserved memory function, as mediated through neuronal endosomal dysfunction, and identify new targets for further mechanistic interrogation.

Linkage and association of rs3110045 and rs28499085 variants in the thyrotropin-releasing hormone receptor (TRHR) gene with the risk of familial type 2 diabetes

Type 2 diabetes (T2D) is a chronic and prevalent multisystemic disease that significantly increases morbidity and mortality. Dysfunction of the thyroid hormone system is common in patients with T2D, increasing their risk of both hyperthyroidism and hypothyroidism. Several components of the thyroid system are candidate risk genes for T2D. The thyrotropin-releasing hormone receptor (TRHR) gene encoding for TRHR is of particular interest since it is expressed by the dorsomedial hypothalamus neurons, which are known to regulate food intake. In humans, a variant in the TRHR gene has been previously reported in T2D patients in a population-based case-control study but not in familial T2D. We recruited 212 multigenerational families with T2D originated from the Italian peninsula with multiple cases of T2D and tested, via Pseudomarker 9 single nucleotide polymorphisms (SNPs) in the TRHR gene for linkage and linkage disequilibrium (i.e., linkage plus association) to/with T2D. We identified 2 novel risk variants (rs3110045 and rs28499085) significantly linked to and associated with the risk of T2D in the Italian families across several inheritance models. Our study is the first to confirm the previously reported association of TRHR gene with T2D and extends the risk to familial inheritance. However, functional and replication studies are still needed to confirm these results.

Integration of Alzheimer’s Disease GWAS with Molecular QTLs Reveals Prioritized Risk Genes and Disease‐associated Molecular Alterations

AbstractBackgroundDespite the success of genome‐wide association studies (GWAS) to identify genetic variants associated with complex diseases, as the majority of these variants reside in non‐coding genome, one important task is to link these variants to disease‐associated molecular alterations in risk genes. To this end, we developed a systematic gene prioritization methodology that integrated our recent Alzheimer’s disease (AD) GWAS meta‐analysis with molecular quantitative trait loci (QTL) in disease‐relevant tissues and identified highly likely candidate risk genes and molecular alterations within the majority of 42 novel loci identified. In this subsequent study, we updated our pipeline with new molecular QTL catalogues and analyzed the complete landscape of AD risk.MethodWe performed GWAS‐QTL integration in AD‐relevant tissues and cells by querying the lead variants in each locus in the molecular QTL catalogues, (ii) investigating genetic colocalization between GWAS and molecular QTL signals using coloc, and (iii) conducting transcriptome, methylome, and proteome‐wide association studies (TWAS/MWAS/PWAS) using the EADB stage I meta‐analysis GWAS on 85,934 cases and 401,577 controls. The updated gene prioritization pipeline included new analyses based on new brain protein expression QTL (pQTL), cell‐type‐specific expression QTL (ct‐eQTL), expression QTL (eQTL), histone acetylation QTL (haQTL), and methylation QTL (mQTL) catalogues. Moreover, we used Nanopore RNA sequencing to further investigate risk‐associated splicing events revealed by splicing QTL (sQTL) integration.ResultOur brain ct‐eQTL coloc results showed that while the most risk‐associated gene expression alterations are specific to microglia, the proportion of astrocyte‐specific eQTL colocalizations were doubled compared to the previous studies. These included EGFR, a previously prioritized gene due to bulk brain eQTL coloc hits, whose downregulation was regulated by the protective signal specifically in astrocytes. Importantly, in a previously unresolved locus we could prioritize HS3ST5 through astrocyte‐specific eQTLs as well. Furthermore, pQTL coloc and PWAS prioritized SNX1, a previously prioritized gene at a lower confidence together with neighboring FAM96A gene, by implicating its genetic downregulation with increased AD risk.ConclusionOur updated pipeline systematically prioritized AD risk genes and disease‐associated molecular alterations in the majority of risk loci, and contributed to better interpretation of AD GWAS and understanding of genetically‐associated molecular mechanisms underlying AD risk.

Integrated analysis of Mendelian Randomization and Bayesian colocalization reveals bidirectional causal association between inflammatory bowel disease and psoriasis

Background Observational studies have suggested an association between inflammatory bowel disease [IBD] and psoriasis. However, the detailed genetic basis, causality, and direction of this association remain unclear. Methods Bidirectional two-sample Mendelian Randomization [MR] analysis was conducted using summary statistics from published genome-wide association studies. Bayesian Colocalization and multivariable MR [MVMR] analyses were performed to identify candidate variants and risk genes involved in the shared genetic basis between IBD, psoriasis, and their subtypes. Results Genetically predicted IBD and Crohn’s disease [CD] were associated with an increased risk of psoriasis, psoriasis vulgaris [PsV], and psoriatic arthritis [PsA] (IBD on psoriasis: pooled odds ratio [OR] 1.09, 95% confidence interval [CI] 1.04–1.14, p = .0001; CD on psoriasis: pooled OR 1.10, 95% CI 1.06–1.15, p < .0001) and vice versa (psoriasis on IBD: pooled OR 1.11, 95%CI 1.02–1.21), whereas CD only exhibited a unidirectional association with psoriasis. Colocalization analysis revealed eight candidate genetic variants and risk genes (including LINC00824, CDKAL1, IL10, IL23R, DNAJC27, LPP, RUNX3, and RGS14) associated with a shared genetic basis. Among these, IL23R, DNAJC27, LPP, and RGS14 were further validated by MVMR analysis. Conclusion Our findings indicated bidirectional causal associations between IBD and psoriasis (including PsV and PsA), which were attributed primarily to CD rather than Ulcerative colitis [UC]. Furthermore, we identified several candidate variants and risk genes involved in the shared genetic basis of IBD and psoriasis. Acquiring a better understanding of the shared genetic architecture underlying IBD and psoriasis would help improve clinical strategies.

A large meta-analysis identifies genes associated with anterior uveitis

Anterior Uveitis (AU) is the inflammation of the anterior part of the eye, the iris and ciliary body and is strongly associated with HLA-B*27. We report AU exome sequencing results from eight independent cohorts consisting of 3,850 cases and 916,549 controls. We identify common genome-wide significant loci in HLA-B (OR = 3.37, p = 1.03e-196) and ERAP1 (OR = 0.86, p = 1.1e-08), and find IPMK (OR = 9.4, p = 4.42e-09) and IDO2 (OR = 3.61, p = 6.16e-08) as genome-wide significant genes based on the burden of rare coding variants. Dividing the cohort into HLA-B*27 positive and negative individuals, we find ERAP1 haplotype is strongly protective only for B*27-positive AU (OR = 0.73, p = 5.2e-10). Investigation of B*27-negative AU identifies a common signal near HLA-DPB1 (rs3117230, OR = 1.26, p = 2.7e-08), risk genes IPMK and IDO2, and several additional candidate risk genes, including ADGFR5, STXBP2, and ACHE. Taken together, we decipher the genetics underlying B*27-positive and -negative AU and identify rare and common genetic signals for both subtypes of disease.

VBASS enables integration of single cell gene expression data in Bayesian association analysis of rare variants

Rare or de novo variants have substantial contribution to human diseases, but the statistical power to identify risk genes by rare variants is generally low due to rarity of genotype data. Previous studies have shown that risk genes usually have high expression in relevant cell types, although for many conditions the identity of these cell types are largely unknown. Recent efforts in single cell atlas in human and model organisms produced large amount of gene expression data. Here we present VBASS, a Bayesian method that integrates single-cell expression and de novo variant (DNV) data to improve power of disease risk gene discovery. VBASS models disease risk prior as a function of expression profiles, approximated by deep neural networks. It learns the weights of neural networks and parameters of Gamma-Poisson likelihood models of DNV counts jointly from expression and genetics data. On simulated data, VBASS shows proper error rate control and better power than state-of-the-art methods. We applied VBASS to published datasets and identified more candidate risk genes with supports from literature or data from independent cohorts. VBASS can be generalized to integrate other types of functional genomics data in statistical genetics analysis.

A novel angiogenesis-related scoring model predicts prognosis risk and treatment responsiveness in diffuse large B-cell lymphoma.

Diffuse large B cell lymphoma (DLBCL) is a highly heterogeneous disease with varying therapeutic responses and prognoses. Angiogenesis is a crucial factor in lymphoma growth and progression, but no scoring model based on angiogenesis-related genes (ARGs) has been developed for prognostic evaluation of DLBCL patients. In this study, we used univariate Cox regression to identify prognostic ARGs and found two distinct clusters of DLBCL patients in the GSE10846 dataset based on the expression of these prognostic ARGs. These two clusters had different prognoses and immune cell infiltration. Using LASSO regression analysis, we constructed a novel seven-ARG-based scoring model in GSE10846 dataset, and it was further validated in the GSE87371 dataset. The DLBCL patients were divided into high- and low-score groups based on the median risk score as a cut-off. The high-score group had a worse prognosis and showed higher expression of immune checkpoints, M2 macrophages, myeloid-derived suppressor cells, and regulatory T cells, indicating a stronger immunosuppressive environment. DLBCL patients in high-score group were resistant to doxorubicin and cisplatin, which are components of frequently used chemotherapy regimens, but more sensitive to gemcitabine and temozolomide. Using RT-qPCR, we found that two candidate risk genes, RAPGEF2 and PTGER2, were over-expressed in DLBCL tissues compared with control tissues. Taken together, the ARG-based scoring model provides a promising direction for the prognosis and immune status of DLBCL patients, and benefits the development of personalized treatment for DLBCL patients.

Emerging concepts involving inhibitory and activating RNA functionalization towards the understanding of microcephaly phenotypes and brain diseases in humans.

Microcephaly is characterized as a small head circumference, and is often accompanied by developmental disorders. Several candidate risk genes for this disease have been described, and mutations in non-coding regions are occasionally found in patients with microcephaly. Various non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), SINEUPs, telomerase RNA component (TERC), and promoter-associated lncRNAs (pancRNAs) are now being characterized. These ncRNAs regulate gene expression, enzyme activity, telomere length, and chromatin structure through RNA binding proteins (RBPs)-RNA interaction. Elucidating the potential roles of ncRNA-protein coordination in microcephaly pathogenesis might contribute to its prevention or recovery. Here, we introduce several syndromes whose clinical features include microcephaly. In particular, we focus on syndromes for which ncRNAs or genes that interact with ncRNAs may play roles. We discuss the possibility that the huge ncRNA field will provide possible new therapeutic approaches for microcephaly and also reveal clues about the factors enabling the evolutionary acquisition of the human-specific "large brain."

Mapping Genetic Susceptibility to Stenosis in the Proximal Airway.

Recent translational scientific efforts in subglottic stenosis (SGS) support a disease model where epithelial alterations facilitate microbiome displacement, dysregulated immune activation, and localized fibrosis. Yet despite recent advances, the genetic basis of SGS remains poorly understood. We sought to identify candidate risk genes associated with an SGS phenotype, investigate their biological function, and identify the cell types enriched for their expression. The Online Mendelian Inheritance in Man (OMIM) database was queried for single gene variants associated with an SGS phenotype. The functional intersections and molecular roles of the identified genes were explored using pathway enrichment analysis (PEA) computational methods. Cellular localization of the candidate risk genes was measured via transcriptional quantification in an established single cell RNA sequencing (scRNA-seq) atlas of the proximal airway. Twenty genes associated with SGS phenotype were identified. PEA resulted in 24 significantly enriched terms including "cellular response to TGF-β," "epithelial-to-mesenchymal transition," and "adherens junctions." Mapping the 20 candidate risk genes to the scRNA-seq atlas found 3 (15%) genes were enriched in epithelial cells, 3 (15%) in fibroblasts, and 3 (15%) in endothelial cells. 11 (55%) genes were expressed ubiquitously among tissue types. Interestingly, immune cells were not significantly enriched for candidate risk genes. We identify and provide biologic context for 20 genes associated with fibrotic disease of the proximal airway and form the foundation for future detailed genetic study. N/A Laryngoscope, 133:3049-3056, 2023.

Identification of risk genes in Chinese nonobstructive azoospermia patients based on whole-exome sequencing.

Nonobstructive azoospermia (NOA) is a severe condition in infertile men, and increasing numbers of causative genes have been identified during the last few decades. Although certain causative genes can explain the presence of NOA in some patients, a proportion of NOA patients remain to be addressed. This study aimed to investigate potential high-risk genes associated with spermatogenesis in idiopathic NOA patients by whole-exome sequencing. Whole-exome sequencing was performed in 46 male patients diagnosed with NOA. First, screening was performed for 119 genes known to be related to male infertility. Next, further screening was performed to determine potential high-risk causative genes for NOA by comparisons with 68 healthy male controls. Finally, risk genes with high/specific expression in the testes were selected and their expression fluctuations during spermatogenesis were graphed. The frequency of cystic fibrosis transmembrane conductance regulator (CFTR) gene pathogenic variant carriers was higher in the NOA patients compared with the healthy controls. Potential risk genes that may be causes of NOA were identified, including seven genes that were highly/specifically expressed in the testes. Four risk genes previously reported to be involved in spermatogenesis (MutS homolog 5 [MSH5], cilia- and flagella-associated protein 54 [CFAP54], MAP7 domain containing 3 [MAP7D3], and coiled-coil domain containing 33 [CCDC33]) and three novel risk genes (coiled-coil domain containing 168 [CCDC168], chromosome 16 open reading frame 96 [C16orf96], and serine protease 48 [PRSS48]) were identified to be highly or specifically expressed in the testes and significantly different in the 46 NOA patients compared with 68 healthy controls. This study on clinical NOA patients provides further evidence for the four previously reported risk genes. The present findings pave the way for further functional investigations and provide candidate risk genes for genetic diagnosis of NOA.

Identification of Novel, Replicable Genetic Risk Loci for Suicidal Thoughts and Behaviors Among US Military Veterans

Suicide is a leading cause of death; however, the molecular genetic basis of suicidal thoughts and behaviors (SITB) remains unknown. To identify novel, replicable genomic risk loci for SITB. This genome-wide association study included 633 778 US military veterans with and without SITB, as identified through electronic health records. GWAS was performed separately by ancestry, controlling for sex, age, and genetic substructure. Cross-ancestry risk loci were identified through meta-analysis. Study enrollment began in 2011 and is ongoing. Data were analyzed from November 2021 to August 2022. SITB. A total of 633 778 US military veterans were included in the analysis (57 152 [9%] female; 121 118 [19.1%] African ancestry, 8285 [1.3%] Asian ancestry, 452 767 [71.4%] European ancestry, and 51 608 [8.1%] Hispanic ancestry), including 121 211 individuals with SITB (19.1%). Meta-analysis identified more than 200 GWS (P < 5 × 10-8) cross-ancestry risk single-nucleotide variants for SITB concentrated in 7 regions on chromosomes 2, 6, 9, 11, 14, 16, and 18. Top single-nucleotide variants were largely intronic in nature; 5 were independently replicated in ISGC, including rs6557168 in ESR1, rs12808482 in DRD2, rs77641763 in EXD3, rs10671545 in DCC, and rs36006172 in TRAF3. Associations for FBXL19 and AC018880.2 were not replicated. Gene-based analyses implicated 24 additional GWS cross-ancestry risk genes, including FURIN, TSNARE1, and the NCAM1-TTC12-ANKK1-DRD2 gene cluster. Cross-ancestry enrichment analyses revealed significant enrichment for expression in brain and pituitary tissue, synapse and ubiquitination processes, amphetamine addiction, parathyroid hormone synthesis, axon guidance, and dopaminergic pathways. Seven other unique European ancestry-specific GWS loci were identified, 2 of which (POM121L2 and METTL15/LINC02758) were replicated. Two additional GWS ancestry-specific loci were identified within the African ancestry (PET112/GATB) and Hispanic ancestry (intergenic locus on chromosome 4) subsets, both of which were replicated. No GWS loci were identified within the Asian ancestry subset; however, significant enrichment was observed for axon guidance, cyclic adenosine monophosphate signaling, focal adhesion, glutamatergic synapse, and oxytocin signaling pathways across all ancestries. Within the European ancestry subset, genetic correlations (r > 0.75) were observed between the SITB phenotype and a suicide attempt-only phenotype, depression, and posttraumatic stress disorder. Additionally, polygenic risk score analyses revealed that the Million Veteran Program polygenic risk score had nominally significant main effects in 2 independent samples of veterans of European and African ancestry. The findings of this analysis may advance understanding of the molecular genetic basis of SITB and provide evidence for ESR1, DRD2, TRAF3, and DCC as cross-ancestry candidate risk genes. More work is needed to replicate these findings and to determine if and how these genes might impact clinical care.

Functional Interpretation of Alzheimer’s Disease Genetic Risk and Systematic Gene Prioritization

AbstractBackgroundGenome‐wide association studies (GWAS) of Alzheimer’s disease (AD) risk identified over 100 genome‐wide significant susceptibility loci in the past 15 years, but remaining challenges include resolving the mechanism of action of how these associations functionally contribute to AD risk and prioritizing the candidate risk genes in the complex risk loci. To address these, we have recently developed a systematic gene prioritization pipeline that nominated highly likely candidate risk genes within the majority of 42 new loci identified by our recent AD GWAS meta‐analysis. In this subsequent work, we investigated the complete landscape of AD risk‐associated molecular phenotypes and further extended our analyses to known AD risk loci and subthreshold significant loci.MethodWe investigated downstream effects of AD‐associated variants on molecular phenotypes (e.g. gene expression, splicing, methylation, protein expression, histone modifications) through molecular quantitative trait loci (QTL) catalogues measured in disease‐relevant tissues and cells. We queried the lead variants in these QTL catalogues and performed genetic colocalization analyses (coloc) &amp; transcriptome/methylome/proteome‐wide association studies (TWAS/EWAS/PWAS) using the EADB stage I meta‐analysis GWAS on 85,934 cases and 401,577 controls. Evidence for candidate genes in new, known, and subthreshold loci were assessed by the gene prioritization pipeline that was updated with new analyses. Moreover, we investigated AD‐associated cryptic splicing events using Nanopore sequencing.ResultOur results implicated &gt;140 protein‐coding genes with AD risk in 42 new loci, &gt;275 in 34 known loci, and over &gt;65 in 31 subthreshold loci. Within MINDY2 known locus, evidence from multiple QTL‐GWAS integration domains confirmed ADAM10 as a prioritized gene. Moreover, we obtained first QTL‐based evidences for candidate genes within previously‐unresolved new risk loci such as RASGEF1C. In a subthreshold locus, fine‐mapped expression TWAS associations were observed for SNX31 (a member of sorting nexins together with SNX1 in novel loci) consistently across multiple brain expression reference panels, implicating genetic upregulation of SNX31 with increased AD risk. Furthermore, we could confirm AD‐associated novel splicing events within prioritized genes such as DOC2A. ConclusionOur comprehensive study effectively and systematically prioritized AD risk genes and aided functional interpretation of AD GWAS by contributing to better understanding of genetically‐associated molecular mechanisms underlying AD risk.

Tissue-wide cell-specific proteogenomic modeling reveals novel candidate risk genes in autism spectrum disorders

Autism spectrum disorders (ASD) are a set of complex neurodevelopmental diseases characterized with repetitive behavioral patterns and communication disabilities. Using a systems biology method called MAPSD (Markov Affinity-based Proteogenomic Signal Diffusion) for joint modeling of proteome dynamics and a wide array of omics datasets, we identified a list of candidate ASD risk genes. Leveraging the collected biological signals as well as a large-scale protein-protein interaction network adjusted based on single cell resolution proteome properties in four brain regions, we observed an agreement between the known and the newly identified candidate genes that are spatially enriched in neuronal cells within cerebral cortex at the protein level. Moreover, we created a detailed subcellular localization enrichment map of the known and the identified genes across 32 micro-domains and showed that neuronal cells and neuropils share the largest fraction of signal enrichment in cerebral cortex. Notably, we showed that the identified genes are among the transcriptional biomarkers of inhibitory and excitatory neurons in human frontal cortex. Intersecting the identified genes with a single cell RNA-seq data on ASD brains further evidenced that 20 candidate genes, including GRIK1, EMX2, STXBP6, and KCNJ3 are disrupted in distinct cell-types. Moreover, we showed that ASD risk genes are predominantly distributed in certain human interactome modules, and that the identified genes may act as the regulator for some of the known ASD loci. In summary, our study demonstrated how tissue-wide cell-specific proteogenomic modeling can reveal candidate genes for brain disorders that can be supported by convergent lines of evidence.

Genetic evidence for a causal relationship between type 2 diabetes and peripheral artery disease in both Europeans and East Asians

BackgroundObservational studies have revealed that type 2 diabetes (T2D) is associated with an increased risk of peripheral artery disease (PAD). However, whether the two diseases share a genetic basis and whether the relationship is causal remain unclear. It is also unclear as to whether these relationships differ between ethnic groups.MethodsBy leveraging large-scale genome-wide association study (GWAS) summary statistics of T2D (European-based: Ncase = 21,926, Ncontrol = 342,747; East Asian-based: Ncase = 36,614, Ncontrol = 155,150) and PAD (European-based: Ncase = 5673, Ncontrol = 359,551; East Asian-based: Ncase = 3593, Ncontrol = 208,860), we explored the genetic correlation and putative causal relationship between T2D and PAD in both Europeans and East Asians using linkage disequilibrium score regression and seven Mendelian randomization (MR) models. We also performed multi-trait analysis of GWAS and two gene-based analyses to reveal candidate variants and risk genes involved in the shared genetic basis between T2D and PAD.ResultsWe observed a strong genetic correlation (rg) between T2D and PAD in both Europeans (rg = 0.51; p-value = 9.34 × 10−15) and East Asians (rg = 0.46; p-value = 1.67 × 10−12). The MR analyses provided consistent evidence for a causal effect of T2D on PAD in both ethnicities (odds ratio [OR] = 1.05 to 1.28 for Europeans and 1.15 to 1.27 for East Asians) but not PAD on T2D. This putative causal effect was not influenced by total cholesterol, body mass index, systolic blood pressure, or smoking initiation according to multivariable MR analysis, and the genetic overlap between T2D and PAD was further explored employing an independent European sample through polygenic risk score regression. Multi-trait analysis of GWAS revealed two novel European-specific single nucleotide polymorphisms (rs927742 and rs1734409) associated with the shared genetic basis of T2D and PAD. Gene-based analyses consistently identified one gene ANKFY1 and gene-gene interactions (e.g., STARD10 [European-specific] to AP3S2 [East Asian-specific]; KCNJ11 [European-specific] to KCNQ1 [East Asian-specific]) associated with the trans-ethnic genetic overlap between T2D and PAD, reflecting a common genetic basis for the co-occurrence of T2D and PAD in both Europeans and East Asians.ConclusionsOur study provides the first evidence for a genetically causal effect of T2D on PAD in both Europeans and East Asians. Several candidate variants and risk genes were identified as being associated with this genetic overlap. Our findings emphasize the importance of monitoring PAD status in T2D patients and suggest new genetic biomarkers for screening PAD risk among patients with T2D.

Evidence for the contribution of HCN1 gene polymorphism (rs1501357) to working memory at both behavioral and neural levels in schizophrenia patients and healthy controls

Gene HCN1 polymorphism (rs1501357) has been proposed to be one of the candidate risk genes for schizophrenia in the second report of the Psychiatric Genomics Consortium–Schizophrenia Workgroup. Although animal studies repeatedly showed a role of this gene in working memory, its contribution to working memory in human samples, especially in schizophrenia patients, is still unknown. To explore the association between rs1501357 and working memory at both behavioral (Study 1) and neural (Study 2) levels, the current study involved two independent samples. Study 1 included 876 schizophrenia patients and 842 healthy controls, all of whom were assessed on a 2-back task, a dot pattern expectancy task (DPX), and a digit span task. Study 2 included 56 schizophrenia patients and 155 healthy controls, all of whom performed a 2-back task during functional magnetic resonance imaging (fMRI) scanning. In both studies, we consistently found significant genotype-by-diagnosis interaction effects. For Study 1, the interaction effects were significant for the three tasks. Patients carrying the risk allele performed worse than noncarriers, while healthy controls showed the opposite pattern. For Study 2, the interaction effects were observed at the parietal cortex and the medial frontal cortex. Patients carrying the risk allele showed increased activation at right parietal cortex and increased deactivation at the medial frontal cortex, while healthy controls showed the opposite pattern. These results suggest that the contributions of rs1501357 to working memory capability vary in different populations (i.e., schizophrenia patients vs. healthy controls), which expands our understanding of the functional impact of the HCN1 gene. Future studies should examine its associations with other cognitive functions.

Bioinformatics analysis and experimental verification of the prognostic and biological significance mediated by fatty acid metabolism related genes for hepatocellular carcinoma.

BackgroundLiver cancer is among the leading causes of death related to cancer around the world. The most frequent type of human liver cancer is hepatocellular carcinoma (HCC). Fatty acid (FA) metabolism is an emerging hallmark that plays a promoting role in numerous malignancies. This study aimed to discover a FA metabolism-related risk signature and formulate a better model for HCC patients’ prognosis prediction.MethodsWe collected mRNA expression data and clinical parameters of patients with HCC using the TCGA databases, and the differential FA metabolism-related genes were explored. To create a risk prognostic model, we carried out the consensus clustering as well as univariate and multivariate Cox regression analyses. 16 genes were used to establish a prognostic model, which was then validated in the ICGC dataset. The accuracy of the model was performed using receiver operating characteristic (ROC) analyses, decision curve analysis (DCA) and nomogram. The immune cell infiltration level of risk genes was evaluated with single-sample GSEA (ssGSEA) algorithm. To reflect the response to immunotherapy, immunophenoscore (IPS) was obtained from TCGA-LIHC. Then, the expression of the candidate risk genes (p < 0.05) was validated by qRT-PCR, Western blotting and single-cell transcriptomics. Cellular function assays were performed to revealed the biological function of HAVCR1.ResultsAccording to the TCGA-LIHC cohort analysis, the majority of the FA metabolism-related genes were expressed differentially in the HCC and normal tissues. The prognosis of patients with high-risk scores was observed to be worse. Multivariate COX regression analysis confirmed that the model can be employed as an independent prognosis factor for HCC patients. Furthermore, ssGSEA analysis revealed a link between the model and the levels of immune cell infiltration. Our model scoring mechanism also provides a high predictive value in HCC patients receiving anti-PDL1 immunotherapy. One of the FA metabolism-related genes, HAVCR1, displays a significant differential expression between normal and HCC cell lines. Hepatocellular carcinoma cells (Huh7, and HepG2) proliferation, motility, and invasion were all remarkably inhibited by HAVCR1 siRNA.ConclusionOur study identified a novel FA metabolism-related prognostic model, revealing a better potential treatment and prevention strategy for HCC.

Genetics of the human microglia regulome refines Alzheimer's disease risk loci.

Microglia are brain myeloid cells that play a critical role in neuroimmunity and the etiology of Alzheimer's disease (AD), yet our understanding of how the genetic regulatory landscape controls microglial function and contributes to AD is limited. Here, we performed transcriptome and chromatin accessibility profiling in primary human microglia from 150 donors to identify genetically driven variation and cell-specific enhancer-promoter (E-P) interactions. Integrative fine-mapping analysis identified putative regulatory mechanisms for 21 AD risk loci, of which 18 were refined to a single gene, including 3 new candidate risk genes (KCNN4, FIBP and LRRC25). Transcription factor regulatory networks captured AD risk variation and identified SPI1 as a key putative regulator of microglia expression and AD risk. This comprehensive resource capturing variation in the human microglia regulome provides insights into the etiology of neurodegenerative disease.

DbBIP: a comprehensive bipolar disorder database for genetic research.

Bipolar disorder (BIP) is one of the most common hereditary psychiatric disorders worldwide. Elucidating the genetic basis of BIP will play a pivotal role in mechanistic delineation. Genome-wide association studies (GWAS) have successfully reported multiple susceptibility loci conferring BIP risk, thus providing insight into the effects of its underlying pathobiology. However, difficulties remain in the extrication of important and biologically relevant data from genetic discoveries related to psychiatric disorders such as BIP. There is an urgent need for an integrated and comprehensive online database with unified access to genetic and multi-omics data for in-depth data mining. Here, we developed the dbBIP, a database for BIP genetic research based on published data. The dbBIP consists of several modules, i.e.: (i) single nucleotide polymorphism (SNP) module, containing large-scale GWAS genetic summary statistics and functional annotation information relevant to risk variants; (ii) gene module, containing BIP-related candidate risk genes from various sources and (iii) analysis module, providing a simple and user-friendly interface to analyze one’s own data. We also conducted extensive analyses, including functional SNP annotation, integration (including summary-data-based Mendelian randomization and transcriptome-wide association studies), co-expression, gene expression, tissue expression, protein–protein interaction and brain expression quantitative trait loci analyses, thus shedding light on the genetic causes of BIP. Finally, we developed a graphical browser with powerful search tools to facilitate data navigation and access. The dbBIP provides a comprehensive resource for BIP genetic research as well as an integrated analysis platform for researchers and can be accessed online at http://dbbip.xialab.info. Database URL: http://dbbip.xialab.info