Genetic control of RNA splicing and its distinct role in complex trait variation

Genetic insights into the role of mitochondria-related genes in mental disorders: An integrative multi-omics analysis

BACKGROUND

Mitochondrial dysfunction has been implicated in the development of mental disorders, yet the underlying mechanisms remain unclear. In this study, we employed summary-data-based Mendelian randomization (SMR) analysis to explore the associations between mitochondrial-related genes and seven common mental disorders across gene expression, DNA methylation, and protein levels.

METHOD

Summary statistics from genome-wide association studies were used for seven mental disorders, including attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder, anxiety, bipolar disorder, major depressive disorder, post-traumatic stress disorder, and schizophrenia (SCZ). Instrumental variables associated with 1136 mitochondria-related genes were derived from summary statistics for DNA methylation, gene expression, and protein quantitative trait loci. SMR analyses and colocalization analyses were then conducted across these three biological levels to explore the associations with each of the seven mental disorders.

RESULTS

We identified mitochondria-related genes associated with mental disorders with multi-omics evidence: RMDN1 for ADHD, and ACADVL, ETFA, MMAB, and PPA2 for SCZ. Specifically, an increase of one standard deviation in the level of RMDN1 was linked to a 12 % decrease in the risk of developing ADHD (OR = 0.88, 95 % CI: 0.83-0.94). Increased levels of ETFA (OR = 1.79, 95 % CI: 1.24-2.60) and MMAB (OR = 1.10, 95 % CI: 1.05-1.16) were significantly associated with increased risk of SCZ. Conversely, high levels of ACADVL (OR = 0.50, 95 % CI: 0.33-0.77) and PPA2 (OR = 0.68, 95 % CI: 0.55-0.85) were associated with a reduced risk of SCZ.

CONCLUSIONS

These findings suggested that dysfunction in mitochondria-related genes may underlie the molecular mechanisms of ADHD and SCZ, providing novel biomarkers for diagnosis and therapeutic interventions.

Integrated multi-omics insight into the molecular networks of oxidative stress in triggering multiple sclerosis

Oxidative stress (OS) is a key pathophysiological mechanism in multiple sclerosis (MS). However, the underlying mechanisms by which OS triggered MS remain unknown. To identify potential causal targets of 1216 OS-related genes for MS, a summary-data-based Mendelian randomization (SMR) method was applied. Given that genes can exert their biological functions through different omics levels, the multi-omics SMR integrating expression, methylation, and protein quantitative trait loci (eQTL, mQTL, and pQTL) of OS-related genes from blood and brain tissues was utilized. Bayesian colocalization test was conducted to examine potential regulatory mechanisms of QTL risk variation in MS. To verify the robustness of our results, we validated these findings in FinnGen cohort. Furthermore, the QTL evidence levels, colocalization findings, and replication cohort results were integrated and potential target genes were categorized into three levels. Consequently, three genes (BACH2, TRAF3, and MAPK3) were identified as potential contributors to MS in blood, and four genes (HMGCL, TSFM, TRAF3 and HLA-B) were identified as potential contributors to MS in brain tissue. Additionally, HMGCL and TSFM from brain tissue were supported by first-level evidence related to MS and were validated via in vitro experiments. This research not only contributed to fundamental research of OS in MS but also supported the identification of potential targets for clinical interventions in MS.

Alternative Splicing Regulation in Metabolic Disorders

Alternative splicing (AS) is a fundamental mechanism for enhancing transcriptome diversity and regulating gene expression, crucial for various cellular processes and the development of complex traits. This review examines the role of AS in metabolic disorders, including obesity, weight loss, dyslipidemias, and metabolic syndrome. We explore the molecular mechanisms underlying AS regulation, focusing on the interplay between cis-acting elements and trans-acting factors, and the influence of RNA-binding proteins (RBPs). Advances in high-throughput sequencing and bioinformatics have unveiled the extensive landscape of AS events across different tissues and conditions, highlighting the importance of tissue-specific splicing in metabolic regulation. We discuss the impact of genetic variants on AS, with a particular emphasis on splicing quantitative trait loci (sQTLs) and their association with cardiometabolic traits. The review also covers the regulation of spliceosome components by phosphorylation, the role of m6A modification in AS, and the interaction between transcription and splicing. Additionally, we address the clinical relevance of AS, illustrating how splicing misregulation contributes to metabolic diseases and the potential for therapeutic interventions targeting splicing mechanisms. This comprehensive overview underscores the significance of AS in metabolic health and disease, advocating for further research to harness its therapeutic potential.

Morphological alterations and gene expression levels in the cerebral cortex causally influence susceptibility to type 2 diabetes: A Mendelian randomization study

BACKGROUND

The associations between type 2 diabetes (T2D) and neurological as well as psychiatric disorders have garnered growing interest. Previous evidence has indicated a correlation between the cerebral cortex and these conditions. However, the causal direction between the cerebral cortex and T2D remains ambiguous.

METHODS

We conducted a cerebral cortex-focused systematic Mendelian randomization (MR) study based on multiple data sourced from genome-wide association studies and expression quantitative trait locus.

RESULTS

The surficial area (SA) of Pars Opercularis and the thickness (TH) of the Supramarginal gyrus were found as significant contributors to the risk of T2D. Conversely, thickening in the Precentral area, Caudal Anterior Cingulate cortex, and banks of the Superior Temporal Sulcus, as well as SA amplification of the Precentral area, were associated with a reduced risk of T2D. There was no evidence of reverse causation. These alterations also have an impact on susceptibility to T2D complications. Combining the summary-data-based MR (SMR) analysis and colocalization analysis, we prioritized the expression of three causal genes in the cerebral cortex with genetic evidence for influencing T2D susceptibility. Elevated expression levels of NUDC and PACC1 increased susceptibility to T2D, whereas RAB29 expression exhibits an inverse association with T2D susceptibility. Mediation MR analysis revealed that TH of the Banks of the Superior Temporal Sulcus, SA of Precentral area, SA of Pars Opercularis, and SA of Supramarginal gyrus mediated the effect of RAB29 on T2D. Cross-tissue colocalization analysis demonstrated that the expression pattern of NUDC displayed brain tissue specificity. PACC1 and RAB29 also exhibited colocalization signals in several specific tissues beyond brain tissue. The phenome-wide association study suggested that these genes underscore the shared genetic burden of T2D with a range of disease phenotypes including mental disorders, cardiovascular disease, and malignancies.

CONCLUSIONS

These findings underscore the novel role of the central nervous system in genetic liability to T2D and provide valuable clues for future mechanism studies.

Multi-omics identify ribosome related causal genes methylation, splicing, and expression in prostate cancer

BACKGROUND

Understanding the molecular underpinnings of prostate cancer remains a critical challenge in oncology. Ribosomes, essential cellular organelles responsible for protein synthesis, have emerged as potential regulators in cancer development. Previous studies suggest that dysfunction in ribosomal processes may contribute significantly to prostate cancer progression. We used summary-data-based Mendelian randomization (SMR) and colocalization analysis, as well as single-cell analysis, to investigate the association between ribosome-related genes and prostate cancer by integrating multi-omics.

METHOD

In this study, we employed a multi-omics approach integrating genomics and transcriptomics data to investigate the role of ribosome-related genes in prostate cancer. Summary-level data for prostate cancer were obtained from The Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome and FinnGen studies. SMR analyses were performed to assess the relevance of ribosomal gene-related molecular signatures to prostate cancer. We further performed colocalization analysis to assess whether the identified signal pairs shared causal genetic variants. Genes were then validated with single-cell sequencing analysis.

RESULTS

We identified significant causal effects of ribosome gene methylation on prostate cancer. After integrating the multi-omics data of mQTL, sQTL and eQTL, we identified two ribosomal genes, NSUN4 and MPHOSPH6. Methylation and splicing at different sites on the NSUN4 gene showed increased and decreased risks for prostate cancer, indicating complex gene regulation mechanisms. For instance, NSUN4 methylation site of cg10215817 was genetically associated with the increased prostate cancer risk (OR 1.20, 95% CI 1.10,1.30), while NSUN4 methylation site of cg00937489 was genetically associated with the decreased prostate cancer risk (OR 0.84, 95% CI 0.74,0.94); NSUN4 chr1:46341497:46344801 splicing (OR 1.11, 95% CI 1.05-1.17) were positively associated with prostate cancer risk, while NSUN4 chr1:46340919:46344801 splicing (OR 0.95, 95% CI 0.92-0.97) were negatively associated with prostate cancer risk. Expression analysis indicated significant associations between prostate cancer risk and increased expression levels of NSUN4 (OR 1.06, 95% CI 1.03-1.09; PPH4 = 0.79) and MPHOSPH6 (OR 1.07, 95% CI 1.04-1.10; PPH4 = 0.70). In-depth single-cell analysis showed that NSUN4 highly expresses in epithelial cells, while MPHOSPH6 highly expresses in myeloid cells.

CONCLUSION

The study found that ribosome NSUN4 and MPHOSPH6 genes were associated with prostate cancer risk. This integrative multi-omics study underscores the significance of ribosome-related genes in prostate cancer etiology. By elucidating the molecular mechanisms underlying ribosome dysfunction, our research identifies potential therapeutic targets for mitigating disease progression. These findings not only enhance our understanding of prostate cancer biology but also pave the way for personalized therapeutic strategies targeting ribosomal pathways to improve clinical outcomes.

Long-read sequencing reveals novel isoform-specific eQTLs and regulatory mechanisms of isoform expression in human B cells

BACKGROUND

Genetic variations linked to changes in gene expression are known as expression quantitative loci (eQTLs). The identification of eQTLs helps to understand the mechanisms governing gene expression. However, prior studies have primarily utilized short-read sequencing techniques, and the analysis of eQTLs on isoforms has been relatively limited.

RESULTS

In this study, we employ long-read sequencing technology (Oxford Nanopore) on B cells from 67 healthy Japanese individuals to explore genetic variations associated with isoform expression levels, referred to as isoform eQTLs (ieQTLs). Our analysis reveals 17,119 ieQTLs, with 70.6% remaining undetected by a gene-level analysis. Additionally, we identify ieQTLs that have significantly different effects on isoform expression levels within a gene. A functional feature analysis demonstrates a significant enrichment of ieQTLs at splice sites and specific histone marks, such as H3K36me3, H3K4me1, H3K4me3, and H3K79me2. Through an experimental validation using genome editing, we observe that a distant genomic region can modulate isoform-specific expression. Moreover, an ieQTL analysis and minigene splicing assays unveils functionally crucial variants in splicing that splicing prediction software did not assign a high prediction score. A comparison with GWAS data reveals a higher number of colocalizations between ieQTLs and GWAS findings compared to gene eQTLs.

CONCLUSIONS

These findings highlight the substantial contribution of ieQTLs identified through long-read analysis in our understanding of the functional implications of genetic variations and the regulatory mechanisms governing isoforms.

Identifying the mediating role of brain atrophy on the relationship between DNA damage repair pathway and Alzheimer's disease: A Mendelian randomization analysis and mediation analysis

BackgroundDNA damage and repair (DDR) and structural atrophies in different brain regions were recognized as critical factors in the onset of Alzheimer's disease (AD).ObjectiveWe utilized Mendelian randomization (MR) to examine the causal effects of the DDR-related molecular traits on AD and the potential mediating roles of different brain region volumes.MethodsIn primary analysis, we utilized public genome-wide association studies of AD and summary data from existing molecular traits datasets, including gene expression, DNA methylation, and protein levels quantitative trait loci (eQTL, mQTL, and pQTL) in both blood and brain to examine their causal associations by summary-data-based MR analysis and additional five two-sample MR methods. Subsequently, mediation analysis explored the potential mediate roles of 13 imaging-derived brain volume phenotypes in the associations between the DDR pathways and AD through a network MR design.ResultsWe found that the volumes of the right thalamus proper and global cerebral white matter mediated the causal pathways from EGFR to AD and relatively weak mediation effects of the right lateral ventricle volume in the causal pathways involving CHRNE, DNTT, and AD.ConclusionsWe identified causal relationships among DDR pathways, specific brain region volumes, and AD. Monitoring the molecular traits of these DDR-related genes and developing targeted drugs may help detect and interrupt the early progression of AD.

Integrating a multi-omics strategy framework to screen potential targets in cognitive impairment-related epilepsy

Epilepsy is a prevalent neurological disorder that affects over 70 million individuals worldwide and is often associated with cognitive impairments. Despite the widespread impact of epilepsy and cognitive impairments, the genetic basis and causal relationships underlying these conditions remain uncertain, prompting us to conduct a comprehensive investigation into the molecular mechanisms involved. In this study, we utilized statistical data from the third National Health and Nutrition Examination Survey (NHANES III) to evaluate correlation and large-scale pan-phenotype genome-wide association study (GWAS) data to establish genetic correlation and causality. Leveraging multi-omics datasets, we performed a comprehensive post-analysis that included variant prioritization, gene analysis, tissue and cell type enrichment, and pathway annotation. An integrated strategy-multi-trait analysis of GWAS (MTAG), transcriptome-wide association study (TWAS), summary-data-based Mendelian Randomization (SMR), and protein quantitative trait locus (pQTL)-MR-was performed to investigate the shared genetic architecture. Based on multiple orthogonal lines of evidence, we thereby identified 40 single nucleotide polymorphisms (SNPs) and 85 genes common to both conditions. Additionally, we optimized candidate genes such as GNAQ, FADS1, and PTK2 by single-cell expression analysis and molecular pathway mechanisms, thereby highlighting potential shared genetic pathways. These findings elucidate the genetic interplay and co-occurring mechanisms between epilepsy and cognitive impairments, providing crucial insights for future research and therapeutic strategies.

TEX10: A Novel Drug Target and Potential Therapeutic Direction for Sleep Apnea Syndrome

Background

Sleep apnea syndrome (SAS) is a prevalent sleep disorder strongly associated with obesity, metabolic dysregulation, and cardiovascular diseases. While its underlying pathophysiological mechanisms remain incompletely understood, genetic factors likely play a pivotal role in SAS pathogenesis. This study investigates the causal relationships between potential drug target genes and SAS using multiple statistical approaches, aiming to provide novel insights for targeted therapeutic development.

Methods

We conducted a comprehensive genetic analysis integrating multiple methodologies to investigate gene-SAS relationships. Using publicly available GWAS and eQTL databases, we performed Mendelian Randomization (MR) analysis with the inverse variance weighted (IVW) method, validated by weighted median and MR-Egger approaches. Summary-data-based MR (SMR) analysis, coupled with HEIDI testing, assessed direct gene expression-SAS associations while controlling for linkage disequilibrium (LD). Colocalization analysis evaluated the probability of shared causal variants between SNPs, gene expression, and SAS. Statistical significance was determined using Benjamini-Hochberg multiple testing correction (FDR < 0.05). Additionally, mediation analysis explored TEX10's influence on SAS through metabolic intermediates including BMI, waist circumference, and HDL cholesterol.

Results

We identified 18 candidate drug target genes significantly associated with SAS, with MAPKAPK3, TNXB, MPHOSPH8, and TEX10 showing consistent associations across multiple analyses. TEX10, in particular, exhibited significant associations with SAS risk in blood, cerebral cortex, hippocampus, and basal ganglia (PP.H4 > 0.9). Mediation analysis suggested that TEX10 might influence SAS risk indirectly through BMI, waist circumference, and HDL cholesterol levels.

Conclusion

Our study identified multiple potential therapeutic targets causally linked to SAS, with TEX10 emerging as a key candidate gene. These findings advance our understanding of SAS pathogenesis and offer promising directions for personalized diagnostics and targeted therapies.

Association among blood pressure, antihypertensive drugs, and amyotrophic lateral sclerosis

BACKGROUND

 Amyotrophic lateral sclerosis (ALS) is a fatal and incurable neurodegenerative disease. The impacts of antihypertensive drugs and blood pressure (BP) on ALS are currently debatable.

OBJECTIVE

 To evaluate the causal relationship involving antihypertensive drugs, BP, and ALS through a Mendelian randomization (MR) analysis.

METHODS

 The causal relationship between BP and ALS was evaluated by a bidirectional two-sample MR analysis. Then, a sensitivity analysis was performed using a secondary BP genome-wide association study. The drug-target MR was employed to evaluate the impact of antihypertensive drugs on ALS. Furthermore, we used cis-expression quantitative trait loci (cis-eQTLs) data from brain tissue and blood to validate the positive results by a summary-based MR method.

RESULTS

 We found that an increment in systolic BP (SBP) could elevate the risk of ALS (inverse-variance weighted [IVW] odds ratio [OR] = 1.003; 95% confidence interval [95%CI]: 1.001-1.006; per 10-mmHg increment) and ALS might be protected by angiotensin-converting enzyme inhibitors (ACEIs; OR = 0.970; 95%CI: 0.956-0.984; p = 1.96 × 10-5; per 10-mmHg decrement). A causal relationship was not observed between diastolic BP and other antihypertensive drugs in ALS.

CONCLUSION

 In the present study, genetic support for elevated SBP serves as a risk factor for ALS. Besides, ACEIs hold promise as a candidate for ALS.

Genetic regulation of gene expression across multiple tissues in chickens

The chicken is a valuable model for understanding fundamental biology and vertebrate evolution and is a major global source of nutrient-dense and lean protein. Despite being the first non-mammalian amniote to have its genome sequenced, a systematic characterization of functional variation on the chicken genome remains lacking. Here, we integrated bulk RNA sequencing (RNA-seq) data from 7,015 samples, single-cell RNA-seq data from 127,598 cells and 2,869 whole-genome sequences to present a pilot atlas of regulatory variants across 28 chicken tissues. This atlas reveals millions of regulatory effects on primary expression (protein-coding genes, long non-coding RNA and exons) and post-transcriptional modifications (alternative splicing and 3'-untranslated region alternative polyadenylation). We highlighted distinct molecular mechanisms underlying these regulatory variants, their context-dependent behavior and their utility in interpreting genome-wide associations for 39 chicken complex traits. Finally, our comparative analyses of gene regulation between chickens and mammals demonstrate how this resource can facilitate cross-species gene mapping of complex traits.

Identification of Gene Expression and Splicing QTLs in Porcine Muscle Associated with Meat Quality Traits

Understanding the genetic regulation of gene expression and splicing in muscle tissues is critical for elucidating the molecular mechanisms of meat quality traits. In this study, we integrated large-scale whole-genome sequencing and strand-specific RNA-seq data from 582 F2 hybrid pigs (White Duroc × Erhualian) to characterize the expression and splicing quantitative trait loci (eQTLs/sQTL) in longissimus dorsi muscle. We identified 11,058 cis-eQTL-associated genes (eGenes) and 5139 cis-sQTL-associated genes (sGenes), of which 29% of eGenes and 80% of sGenes were previously unreported in the PigGTEx database. Functional analyses revealed distinct genomic features: eQTLs were enriched near transcription start sites (TSSs) and associated with active TSS-proximal transcribed regions and enhancers, whereas sQTLs clustered at splice junctions, underscoring their distinct roles in gene expression and splicing regulation. Colocalization analysis of e/sQTLs with GWAS signals prioritized PHKG1 as a key candidate gene (PPH4 > 0.9) for glycogen metabolism. Notably, we confirmed that an sQTL-driven alternative splicing event in exon 10 of PHKG1 was significantly correlated with phenotypic variation (R = -0.39, p = 9.5 × 10-21). Collectively, this study provides novel insights into the genetic regulation of gene expression and alternative splicing in porcine muscle tissue, advancing our understanding of the molecular mechanisms underlying economically important meat quality traits.

Functional impact of splicing variants in the elaboration of complex traits in cattle

GWAS conducted directly on imputed whole genome sequence have led to the identification of numerous genetic variants associated with agronomic traits in cattle. However, such variants are often simply markers in linkage disequilibrium with the actual causal variants, which is a limiting factor for the development of accurate genomic predictions. It is possible to identify causal variants by integrating information on how variants impact gene expression into GWAS output. RNA splicing plays a major role in regulating gene expression. Thus, assessing the effect of variants on RNA splicing may explain their function. Here, we use a high-throughput strategy to functionally analyse putative splice-disrupting variants in the bovine genome. Using GWAS, massively parallel reporter assay and deep learning algorithms designed to predict splice-disrupting variants, we identify 38 splice-disrupting variants associated with complex traits in cattle, three of which could be classified as causal. Our results indicate that splice-disrupting variants are widely found in the quantitative trait loci related to these phenotypes. Using our combined approach, we also assess the validity of splicing predictors originally developed to analyse human variants in the context of the bovine genome.

A global overview of shared genetic architecture between smoking behaviors and major depressive disorder in European and East Asian ancestry

BACKGROUND

The co-occurrence of smoking behaviors and major depressive disorder (MDD) has been widely documented in populations. However, the underlying mechanism of this association remains unclear.

METHODS

Genome-wide association studies of smoking behaviors and MDD, combined with multi-omics datasets, were used to characterise genetic correlations, identify shared loci and genes, and explore underlying biological mechanisms. Mendelian randomization (MR) analyses were conducted to infer causal relationships between smoking behaviors and MDD. Druggability analyses were performed to identify potential drugs with both antidepressant and smoking cessation effects.

RESULTS

Extensive overall genetic correlations were found between smoking behaviors and MDD. Furthermore, eighteen local regions showed significant genetic correlations, which could be partly explained by gene co-expression patterns. We identified 24 shared loci and 120 genes, which were enriched in limbic system, GABAergic and dopaminergic neurons, as well as in synaptic pathways. Through integrating with tissue specific information, seven key genes (ANKK1, NEGR1, USP4, TCTA, SORCS5, SPPL3, and USP28) were pinpointed. Notably, druggability analyses supported ANKK1 as a potential drug target for the treatment of MDD and tobacco dependence. MR analyses suggested a bidirectional causal relationship between smoking initiation and MDD. Although findings in East Asian ancestry were limited, the shared locus (chr15:47613403-47,685,504) identified in European ancestry remained significant in East Asian ancestry.

CONCLUSIONS

Our findings suggest the extensive genetic overlap between smoking behaviors and MDD, support the role of limbic system and synapse involved in shared mechanisms, and implicate for prevention, intervention and treatment.

Gene discovery and pleiotropic architecture of chronic pain in a genome-wide association study of >1.2 million individuals

Chronic pain is highly prevalent worldwide, and genome-wide association studies (GWAS) have identified a growing number of chronic pain loci. To further elucidate its genetic architecture, we leveraged data from 1,235,695 European ancestry individuals across three biobanks. In a meta-analytic GWAS, we identified 343 independent loci for chronic pain, 92 of which were new. Sex-specific meta-analyses revealed 115 independent loci (12 of which were new) for males (N = 583,066) and 12 loci (two of which were new) for females (N = 241,266). Multi-omics gene prioritization analyses highlighted 490 genes associated with chronic pain through their effects on brain- and blood-specific regulation. Loci associated with increased risk for chronic pain were also associated with increased risk for multiple other traits, with Mendelian randomization analyses showing that chronic pain was causally associated with psychiatric disorders, substance use disorders, and C-reactive protein levels. Chronic pain variants also exhibited pleiotropic associations with cortical area brain structures. This study expands our knowledge of the genetics of chronic pain and its pathogenesis, highlighting the importance of its pleiotropy with multiple disorders and elucidating its multi-omic pathophysiology.

Cross-ancestry genome-wide association study and systems-level integrative analyses implicate new risk genes and therapeutic targets for depression

Deciphering the genetic architecture of depression is pivotal for characterizing the associated pathophysiological processes and development of new therapeutics. Here we conducted a cross-ancestry genome-wide meta-analysis on depression (416,437 cases and 1,308,758 controls) and identified 287 risk loci, of which 49 are new. Variant-level fine mapping prioritized potential causal variants and functional genomic analysis identified variants that regulate the binding of transcription factors. We validated that 80% of the identified functional variants are regulatory variants, and expression quantitative trait loci analysis uncovered the potential target genes regulated by the prioritized risk variants. Gene-level analysis, including transcriptome and proteome-wide association studies, colocalization and Mendelian randomization-based analyses, prioritized potential causal genes and drug targets. Gene prioritization analyses highlighted likely causal genes, including TMEM106B, CTNND1, AREL1 and so on. Pathway analysis indicated significant enrichment of depression risk genes in synapse-related pathways. Finally, knockdown of Tmem106b in mice resulted in depression-like behaviours, supporting the involvement of Tmem106b in depression. Our study identified new risk loci, likely causal variants and genes for depression, providing important insights into the genetic architecture of depression and potential therapeutic targets.

Genome-Wide Association Study Meta-Analysis of 9619 Cases With Tic Disorders

BACKGROUND

Despite the significant personal and societal burden of tic disorders (TDs), treatment outcomes remain modest, necessitating a deeper understanding of their etiology. Family history is the biggest known risk factor, and identifying risk genes could accelerate progress in the field.

METHODS

Expanding upon previous sample size limitations, we added 4800 new TD cases and 971,560 controls and conducted a genome-wide association study (GWAS) meta-analysis with 9619 cases and 981,048 controls of European ancestry. We attempted to replicate the results in an independent deCODE genetics GWAS (885 TD cases and 310,367 controls). To characterize GWAS findings, we conducted several post-GWAS gene-based and enrichment analyses.

RESULTS

A genome-wide significant hit (rs79244681, p = 2.27 × 10-8) within MCHR2-AS1 was identified, although it was not replicated. Post-GWAS analyses revealed a 13.8% single nucleotide polymorphism heritability and 3 significant genes: BCL11B, NDFIP2, and RBM26. Common variant risk for TD was enriched within genes preferentially expressed in the cortico-striato-thalamo-cortical circuit (including the putamen, caudate, nucleus accumbens, and Brodmann area 9) and 5 brain cell types (excitatory and inhibitory telencephalon neurons, inhibitory diencephalon and mesencephalon neurons, and hindbrain and medium spiny neurons). TD polygenic risk was enriched within loss-of-function intolerant genes (p = .0017) and high-confidence neurodevelopmental disorder genes (p = .0108). Of 112 genetic correlations, 43 were statistically significant, showing high positive correlations with most psychiatric disorders. Of the 2 single nucleotide polymorphisms previously associated with TDs, one (rs2453763) replicated in an independent subsample of our GWAS (p = .00018).

CONCLUSIONS

This GWAS was still underpowered to identify high-confidence, replicable loci, but the results suggest imminent discovery of common genetic variants for TDs.

The INO80E at 16p11.2 locus increases risk of schizophrenia in humans and induces schizophrenia-like phenotypes in mice

BACKGROUND

Chromosome 16p11.2 is one of the most significant loci in the genome-wide association studies (GWAS) of schizophrenia. Despite several integrative analyses and functional genomics studies having been carried out to identify possible risk genes, their impacts in the pathogenesis of schizophrenia remain to be fully characterized.

METHODS

We performed expression quantitative trait loci (eQTL) and summary-data-based Mendelian randomization (SMR) analyses to identify schizophrenia risk genes in the 16p11.2 GWAS locus. We constructed a murine model with dysregulated expression of risk gene in the medial prefrontal cortex (mPFC) using stereotaxic injection of adeno-associated virus (AAV), followed by behavioural assessments, dendritic spine analyses and RNA sequencing.

FINDINGS

We identified significant associations between elevated INO80E mRNA expression in the frontal cortex and risk of schizophrenia. The mice overexpressing Ino80e in mPFC (Ino80e-OE) exhibited schizophrenia-like behaviours, including increased anxiety behaviour, anhedonia, and impaired prepulse inhibition (PPI) when compared with control group. The neuronal sparse labelling assay showed that the density of stubby spines in the pyramidal neurons of mPFC was significantly increased in Ino80e-OE mice compared with control mice. Transcriptomic analysis in the mPFC revealed significant alterations in the mRNA levels of schizophrenia-related genes and processes related to synapses upon overexpressing Ino80e.

INTERPRETATION

Our results suggest that upregulation of the Ino80e gene in mPFC may induce schizophrenia-like behaviours in mice, further supporting the hypothesis that INO80E is an authentic risk gene.

FUNDING

This project received support from the National Key Research and Development Program of China, National Natural Science Foundation of China, Key Research and Development Projects of Hunan Provincial Science and Technology Department, Science and Technology Innovation team of Hunan Province, etc.

Mendelian Randomization Reveals Causalities Between DNA Methylation and Schizophrenia

BACKGROUND

Epigenetic factors (such as DNA methylation) have been widely reported to be associated with schizophrenia (SCZ). However, the causal relationships between epigenetic factors and SCZ remain largely unknown.

METHODS

Here, we conducted a Mendelian randomization (MR) study to investigate the causal relationships between DNA methylation and SCZ. Brain methylation quantitative trait loci (mQTL) (N = 1160) and blood mQTL (N = 27,750) data were used as exposures, and genome-wide association data of SCZ (53,386 cases and 77,258 controls) were used as the outcome.

RESULTS

We identified 172 (mapped to 160 genes) and 157 (mapped to 155 genes) methylation sites whose methylation levels in brain and blood are causally associated with SCZ, respectively. Among the mapped genes, 36 overlapping genes were identified. Interestingly, 3 methylation sites (near BRD2, CNNM2, and RERE) showed significant associations in both brain and blood, with the same direction of effect. We also performed MR analysis using brain expression quantitative trait loci (eQTLs) as exposures and identified 123 genes whose expression levels were causally associated with SCZ. Comparing the significant genes from eQTLs and brain mQTLs prioritized 15 overlapping genes, suggesting that both epigenetic modification and expression of these genes confer risk of SCZ. Finally, we validated our findings with genome editing and animal model experiments.

CONCLUSIONS

Our study identified methylation sites whose methylation levels are causally associated with SCZ and demonstrated the important roles of epigenetic factors in SCZ. Our findings also reveal pivotal risk genes whose expression and epigenetic regulation are causally associated with SCZ.

Unraveling the genetic links between depression and type 2 diabetes

BACKGROUND

Type 2 diabetes (T2D) is a chronic metabolic disorder that has high comorbidity with mental disorders. The genetic relationships between T2D and depression are far from being well understood.

METHODS

We performed genetic correlation, polygenic overlap, Mendelian randomization (MR) analyses, cross-trait meta-analysis, and Bayesian colocalization analysis to assess genetic relationships between T2D and depression, in the forms of major depressive disorder (MDD) and depressed affect (DAF). Then, the summary data-based MR (SMR) analysis was performed to prioritize genes contributing to MDD and to T2D from functional perspective. MDD-driven signaling pathways were constructed to understand the influence of MDD on T2D at the molecular level.

RESULTS

T2D has positive genetic correlations both with MDD (rg = 0.14) and with DAF (rg = 0.19). The polygenic overlap analysis showed that about 60 % of causal variants for T2D are shared with MDD and DAF. The MR analysis indicated that genetic liabilities to both MDD (OR: 1.24, 95 % CI: 1.11-1.38) and DAF (OR: 1.48, 95 % CI: 1.23-1.78) are associated with an increased risk for T2D, while genetic liability to T2D is not associated with the risk for MDD (OR: 1.00, 95 % CI: 0.99-1.01) or DAF (OR: 1.01, 95 % CI: 1.00-1.02). The cross-trait meta-analysis identified 271 genomic loci, of which 29 were novel. Genetic predisposition to MDD and T2D shares six overlapping loci, involving some well-characterized genes, such as TCF4 and NEGR1. Colocalization analysis revealed three shared chromosome regions between MDD and T2D, which covers mediator genes including SCYL1, DENND1A, and MAD1L1. Molecular pathway analysis suggests mechanisms that promote the development of T2D through inflammatory pathways overactive in patients with MDD. The SMR analysis and the meta-analysis highlighted seven genes with functional implications for both MDD and T2D, including TNKS2, CCDC92, FADS1, ERI1, THUMPD3, NUCKS1, and PM20D1.

CONCLUSIONS

Our study points out that depression, in the forms of MDD and DAF, may increase the risk of T2D. Analysis of underlying genetic variation and the molecular pathways, connecting depression and T2D, indicate that the pathophysiological foundations of these two conditions have a notable overlap.

Mapping fatigue: discovering brain regions and genes linked to fatigue susceptibility

BACKGROUND

The relationship between the brain and fatigue is gaining increasing attention, with numerous studies indicating that certain specific brain regions may be closely linked to fatigue. Our study aimed to identify brain regions exhibiting significant causal relationships to fatigue and discover potential neurotherapeutic targets associated with fatigue, in the pursuit of seeking new approaches for fatigue treatment.

METHODS

A bidirectional two-sample Mendelian randomization (TSMR) method was employed to investigate causal relationships between cortical and subcortical gray matter volumes in 83 regions and fatigue. Then, we utilized frontal cortex expression Quantitative Trait Loci data, employing the methods of Summary-data-based Mendelian Randomization (SMR) and Bayesian colocalization to identify genes that exhibit significant association with fatigue. Finally, the transcription levels of candidate genes were assessed in a central fatigue rat model using RT-qPCR.

RESULTS

The results of the TSMR analysis revealed that an increased in the volume of the right lateral orbitofrontal, left caudal middle frontal, right caudal middle frontal, and right rostral middle frontal cortices may be correlated with a diminished susceptibility to fatigue. The SMR and Bayesian colocalization analysis identified ECE2, GPX1, METTL21EP, RP11-665J16.1, and SNF8 as candidate genes associated with fatigue. RT-qPCR results confirmed significantly elevated transcription levels of Ece2, Gpx1, and Snf8 in the frontal cortex of central fatigue model rats compared to controls.

CONCLUSIONS

Our findings afford substantial theoretical support for the connection between the brain and fatigue, while also providing novel insights into the genetic mechanisms and therapeutic targets for fatigue, particularly central fatigue.

Association and shared biological bases between birth weight and cortical structure

Associations between birth weight and cortical structural phenotypes have been detected; however, the understanding is incomprehensive, and the potential biological bases are not well defined. Leveraging data from genome-wide association studies, we investigated the associations and the shared transcriptomic, proteomic and cellular bases of birth weight and 13 cortical structural phenotypes. Mendelian randomization analyses were performed to examine associations between birth weight and cortical structure. Downstream transcriptome-wide association study (TWAS), proteome-wide association study (PWAS) and summary-based Mendelian randomization (SMR) analyses were utilized to identify the shared cis-regulated gene expressions and proteins. Finally, cell-type expression-specific integration for complex traits (CELLECT) analyses were conducted to explore the enriched cell types. The Mendelian randomization analyses found positive associations between birth weight and global cortical folding index, intrinsic curvature index, local gyrification index, surface area and volume. Downstream transcriptomic-level TWAS and SMR identified three gene expressions both linked to birth weight and at least one cortical structural phenotype (CNNM2, RABGAP1 and CENPW). Parallel PWAS and SMR analyses at the proteomic level identified four proteins linked to both phenotypes (CNNM2, RAB7L1, RAB5B and PPA2), of which CNNM2 was replicated. CELLECT analyses revealed brain cell types enriched in birth weight, including pericytes, inhibitory GABAergic neurons and cerebrovascular cells. These findings support the importance of early life growth to cortical structure, and suggest underlying transcriptomic, proteomic and cellular bases. These results provide intriguing targets for further research into the mechanisms of cortical development.

Gene discovery and pleiotropic architecture of Chronic Pain in a Genome-wide Association Study of >1.2 million Individuals

Chronic pain is highly prevalent worldwide, and genome-wide association studies (GWAS) have identified a growing number of chronic pain loci. To further elucidate its genetic architecture, we leveraged data from 1,235,695 European ancestry individuals across three biobanks. In a meta-analytic GWAS, we identified 343 independent loci for chronic pain, 92 of which were new. Sex-specific meta-analyses revealed 115 independent loci (12 of which were new) for males (N = 583,066) and 12 loci (two of which were new) for females (N = 241,266). Multi-omics gene prioritization analyses highlighted 490 genes associated with chronic pain through their effects on brain- and blood-specific regulation. Loci associated with increased risk for chronic pain were also associated with increased risk for multiple other traits, with Mendelian randomization analyses showing that chronic pain was causally associated with psychiatric disorders, substance use disorders, and C-reactive protein levels. Chronic pain variants also exhibited pleiotropic associations with cortical area brain structures. This study expands our knowledge of the genetics of chronic pain and its pathogenesis, highlighting the importance of its pleiotropy with multiple disorders and elucidating its multi-omic pathophysiology.

Identification of cis-sQTL demonstrates genetic associations and functional implications of inflammatory processes in Nelore cattle muscle tissue

This study aimed to identify splicing quantitative trait loci (cis-sQTL) in Nelore cattle muscle tissue and explore the involvement of spliced genes (sGenes) in immune system-related biological processes. Genotypic data from 80 intact male Nelore cattle were obtained using SNP-Chip technology, while RNA-Seq analysis was performed to measure gene expression levels, enabling the integration of genomic and transcriptomic datasets. The normalized expression levels of spliced transcripts were associated with single nucleotide polymorphisms (SNPs) through an analysis of variance using an additive linear model with the MatrixEQTL package. A permutation analysis then assessed the significance of the best SNPs for each spliced transcript. Functional enrichment analysis was performed on the sGenes to investigate their roles in the immune system. In total, 3,187 variants were linked to 3,202 spliced transcripts, with 83 sGenes involved in immune system processes. Of these, 31 sGenes were enriched for five transcription factors. Most cis-sQTL effects were found in intronic regions, with 27 sQTL variants associated with disease susceptibility and resistance in cattle. Key sGenes identified, such as GSDMA, NLRP6, CASP6, GZMA, CASP4, CASP1, TREM2, NLRP1, and NAIP, were related to inflammasome formation and pyroptosis. Additionally, genes like PIDD1, OPTN, NFKBIB, STAT1, TNIP3, and TREM2 were involved in regulating the NF-kB pathway. These findings lay the groundwork for breeding disease-resistant cattle and enhance our understanding of genetic mechanisms in immune responses.

Gene-level analysis reveals the genetic aetiology and therapeutic targets of schizophrenia

Genome-wide association studies (GWASs) have reported multiple risk loci for schizophrenia (SCZ). However, the majority of the associations were from populations of European ancestry. Here we conducted a large-scale GWAS in Eastern Asian populations (29,519 cases and 44,392 controls) and identified ten Eastern Asian-specific risk loci, two of which have not been previously reported. A further cross-ancestry GWAS meta-analysis (96,806 cases and 492,818 controls) including populations from diverse ancestries identified 61 previously unreported risk loci. Systematic variant-level analysis, including fine mapping, functional genomics and expression quantitative trait loci, prioritized potential causal variants. Gene-level analyses, including transcriptome-wide association study, proteome-wide association study and Mendelian randomization, nominated the potential causal genes. By integrating evidence from layers of different analyses, we prioritized the most plausible causal genes for SCZ, such as ACE, CNNM2, SNAP91, ABCB9 and GATAD2A. Finally, drug repurposing showed that ACE, CA14, MAPK3 and MAPT are potential therapeutic targets for SCZ. Our study not only showed the power of cross-ancestry GWAS in deciphering the genetic aetiology of SCZ, but also uncovered new genetic risk loci, potential causal variants and genes and therapeutic targets for SCZ.

Association of antihypertensive drug target genes with stroke subtypes: A Mendelian randomization study

OBJECTIVE

Epidemiological and genetic studies have elucidated the effect of antihypertensive medication (AHM) on stroke subtypes varying upon drug classes, but which drug target genes, how, and where mediated this association remains unknown. We aimed to investigate the impact of AHM on stroke subtypes.

METHODS

Genetic instruments for the expression of AHM target genes were identified with expression quantitative trait loci in blood, which should be associated with systolic blood pressure (SBP) to proxy for the effect of AHM. Sensitivity analysis, including reverse causality detection, horizontal pleiotropy, phenotype scanning, tissue enrichment analyses, Bayesian colocalization, and linkage disequilibrium check, were utilized to validate our findings.

RESULTS

A 1-standard deviation (SD) decrease of KCNJ11 gene expression (acting on arteriolar smooth muscle) was associated with a decrease of 2.19 (95 % confidence interval (CI), 1.67-2.71) mmHg of SBP, and a decreased risk of stroke subtypes (Any stroke: odds ratio (OR): 0.80, 95 % CI: 0.70-0.90; Ischemic stroke: OR, 0.79; 95 % CI, 0.69-0.90), respectively. Similarly, a negative association was found between the gene expression of ADRB1 and the risk of small vessel stroke (SVS) (OR, 0.61; 95 % CI, 0.49-0.75). Colocalization supported the probability of shared causal variants for the KCNJ11 and ADRB1 genes in different stroke subtypes. NHLRC2, the nearby gene of ADRB1, was also associated with a higher risk of SVS.

CONCLUSION

Our study implies that changes in expression of KCNJ11 and ADRB1 mediated possibly via AHM may decrease stroke subtypes' risk and NHLRC2 is a potential therapy target gene of SVS.

Long-read RNA sequencing atlas of human microglia isoforms elucidates disease-associated genetic regulation of splicing

Microglia, the innate immune cells of the central nervous system, have been genetically implicated in multiple neurodegenerative diseases. Mapping the genetics of gene expression in human microglia has identified several loci associated with disease-associated genetic variants in microglia-specific regulatory elements. However, identifying genetic effects on splicing is challenging because of the use of short sequencing reads. Here, we present the isoform-centric microglia genomic atlas (isoMiGA), which leverages long-read RNA sequencing to identify 35,879 novel microglia isoforms. We show that these isoforms are involved in stimulation response and brain region specificity. We then quantified the expression of both known and novel isoforms in a multi-ancestry meta-analysis of 555 human microglia short-read RNA sequencing samples from 391 donors, and found associations with genetic risk loci in Alzheimer's and Parkinson's disease. We nominate several loci that may act through complex changes in isoform and splice-site usage.

Genomics yields biological and phenotypic insights into bipolar disorder

Bipolar disorder is a leading contributor to the global burden of disease1. Despite high heritability (60-80%), the majority of the underlying genetic determinants remain unknown2. We analysed data from participants of European, East Asian, African American and Latino ancestries (n = 158,036 cases with bipolar disorder, 2.8 million controls), combining clinical, community and self-reported samples. We identified 298 genome-wide significant loci in the multi-ancestry meta-analysis, a fourfold increase over previous findings3, and identified an ancestry-specific association in the East Asian cohort. Integrating results from fine-mapping and other variant-to-gene mapping approaches identified 36 credible genes in the aetiology of bipolar disorder. Genes prioritized through fine-mapping were enriched for ultra-rare damaging missense and protein-truncating variations in cases with bipolar disorder4, highlighting convergence of common and rare variant signals. We report differences in the genetic architecture of bipolar disorder depending on the source of patient ascertainment and on bipolar disorder subtype (type I or type II). Several analyses implicate specific cell types in the pathophysiology of bipolar disorder, including GABAergic interneurons and medium spiny neurons. Together, these analyses provide additional insights into the genetic architecture and biological underpinnings of bipolar disorder.

Shared genetic architecture and causal relationship between frailty and schizophrenia

The complex relationship between frailty and schizophrenia has yet to be fully understood. This study aims to clarify their relationship by investigating their genetic links. We hypothesize a shared genetic architecture and a bidirectional causal relationship between the two conditions. Utilizing summary genetic data from European genome-wide association studies, we analyzed genetic associations through global and local correlations, shared genomic loci, tissue enrichments, and functional genes. Bidirectional Mendelian Randomization (MR) was employed to infer causality. Our findings show a positive genetic correlation between frailty and schizophrenia (LDSC: rg = 0.117, p = 6.686 × 10-7; HDL: rg = 0.101, p = 5.63 × 10-13) and local correlations in three genomic regions (chr9: 94167203-96671698, p = 2.21 × 10-6; chr11: 112459488-114257728, p = 1.01 × 10-5; and chr18: 77149991-78017158, p = 9.57 × 10-6). We identified 111 genomic loci associated with both conditions and demonstrated that genetic variants for frailty and schizophrenia share tissue enrichments and functional genes in brain. MR analysis suggests that frailty increases the likelihood of schizophrenia (OR: 1.763, 95% CI: 1.259-2.468, p = 0.001) and vice versa (β: 0.012, 95% CI: 0.006-0.018, p < 0.001). Our research supports the presence of a shared genetic basis and bidirectional causality between frailty and schizophrenia. These findings necessitate further investigation in diverse populations to confirm and expand on this genetic understanding.

Human mood disorder risk gene Synaptotagmin-14 contributes to mania-like behaviors in mice

Bipolar disorder (BD) and major depressive disorder (MDD) are the most prevalent mood disorders and cause considerable burden worldwide. Compelling evidence suggests a pronounced overlap between these two disorders in clinical symptoms, treatment strategies, and genetic etiology. Here we leverage a BD GWAS (1822 cases and 4650 controls) and a MDD GWAS (5303 cases and 5337 controls), followed by independent replications, to investigate their shared genetic basis among Han Chinese. We have herein identified a lead SNP rs126277 at the 1q32.2 locus, which also exhibited nominal associations with mood disorders and several relevant sub-clinical phenotypes (e.g., mania) in European populations. Bulk tissue and single-cell eQTL analyses suggest that the risk G-allele of rs126277 predicted lower SYT14 mRNA expression in human brains. We generated mice lacking Syt14 (Syt14-/-) and mice with insufficient expression of Syt14 in the hippocampus (Syt14-KD), and found that depletion of Syt14 resulted in mania-like behaviors including hyperactivity and anti-depressive behaviors, resembling aspects of mood disorders. We also confirmed that deficiency of this gene in the hippocampus was sufficient to induce hyperactivity in mice. RNA-sequencing analyses of the hippocampus of Syt14-/- mice revealed significant upregulation of Per1 as well as downregulation of Slc7a11 and Ptprb. Ultrastructural analyses showed significant alteration of the number of vesicles within 50 nm to the active zone and the width of synaptic cleft in the ventral hippocampus of Syt14-/- mice compared with the control mice. Overall, we have identified a novel mood disorder risk gene SYT14, and confirmed its impact on mania-like behaviors. While the current study identifies an essential mood disorder risk gene, further investigations elucidating the detailed mechanisms by which SYT14 contributes to the pathogenesis of the illnesses are needed.

NEK4: prediction of available drug targets and common genetic linkages in bipolar disorder and major depressive disorder

Background

Bipolar disorder (BD) is a mental illness characterized by alternating episodes of elevated mood and depression, while major depressive disorder (MDD) is a debilitating condition that ranks second globally in terms of disease burden. Pharmacotherapy plays a crucial role in managing both BD and MDD. We investigated the genetic differences in populations of individuals with MDD and BD, and from a genetic perspective, we offered new insights into potential drug targets. This will provide clues to potential drug targets.

Methods

This study employed genome-wide association studies (GWAS) and summary-data-based Mendelian randomization (SMR) methods to investigate the genetic underpinnings of patients with bipolar disorder (BD) and major depressive disorder (MDD) and to predict potential drug target genes. Genetic variants associated with BD and MDD were identified through large-scale GWAS datasets. For BD, the study utilized a comprehensive meta-analysis comprising 57 BD cohorts from Europe, North America, and Australia, including 41,917 BD cases and 371,549 controls of European ancestry. This dataset included both type 1 and type 2 BD cases diagnosed based on DSM-IV, ICD-9, or ICD-10 criteria through standardized assessments. For MDD, we used data from a meta-analysis by Howard DM et al., which integrated the largest GWAS studies of MDD, totaling 246,363 cases and 561,190 controls. The SMR approach, combined with expression quantitative trait loci (eQTL) data, was then applied to assess causal associations between these genetic variants and gene expression, aiming to identify genetic markers and potential drug targets associated with BD and MDD. Furthermore, two-sample Mendelian randomization (TSMR) analyses were performed to explore causal links between protein quantitative trait loci (pQTL) and these disorders.

Results

The SMR analysis revealed 41 druggable genes associated with BD, of which five genes appeared in both brain tissue and blood eQTL datasets and were significantly associated with BD risk. Furthermore, 45 druggable genes were found to be associated with MDD by SMR analysis, of which three genes appeared simultaneously in both datasets and were significantly associated with MDD risk. NEK4, a common drug candidate gene for BD and MDD, was also significantly associated with a high risk of both diseases and may help differentiate between type 1 and type 2 BD. Specifically, NEK4 showed a strong association with BD (β brain=0.126, P FDR=0.001; βblood=1.158, P FDR=0.003) and MDD (β brain=0.0316, P FDR=0.022; βblood=0.254, P FDR=0.045). Additionally, NEK4 was notably linked to BD type 1 (βbrain=0.123, P FDR=2.97E-05; βblood=1.018, P FDR=0.002), but showed no significant association with BD type 2.Moreover, TSMR analysis identified four proteins (BMP1, F9, ITIH3, and SIGIRR) affecting the risk of BD, and PSMB4 affecting the risk of MDD.

Conclusion

Our study identified NEK4 as a key gene linked to both bipolar disorder (BD) and major depressive disorder (MDD), suggesting its potential as a drug target and a biomarker for differentiating BD subtypes. Using GWAS, SMR, and TSMR approaches, we revealed multiple druggable genes and protein associations with BD and MDD risk, providing new insights into the genetic basis of these disorders. These findings offer promising directions for precision medicine and novel therapeutic strategies in mental health treatment.

The Utility of Ultra-Deep RNA sequencing in Mendelian Disorder Diagnostics

Clinical RNA-seq has become an essential tool for resolving variants of uncertain significance (VUS), particularly those affecting gene expression and splicing. However, most reference data and diagnostic protocols employ relatively modest sequencing depths (∼50-150 million reads), which may fail to capture low-abundance transcripts and rare splicing events critical for accurate diagnoses. We evaluated the diagnostic and translational utility of ultra-high-depth (up to ∼one billion unique reads) RNA-seq in four clinically accessible tissues (blood, fibroblast, LCL, and iPSC) using Ultima sequencing platform. After validating the performance of Ultima RNA-seq, we investigated how increasing depth affects gene and isoform detection, splicing variant discovery, and clinical interpretation of VUS. Deep RNA-seq substantially improved sensitivity for detecting lowly-expressed genes and isoforms. At ∼1 billion reads, near-saturation was achieved for gene-level detection, although isoform-level coverage continued to benefit from even deeper sequencing. In two clinical cases with VUS, pathogenic splicing abnormalities were undetected at ∼50 million reads but emerged at 200 million reads, becoming even more pronounced at ∼one billion reads. Using deep RNA-seq data, we constructed a novel resource, MRSD-deep, to estimate the minimum required sequencing depth to achieve desired coverage thresholds. MRSD-deep provided gene- and junction-level guidelines, aiding labs in selecting suitable coverage targets for specific applications. Leveraging deep RNA-seq data on fibroblast, we also built an expanded splicing-variation reference that successfully identified rare splicing events missed by standard-depth data. Our findings underscore the diagnostic and research benefits of deep RNA-seq for Mendelian disease investigations. By capturing rare transcripts and splicing events, ultra-high-depth RNA-seq can facilitate more definitive variant interpretations and enrich splicing-reference databases. We anticipate that cost-effective deep sequencing technologies and robust reference cohorts will further advance RNA-based diagnostics in precision medicine.

Causal Impacts of Psychiatric Disorders on Cognition and the Mediating Effect of Oxidative Stress: A Mendelian Randomization Study

Many psychiatric disorders are associated with major cognitive deficits. However, it is uncertain whether these deficits develop as a result of psychiatric disorders and what shared risk factors might mediate this relationship. Here, we utilized the Mendelian randomization (MR) analysis to investigate the complex causal relationship between nine major psychiatric disorders and three cognitive phenotypes, while also examining the potential mediating role of oxidative stress as a shared biological underpinning. Schizophrenia (SZ), major depressive disorder (MDD), and attention deficit hyperactivity disorder (ADHD) showed a decreasing effect on cognitive performance, intelligence, and education, while bipolar disorder (BPD) increased educational attainment. MR-Clust results exhibit the shared genetic basis between SZ and other psychiatric disorders in relation to cognitive function. Furthermore, when oxidative stress was considered as a potential mediating factor, the associations between SZ and the three dimensions of cognition, as well as between MDD and intelligence and ADHD and intelligence, exhibited larger effect sizes than the overall. Mediation MR analysis also supported the causal effects between psychiatric disorders and cognition via oxidative stress traits, including carotene, vitamin E, bilirubin, and uric acid. Finally, summary-based MR identified 29 potential causal associations of oxidative stress genes with both cognitive performance and psychiatric disorders. Our findings highlight the importance of considering oxidative stress in understanding and potentially treating cognitive impairments associated with psychiatric conditions.

A network-based systems genetics framework identifies pathobiology and drug repurposing in Parkinson's disease

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder. However, current treatments only manage symptoms and lack the ability to slow or prevent disease progression. We utilized a systems genetics approach to identify potential risk genes and repurposable drugs for PD. First, we leveraged non-coding genome-wide association studies (GWAS) loci effects on five types of brain-specific quantitative trait loci (xQTLs, including expression, protein, splicing, methylation and histone acetylation) under the protein-protein interactome (PPI) network. We then prioritized 175 PD likely risk genes (pdRGs), such as SNCA, CTSB, LRRK2, DGKQ, and CD44, which are enriched in druggable targets and differentially expressed genes across multiple human brain-specific cell types. Integrating network proximity-based drug repurposing and patient electronic health record (EHR) data observations, we identified Simvastatin as being significantly associated with reduced incidence of PD (hazard ratio (HR) = 0.91 for fall outcome, 95% confidence interval (CI): 0.87-0.94; HR = 0.88 for dementia outcome, 95% CI: 0.86-0.89) after adjusting for 267 covariates. In summary, our network-based systems genetics framework identifies potential risk genes and repurposable drugs for PD and other neurodegenerative diseases if broadly applied.

Exploring the genetic landscape of the brain-heart axis: A comprehensive analysis of pleiotropic effects between heart disease and psychiatric disorders

BACKGROUND

The genetic links between heart disease and psychiatric disorders are complex and not well understood. This study uses genome-wide association studies (GWAS) and advanced multilevel analyses to explore these connections.

METHODS

We analyzed GWAS data from seven psychiatric disorders and five types of heart disease. Genetic correlations and overlaps were examined using linkage disequilibrium score regression (LDSC), high-definition likelihood (HDL), and Genetic analysis incorporating Pleiotropy and Annotation (GPA). Pleiotropic single-nucleotide variations (SNVs) were identified with pleiotropic analysis under the composite null hypothesis (PLACO) and annotated via Functional mapping and annotation of genetic associations (FUMA). Potential pleiotropic genes were identified using Multi-marker Analysis of GenoMic Annotation (MAGMA) and Summary data-based Mendelian Randomization (SMR).

RESULTS

Among 35 trait pairs, 32 showed significant genetic correlations or overlaps. PLACO identified 15,077 SNVs, with 287 recognized as pleiotropic loci and 20 colocalization sites. MAGMA and SMR revealed 75 potential pleiotropic genes involved in diverse pathways, including cancer, neurodevelopment, and cellular organization. Mouse Genome Informatics (MGI) queries provided evidence linking multiple genes to heart or psychiatric disorders.

CONCLUSIONS

This analysis reveals loci and genes with pleiotropic effects between heart disease and psychiatric disorders, highlighting shared biological pathways. These findings illuminate the genetic mechanisms underlying the brain-heart axis and suggest shared biological foundations for these conditions, offering potential targets for future prevention and treatment strategies.

Association of antihypertensive drug target genes with alzheimer's disease: a mendelian randomization study

BACKGROUND

Epidemiological and genetic studies have elucidated associations between antihypertensive medication and Alzheimer's disease (AD), with the directionality of these associations varying upon the specific class of antihypertensive agents.

METHODS

Genetic instruments for the expression of antihypertensive drug target genes were identified using expression quantitative trait loci (eQTL) in blood, which are associated with systolic blood pressure (SBP). Exposure was derived from existing eQTL data in blood from the eQTLGen consortium and in the brain from the PsychENCODE and subsequently replicated in GTEx V8 and BrainMeta V2. We performed two-sample Mendelian randomization (MR) to estimate the potential effect of different antihypertensive drug classes on AD using summary statistics from a meta-analysis (111,326 cases and 677,663 controls) and further replicated in FinnGen cohorts (9301 cases and 367,976 controls). The reverse causality detection, assessing horizontal pleiotropy, Bayesian co-localization, phenotype scanning, and protein quantitative trait loci (pQTL) analysis were implemented to consolidate the MR findings further.

RESULTS

A 1-standard deviation (SD) lower expression of the angiotensin-converting enzyme (ACE) gene in blood was associated with a lower SBP of 3.92 (95% confidence interval (CI), 2.69-5.15) mmHg but an increased risk of AD (odds ratio (OR), 2.46; 95% CI, 1.82-3.33). A similar direction of association was also observed between ACE expression in prefrontal cortex (OR, 1.19; 95% CI, 1.10-1.28), frontal cortex (OR, 1.19; 95% CI, 1.11-1.27), cerebellum (OR, 1.13; 95% CI, 1.09-1.17), cortex (OR, 1.59; 95% CI, 1.33-1.28) and ACE protein levels in plasma (OR, 1.13; 95% CI, 1.09-1.17) and AD risk. Colocalization supports these results. Similar results were found in external validation. We found no evidence for an association between genetically estimated blood pressure (BP) and AD risk.

CONCLUSIONS

There findings suggest an adverse association of lower ACE messenger RNA and protein levels with an elevated risk of AD, irrespective of its BP-lowering effects. These findings warrant greater pharmacovigilance and further investigation into the effect of ACE inhibitors, particularly those that are centrally acting, on neurodegenerative symptoms in patients with AD.

Multi-Omics Analysis for Identifying Cell-Type-Specific Druggable Targets in Alzheimer's Disease

Background

Analyzing disease-linked genetic variants via expression quantitative trait loci (eQTLs) is important for identifying potential disease-causing genes. Previous research prioritized genes by integrating Genome-Wide Association Study (GWAS) results with tissue-level eQTLs. Recent studies have explored brain cell type-specific eQTLs, but they lack a systematic analysis across various Alzheimer's disease (AD) GWAS datasets, nor did they compare effects between tissue and cell type levels or across different cell type-specific eQTL datasets. In this study, we integrated brain cell type-specific eQTL datasets with AD GWAS datasets to identify potential causal genes at the cell type level.

Methods

To prioritize disease-causing genes, we used Summary Data-Based Mendelian Randomization (SMR) and Bayesian Colocalization (COLOC) to integrate AD GWAS summary statistics with cell-type-specific eQTLs. Combining data from five AD GWAS, three single-cell eQTL datasets, and one bulk tissue eQTL meta-analysis, we identified and confirmed both novel and known candidate causal genes. We investigated gene regulation through enhancer activity using H3K27ac and ATAC-seq data, performed protein-protein interaction and pathway enrichment analyses, and conducted a drug/compound enrichment analysis with the Drug Signatures Database (DSigDB) to support drug repurposing for AD.

Results

We identified 27 candidate causal genes for AD using cell type-specific eQTL datasets, with the highest numbers in microglia, followed by excitatory neurons, astrocytes, inhibitory neurons, oligodendrocytes, and oligodendrocyte precursor cells (OPCs). PABPC1 emerged as a novel astrocyte-specific gene. Our analysis revealed protein-protein interaction (PPI) networks for these causal genes in microglia and astrocytes. We found the "regulation of aspartic-type peptidase activity" pathway being the most enriched among all the causal genes. AD-risk variants associated with candidate causal gene PABPC1 is located near or within enhancers only active in astrocytes. We classified the genes into three drug tiers and identified druggable interactions, with imatinib mesylate emerging as a key candidate. A drug-target gene network was created to explore potential drug targets for AD.

Conclusions

We systematically prioritized AD candidate causal genes based on cell type-specific molecular evidence. The integrative approach enhances our understanding of molecular mechanisms of AD-related genetic variants and facilitates the interpretation of AD GWAS results.

xQTLatlas: a comprehensive resource for human cellular-resolution multi-omics genetic regulatory landscape

Understanding how genetic variants influence molecular phenotypes in different cellular contexts is crucial for elucidating the molecular and cellular mechanisms behind complex traits, which in turn has spurred significant advances in research into molecular quantitative trait locus (xQTL) at the cellular level. With the rapid proliferation of data, there is a critical need for a comprehensive and accessible platform to integrate this information. To meet this need, we developed xQTLatlas (http://www.hitxqtl.org.cn/), a database that provides a multi-omics genetic regulatory landscape at cellular resolution. xQTLatlas compiles xQTL summary statistics from 151 cell types and 339 cell states across 55 human tissues. It organizes these data into 20 xQTL types, based on four distinct discovery strategies, and spans 13 molecular phenotypes. Each entry in xQTLatlas is meticulously annotated with comprehensive metadata, including the origin of the tissue, cell type, cell state and the QTL discovery strategies utilized. Additionally, xQTLatlas features multiscale data exploration tools and a suite of interactive visualizations, facilitating in-depth analysis of cell-level xQTL. xQTLatlas provides a valuable resource for deepening our understanding of the impact of functional variants on molecular phenotypes in different cellular environments, thereby facilitating extensive research efforts.

Shared genetic architecture and bidirectional clinical risks within the psycho-metabolic nexus

BACKGROUND

Increasing evidence suggests a complex interplay between psychiatric disorders and metabolic dysregulations. However, most research has been limited to specific disorder pairs, leaving a significant gap in our understanding of the broader psycho-metabolic nexus.

METHODS

This study leveraged large-scale cohort data and genome-wide association study (GWAS) summary statistics, covering 8 common psychiatric disorders and 43 metabolic traits. We introduced a comprehensive analytical strategy to identify shared genetic bases sequentially, from key genetic correlation regions to local pleiotropy and pleiotropic genes. Finally, we developed polygenic risk score (PRS) models to translate these findings into clinical applications.

FINDINGS

We identified significant bidirectional clinical risks between psychiatric disorders and metabolic dysregulations among 310,848 participants from the UK Biobank. Genetic correlation analysis confirmed 104 robust trait pairs, revealing 1088 key genomic regions, including critical hotspots such as chr3: 47588462-50387742. Cross-trait meta-analysis uncovered 388 pleiotropic single nucleotide variants (SNVs) and 126 shared causal variants. Among variants, 45 novel SNVs were associated with psychiatric disorders and 75 novel SNVs were associated with metabolic traits, shedding light on new targets to unravel the mechanism of comorbidity. Notably, RBM6, a gene involved in alternative splicing and cellular stress response regulation, emerged as a key pleiotropic gene. When psychiatric and metabolic genetic information were integrated, PRS models demonstrated enhanced predictive power.

INTERPRETATION

The study highlights the intertwined genetic and clinical relationships between psychiatric disorders and metabolic dysregulations, emphasising the need for integrated approaches in diagnosis and treatment.

FUNDING

The National Key Research and Development Program of China (2023YFC2506200, SHH). The National Natural Science Foundation of China (82273741, SY).

Integrating Whole Genome and Transcriptome Sequencing to Characterize the Genetic Architecture of Isoform Variation and its Implications for Health and Disease

We created a comprehensive whole blood splice variation quantitative trait locus (sQTL) resource by analyzing isoform expression ratio (isoform-to-gene) in Framingham Heart Study (FHS) participants (discovery: n=2,622; validation: n=1,094) with whole genome (WGS) and transcriptome sequencing (RNA-seq) data. External replication was conducted using WGS and RNA-seq from the Jackson Heart Study (JHS, n=1,020). We identified over 3.5 million cis -sQTL-isoform pairs ( p <5e-8), comprising 1,176,624 cis -sQTL variants and 10,883 isoform transcripts from 4,971 sGenes, with significant change in isoform-to-gene ratio due to allelic variation. We validated 61% of these pairs in the FHS validation sample ( p <1e-4). External validation ( p <1e-4) in JHS for the top 10,000 and 100,000 most significant cis -sQTL-isoform pairs was 88% and 69%, respectively, while overall pairs validated at 23%. For 20% of cis -sQTLs in the FHS discovery sample, allelic variation did not significantly correlate with overall gene expression. sQTLs are enriched in splice donor and acceptor sites, as well as in GWAS SNPs, methylation QTLs, and protein QTLs. We detailed several sentinel cis -sQTLs influencing alternative splicing, with potential causal effects on cardiovascular disease risk. Notably, rs12898397 (T>C) affects splicing of ULK3 , lowering levels of the full-length transcript ENST00000440863.7 and increasing levels of the truncated transcript ENST00000569437.5, encoding proteins of different lengths. Mendelian randomization analysis demonstrated that a lower ratio of the full-length isoform is causally associated with lower diastolic blood pressure and reduced lymphocyte percentages. This sQTL resource provides valuable insights into how transcriptomic variation may influence health outcomes.

Single-cell RNA sequencing of peripheral blood links cell-type-specific regulation of splicing to autoimmune and inflammatory diseases

Alternative splicing contributes to complex traits, but whether this differs in trait-relevant cell types across diverse genetic ancestries is unclear. Here we describe cell-type-specific, sex-biased and ancestry-biased alternative splicing in ~1 M peripheral blood mononuclear cells from 474 healthy donors from the Asian Immune Diversity Atlas. We identify widespread sex-biased and ancestry-biased differential splicing, most of which is cell-type-specific. We identify 11,577 independent cis-splicing quantitative trait loci (sQTLs), 607 trans-sGenes and 107 dynamic sQTLs. Colocalization between cis-eQTLs and trans-sQTLs revealed a cell-type-specific regulatory relationship between HNRNPLL and PTPRC. We observed an enrichment of cis-sQTL effects in autoimmune and inflammatory disease heritability. Specifically, we functionally validated an Asian-specific sQTL disrupting the 5' splice site of TCHP exon 4 that putatively modulates the risk of Graves' disease in East Asian populations. Our work highlights the impact of ancestral diversity on splicing and provides a roadmap to dissect its role in complex diseases at single-cell resolution.

Genome-wide association study and polygenic risk score analysis for schizophrenia in a Korean population

Although large-scale genome-wide association studies (GWASs) have revealed the genetic architecture of schizophrenia, these studies have mainly focused on populations of European ancestry. This study aimed to identify common genetic variants associated with schizophrenia in the Korean population and evaluate the performance of polygenic risk scores (PRSs) derived from large-scale GWASs across ancestries. In the Korean psychiatric GWAS project (KPGP), seven academic institutes and their affiliated hospitals across South Korea recruited a cohort of 1670 patients with DSM-IV-defined schizophrenia and 2271 healthy controls. A total of 6690,822 SNPs were tested for association with schizophrenia. We identified one previously unreported genome-wide significant locus rs2423464 (P = 2.83 × 10-11; odds ratio = 1.65; 95 % confidence interval = 1.43-1.91, minor allele frequency = 0.126). This variant was also associated with increased lysosomal-associated membrane protein family member 5 (LAMP5) gene expression. The PRS derived from the meta-analysis results of East Asian and European GWASs explained a larger proportion of the phenotypic variance in the Korean schizophrenia sample than the PRS of an East Asian or European GWAS. (R2 = 0.073 for meta-analysis; 0.028 for East Asian GWAS; 0.037 for European GWAS). GWASs involving diverse ethnic groups will expand our understanding of the genetic architecture of schizophrenia.

Genetic architecture of epigenetic cortical clock age in brain tissue from older individuals: alterations in CD46 and other loci

The cortical epigenetic clock was developed in brain tissue as a biomarker of brain aging. As one way to identify mechanisms underlying aging, we conducted a GWAS of cortical age. We leveraged postmortem cortex tissue and genotyping array data from 694 participants of the Rush Memory and Aging Project and Religious Orders Study (ROSMAP; 11000,000 SNPs), and meta-analysed ROSMAP with 522 participants of Brains for Dementia Research (5,000,000 overlapping SNPs). We confirmed results using eQTL (cortical bulk and single nucleus gene expression), cortical protein levels (ROSMAP), and phenome-wide association studies (clinical/neuropathologic phenotypes, ROSMAP). In the meta-analysis, the strongest association was rs4244620 (p = 1.29 × 10-7), which also exhibited FDR-significant cis-eQTL effects for CD46 in bulk and single nucleus (microglia, astrocyte, oligodendrocyte, neuron) cortical gene expression. Additionally, rs4244620 was nominally associated with lower cognition, faster slopes of cognitive decline, and greater Parkinsonian signs (n ~ 1700 ROSMAP with SNP/phenotypic data; all p ≤ 0.04). In ROSMAP alone, the top SNP was rs4721030 (p = 8.64 × 10-8) annotated to TMEM106B and THSD7A. Further, in ROSMAP (n = 849), TMEM106B and THSD7A protein levels in cortex were related to many phenotypes, including greater AD pathology and lower cognition (all p ≤ 0.0007). Overall, we identified converging evidence of CD46 and possibly TMEM106B/THSD7A for potential roles in cortical epigenetic clock age.

Landscape of multiple tissues' gene expression pattern associated with severe sepsis: Genetic insights from Mendelian randomization and trans-omics analysis

BACKGROUND

Sepsis, a systemic syndrome often culminating in multiple organ failure (MOF), poses a substantial global health threat. However, the gene expression pattern of various tissues associated with severe sepsis remains elusive.

METHODS

Applying the summary data-based Mendelian randomization (SMR) method, we integrated sepsis genome-wide association study (GWAS) data and expression quantitative trait loci (eQTLs) summaries. This facilitated the investigation of gene causality across 12 tissue types within 26 cohorts linked to adverse sepsis outcomes, including critical care and 28-day mortality. Additionally, trans-omics analyses, including blood transcriptome and single-cell RNA sequencing, were conducted to examine cellular origins and gene functions. The effects of ST7L on sepsis were validated in vivo and in vitro.

RESULTS

We identified 127 genes associated with severe sepsis across diverse tissues. Cross-tissue analysis highlighted ST7L as a significant pan-tissue risk factor for severe sepsis, displaying significance across 11 tissues for both critical care sepsis (meta OR 1.19, 95 % CI: 1.14-1.25, meta p < 0.0001) and 28-day-death sepsis (meta OR: 1.22, 95 % CI: 1.17-1.27, meta p < 0.0001). Notably, independent blood single-cell RNA sequencing data showed specific expression of ST7L in dendritic cells (DCs). ST7L+ DCs were elevated in non-surviving sepsis patients and exhibited an augmented inflammatory molecular pattern compared to ST7L- DCs. Both transcription and translation level of ST7L in DCs exhibited a dose-dependent pattern with LPS. Knocking down ST7L by siRNA was sufficient to alleviate the inflammation phenotype of DCs, including inhibiting p65/NF-kB pathway and inflammatory factors.

CONCLUSION

Our findings underscore ST7L as a pan-tissue risk factor for severe sepsis, specifically manifested in DCs and associated with an inflammatory phenotype. These results offer essential insights into the gene expression profiles across multiple tissues in severe sepsis, potentially identifying therapeutic targets for effective sepsis management.

Multimodal analysis of RNA sequencing data powers discovery of complex trait genetics

RNA sequencing has the potential to reveal many modalities of transcriptional regulation, such as various splicing phenotypes, but studies on gene regulation are often limited to gene expression due to the complexity of extracting and analyzing multiple RNA phenotypes. Here, we present Pantry, a framework to efficiently generate diverse RNA phenotypes from RNA sequencing data and perform downstream integrative analyses with genetic data. Pantry generates phenotypes from six modalities of transcriptional regulation (gene expression, isoform ratios, splice junction usage, alternative TSS/polyA usage, and RNA stability) and integrates them with genetic data via QTL mapping, TWAS, and colocalization testing. We apply Pantry to Geuvadis and GTEx data, finding that 4768 of the genes with no identified eQTL in Geuvadis have QTL in at least one other transcriptional modality, resulting in a 66% increase in genes over eQTL mapping. We further found that the QTL exhibit modality-specific functional properties that are further reinforced by joint analysis of different RNA modalities. We also show that generalizing TWAS to multiple RNA modalities approximately doubles the discovery of unique gene-trait associations, and enhances identification of regulatory mechanisms underlying GWAS signal in 42% of previously associated gene-trait pairs.

Plasma pQTL and brain eQTL integration identifies PNKP as a therapeutic target and reveals mechanistic insights into migraine pathophysiology

BACKGROUND

Migraine is a prevalent neurological disorder affecting 14.1% of the global population. Despite advances in genetic research, further investigation is needed to identify therapeutic targets and better understand its mechanisms. In this study, we aimed to identify drug targets and explore the relationships between gene expression, protein levels, and migraine pathophysiology.

METHODS

We utilized cis-pQTL data from deCODE Genetics, combined with migraine GWAS data from the GERA + UKB cohort as the discovery cohort and the FinnGen R10 cohort as the replication cohort. SMR and MR analyses identified migraine-associated protein loci. Brain eQTL data from GTEx v8 and BrainMeta v2 were used to explore causal relationships between gene expression, protein levels, and migraine risk. Mediation analysis assessed the role of metabolites, and PheWAS evaluated potential side effects.

RESULTS

Four loci were identified: PNKP, MRVI1, CALCB, and INPP5B. PNKP and MRVI1 showed a high level of evidence and opposing effects at the gene and protein levels. PNKP gene expression in certain brain regions was protective against migraine, while its plasma protein levels were positively associated with migraine risk. MRVI1 showed protective effects at the protein level but had the opposite effect at the gene expression level. Mediation analysis revealed that the glutamate to pyruvate ratio and 3-CMPFP mediated PNKP's effects on migraine. PheWAS indicated associations between PNKP and body composition traits, suggesting drug safety considerations.

CONCLUSION

PNKP and MRVI1 exhibit dual mechanisms of action at the gene and protein levels, potentially involving distinct mechanistic pathways. Among them, PNKP emerges as a promising drug target for migraine treatment, supported by multi-layered validation.

Response splicing QTLs in primary human chondrocytes identifies putative osteoarthritis risk genes

Osteoarthritis affects millions worldwide, yet effective treatments remain elusive due to poorly understood molecular mechanisms. While genome-wide association studies (GWAS) have identified over 100 OA-associated loci, identifying the genes impacted at each locus remains challenging. Several studies have mapped expression quantitative trait loci (eQTL) in chondrocytes and colocalized them with OA GWAS variants to identify putative OA risk genes; however, the degree to which genetic variants influence OA risk via alternative splicing has not been explored. We investigated the role of alternative splicing in OA pathogenesis using RNA-seq data from 101 human chondrocyte samples treated with PBS (control) or fibronectin fragment (FN-f), an OA trigger. We identified 590 differentially spliced genes between conditions, with FN-f inducing splicing events similar to those in primary OA tissue. We used CRISPR/Cas9 to mimic an SNRNP70 splicing event observed in OA and FN-f-treated chondrocytes and found that it induced an OA-like expression pattern. Integration with genotyping data revealed 7,188 splicing quantitative trait loci (sQTL) affecting 3,056 genes. While many sQTLs were shared, we identified 738 and 343 condition-specific sQTLs for control and FN-f, respectively. We identified 15 RNA binding proteins whose binding sites were enriched at sQTL splice junctions and found that expression of those RNA binding proteins correlated with exon inclusion. Colocalization with OA GWAS identified 6 putative risk genes, including a novel candidate, PBRM1. Our study highlights the significant impact of alternative splicing in OA and provides potential therapeutic targets for future research.

Multivariate genomic analysis of 5 million people elucidates the genetic architecture of shared components of the metabolic syndrome

Metabolic syndrome (MetS) is a complex hereditary condition comprising various metabolic traits as risk factors. Although the genetics of individual MetS components have been investigated actively through large-scale genome-wide association studies, the conjoint genetic architecture has not been fully elucidated. Here, we performed the largest multivariate genome-wide association study of MetS in Europe (nobserved = 4,947,860) by leveraging genetic correlation between MetS components. We identified 1,307 genetic loci associated with MetS that were enriched primarily in brain tissues. Using transcriptomic data, we identified 11 genes associated strongly with MetS. Our phenome-wide association and Mendelian randomization analyses highlighted associations of MetS with diverse diseases beyond cardiometabolic diseases. Polygenic risk score analysis demonstrated better discrimination of MetS and predictive power in European and East Asian populations. Altogether, our findings will guide future studies aimed at elucidating the genetic architecture of MetS.

A network-based systems genetics framework identifies pathobiology and drug repurposing in Parkinson's disease

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder. However, current treatments are directed at symptoms and lack ability to slow or prevent disease progression. Large-scale genome-wide association studies (GWAS) have identified numerous genomic loci associated with PD, which may guide the development of disease-modifying treatments. We presented a systems genetics approach to identify potential risk genes and repurposable drugs for PD. First, we leveraged non-coding GWAS loci effects on multiple human brain-specific quantitative trait loci (xQTLs) under the protein-protein interactome (PPI) network. We then prioritized a set of PD likely risk genes (pdRGs) by integrating five types of molecular xQTLs: expression (eQTLs), protein (pQTLs), splicing (sQTLs), methylation (meQTLs), and histone acetylation (haQTLs). We also integrated network proximity-based drug repurposing and patient electronic health record (EHR) data observations to propose potential drug candidates for PD treatments. We identified 175 pdRGs from QTL-regulated GWAS findings, such as SNCA, CTSB, LRRK2, DGKQ, CD38 and CD44. Multi-omics data validation revealed that the identified pdRGs are likely to be druggable targets, differentially expressed in multiple cell types and impact both the parkin ubiquitin-proteasome and alpha-synuclein (a-syn) pathways. Based on the network proximity-based drug repurposing followed by EHR data validation, we identified usage of simvastatin as being significantly associated with reduced incidence of PD (fall outcome: hazard ratio (HR) = 0.91, 95% confidence interval (CI): 0.87-0.94; for dementia outcome: HR = 0.88, 95% CI: 0.86-0.89), after adjusting for 267 covariates. Our network-based systems genetics framework identifies potential risk genes and repurposable drugs for PD and other neurodegenerative diseases if broadly applied.

TWAS facilitates gene-scale trait genetic dissection through gene expression, structural variations, and alternative splicing in soybean

A genome-wide association study (GWAS) identifies trait-associated loci, but identifying the causal genes can be a bottleneck, due in part to slow decay of linkage disequilibrium (LD). A transcriptome-wide association study (TWAS) addresses this issue by identifying gene expression-phenotype associations or integrating gene expression quantitative trait loci with GWAS results. Here, we used self-pollinated soybean (Glycine max [L.] Merr.) as a model to evaluate the application of TWAS to the genetic dissection of traits in plant species with slow LD decay. We generated RNA sequencing data for a soybean diversity panel and identified the genetic expression regulation of 29 286 soybean genes. Different TWAS solutions were less affected by LD and were robust to the source of expression, identifing known genes related to traits from different tissues and developmental stages. The novel pod-color gene L2 was identified via TWAS and functionally validated by genome editing. By introducing a new exon proportion feature, we significantly improved the detection of expression variations that resulted from structural variations and alternative splicing. As a result, the genes identified through our TWAS approach exhibited a diverse range of causal variations, including SNPs, insertions or deletions, gene fusion, copy number variations, and alternative splicing. Using this approach, we identified genes associated with flowering time, including both previously known genes and novel genes that had not previously been linked to this trait, providing insights complementary to those from GWAS. In summary, this study supports the application of TWAS for candidate gene identification in species with low rates of LD decay.