Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses

Bidirectional Mendelian randomization and colocalization analysis of gut microbiota on lipid profile

The gut microbiota plays a crucial role in human health, but its impact on lipid metabolism remains unclear. Understanding the causal relationship between gut bacteria and lipid profiles is essential for developing strategies to prevent and treat dyslipidemia and cardiovascular diseases. This study aimed to assess this relationship using two-sample Mendelian randomization (MR). Data for both exposure and outcomes were obtained from the IEU-GWAS database, with lipid profile data sourced from a publication. Genome-wide significant single nucleotide polymorphisms (SNPs), which were independent of outcome factors but correlated with exposure variables, were identified as instrumental variables. Several MR methods, including weighted analysis, maximum likelihood, inverse variance weighting (IVW), MR-Egger, and weighted median, were applied. Colocalization analysis further validated the findings. The analysis revealed microbial groups with causal relationships to ApoA1, ApoB, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, total cholesterol, and triglycerides. Reverse MR and colocalization analysis provided additional confirmation of these results. This study offers new evidence of the causal link between gut microbiota and lipid profiles, providing insights for improving lipid profiles and reducing cardiovascular disease risk.

Genetic Distinctions Between Reticular Pseudodrusen and Drusen: A Genome-Wide Association Study

OBJECTIVE

To identify genetic determinants specific to reticular pseudodrusen (RPD) compared with drusen.

DESIGN

Genome-wide association study (GWAS) SUBJECTS: Participants with RPD, drusen, and controls from the UK Biobank (UKBB), a large, multisite, community-based cohort.

METHODS

Participants with RPD, drusen, and controls from the UK Biobank (UKBB), a large, multisite, community-based cohort, were included. A deep learning framework analyzed 169,370 optical coherence tomography (OCT) volumes to identify cases and controls within the UKBB. Five retina specialists validated the cohorts using OCT and color fundus photographs. Several GWAS were undertaken utilizing the quantity and presence of RPD and drusen. Genome-wide significance was defined as P < 5e-8.

MAIN OUTCOMES MEASURES

Genetic associations were examined with the number of RPD and drusen within 'pure' cases, where only RPD or drusen were present in either eye. A candidate approach assessed 46 previously known AMD loci. Secondary GWAS were conducted for number of RPD and drusen in mixed cases, and binary case-control analyses for pure RPD and pure drusen.

RESULTS

The study included 1787 participants: 1037 controls, 361 pure drusen, 66 pure RPD, and 323 mixed cases. The primary pure RPD GWAS identified four genome-wide significant loci: rs11200630 near ARMS2-HTRA1 (P = 1.9e-09), rs79641866 at PARD3B (P = 1.3e-08), rs143184903 near ITPR1 (P = 8.1e-09), and rs76377757 near SLN (P = 4.3e-08). The latter three are uncommon variants (minor allele frequency <5%). A significant association at the CFH locus was also observed using a candidate approach (P = 1.8e-04). For pure drusen, two loci reached genome-wide significance: rs10801555 at CFH (P = 6.0e-33) and rs61871744 at ARMS2-HTRA1 (P = 4.2e-20).

CONCLUSIONS

The study highlights a clear association between the ARMS2-HTRA1 locus and higher RPD load. Although the CFH locus association did not achieve genome-wide significance, a suggestive link was observed. Three novel associations unique to RPD were identified, albeit for uncommon genetic variants. Further studies with larger sample sizes are needed to explore these findings.

Distinct pathway-based effects of blood pressure and body mass index on cardiovascular traits: comparison of novel Mendelian randomization approaches

BACKGROUND

Mendelian randomization (MR) leverages trait associated genetic variants as instrumental variables (IVs) to determine causal relationships in epidemiology. However, genetic IVs for complex traits are typically highly heterogeneous and, at a molecular level, exert effects on different biological processes. Exploration of the biological underpinnings of such heterogeneity can enhance our understanding of disease mechanisms and inform therapeutic strategies. Here, we introduce a new approach to instrument partitioning based on enrichment of Mendelian disease categories (pathway-partitioned) and compare it to an existing method based on genetic colocalization in contrasting tissues (tissue-partitioned).

METHODS

We employed individual- and summary-level MR methodologies using SNPs grouped by pathway informed by proximity to Mendelian disease genes affecting the renal system or vasculature (for blood pressure (BP)), or mental health and metabolic disorders (for body mass index (BMI)). We compared the causal effects of pathway-partitioned SNPs on cardiometabolic outcomes with those derived using tissue-partitioned SNPs informed by colocalization with gene expression in kidney, artery (BP), or adipose and brain tissues (BMI). Additionally, we assessed the likelihood that estimates observed for partitioned exposures could emerge by chance using random SNP sampling.

RESULTS

Our pathway-partitioned findings suggest the causal relationship between systolic BP and heart disease is predominantly driven by vessel over renal pathways. The stronger effect attributed to kidney over artery tissue in our tissue-partitioned MR hints at a multifaceted interplay between pathways in the disease aetiology. We consistently identified a dominant role for vessel (pathway) and artery (tissue) driving the negative directional effect of diastolic BP on left ventricular stroke volume and positive directional effect of systolic BP on type 2 diabetes. We also found when dissecting the BMI pathway contribution to atrial fibrillation that metabolic-pathway and brain-tissue IVs predominantly drove the causal effects relative to mental health and adipose in pathway- and tissue-partitioned MR analyses, respectively.

CONCLUSIONS

This study presents a novel approach to dissecting heterogeneity in MR by integrating clinical phenotypes associated with Mendelian disease. Our findings emphasize the importance of understanding pathway-/tissue-specific contributions to complex exposures when interpreting causal relationships in MR. Importantly, we advocate caution and robust validation when interpreting pathway-partitioned effect size differences.

Variant-to-function approaches for adipose tissue: Insights into cardiometabolic disorders

Genome-wide association studies (GWASs) have identified thousands of genetic loci associated with cardiometabolic disorders. However, the functional interpretation of these loci remains a daunting challenge. This is particularly true for adipose tissue, a critical organ in systemic metabolism and the pathogenesis of various cardiometabolic diseases. We discuss how variant-to-function (V2F) approaches are used to elucidate the mechanisms by which GWAS loci increase the risk of cardiometabolic disorders by directly influencing adipose tissue. We outline GWAS traits most likely to harbor adipose-related variants and summarize tools to pinpoint the putative causal variants, genes, and cell types for the associated loci. We explain how large-scale perturbation experiments, coupled with imaging and multi-omics, can be used to screen variants' effects on cellular phenotypes and how these phenotypes can be tied to physiological mechanisms. Lastly, we discuss the challenges and opportunities that lie ahead for V2F research and propose a roadmap for future studies.

Genome-wide analyses identify 30 loci associated with obsessive-compulsive disorder

Obsessive-compulsive disorder (OCD) affects ~1% of children and adults and is partly caused by genetic factors. We conducted a genome-wide association study (GWAS) meta-analysis combining 53,660 OCD cases and 2,044,417 controls and identified 30 independent genome-wide significant loci. Gene-based approaches identified 249 potential effector genes for OCD, with 25 of these classified as the most likely causal candidates, including WDR6, DALRD3 and CTNND1 and multiple genes in the major histocompatibility complex (MHC) region. We estimated that ~11,500 genetic variants explained 90% of OCD genetic heritability. OCD genetic risk was associated with excitatory neurons in the hippocampus and the cortex, along with D1 and D2 type dopamine receptor-containing medium spiny neurons. OCD genetic risk was shared with 65 of 112 additional phenotypes, including all the psychiatric disorders we examined. In particular, OCD shared genetic risk with anxiety, depression, anorexia nervosa and Tourette syndrome and was negatively associated with inflammatory bowel diseases, educational attainment and body mass index.

Genome-wide association study for lung cancer in 6531 African Americans reveals new susceptibility loci

Despite lung cancer affecting all races and ethnicities, disparities are observed in incidence and mortality rates among different ethnic groups in the United States. Non-Hispanic African Americans had a high incidence rate of lung cancer at 55.8 per 100 000 people, as well as the highest death rate at 37.2 per 100 000 people from 2016 to 2020. While previous genome-wide association studies (GWAS) have identified over 45 susceptibility risk loci that influence lung cancer development, few GWAS have investigated the etiology of lung cancer in African Americans. To address this gap in knowledge, we conducted GWAS of lung cancer focused on studying African Americans, comprising 2267 lung cancer cases and 4264 controls. We identified three loci associated with lung cancer, one with lung adenocarcinoma, and four with lung squamous cell carcinoma in this population at the genomic-wide significance level. Among them, three novel loci were identified near VWF at 12p13.31 for overall lung cancer and GACAT3 at 2p24.3 and LMAN1L at 15q24.1 for lung squamous cell carcinoma. In addition, we confirmed previously reported risk loci with known or new lead variants near CHRNA5 at 15q25.1 and CYP2A6 at 19q13.2 associated with lung cancer and TRIP13 at 5p15.33 and ERC1 at 12p13.33 associated with lung squamous cell carcinoma. Further multi-step functional analyses shed light on biological mechanisms underlying these associations of lung cancer in this population. Our study highlights the importance of ancestry-specific studies for the potential alleviation of lung cancer burden in African Americans.

Genome-wide association meta-analysis of age at onset of walking in over 70,000 infants of European ancestry

Age at onset of walking is an important early childhood milestone which is used clinically and in public health screening. In this genome-wide association study meta-analysis of age at onset of walking (N = 70,560 European-ancestry infants), we identified 11 independent genome-wide significant loci. SNP-based heritability was 24.13% (95% confidence intervals = 21.86-26.40) with ~11,900 variants accounting for about 90% of it, suggesting high polygenicity. One of these loci, in gene RBL2, co-localized with an expression quantitative trait locus (eQTL) in the brain. Age at onset of walking (in months) was negatively genetically correlated with ADHD and body-mass index, and positively genetically correlated with brain gyrification in both infant and adult brains. The polygenic score showed out-of-sample prediction of 3-5.6%, confirmed as largely due to direct effects in sib-pair analyses, and was separately associated with volume of neonatal brain structures involved in motor control. This study offers biological insights into a key behavioural marker of neurodevelopment.

The genetic overlap and causal relationship between attention deficit hyperactivity disorder and obstructive sleep apnea: a large-scale genomewide cross-trait analysis

BACKGROUND

Attention deficit hyperactivity disorder (ADHD) and Obstructive sleep apnea (OSA) are highly clinically co-occurring, but the mechanisms behind this remain unclear, so this article analyzes the reasons for the co-morbidities from a genetic perspective.

METHODS

We examined the genetic architecture of ADHD and OSA based on the large genome-wide association studies (GWAS). The global genetic relationship between OSA and ADHD was explored. Cross-trait analysis from single nucleotide polymorphism (SNP) and gene level was performed subsequently to detect the crucial genomic regions. Finally, we revealed the anatomical change on which genetic overlap relies and further explored whether genetic factors exert a causal effect.

RESULTS

After using both linkage disequilibrium score regression (LDSC) and High-definition likelihood inference (HDL) methods, we identified a significant genetic correlation between OSA and ADHD (PLDSC = 2.45E-28, PHDL = 1.09E-25), demonstrating a consistent direction. Furthermore, through the application of various cross-trait methods, we pinpointed 5 loci and 57 genes involved in regulating the co-occurrence of these disorders. These genetic regions were thought to be associated with the prefrontal lobes (P = 3.07E-06) and the nucleus accumbens basal ganglia (P = 2.85E-06). Lastly, utilizing Mendelian randomization (MR), we established a link indicating that individuals with ADHD were at an elevated risk of developing OSA (PIVM = 0.02, OR (95%CI):1.09 (1.01-1.17)).

CONCLUSIONS

This study reveals a strong genetic correlation between ADHD and OSA. It offers insights for future drug target development and sleep management in ADHD.

Contrastive sequence signatures between the both sides of a recombination spot reveal an adaptation at PPARD locus from standing variation for pleiotropy since out-of-Africa dispersal

BACKGROUND

Drug metabolism and transporter genes are a specialized class of genes involved in absorption, distribution, metabolism and excretion. They easily present distinct genetic population differentiation and are vulnerable to natural selection.

RESULTS

We initiated a study using a special panel of informative genetic markers in such genes and dissected the genetic structure in representative Chinese and worldwide populations. A distinctive sub-population stratification was discovered in extensive Eurasians and resulted from divergence at the PPARD locus. The contrastive sequence signatures between the both sides of a recombination spot prove a selective sweep on this locus for genetic hitchhiking effect. A genealogy-based framework demonstrates the positive selection acting from standing variation exerted a moderate pressure in Eurasians, and drove the adaptive allele up to a high frequency. The timing and tempo estimations for the genetic adaptation indicate its onset coincided with the early out-of-Africa migration of modern humans and it lasted over a prolonged evolutionary history. A phenome-wide association analysis reveals an extended cis-regulation on the local gene expression and the pleiotropy implicated in a variety of complex traits. The colocalization analyses between the genetic associations from cis-acting gene expression and complex traits signify the most likely selective pressure from physical capacity, energy metabolism, and immune-related involvement, and provide prioritization for the effective genes and casual variants.

CONCLUSIONS

This work has laid a foundation for following efforts to make full sense of the biological mechanisms underlying the genetic adaptation.

Prioritizing Parkinson's disease risk genes in genome-wide association loci

Many drug targets in ongoing Parkinson's disease (PD) clinical trials have strong genetic links. While genome-wide association studies (GWAS) nominate regions associated with disease, pinpointing causal genes is challenging. Our aim was to prioritize additional druggable genes underlying PD GWAS signals. The polygenic priority score (PoPS) integrates genome-wide information from MAGMA gene-level associations and over 57,000 gene-level features. We applied PoPS to East Asian and European PD GWAS data and prioritized genes based on PoPS, distance to the GWAS signal, and non-synonymous credible set variants. We prioritized 46 genes, including well-established PD genes (SNCA, LRRK2, GBA1, TMEM175, VPS13C), genes with strong literature evidence supporting a mechanistic link to PD (RIT2, BAG3, SCARB2, FYN, DYRK1A, NOD2, CTSB, SV2C, ITPKB), and genes relatively unexplored in PD. Many hold potential for drug repurposing or development. We prioritized high-confidence genes with strong links to PD pathogenesis that may represent our next-best candidates for developing disease-modifying therapeutics.

Exploring the shared genetic basis of attention-deficit/hyperactivity disorder and obstructive sleep apnea: A multi-omics analysis

BACKGROUND

Observational studies have suggested an association between attention-deficit/hyperactivity disorder (ADHD) and obstructive sleep apnea (OSA), but these findings are often inconsistent due to potential biases from medication use, and varying diagnostic criteria. Genetic analyses can help mitigate these confounding factors, providing additional evidence.

METHODS

This study evaluated the genetic correlations between ADHD and OSA using Genome-wide association study (GWAS) summary data, applying linkage disequilibrium score regression (LDSC) and SUPER GeNetic cOVariance Analyzer (SUPERGNOVA). Cross-trait association and colocalization analysis identify potential pleiotropic loci. Tissue enrichment analysis and gene-level analysis of shared genes between OSA and ADHD was conducted. Additionally, bidirectional Mendelian randomization was used to assess potential causal relationships.

RESULTS

We found significant genetic correlations between ADHD and OSA (rg = 0.309, p = 3.252E-27), and identified 8 novel pleiotropic loci through cross-trait association analysis. Tissue enrichment analysis showed that these shared genes were primarily concentrated in brain tissues, particularly in deep gray matter regions, and were associated with immune and inflammatory pathways. Forward Mendelian Randomization analysis showed that ADHD was significantly associated with the risk of OSA (OR 1.070, 95 % CI 1.013-1.130, p = 0.016), and reverse analysis showed that OSA was significantly associated with the risk of ADHD (OR 1.240, 95 % CI 1.106-1.390, p = 2.213E-4).

CONCLUSION

The findings of this study show a significant positive genetic correlation between ADHD and OSA and each is a risk factor for the other. Inflammation in specific brain regions may be the underlying mechanism for their comorbidity.

Genome-wide analyses identify 25 infertility loci and relationships with reproductive traits across the allele frequency spectrum

Genome-wide association studies (GWASs) may help inform the etiology of infertility. Here, we perform GWAS meta-analyses across seven cohorts in up to 42,629 cases and 740,619 controls and identify 25 genetic risk loci for male and female infertility. We additionally identify up to 269 genetic loci associated with follicle-stimulating hormone, luteinizing hormone, estradiol and testosterone through sex-specific GWAS meta-analyses (n = 6,095-246,862). Exome sequencing analyses reveal that women carrying testosterone-lowering rare variants in some genes are at risk of infertility. However, we find no local or genome-wide genetic correlation between female infertility and reproductive hormones. While infertility is genetically correlated with endometriosis and polycystic ovary syndrome, we find limited genetic overlap between infertility and obesity. Finally, we show that the evolutionary persistence of infertility-risk alleles may be explained by directional selection. Taken together, we provide a comprehensive view of the genetic determinants of infertility across multiple diagnostic criteria.

GWAS by Subtraction to Disentangle RBD Genetic Background from α-Synucleinopathies

Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by loss of muscle atonia and abnormal behaviors occurring during REM sleep. Idiopathic RBD (iRBD) is recognized as the strongest prodromal hallmark of α-synucleinopathies, with an established conversion rate to a neurodegenerative condition that reaches up to 96.6% at 15 years of follow-up. Moreover, RBD-converters display a more severe clinical trajectory compared to those that do not present with RBD. However, the extent to which iRBD represents a distinct genetic entity or an early manifestation of neurodegeneration remains unclear. To address this, we applied Genomic Structural Equation Modeling (GenomicSEM) using a GWAS-by-subtraction approach to disentangle the genetic architecture of iRBD from the shared genomic liability across α-synucleinopathies. Our findings highlight the SNCA locus as a key genetic regulator of iRBD susceptibility. While iRBD exhibits a partially distinct genetic signature, residual genomic overlap with neurodegenerative traits suggests that its genetic architecture exists along a continuum of α-synucleinopathy risk. In this scenario, the associations with neuroanatomical correlates may serve as early indicators of a trajectory toward future neurodegeneration. These findings provide a framework for identifying biomarkers that could aid in disease stratification and risk prediction, potentially improving early intervention strategies.

Transcriptome-wide association study of alternative polyadenylation identifies susceptibility genes in non-small cell lung cancer

Alternative polyadenylation (APA) plays a crucial role in cancer development and prognosis. However, the molecular characteristics of APA related to non-small cell lung cancer (NSCLC) susceptibility remain understudied, especially in East Asian populations. In this study, we constructed an atlas of APA-regulated 3' untranslated region (3'UTR) and profiled its genetic regulation in 747 lung tissue samples (including tumors and paired normal tissues) from 417 NSCLC Chinese patients. We verified a significant global shortening of 3'UTRs in tumor samples compared to normal samples and underscored the value of APA-regulation as a prognostic marker. The 3'UTR APA quantitative trait loci (3'aQTL) was identified by regressing the percentage of distal poly(A) site usage index (PDUI) value on genetic variants. We found that a significant proportion 3'aQTLs are independent of genetic regulation of expression and are specific in Chinese. We also conducted a 3'UTR APA transcriptome-wide association study (3'aTWAS) by integrating the APA regulation atlas with a genome-wide association study (GWAS) for NSCLC involving 7035 cases and 185,413 cancer-free controls. We identified NSCLC-associated genes, highlighting TUBB, TEAD3, and PPP1R10. Combining the consistent results from colocalization analysis, differential APA analysis, and survival analysis, we provide novel evidence for the role TUBB APA regulation in NSCLC and identified potential upstream regulators. Overall, our study profiled the APA regulation and highlighted the substantial role of APA in NSCLC carcinogenesis and prognosis in East Asian populations.

Cross-ancestry analyses of Chinese and European populations reveal insights into the genetic architecture and disease implication of metabolites

Differential susceptibilities to various diseases and corresponding metabolite variations have been documented across diverse ethnic populations, but the genetic determinants of these disparities remain unclear. Here, we performed large-scale genome-wide association studies of 171 directly quantifiable metabolites from a nuclear magnetic resonance-based metabolomics platform in 10,792 Han Chinese individuals. We identified 15 variant-metabolite associations, eight of which were successfully replicated in an independent Chinese population (n = 4,480). By cross-ancestry meta-analysis integrating 213,397 European individuals from the UK Biobank, we identified 228 additional variant-metabolite associations and improved fine-mapping precision. Moreover, two-sample Mendelian randomization analyses revealed evidence that genetically predicted levels of triglycerides in high-density lipoprotein were associated with a higher risk of coronary artery disease and that of glycine with a lower risk of heart failure in both ancestries. These findings enhance our understanding of the shared and specific genetic architecture of metabolites as well as their roles in complex diseases across populations.

Therapeutic Target Discovery for Multiple Myeloma: Identifying Druggable Genes via Mendelian Randomization

Background: Multiple myeloma (MM) is a hematological malignancy originating from the plasma cells present in the bone marrow. Despite significant therapeutic advancements, relapse and drug resistance remain major clinical challenges, highlighting the urgent need for novel therapeutic targets. Methods: To identify potential druggable genes associated with MM, we performed Mendelian randomization (MR) analysis. Causal candidates were further validated using a single-tissue transcriptome-wide association study (TWAS), and colocalization analysis was conducted to assess shared genetic signals between gene expression and disease risk. Potential off-target effects were assessed through an MR phenome-wide association study (MR-PheWAS). Additionally, molecular docking and functional assays were used to evaluate candidate drug efficacy. Results: The MR analysis identified nine druggable genes (FDR < 0.05), among which Orosomucoid 1 (ORM1) and Oviductal Glycoprotein 1 (OVGP1) were supported by both TWAS and colocalization evidence (PPH4 > 0.75). Experimental validation demonstrated the significant downregulation of ORM1 and OVGP1 in MM cells (p < 0.05). Pregnenolone and irinotecan, identified as agonists of ORM1 and OVGP1, respectively, significantly inhibited MM cell viability, while upregulating their expression (p < 0.05). Conclusions: Our study highlights ORM1 and OVGP1 as novel therapeutic targets for MM. The efficacy of pregnenolone and irinotecan in suppressing MM cell growth suggests their potential for clinical application. These findings provide insights into MM pathogenesis and offer a promising strategy for overcoming drug resistance.

Bidirectional causal associations between plasma metabolites and bipolar disorder

Altered levels of human plasma metabolites have been implicated in the etiology of bipolar disorder (BD). However, the causality between metabolites and the disease was not well described. We performed a bidirectional metabolome-wide Mendelian randomization (MR) analysis to evaluate the potential causal relationships between 871 plasma metabolites and BD. We used DrugBank and ChEMBL to evaluate whether related metabolites are potential therapeutic targets. Finally, Bayesian colocalization analysis was performed to identify shared genomic loci BD and identified metabolites. Our MR results showed that six metabolites were significantly associated with a reduced risk of BD, including arachidonate (20:4n6) (OR: 0.90, 95% CI: 0.84-0.95) and sphingomyelin (d18:2/24:1, d18:1/24:2) (OR: 0.92, 95% CI: 0.87-0.96), while five metabolites were significantly associated with an increased risk of BD, including 1-palmitoyl-2-linoleoyl-GPE (16:0/18:2) (OR: 1.09, 95% CI: 1.05-1.13). However, our reverse MR analysis showed that BD was not associated with the levels of any metabolite. Additionally, the leave-one-out analysis revealed SNPs within chromosome 11 loci harboring MYRF, FADS1, and FADS2 as ones with the potential to influence partial causal effects. Druggability evaluation showed that 10 of the BD-related metabolites, such as sphingomyelin and cytidine, have been targeted by pharmacologic intervention. Colocalization analysis highlighted one colocalized region (chromosome 11q12) shared by 11 metabolites and BD and pointed to some genes as possible players, including FADS1, FADS2, FADS3, and SYT7. Our study supported a causal role of plasma metabolites in the susceptibility to BD, and the identified metabolites may provide a new avenue for the prevention and treatment of BD.

The identification of blood-derived response eQTLs reveals complex effects of regulatory variants on inflammatory and infectious disease risk

Hundreds of risk loci for immune mediated inflammatory and infectious diseases have been identified by genome-wide association studies (GWAS). Yet, what causal variants and genes in risk loci underpin the observed associations remains poorly understood for most. The identification of colocalized cis-expression Quantitative Trait Loci (cis-eQTLs) is a promising way to identify candidate causative genes. The catalogue of cis-eQTLs of the immune system is likely incomplete as many cis-eQTLs may be context-specific. We built a large cohort of 406 healthy individuals and expanded the immune cis-regulome through their whole blood transcriptome obtained after stimulation with specific toll-like receptor (TLR) agonists and T-cell receptor (TCR) antagonist. We report three mechanisms that may explain why an eQTL could only be revealed after immune stimulation. More than half of the cis-eQTLs detected in this study would have been overlooked without specific immune stimulations. We then mined this new catalogue of response (r)eQTLs, with public GWAS summary statistics of three diseases through a colocalization approach: inflammatory bowel diseases, rheumatoid arthritis and COVID-19 disease. We identified reQTL-specific colocalizations for risk loci for which no matching eQTL were reported before, revealing interesting new candidate causal genes.

Probabilistic classification of gene-by-treatment interactions on molecular count phenotypes

Genetic variation can modulate response to treatment (G×T) or environmental stimuli (G×E), both of which can be highly consequential in biomedicine. An effective approach to identifying G×T signals and gaining insight into molecular mechanisms is mapping quantitative trait loci (QTL) of molecular count phenotypes, such as gene expression and chromatin accessibility, under multiple treatment conditions, which is termed response molecular QTL mapping. Although standard approaches evaluate the interaction between genetics and treatment conditions, they do not distinguish between meaningful interpretations such as whether a genetic effect is observed only in the treated condition or whether a genetic effect is observed always but accentuated in the treated condition. To address this gap, we have developed a downstream method for classifying response molecular QTLs into subclasses with meaningful genetic interpretations. Our method uses Bayesian model selection and assigns posterior probabilities to different types of G×T interactions for a given feature-SNP pair. We compare linear and nonlinear regression of log ⁡ -scale counts, noting that the latter accounts for an expected biological relationship between the genotype and the molecular count phenotype. Through simulation and application to existing datasets of molecular response QTLs, we show that our method provides an intuitive and well-powered framework to report and interpret G×T interactions. We provide a software package, ClassifyGxT [1].

Identification of druggable targets in acute kidney injury by proteome- and transcriptome-wide Mendelian randomization and bioinformatics analysis

BACKGROUND

Acute kidney injury (AKI) remains a critical condition with limited therapeutic options, predominantly managed by renal replacement therapy. The challenge of developing targeted treatments persists.

METHODS

We integrated genetic data related to druggable proteins and gene expression with AKI genome-wide association study (GWAS) findings. Based on multi-omics Mendelian randomization (MR), we identified the potential causal influence of 5,883 unique proteins and genes on AKI. We also performed using reverse MR and external cohort-based analysis to verify the robustness of this causal relationship. Expression patterns of these targets were examined using bulk transcriptome and single-cell transcriptome data. In addition, drug repurposing analyses were conducted to explore the potential of existing medications. We also constructed a molecular interaction network to explore the interplay between identified targets and known drugs.

RESULTS

Genetically predicted levels of seven proteins and twelve genes were associated with an increased risk of AKI. Of these, six targets (NCF1, TNFRSF1B, APEH, ACADSB, ADD1, and FAM3B) were prioritized based on robust evidence and validated in independent cohorts. Reverse MR showed a one-way causal relationship of targets. These targets are predominantly expressed in proximal tubular cells, endothelial cells, collecting duct-principal cells, and immune cells within both AKI-affected and normal tissues. Several promising drug repurposing opportunities were identified, such as telmisartan-NCF1, calcitriol-ACADSB, and ethinyl estradiol-ACADSB. The molecular interaction mapping and pathway integration analysis provided further insights, suggesting potential strategies for combinatorial therapies.

CONCLUSIONS

This extensive investigation identified several promising therapeutic targets for AKI and highlighted opportunities for drug repurposing. These findings offer valuable insights that could shape future research and the development of targeted treatments.

Unveiling circulating targets in pancreatic cancer: Insights from proteogenomic evidence and clinical cohorts

Pancreatic cancer (PC), characterized by the absence of effective biomarkers and therapies, remains highly fatal. Data regarding the correlations between PC risk and individual plasma proteome known for minimally invasive biomarkers are scarce. Here, we analyzed 1,345 human plasma proteins using proteome-wide association studies, identifying 78 proteins significantly associated with PC risk. Of these, four proteins (ROR1, FN1, APOA5, and ABO) showed the most substantial causal link to PC, confirmed through Mendelian randomization and colocalization analyses. Data from two clinical cohorts further demonstrated that FN1 and ABO were notably overexpressed in both blood and tumor samples from PC patients, compared to healthy controls or para-tumor tissues. Additionally, elevated FN1 and ABO levels correlated with shorter median survival in patients. Multiple drugs targeting FN1 or ROR1 are available or in clinical trials. These findings suggest that plasma protein FN1 associated with PC holds potential as both prognostic biomarkers and therapeutic targets.

Genome-wide association and multi-omics analyses provide insights into the disease mechanisms of central serous chorioretinopathy

Central serous chorioretinopathy (CSC) is a major cause of vision loss, especially in middle-aged men, and its chronic subtype can lead to legal blindness. Despite its clinical importance, the underlying mechanisms of CSC need further clarification. In this study, we conducted a meta-analysis of three genome-wide association studies (GWASs) for CSC consisting of 8811 Asians and Caucasians, followed by replication in an additional 4338 Asians. We identified four genome-wide hits, including a novel hit (rs12960630 at LINC01924-CDH7, Pmeta = 2.97 × 10-9). A phenome-wide association study for rs12960630 showed a positive correlation between its CSC risk allele with plasma cortisol concentration. Expression/splicing quantitative trait loci (QTL) analyses showed an association of all these hits with the expression and/or splicing of genes in genital organs, which may explain the sex differences in CSC. Protein QTL also suggested the protein-level contribution of the complement factor H pathway to CSC pathogenesis.

Genetic overlap between multi-site chronic pain and cognition: a large-scale genome-wide cross-trait analysis

Background

Different studies have consistently demonstrated a positive correlation between chronic pain and cognitive changes. This study aimed to explore the genetic factors underlying the relationship between chronic pain and cognitive traits, and to investigate whether an inherent causal connection exists between them.

Method

The genetic contributions of chronic multi-site pain and eight cognitive traits were investigated based on Genome-wide association studies (GWAS) data. Linkage disequilibrium score regression (LDSC) was employed to assess the genetic correlations between each pair of traits. The shared genetic components of these traits were investigated by identifying single nucleotide polymorphisms (SNPs) with pleiotropic effects using the Cross Phenotype Association (CPASSOC) method. Furthermore, enrichment analysis and transcriptome-wide association studies (TWAS) were performed to characterize the significant associations between genetic traits. The latent causal variable model (LCV) was employed to explore the potential causal relationship between both traits.

Results

A significant negative genetic correlation was found between chronic pain and several cognitive functions, particularly intelligence (rg = -0. 11, p = 7.77 × 10-64). CPASSOC identified 150 pleiotropic loci. A co-localization analysis was conducted, which identified 20 loci exhibiting pleiotropic effects at the same genomic position. The LCV analysis indicated no causal relationship between both traits.

Conclusion

The present work contributed to an enhanced understanding of the complex genetic interplay between cognitive function and chronic pain.

Identification and validation of transcriptome-wide association study-derived genes as potential druggable targets for osteoarthritis

Aims

Osteoarthritis (OA) is a widespread chronic degenerative joint disease with an increasing global impact. The pathogenesis of OA involves complex interactions between genetic and environmental factors. Despite this, the specific genetic mechanisms underlying OA remain only partially understood, hindering the development of targeted therapeutic strategies.

Methods

A transcriptome-wide association study (TWAS) was conducted for site-specific OA phenotypes using functional summary-based imputation (FUSION). High-confidence candidate genes were identified through rigorous quality control measures, including joint/conditional analysis, permutation tests, best model evaluation, and colocalization analysis. Co-expression network analysis was performed to elucidate the functional biology of these candidate genes. Druggable gene targets and their structural models were retrieved from the DrugBank and SWISS-MODEL databases. Finally, the enrichment of mitogen-activated protein kinase 3 (MAPK3) and SMAD3 in OA was validated biochemically using in vitro and in vivo OA models, as well as human histological sections.

Results

Utilizing the FUSION algorithm, TWAS identified 794 candidate genes for OA. After quality control, 14 genes were classified as high-confidence genes, with seven recognized as potential drug targets including GCAT, MAPK3, MST1R, PFKM, RAD9A, SMAD3, and USAP8. Co-expression analysis revealed a strong biological association between SMAD3 and MAPK3. Both in vitro and in vivo experiments demonstrated high activity and enriched expression of these two genes in OA.

Conclusion

The present study identified tissue-specific candidate genes and validated high-confidence druggable targets for OA, providing new insights into the genetic landscape and biological processes involved in OA. Further functional studies are warranted to confirm these findings.

Biomarker identification for Alzheimer's disease through integration of comprehensive Mendelian randomization and proteomics data

BACKGROUND

Alzheimer's disease (AD) is the main cause of dementia with few effective therapies. We aimed to identify potential plasma biomarkers or drug targets for AD by investigating the causal association between plasma proteins and AD by integrating comprehensive Mendelian randomization (MR) and multi-omics data.

METHODS

Using two-sample MR, cis protein quantitative trait loci (cis-pQTLs) for 1,916 plasma proteins were used as an exposure to infer their causal effect on AD liability in individuals of European ancestry, with two large-scale AD genome-wide association study (GWAS) datasets as the outcome for discovery and replication. Significant causal relationships were validated by sensitivity analyses, reverse MR analysis, and Bayesian colocalization analysis. Additionally, we investigated the causal associations at the transcriptional level with cis gene expression quantitative trait loci (cis-eQTLs) data across brain tissues and blood in European ancestry populations, as well as causal plasma proteins in African ancestry populations.

RESULTS

In those of European ancestry, the genetically predicted levels of five plasma proteins (BLNK, CD2AP, GRN, PILRA, and PILRB) were causally associated with AD. Among these five proteins, GRN was protective against AD, while the rest were risk factors. Consistent causal effects were found in the brain for cis-eQTLs of GRN, BLNK, and CD2AP, while the same was true for PILRA in the blood. None of the plasma proteins were significantly associated with AD in persons of African ancestry.

CONCLUSIONS

Comprehensive MR analyses with multi-omics data identified five plasma proteins that had causal effects on AD, highlighting potential biomarkers or drug targets for better diagnosis and treatment for AD.

Protein Drug Targets for Abdominal Aortic Aneurysm and Proteomic Associations Between Modifiable Risk Factors and Abdominal Aortic Aneurysm

BACKGROUND

Abdominal aortic aneurysm (AAA) is a severe aortic disease for which no pharmacological interventions have yet been developed. This investigation focused on identifying protein-based therapeutic targets and assessing how proteins mediate the interplay between modifiable risk factors and AAA development.

METHODS

Causal inferences between plasma proteins and AAA were drawn using 2-sample Mendelian randomization, followed by comprehensive sensitivity testing, colocalization, and replication efforts. Further analyses included database interrogation, single-cell RNA data analysis, enrichment analysis, protein-protein interaction networks, and immunohistochemistry to map the tissue-specific expression of these proteins, their expression within AAA tissues, and their biological roles. Mediation Mendelian randomization was employed to evaluate the mediating effects of AAA-related proteins on the associations between AAA and 3 risk factors: hypertension, smoking, and obesity.

RESULTS

A total of 43 proteins were identified as having causal links to AAA. Colocalization analysis pinpointed 13 proteins with strong evidence of colocalization with AAA. Of these, the causal involvement of 10 proteins was substantiated by external validation data. Consistent evidence for PCSK9 (proprotein convertase subtilisin/kexin type 9), IL6R (interleukin-6R), ECM1 (extracellular matrix protein 1), and ANGPTL4 (angiopoietin-related protein 4) was further validated through tissue immunohistochemistry and blood data. Moreover, Mendelian randomization analysis identified 10 proteins as mediators of the influence of hypertension, smoking, and obesity on AAA development.

CONCLUSIONS

This analysis identifies 4 proteins (PCSK9, IL6R, ECM1, and ANGPTL4) as high-priority therapeutic targets for AAA and emphasizes the intermediary role of plasma proteins in linking hypertension, smoking, obesity, and AAA. Further investigations are needed to clarify the specific roles of these proteins in AAA pathology.

Testosterone Supplementation: A Potential Therapeutic Strategy for Amyotrophic Lateral Sclerosis

Objectives: Amyotrophic lateral sclerosis (ALS) is a progressive and fatal disease characterized by the degeneration of spinal cord and brain neurons. Proteomics combined with Mendelian randomization (MR) is an effective method for finding disease treatment targets. Methods: We aimed to seek new therapeutic targets for ALS. A large-scale GWAS on proteomics (4907 circulatory protein) with 35,559 individuals was included as the exposure data; a GWAS with 138,086 ALS patients was used as the outcome data; we found that a high level of sex hormone-binding globulin (SHBG) is a risk factor by MR analysis. Colocalization analyses were used to validate the causality between SHBG and ALS further. Functional enrichment found a high level of SHBG was associated with a low level of bioavailable testosterone. Two-sample MR confirmed the association of SHBG (400,210 samples), bioavailable testosterone (367,289 samples), and ALS. Results: A high level of SHBG, and a low level of bioavailable testosterone are risk factors for ALS. Conclusions: A low level of bioavailable testosterone is a risk factor for ALS. Although our study is relatively limited and cannot fully confirm that testosterone supplementation has a therapeutic effect on ALS, it offers a promising direction for ALS therapy.

Local genetic covariance analysis with lipid traits identifies novel loci for early-onset Alzheimer's Disease

The genetic component of early-onset Alzheimer disease (EOAD), accounting for ~10% of all Alzheimer's disease (AD) cases, is largely unexplained. Recent studies suggest that EOAD may be enriched for variants acting in the lipid pathway. The current study examines the shared genetic heritability between EOAD and the lipid pathway using genome-wide multi-trait genetic covariance analyses. Summary statistics were obtained from the GWAS meta-analyses of EOAD by the Alzheimer's Disease Genetics Consortium (n=19,668) and five blood lipid traits by the Global Lipids Genetics Consortium (n=1,320,016). The significant results were compared between the EOAD and lipids GWAS and genetic covariance analyses were performed via SUPERGNOVA. Genes in linkage disequilibrium (LD) with top EOAD hits in identified regions of covariance with lipid traits were scored and ranked for causality by combining evidence from gene-based analysis, AD-risk scores incorporating transcriptomic and proteomic evidence, eQTL data, eQTL colocalization analyses, DNA methylation data, and single-cell RNA sequencing analyses. Direct comparison of GWAS results showed 5 loci overlapping between EOAD and at least one lipid trait harboring APOE, TREM2, MS4A4E, LILRA5, and LRRC25. Local genetic covariance analyses identified 3 regions of covariance between EOAD and at least one lipid trait. Gene prioritization nominated 3 likely causative genes at these loci: ANKDD1B, CUZD1, and MS4A64.The current study identified genetic covariance between EOAD and lipids, providing further evidence of shared genetic architecture and mechanistic pathways between the two traits.

Cardio-metabolic-related plasma proteins reveal biological links between cardiovascular diseases and fragility fractures: a cohort and Mendelian randomisation investigation

BACKGROUND

How cardiovascular diseases (CVD) predispose to a higher risk of fragility fractures is not well understood. Both contribute to significant components of disease burden and health expenditure. Poor bone quality, central obesity, sarcopenia, falls, and low grip strength are independent risk factors for hip and other fragility fractures and also for CVD and early death.

METHODS

We used proteomics and a cohort design combined with Mendelian randomisation analysis to understand shared mechanisms for developing CVD and fragility fractures, two significant sources of disease burden and health expenditure. We primarily aimed to discover and replicate the association of 274 cardio-metabolic-related proteins with future rates of hip and any fracture in two separate population-based cohorts, with a total of 12,314 women and men.

FINDINGS

The average age at baseline was 68 years in the discovery cohort of women and 74 years in the mixed-sex replication cohort. During 100,619 person-years of follow-up, 2168 had any fracture, and 538 had a hip fracture. Our analysis resulted in 24 cardiometabolic proteins associated with fracture risk: 20 with hip fracture, 9 with any fracture, and 5 with both. The associations remained even if protein concentrations were measured from specimens taken during preclinical stages of cardio-metabolic diseases, and 19 associations remained after adjustment for bone mineral density. Twenty-two of the proteins were associated with total body fat mass or lean body mass. Mendelian randomisation (MR) analysis supported causality since genetically predicted levels of SOST (Sclerostin), CCDC80 (Coiled-coil domain-containing protein 80), NT-proBNP (N-terminal prohormone brain natriuretic peptide), and BNP (Brain natriuretic peptide) were associated with risk of hip fracture. MR analysis also revealed a possible negative impact on bone mineral density (BMD) by genetically predicted higher levels of SOST, CCDC80, and TIMP4 (Metalloproteinase inhibitor 4). The MR association with BMD was positive for PTX3 (Pentraxin-related protein) and SPP1 (Osteopontin). Genetically predicted higher concentrations of SOST and lower concentrations of SPP1 also conferred a higher risk of falls and lowered grip strength. The genetically determined concentration of nine proteins influenced fat mass, and one influenced lean body mass.

INTERPRETATION

These data reveal biological links between cardiovascular diseases and fragility fractures. The proteins should be further evaluated as shared targets for developing pharmacological interventions to prevent fractures and cardiovascular disease.

FUNDING

The study was supported by funding from the Swedish Research Council (https://www.vr.se; grants No. 2015-03257, 2017-00644, 2017-06100, and 2019-01291 to Karl Michaëlsson) and funding from Olle Engkvist Byggmästares stiftelse (SOEB).

Cell type-specific epigenetic regulatory circuitry of coronary artery disease loci

Coronary artery disease (CAD) is the leading cause of death worldwide. Recently, hundreds of genomic loci have been shown to increase CAD risk, however, the molecular mechanisms underlying signals from CAD risk loci remain largely unclear. We sought to pinpoint the candidate causal coding and non-coding genes of CAD risk loci in a cell type-specific fashion. We integrated the latest statistics of CAD genetics from over one million individuals with epigenetic data from 45 relevant cell types to identify genes whose regulation is affected by CAD-associated single nucleotide variants (SNVs) via epigenetic mechanisms. Applying two statistical approaches, we identified 1,580 genes likely involved in CAD, about half of which have not been associated with the disease so far. Enrichment analysis and phenome-wide association studies linked the novel candidate genes to disease-specific pathways and CAD risk factors, corroborating their disease relevance. We showed that CAD-SNVs are enriched to regulate gene expression by affecting the binding of transcription factors (TFs) with cellular specificity. Of all the candidate genes, 23.5% represented non-coding RNAs (ncRNA), which likewise showed strong cell type specificity. We conducted a proof-of-concept biological validation for the novel CAD ncRNA gene IQCH-AS1 . CRISPR/Cas9-based gene knockout of IQCH-AS1 , in a human preadipocyte strain, resulted in reduced preadipocyte proliferation, less adipocyte lipid accumulation, and atherogenic cytokine profile. The cellular data are in line with the reduction of IQCH-AS1 in adipose tissues of CAD patients and the negative impact of risk alleles on its expression, suggesting IQCH-AS1 to be protective for CAD. Our study not only pinpoints CAD candidate genes in a cell type-specific fashion but also spotlights the roles of the understudied ncRNA genes in CAD genetics.

Genetic insights into the shared molecular mechanisms of Crohn's disease and breast cancer: a Mendelian randomization and deep learning approach

The objective of this study was to explore the potential genetic link between Crohn's disease and breast cancer, with a focus on identifying druggable genes that may have therapeutic relevance. We assessed the causal relationship between these diseases through Mendelian randomization and investigated gene-drug interactions using computational predictions. This study sought to identify common genetic pathways possibly involved in immune responses and cancer progression, providing a foundation for future targeted treatment research. The dataset comprises single nucleotide polymorphisms used as instrumental variables for Crohn's disease, analyzed to explore their possible impact on breast cancer risk. Gene ontology and pathway enrichment analyses were conducted to identify genes shared between the two conditions, supported by protein-protein interaction networks, colocalization analyses, and deep learning-based predictions of gene-drug interactions. The identified hub genes and predicted gene-drug interactions offer preliminary insights into possible therapeutic targets for breast cancer and immune-related conditions. This dataset may be valuable for researchers studying genetic links between autoimmune diseases and cancer and for those interested in the early identification of potential drug targets.

Insights Into Causal Effects of Genetically Proxied Lipids and Lipid-Modifying Drug Targets on Cardiometabolic Diseases

BACKGROUND

The differential impact of serum lipids and their targets for lipid modification on cardiometabolic disease risk is debated. This study used Mendelian randomization to investigate the causal relationships and underlying mechanisms.

METHODS

Genetic variants related to lipid profiles and targets for lipid modification were sourced from the Global Lipids Genetics Consortium. Summary data for 10 cardiometabolic diseases were compiled from both discovery and replication data sets. Expression quantitative trait loci data from relevant tissues were employed to evaluate significant lipid-modifying drug targets. Comprehensive analyses including colocalization, mediation, and bioinformatics were conducted to validate the results and investigate potential mediators and mechanisms.

RESULTS

Significant causal associations were identified between lipids, lipid-modifying drug targets, and various cardiometabolic diseases. Notably, genetic enhancement of LPL (lipoprotein lipase) was linked to reduced risks of myocardial infarction (odds ratio [OR]1, 0.65 [95% CI, 0.57-0.75], P1=2.60×10-9; OR2, 0.59 [95% CI, 0.49-0.72], P2=1.52×10-7), ischemic heart disease (OR1, 0.968 [95% CI, 0.962-0.975], P1=5.50×10-23; OR2, 0.64 [95% CI, 0.55-0.73], P2=1.72×10-10), and coronary heart disease (OR1, 0.980 [95% CI, 0.975-0.985], P1=3.63×10-14; OR2, 0.64 [95% CI, 0.54-0.75], P2=6.62×10-8) across 2 data sets. Moreover, significant Mendelian randomization and strong colocalization associations for the expression of LPL in blood and subcutaneous adipose tissue were linked with myocardial infarction (OR, 0.918 [95% CI, 0.872-0.967], P=1.24×10-3; PP.H4, 0.99) and coronary heart disease (OR, 0.991 [95% CI, 0.983-0.999], P=0.041; PP.H4=0.92). Glucose levels and blood pressure were identified as mediators in the total effect of LPL on cardiometabolic outcomes.

CONCLUSIONS

The study substantiates the causal role of lipids in specific cardiometabolic diseases, highlighting LPL as a potent drug target. The effects of LPL are suggested to be influenced by changes in glucose and blood pressure, providing insights into its mechanism of action.

A brain DNA co-methylation network analysis of psychosis in Alzheimer's disease

INTRODUCTION

The presence of psychosis in Alzheimer's disease (AD) is suggested to be associated with distinct molecular and neuropathological profiles in the brain.

METHODS

We assessed brain DNA methylation in AD donors with psychosis (AD+P) and without psychosis (AD-P) using the EPIC array. Weighted gene correlation network analysis identified modules of co-methylated genes in a discovery cohort (PITT-ADRC: N = 113 AD+P, N = 40 AD-P), with validation in an independent cohort (BDR: N = 79 AD+P, N = 117 AD-P), with Gene Ontology and cell-type enrichment analysis. Genetic data were integrated to identify methylation quantitative trait loci (mQTLs), which were co-localized with GWAS for related traits.

RESULTS

We replicated one AD+P associated module, which was enriched for synaptic pathways and in excitatory and inhibitory neurons. mQTLs in this module co-localized with variants associated with schizophrenia and educational attainment.

DISCUSSION

This represents the largest epigenetic study of AD+P to date, identifying pleiotropic relationships between AD+P and related traits.

HIGHLIGHTS

DNA methylation was assessed in the prefrontal cortex in subjects with AD+P and AD-P. WGCNA identified six modules of co-methylated loci associated with AD+P in a discovery cohort. One of the modules was replicated in an independent cohort. This module was enriched for synaptic genes and in excitatory and inhibitory neurons. mQTLs mapping to genes in the module co-localized with GWAS loci for schizophrenia and educational attainment.

Genetic architecture of RNA editing, splicing and gene expression in schizophrenia

Genome wide association studies (GWAS) have been conducted over the past decades to investigate the underlying genetic origin of neuropsychiatric diseases, such as schizophrenia (SCZ). While these studies demonstrated the significance of disease-phenotype associations, there is a pressing need to fully characterize the functional relevance of disease-associated genetic variants. Functional genetic loci can affect transcriptional and post-transcriptional phenotypes that may contribute to disease pathology. Here, we investigate the associations between genetic variation and RNA editing, splicing, and overall gene expression through identification of quantitative trait loci (QTL) in the CommonMind Consortium SCZ cohort. We find that editing QTL (edQTL), splicing QTL (sQTL) and expression QTL (eQTL) possess both unique and common gene targets, which are involved in many disease-relevant pathways, including brain function and immune response. We identified two QTL that fall into all three QTL categories (seedQTL), one of which, rs146498205, targets the lincRNA gene, RP11-156P1.3. In addition, we observe that the RNA binding protein AKAP1, with known roles in neuronal regulation and mitochondrial function, had enriched binding sites among edQTL, including the seedQTL, rs146498205. We conduct colocalization with various brain disorders and find that all QTL have top colocalizations with SCZ and related neuropsychiatric diseases. Furthermore, we identify QTL within biologically relevant GWAS loci, such as in ELA2, an important tRNA processing gene associated with SCZ risk. This work presents the investigation of multiple QTL types in parallel and demonstrates how they target both distinct and overlapping SCZ-relevant genes and pathways.

Low CD3 level is a risk factor for amyotrophic lateral sclerosis: a Mendelian randomization study

Amyotrophic lateral sclerosis (ALS) is a progressive and fatal disease characterized by neuronal degeneration of the spinal cord and brain and believed to be related to the immune system. In this study, our aim is to use Mendelian randomization (MR) to search for immune markers related to ALS. A total of 731 immune cell traits were included in this study. MR analysis was used to identify the causality between 731 immune cell traits (with 3,757 Europeans) and ALS (with 138,086 Europeans). Colocalization analysis was used to verify the found causality, protein-protein interaction prediction was used to look for the interacting proteins that are known to be involved in ALS. We found low expression levels of CD3 on central memory CD8+ T cell is risk factor for ALS (OR = 0.90, 95% CI: 0.86-0.95, P = 0.0000303). CD3 can interact with three ALS-related proteins: VCP, HLA-DRA and HLA-DRB5, which are associated with adaptive immune response. Our study reported for the first time that low-level CD3 is a risk factor for ALS and the possible mechanism, which could provide a potential strategy for ALS diagnosis and therapy.

Human Genetic Evidence to Inform Clinical Development of IL-6 Signaling Inhibition for Abdominal Aortic Aneurysm

BACKGROUND

Abdominal aortic aneurysm (AAA) represents a significant cause of mortality, yet no medical therapies have proven efficacious. The aim of the current study was to leverage human genetic evidence to inform clinical development of IL-6 (interleukin-6) signaling inhibition for the treatment of AAA.

METHODS

Associations of rs2228145, a missense variant in the IL6R gene region, are expressed per additional copy of the C allele, corresponding to the genetically predicted effect of IL-6 signaling inhibition. We consider genetic associations with AAA risk in the AAAgen consortium (39 221 cases and 1 086 107 controls) and UK Biobank (1963 cases and 365 680 controls). To validate against known effects of IL-6 signaling inhibition, we present associations with rheumatoid arthritis, polymyalgia rheumatica, and severe COVID-19. To explore mechanism specificity, we present associations with thoracic aortic aneurysm, intracranial aneurysm, and coronary artery disease. We further explored genetic associations in clinically relevant subgroups of the population.

RESULTS

We observed strong genetic associations with AAA risk in the AAAgen consortium, UK Biobank, and FinnGen (odds ratios: 0.91 [95% CI, 0.90-0.92], P=4×10-30; 0.90 [95% CI, 0.84-0.96], P=0.001; and 0.86 [95% CI, 0.82-0.91], P=7×10-9, respectively). The association was similar for fatal AAA but with greater uncertainty due to the lower number of events. The association with AAA was of greater magnitude than associations with coronary artery disease and even rheumatological disorders for which IL-6 inhibitors have been approved. No strong associations were observed with thoracic aortic aneurysm or intracranial aneurysm. Associations attenuated toward the null in populations with concomitant rheumatological or connective tissue disease.

CONCLUSIONS

Inhibition of IL-6 signaling is a promising strategy for treating AAA but not other types of aneurysmal disease. These findings serve to help inform clinical development of IL-6 signaling inhibition for AAA treatment.

Genetic factors shaping the plasma lipidome and the relations to cardiometabolic risk in children and adolescents

BACKGROUND

Lipid species are emerging as biomarkers for cardiometabolic risk in both adults and children. The genetic regulation of lipid species and their impact on cardiometabolic risk during early life remain unexplored.

METHODS

Using mass spectrometry-based lipidomics, we measured 227 plasma lipid species in 1149 children and adolescents (44.8% boys) with a median age of 11.2 years. We performed genome-wide association analyses to identify genetic variants influencing lipid species. Colocalisation and Mendelian randomisation (MR) analyses were performed to infer causality between lipid species and cardiometabolic outcomes.

FINDINGS

We identified 37 genome-wide significant loci for 52 lipid species, nine of which are previously unreported. Colocalisation analyses revealed that seven lipid loci shared genetic variants associated with adult cardiometabolic outcomes. One-sample MR analysis identified positive causal associations between ceramides and liver enzymes, sphingomyelins and hemoglobin A1c (HbA1c), and phosphatidylethanolamines and high-sensitivity C-reactive protein in children and adolescents. Two-sample MR using adult-based summary statistics showed consistent direction of associations and indicated additional causal links, specifically between ceramides and elevated HbA1c levels, and phosphatidylinositols with elevated liver enzymes.

INTERPRETATION

These findings highlight the potential long-term implications of plasma lipid genetic determinants on cardiometabolic risk.

FUNDING

Novo Nordisk Foundation, The Innovation Fund Denmark, The Danish Heart Foundation, EU Horizon, and LundbeckFonden.

Exploring the role of circulating proteins in multiple myeloma risk: a Mendelian randomization study

Multiple myeloma (MM) is an incurable blood cancer with unclear aetiology. Proteomics is a valuable tool in exploring mechanisms of disease. We investigated the causal relationship between circulating proteins and MM risk, using two of the largest cohorts with proteomics data to-date. We performed bidirectional two-sample Mendelian randomization (MR; forward MR = causal effect estimation of proteins and MM risk; reverse MR = causal effect estimation of MM risk and proteins). Summary statistics for plasma proteins were obtained from genome-wide association studies performed using SomaLogic (N = 35,559; deCODE) and Olink (N = 34,557; UK Biobank; UKB) proteomic platforms and for MM risk from a meta-analysis of UKB and FinnGen (case = 1649; control = 727,247) or FinnGen only (case = 1085; control = 271,463). Cis-SNPs associated with protein levels were used to instrument circulating proteins. We evaluated proteins for the consistency of directions of effect across MR analyses (with 95% confidence intervals not overlapping the null) and corroborating evidence from genetic colocalization. In the forward MR, 994 (SomaLogic) and 1570 (Olink) proteins were instrumentable. 440 proteins were analysed in both deCODE and UKB; 302 (69%) of these showed consistent directions of effect in the forward MR. Seven proteins had 95% confidence intervals (CIs) that did not overlap the null in both forward MR analyses and did not have evidence for an effect in the reverse direction: higher levels of dermatopontin (DPT), beta-crystallin B1 (CRYBB1), interleukin-18-binding protein (IL18BP) and vascular endothelial growth factor receptor 2 (KDR) and lower levels of odorant-binding protein 2b (OBP2B), glutamate-cysteine ligase regulatory subunit (GCLM) and gamma-crystallin D (CRYGD) were implicated in increasing MM risk. Evidence from genetic colocalization did not meet our threshold for a shared causal signal between any of these proteins and MM risk (h4 < 0.8). Our results highlight seven circulating proteins which may be involved in MM risk. Although evidence from genetic colocalization suggests these associations may not be robust to the effects of horizontal pleiotropy, these proteins may be useful markers of MM risk. Future work should explore the utility of these proteins in disease prediction or prevention using proteomic data from patients with MM or precursor conditions.

Multi-omics analysis reveals novel causal pathways in psoriasis pathogenesis

BACKGROUND

To elucidate the genetic and molecular mechanisms underlying psoriasis by employing an integrative multi-omics approach, using summary-data-based Mendelian randomization (SMR) to infer causal relationships among DNA methylation, gene expression, and protein levels in relation to psoriasis risk.

METHODS

We conducted SMR analyses integrating genome-wide association study (GWAS) summary statistics with methylation quantitative trait loci (mQTL), expression quantitative trait loci (eQTL), and protein quantitative trait loci (pQTL) data. Publicly available datasets were utilized, including psoriasis GWAS data from the European Molecular Biology Laboratory-European Bioinformatics Institute and the UK Biobank. Heterogeneity in dependent instruments (HEIDI) test and colocalization analyses were performed to identify shared causal variants, and multi-omics integration was employed to construct potential regulatory pathways.

RESULTS

Our analyses identified significant causal associations between DNA methylation, gene expression, protein abundance, and psoriasis risk. We discovered two pathways involving the long non-coding RNA RP11-977G19.11 and apolipoprotein F (APOF). Methylation at sites cg26804944 and cg02705573 was negatively associated with RP11-977G19.11 expression. Reduced expression of RP11-977G19.11 was linked to increased APOF levels, which were positively associated with a higher risk of psoriasis. Methylation at sites cg00172967, cg00294382, and cg24773560 was positively associated with RP11-977G19.11 expression. Elevated expression of RP11-977G19.11 was associated with decreased APOF levels, reducing the risk of psoriasis. Colocalization analysis highlighted APOF as a key protein in psoriasis pathogenesis. Validation using skin tissue, EBV-transformed lymphocytes data and inflammation-related protein panels confirmed the associations of RP11-977G19.11 and APOF with psoriasis.

CONCLUSIONS

Our multi-omics analysis provides preliminary evidence for potential molecular mechanisms in psoriasis pathogenesis. Through the integration of GWAS and molecular QTL data, we identify candidate pathways that may be relevant to disease biology. While these findings require extensive experimental validation, they offer a framework for future investigations into the molecular basis of psoriasis.

Mapping the Cerebrospinal Fluid Proteome in Bipolar Disorder

BACKGROUND

Bipolar disorder (BD) is a severe psychiatric condition with unclear etiology and no established biomarkers. Here, we aimed to characterize the cerebrospinal fluid (CSF) proteome in euthymic individuals with BD to identify potential protein biomarkers.

METHODS

We used nano-flow liquid chromatography coupled to high-resolution mass spectrometry to quantify over 2000 CSF proteins in 374 individuals from two independent clinical cohorts (n = 164 cases + 89 controls and 66 cases + 55 controls, respectively). A subset of the cases was followed longitudinally and reexamined after a median of 6.5 years.

RESULTS

Differential abundance analysis revealed 41 proteins with robust case-control association in both cohorts. These included lower levels of synaptic proteins (e.g., APP, CLSTN1, NPTX2, NRXN1) and axon guidance and cell adhesion molecules (e.g., NEO1, NCAM1, SEMA7A) and higher levels of blood-brain barrier integrity proteins (e.g., VTN, SERPIN3) and complement components (e.g., C1RL, C3, C5). The findings were consistently driven by the BD type 1 subtype. Comparing BD type 1 participants with control participants increased discoverability, revealing 86 replicated associations despite a loss of statistical power. Moreover, longitudinal analyses of coexpression modules revealed dynamic changes in the CSF proteome composition that correlated with clinical outcomes, including disease severity, future manic episodes, and symptom improvement. Finally, we conducted association analyses of CSF proteins with genetic risk loci for BD and schizophrenia.

CONCLUSIONS

This study represents the first large-scale untargeted profiling of the CSF proteome in BD, unveiling potential biomarkers and providing in vivo support for altered synaptic and brain connectivity processes, impaired neurovascular integrity, and complement activation in the pathology of BD.

Investigating the shared genetic links between hypothyroidism and psychiatric disorders: a large-scale genomewide cross-trait analysis

BACKGROUND

Associations between thyroid diseases and psychiatric disorders have been mainly described before. However, the genetic mechanism behind hypothyroidism and psychiatric disorders remains unexplained.

METHODS

We examined the genetic architecture of hypothyroidism and 8 psychiatric disorders. Firstly, the global and local genetic relationship between the paired traits was explored. Secondly, cross-trait analysis was performed to investigate the genomic loci and genes between psychiatric disorders and hypothyroidism. Thirdly, the significant expression of these genes and the causal relationships were investigated. Lastly, enrichment analysis was conducted on these genes to explore their biological mechanisms.

RESULTS

We observed significant positive genetic correlations between psychiatric disorders and hypothyroidism. The cross-trait meta-analysis identified 62 shared genetic loci between hypothyroidism and psychiatric disorders. The colocalization analysis additionally revealed 15 potential pleiotropic loci with a posterior probabilities.H4 (PP·H4) value >0.7. We also found 2308 genes shared between both traits, which were highly enriched in biological pathways such as immune cell differentiation and autoimmune diseases, as well as in tissue structures like the frontal cortex and cerebral cortex. Especially, many pleiotropic genes were significantly expressed for multiple pairwise traits, such as BCL11B, RERE, and SUOX. Lastly, the Latent causal variable model (LCV) analysis did not find any causal components in the genetic structure between them.

LIMITATIONS

The limitations of this study include that the conclusions were drawn from a European population.

CONCLUSIONS

These findings not only deepens our understanding of their biological mechanisms but also has significant implications for the intervention and treatment of these diseases.

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.

Genome-wide association study reveals shared and distinct genetic architecture of fatty acids and oxylipins in the Hispanic Community Health Study/Study of Latinos

Bioactive fatty acid-derived oxylipin molecules play key roles mediating inflammation and oxidative stress. Circulating levels of fatty acids and oxylipins are influenced by environmental and genetic factors; characterizing the genetic architecture of bioactive lipids could yield new insights into underlying biology. We performed a genome-wide association study (GWAS) of 81 fatty acids and oxylipins in 11,584 Hispanic Community Health Study/Study of Latinos (HCHS/SOL) participants with genetic and lipidomic data measured at study baseline (58.6% female, mean age = 46.1 years (standard deviation 13.8)). Additionally, given the effects of central obesity on inflammation, we examined interactions with waist circumference using two-degree-of-freedom joint tests. Thirty-three of the 81 oxylipins and fatty acids were significantly heritable (heritability range: 0-32.7%). Forty (49.4%) oxylipins and fatty acids had at least one genome-wide significant (p < 6.94E-11) variant resulting in 19 independent genetic loci. Six loci (lead variant minor allele frequency [MAF] range: 0.08-0.50), including desaturase-encoding FADS and OATP1B1 transporter protein-encoding SLCO1B1, exhibited associations with two or more fatty acids and oxylipins. At several of these loci, there was evidence of colocalization of the top variant across fatty acids and oxylipins. The remaining loci were only associated with one oxylipin or fatty acid and included several CYP loci. We also identified an additional rare variant (MAF = 0.002) near CARS2 in two-degree-of-freedom tests. Our analyses revealed shared and distinct genetic architecture underlying fatty acids and oxylipins, providing insights into genetic factors and motivating work to characterize these compounds and elucidate their roles in disease.

Metformin and the risk of malignant tumors of digestive system: a mendelian randomization study

BACKGROUND

Observational studies suggest that metformin may reduce the risk of malignant tumors of the digestive system (MTDS), but these findings are often confounded by bias and unmeasured variables. Recent meta-analyses have questioned these associations, emphasizing the need for robust causal inference.

METHODS

Mendelian randomization (MR) was used to evaluate the causal relationship between metformin and MTDS. Genetic variants associated with metformin's molecular targets were selected from GTEx, eQTLGen, and UK Biobank and validated using genetic colocalization to ensure instrument validity. GWAS summary statistics for MTDS, encompassing up to 314,193 controls and 6,847 colorectal cancer cases, were obtained from FinnGen and EBI. The primary analysis employed the inverse-variance weighted (IVW) method, supplemented by MR-Egger, weighted median, and weighted mode analyses. Bonferroni correction was applied to adjust for multiple testing across 14 cancer types.

RESULTS

Genetically proxied metformin use was associated with an increased risk of colorectal cancer (OR = 2.38, 95%CI = 1.38-4.09, P = 0.0018) and related subtypes. No causal relationship was found for hepatocellular carcinoma, gastric cancer, pancreatic cancer, or other digestive system cancers. The robustness of these findings was supported by sensitivity analyses, which indicated no significant pleiotropy, and leave-one-out tests.

CONCLUSION

This study provides robust genetic evidence that metformin use increases the risk of colorectal cancer, challenging its role as a preventive agent for digestive cancers. These findings emphasize the need for clinicians to carefully evaluate the risks and benefits of metformin, particularly in populations at higher risk for colorectal cancer. Future research should focus on delineating the mechanisms underlying this association to optimize the use of metformin in clinical practice.

Evaluating the Causal Effect of Circulating Proteome on Glycemic Traits: Evidence From Mendelian Randomization

Exploring the mechanisms underlying abnormal glycemic traits is important for deciphering type 2 diabetes and characterizing novel drug targets. This study aimed to decipher the causal associations of circulating proteins with fasting glucose (FG), 2-h glucose after an oral glucose challenge (2hGlu), fasting insulin (FI), and glycated hemoglobin (HbA1c) using large-scale proteome-wide Mendelian randomization (MR) analyses. Genetic data on plasma proteomes were obtained from 10 proteomic genome-wide association studies. Both cis-protein quantitative trait loci (pQTLs) and cis + trans-pQTLs MR analyses were conducted. Bayesian colocalization, Steiger filtering analysis, assessment of protein-altering variants, and mapping expression QTLs to pQTLs were performed to investigate the reliability of the MR findings. Protein-protein interaction, pathway enrichment analysis, and evaluation of drug targets were performed. Thirty-three proteins were identified with causal effects on FG, FI, or HbA1c but not 2hGlu in the cis-pQTL analysis, and 93 proteins had causal effects on glycemic traits in the cis + trans-pQTLs analysis. Most proteins were either considered druggable or drug targets. In conclusion, many novel circulating protein biomarkers were identified to be causally associated with glycemic traits. These biomarkers enhance the understanding of molecular etiology and provide insights into the screening, monitoring, and treatment of diabetes.

ARTICLE HIGHLIGHTS

Adipose tissue eQTL meta-analysis highlights the contribution of allelic heterogeneity to gene expression regulation and cardiometabolic traits

Complete characterization of the genetic effects on gene expression is needed to elucidate tissue biology and the etiology of complex traits. In the present study, we analyzed 2,344 subcutaneous adipose tissue samples and identified 34,774 conditionally distinct expression quantitative trait locus (eQTL) signals at 18,476 genes. Over half of eQTL genes exhibited at least two eQTL signals. Compared with primary eQTL signals, nonprimary eQTL signals had lower effect sizes, lower minor allele frequencies and less promoter enrichment; they corresponded to genes with higher heritability and higher tolerance for loss of function. Colocalization of eQTLs with genome-wide association study (GWAS) signals for 28 cardiometabolic traits identified 1,835 genes. Inclusion of nonprimary eQTL signals increased discovery of colocalized GWAS-eQTL signals by 46%. Furthermore, 21 genes with ≥2 colocalized GWAS-eQTL signals showed a mediating gene dosage effect on the GWAS trait. Thus, expanded eQTL identification reveals more mechanisms underlying complex traits and improves understanding of the complexity of gene expression regulation.

Identification of shared genetic etiology of cardiovascular and cerebrovascular diseases through common cardiometabolic risk factors

Cardiovascular diseases (CVDs) and cerebrovascular diseases (CeVDs) are closely related vascular diseases, sharing common cardiometabolic risk factors (RFs). Although pleiotropic genetic variants of these two diseases have been reported, their underlying pathological mechanisms are still unclear. Leveraging GWAS summary data and using genetic correlation, pleiotropic variants identification, and colocalization analyses, we identified 11 colocalized loci for CVDs-CeVDs-BP (blood pressure), CVDs-CeVDs-LIP (lipid traits), and CVDs-CeVDs-cIMT (carotid intima-media thickness) triplets. No shared causal loci were found for CVDs-CeVDs-T2D (type 2 diabetes) or CVDs-CeVDs-BMI (body mass index) triplets. The 11 loci were mapped to 12 genes, namely CASZ1, CDKN1A, TWIST1, CDKN2B, ABO, SWAP70, SH2B3, LRCH1, FES, GOSR2, RPRML, and LDLR, where both GOSR2 and RPRML were mapped to one locus. They were enriched in pathways related to cellular response to external stimulus and regulation of the phosphate metabolic process and were highly expressed in endothelial cells, epithelial cells, and smooth muscle cells. Multi-omics analysis revealed methylation of two genes (CASZ1 and LRCH1) may play a causal role in the genetic pleiotropy. Notably, these pleiotropic loci are highly enriched in the targets of antihypertensive drugs, which further emphasizes the role of the blood pressure regulation pathway in the shared etiology of CVDs and CeVDs.

The impact of immunity on the risk of coronary artery disease: insights from a multiomics study

BACKGROUND

Immune inflammation is intricately associated with coronary artery disease (CAD) progression, necessitating the pursuit of more efficacious therapeutic strategies. This study aimed to uncover potential therapeutic targets for CAD and myocardial infarction (MI) by elucidating the causal connection between regulatory immune-related genes (RIRGs) and these disorders.

METHODOLOGY

We performed summary data-based Mendelian randomization analysis to assess the therapeutic targets linked to expression quantitative trait loci and methylation quantitative trait loci of RIRGs in relation to CAD and MI. Independent validation cohorts and datasets from coronary artery and left ventricular heart tissue were analyzed. To strengthen causal inference, colocalization analysis and PhenoScanner phenotype scans were employed.

RESULTS

Utilizing multiomics integration, we pinpointed EIF2B2, FCHO1, and DDT as CAD risk genes. Notably, EIF2B2 and FCHO1 displayed significant associations with MI. High EIF2B2 expression, regulated by cg16144293, heightened CAD and MI risk at rs175438. In contrast, enhanced FCHO1 expression, modulated by cg18329931, reduced CAD and MI risk at rs13382133. DDT upregulation influenced by cg11060661 and cg09664220 was associated with decreased CAD risk at rs5760120. Colocalization analysis firmly established these relationships.

CONCLUSION

EIF2B2, FCHO1, and DDT represent risk loci for CAD progression within RIRGs. Our identification of these genes enhances understanding of CAD pathogenesis and directs future drug development efforts.

NetREm: Network Regression Embeddings reveal cell-type transcription factor coordination for gene regulation

Motivation

Transcription factor (TF) coordination plays a key role in gene regulation via direct and/or indirect protein-protein interactions (PPIs) and co-binding to regulatory elements on DNA. Single-cell technologies facilitate gene expression measurement for individual cells and cell-type identification, yet the connection between TF-TF coordination and target gene (TG) regulation of various cell types remains unclear.

Results

To address this, we introduce our innovative computational approach, Network Regression Embeddings (NetREm), to reveal cell-type TF-TF coordination activities for TG regulation. NetREm leverages network-constrained regularization, using prior knowledge of PPIs among TFs, to analyze single-cell gene expression data, uncovering cell-type coordinating TFs and identifying revolutionary TF-TG candidate regulatory network links. NetREm's performance is validated using simulation studies and benchmarked across several datasets in humans, mice, yeast. Further, we showcase NetREm's ability to prioritize valid novel human TF-TF coordination links in 9 peripheral blood mononuclear and 42 immune cell sub-types. We apply NetREm to examine cell-type networks in central and peripheral nerve systems (e.g. neuronal, glial, Schwann cells) and in Alzheimer's disease versus Controls. Top predictions are validated with experimental data from rat, mouse, and human models. Additional functional genomics data helps link genetic variants to our TF-TG regulatory and TF-TF coordination networks.

Availability and implementation

https://github.com/SaniyaKhullar/NetREm.

Exploring autism spectrum disorder and co-occurring trait associations to elucidate multivariate genetic mechanisms and insights

BACKGROUND

Autism spectrum disorder (ASD) is a partially heritable neurodevelopmental trait, and people with ASD may also have other co-occurring trait such as ADHD, anxiety disorders, depression, mental health issues, learning difficulty, physical health traits and communication challenges. The concomitant development of ASD and other neurological traits is assumed to result from a complex interplay between genetics and the environment. However, only a limited number of studies have performed multivariate genome-wide association studies (GWAS) for ASD.

METHODS

We conducted to-date the largest multivariate GWAS on ASD and 8 ASD co-occurring traits (ADHD, ADHD childhood, anxiety stress (ASDR), bipolar (BIP), disruptive behaviour (DBD), educational attainment (EA), major depression, and schizophrenia (SCZ)) using summary statistics from leading studies. Multivariate associations and central traits were further identified. Subsequently, colocalization and Mendelian randomization (MR) analysis were performed on the associations identified with the central traits containing ASD. To further validate our findings, pathway and quantified trait loci (QTL) resources as well as independent datasets consisting of 112 (45 probands) whole genome sequence data from the GEMMA project were utilized.

RESULTS

Multivariate GWAS resulted in 637 significant associations (p < 5e-8), among which 322 are reported for the first time for any trait. 37 SNPs were identified to contain ASD and one or more traits in their central trait set, including variants mapped to known SFARI ASD genes MAPT, CADPS and NEGR1 as well as novel ASD genes KANSL1, NSF and NTM, associated with immune response, synaptic transmission, and neurite growth respectively. Mendelian randomization analyses found that genetic liability for ADHD childhood, ASRD and DBT has causal effects on the risk of ASD while genetic liability for ASD has causal effects on the risk of ADHD, ADHD childhood, BIP, WA, MDD and SCZ. Frequency differences of SNPs found in NTM and CADPS genes, respectively associated with neurite growth and neural/endocrine calcium regulation, were found between GEMMA ASD probands and controls. Pathway, QTL and cell type enrichment implicated microbiome, enteric inflammation, and central nervous system enrichments.

CONCLUSIONS

Our study, combining multivariate GWAS with systematic decomposition, identified novel genetic associations related to ASD and ASD co-occurring driver traits. Statistical tests were applied to discern evidence for shared and interpretable liability between ASD and co-occurring traits. These findings expand upon the current understanding of the complex genetics regulating ASD and reveal insights of neuronal brain disruptions potentially driving development and manifestation.