The shared genetic landscape of blood cell traits and risk of neurological and psychiatric disorders

Genome-wide analyses of variance in blood cell phenotypes provide new insights into complex trait biology and prediction

Blood cell phenotypes are routinely tested in healthcare to inform clinical decisions. Genetic variants influencing mean blood cell phenotypes have been used to understand disease aetiology and improve prediction; however, additional information may be captured by genetic effects on observed variance. Here, we mapped variance quantitative trait loci (vQTL), i.e. genetic loci associated with trait variance, for 29 blood cell phenotypes from the UK Biobank (N ~ 408,111). We discovered 176 independent blood cell vQTLs, of which 147 were not found by additive QTL mapping. vQTLs displayed on average 1.8-fold stronger negative selection than additive QTL, highlighting that selection acts to reduce extreme blood cell phenotypes. Variance polygenic scores (vPGSs) were constructed to stratify individuals in the INTERVAL cohort (N ~ 40,466), where the genetically most variable individuals had increased conventional PGS accuracy (by ~19%) relative to the genetically least variable individuals. Genetic prediction of blood cell traits improved by ~10% on average combining PGS with vPGS. Using Mendelian randomisation and vPGS association analyses, we found that alcohol consumption significantly increased blood cell trait variances highlighting the utility of blood cell vQTLs and vPGSs to provide novel insight into phenotype aetiology as well as improve prediction.

Prominent role of PM10 in the link between air pollution and incident Parkinson's Disease

Air pollution has been associated with Parkinson's Disease (PD) risk, although this relationship remains unclear. We estimated yearly levels of exposure to ten air pollutants (period 2006-2018) in an Italian population cohort, the Moli-sani study (N = 24,325; ≥35 years; 51.9% women), and derived three principal components, testing their associations with incident PD risk over 23,841 participants (213 cases, median(IQR) follow-up 11.2(2.0) years). This revealed a statistically significant association of PC1 (explaining 38.2% of common variance, tagging PM10 levels), independent on sociodemographic, professional and lifestyles covariates (Hazard Ratio [95%CI] = 1.04[1.02-1.07]). The association was confirmed testing average PM10 levels during follow-up (18[13-24]% increase of PD risk per 1 μg/m3 increase of PM10). Among different circulating markers, lipoprotein a explained a significant proportion of this association (2.8[0.9; 8.4]%). These findings suggest PM10 as a target to lower PD risk at the population level and a potential implication of lipoprotein a in PD etiology.

Genetic Overlap Between Obstructive Sleep Apnea and Ischemic Stroke: A Large-Scale Genome-Wide Cross-Trait Analysis

Background

To further understand the complex relationship between Obstructive Sleep Apnea (OSA) and ischemic stroke, this study explores the role of genetic factors in the comorbidity of these two conditions.

Methods

Based on large-scale available Genome-Wide Association Studies (GWAS) for OSA and ischemic stroke, we conducted a multi-level cross-trait analysis. First, we utilized Linkage Disequilibrium Score Regression (LDSC) to analyze the genetic correlation between the two diseases. Subsequently, we performed cross-trait analysis to identify pleiotropic Single Nucleotide Polymorphisms (SNPs) associated with both OSA and ischemic stroke. On this basis, we applied annotation and Multi-marker Analysis of GenoMic Annotation (MAGMA) analysis to examine results at the gene level. Finally, we conducted Transcriptome-Wide Association Studies (TWAS) to analyze gene expressions significantly related to both traits.

Results

The LDSC analysis revealed a significant positive genetic correlation between OSA and ischemic stroke. Cross-trait analysis identified a total of 90 pleiotropic SNPs, with rs78581380 being the most significant. Combining Functional Mapping and Annotation (FUMA) annotation and MAGMA analysis, we identified 83 genes in total. TWAS analysis discovered 23 gene expressions that were significantly associated with both OSA and ischemic stroke traits.

Conclusion

This study elucidates the shared genetic architecture between OSA and ischemic stroke, emphasizing the crucial role of genetic factors in the comorbidity of these two conditions.

From blood vessels to brain cells: Connecting the circulatory system and Parkinson's disease

Parkinson's disease (PD) is traditionally recognized as a neurodegenerative disorder characterized by motor dysfunction and α-synuclein protein accumulation in the brain. However, recent research suggests that the circulatory system may also contribute to PD pathogenesis through the spread of α-synuclein beyond the brain. The blood-brain barrier (BBB), a key regulator of molecular exchange between the bloodstream and the brain, may become compromised in PD, allowing harmful substances, including pathogenic forms of α-synuclein, to infiltrate the brain and promote neurodegeneration. Transport mechanisms such as P-glycoprotein and the low-density lipoprotein (LDL) receptor-related protein (LRP-1) further modulate the movement of α-synuclein across the BBB, influencing disease progression. Additionally, extracellular vesicles are emerging as crucial mediators in the dissemination of α-synuclein between the brain and peripheral tissues, facilitating its spread and accumulation. The lymphatic system, responsible for clearing α-synuclein, may also contribute to PD pathology when impaired. This review highlights the growing evidence for a circulatory axis in the initiation and progression of PD. We propose that future research should explore the hypothesis that the circulatory system contributes to the pathogenesis of PD by aiding the distribution of α-synuclein throughout the body.

Scrutinizing neurodegenerative diseases: decoding the complex genetic architectures through a multi-omics lens

Neurodegenerative diseases present complex genetic architectures, reflecting a continuum from monogenic to oligogenic and polygenic models. Recent advances in multi-omics data, coupled with systems genetics, have significantly refined our understanding of how these data impact neurodegenerative disease mechanisms. To contextualize these genetic discoveries, we provide a comprehensive critical overview of genetic architecture concepts, from Mendelian inheritance to the latest insights from oligogenic and omnigenic models. We explore the roles of common and rare genetic variants, gene-gene and gene-environment interactions, and epigenetic influences in shaping disease phenotypes. Additionally, we emphasize the importance of multi-omics layers including genomic, transcriptomic, proteomic, epigenetic, and metabolomic data in elucidating the molecular mechanisms underlying neurodegeneration. Special attention is given to missing heritability and the contribution of rare variants, particularly in the context of pleiotropy and network pleiotropy. We examine the application of single-cell omics technologies, transcriptome-wide association studies, and epigenome-wide association studies as key approaches for dissecting disease mechanisms at tissue- and cell-type levels. Our review introduces the OmicPeak Disease Trajectory Model, a conceptual framework for understanding the genetic architecture of neurodegenerative disease progression, which integrates multi-omics data across biological layers and time points. This review highlights the critical importance of adopting a systems genetics approach to unravel the complex genetic architecture of neurodegenerative diseases. Finally, this emerging holistic understanding of multi-omics data and the exploration of the intricate genetic landscape aim to provide a foundation for establishing more refined genetic architectures of these diseases, enhancing diagnostic precision, predicting disease progression, elucidating pathogenic mechanisms, and refining therapeutic strategies for neurodegenerative conditions.

Genome-wide assessment of shared genetic landscape of idiopathic pulmonary fibrosis and its comorbidities

Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease accompanied by both local and systemic comorbidities. Genetic factors play a role in the development of IPF and certain associated comorbidities. Nevertheless, it is uncertain whether there are shared genetic factors underlying IPF and these comorbidities. To bridge this knowledge gap, we conducted a systematic investigation into the shared genetic architecture between IPF and ten prevalent heritable comorbidities (i.e., body mass index [BMI], coronary artery disease [CAD], chronic obstructive pulmonary disease [COPD], gastroesophageal reflux disease, lung cancer, major depressive disorder [MDD], obstructive sleep apnoea, pulmonary hypertension [PH], stroke, and type 2 diabetes), by utilizing large-scale summary data from their respective genome-wide association studies and multi-omics studies. We revealed significant (false discovery rate [FDR] < 0.05) and moderate genetic correlations between IPF and seven comorbidities, excluding lung cancer, MDD and PH. Evidence suggested a partially putative causal effect of IPF on CAD. Notably, we observed FDR-significant genetic enrichments in lung for the cross-trait between IPF and CAD and in liver for the cross-trait between IPF and COPD. Additionally, we identified 65 FDR-significant genes over-represented in 20 biological pathways related to the etiology of IPF, BMI, and COPD, including inflammation-related mucin gene clusters. Several of these genes were associated with clinically relevant drugs for the treatment of IPF, CAD, and/or COPD. Our results underscore the pervasive shared genetic basis between IPF and its common comorbidities and hold future implications for early diagnosis of IPF-related comorbidities, drug repurposing, and the development of novel therapies for IPF.

Revealing genetic causality between blood-based biomarkers and major depression in east Asian ancestry

Introduction

Major Depression (MD) is a common mental disorder. In East Asian ancestry, the association, causality, and shared genetic basis between blood-based biomarkers and MD remain unclear.

Methods

We investigated the relationships between blood-based biomarkers and MD through a cross-sectional study and Mendelian randomization (MR) analysis. Cross-trait analysis and enrichment analyses were used to highlight the shared genetic determinants and biological pathways. We conducted summary data-based MR to identify shared genes, which were then validated using a transcriptome dataset from drug-naïve patients with MD.

Results

In the cross-sectional study, C-Reactive Protein showed the significantly positive correlation with depressive symptoms, while hematocrit, hemoglobin, and uric acid exhibited significantly negative correlations. In MR analysis, basophil count (BASO) and low-density lipoprotein cholesterol (LDLc) had a significant causal effect on MD. The enrichment analysis indicated a significant role of inflammatory cytokines and oxidative stress. The shared genes MFN2, FAM55C, GCC2, and SCAPER were validated, with MFN2 identified as a pleiotropic gene involved in MD, BASO, and LDLc.

Discussion

This study highlighted that BASO and LDLc have a causal effect on MD in East Asian ancestry. The pathological mechanisms of MD are related not only to inflammatory cytokines and oxidative stress but also to down regulation of MFN2 expression and mitochondrial dysfunction.

EndoPRS: Incorporating Endophenotype Information to Improve Polygenic Risk Scores for Clinical Endpoints

Polygenic risk score (PRS) prediction of complex diseases can be improved by leveraging related phenotypes. This has motivated the development of several multi-trait PRS methods that jointly model information from genetically correlated traits. However, these methods do not account for vertical pleiotropy between traits, in which one trait acts as a mediator for another. Here, we introduce endoPRS, a weighted lasso model that incorporates information from relevant endophenotypes to improve disease risk prediction without making assumptions about the genetic architecture underlying the endophenotype-disease relationship. Through extensive simulation analysis, we demonstrate the robustness of endoPRS in a variety of complex genetic frameworks. We also apply endoPRS to predict the risk of childhood onset asthma in UK Biobank by leveraging a paired GWAS of eosinophil count, a relevant endophenotype. We find that endoPRS significantly improves prediction compared to many existing PRS methods, including multi-trait PRS methods, MTAG and wMT-BLUP, which suggests advantages of endoPRS in real-life clinical settings.

Genome-wide analyses of variance in blood cell phenotypes provide new insights into complex trait biology and prediction

Blood cell phenotypes are routinely tested in healthcare to inform clinical decisions. Genetic variants influencing mean blood cell phenotypes have been used to understand disease aetiology and improve prediction; however, additional information may be captured by genetic effects on observed variance. Here, we mapped variance quantitative trait loci (vQTL), i.e. genetic loci associated with trait variance, for 29 blood cell phenotypes from the UK Biobank (N~408,111). We discovered 176 independent blood cell vQTLs, of which 147 were not found by additive QTL mapping. vQTLs displayed on average 1.8-fold stronger negative selection than additive QTL, highlighting that selection acts to reduce extreme blood cell phenotypes. Variance polygenic scores (vPGSs) were constructed to stratify individuals in the INTERVAL cohort (N~40,466), where genetically less variable individuals (low vPGS) had increased conventional PGS accuracy (by ~19%) than genetically more variable individuals. Genetic prediction of blood cell traits improved by ~10% on average combining PGS with vPGS. Using Mendelian randomisation and vPGS association analyses, we found that alcohol consumption significantly increased blood cell trait variances highlighting the utility of blood cell vQTLs and vPGSs to provide novel insight into phenotype aetiology as well as improve prediction.

Parkinson's disease and schizophrenia interactomes contain temporally distinct gene clusters underlying comorbid mechanisms and unique disease processes

Genome-wide association studies suggest significant overlaps in Parkinson's disease (PD) and schizophrenia (SZ) risks, but the underlying mechanisms remain elusive. The protein-protein interaction network ('interactome') plays a crucial role in PD and SZ and can incorporate their spatiotemporal specificities. Therefore, to study the linked biology of PD and SZ, we compiled PD- and SZ-associated genes from the DisGeNET database, and constructed their interactomes using BioGRID and HPRD. We examined the interactomes using clustering and enrichment analyses, in conjunction with the transcriptomic data of 26 brain regions spanning foetal stages to adulthood available in the BrainSpan Atlas. PD and SZ interactomes formed four gene clusters with distinct temporal identities (Disease Gene Networks or 'DGNs'1-4). DGN1 had unique SZ interactome genes highly expressed across developmental stages, corresponding to a neurodevelopmental SZ subtype. DGN2, containing unique SZ interactome genes expressed from early infancy to adulthood, correlated with an inflammation-driven SZ subtype and adult SZ risk. DGN3 contained unique PD interactome genes expressed in late infancy, early and late childhood, and adulthood, and involved in mitochondrial pathways. DGN4, containing prenatally-expressed genes common to both the interactomes, involved in stem cell pluripotency and overlapping with the interactome of 22q11 deletion syndrome (comorbid psychosis and Parkinsonism), potentially regulates neurodevelopmental mechanisms in PD-SZ comorbidity. Our findings suggest that disrupted neurodevelopment (regulated by DGN4) could expose risk windows in PD and SZ, later elevating disease risk through inflammation (DGN2). Alternatively, variant clustering in DGNs may produce disease subtypes, e.g., PD-SZ comorbidity with DGN4, and early/late-onset SZ with DGN1/DGN2.

Mapping the landscape of lineage-specific dynamic regulation of gene expression using single-cell transcriptomics and application to genetics of complex disease

Single-cell transcriptome data can provide insights into how genetic variation influences biological processes involved in human biology and disease. However, the identification of gene-level associations in distinct cell types faces several challenges, including the limited reference resource from population scale studies, data sparsity in single-cell RNA sequencing, and the complex cell-state pattern of expression within individual cell types. Here we develop genetic models of cell type specific and cell state adjusted gene expression in mid-brain neurons in the process of specializing from induced pluripotent stem cells. The resulting framework quantifies the dynamics of the genetic regulation of gene expression and estimates its cell type specificity. As an application, we show that the approach detects known and new genes associated with schizophrenia and enables insights into context-dependent disease mechanisms. We provide a genomic resource from a phenome-wide application of our models to more than 1500 phenotypes from the UK Biobank. Using longitudinal genetically determined expression, we implement a predictive causality framework, evaluating the prediction of future values of a target gene expression using prior values of a putative regulatory gene. Collectively, this work demonstrates the insights that can be gained into the molecular underpinnings of diseases by quantifying the genetic control of gene expression at single-cell resolution.