SEARCH · PubMed Health

Results for “Genetic Pleiotropy”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources

PubMed2025

Genetic pleiotropy underlying obesity and autoimmune disorders: a large-scale cross-trait gwas analysis in European ancestry populations.

BACKGROUND: Obesity and autoimmune disorders represent a significant comorbidity burden, yet their shared genetic architecture is not fully understood. Elucidating the pleiotropic genetic basis underlying both conditions is crucial for unraveling the mechanisms driving their co-occurrence and advancing therapeutic strategies. METHODS: We conducted a large-scale cross-trait analysis integrating genome-wide association study (GWAS) summary data for obesity and 17 autoimmune diseases. Genetic correlations were assessed using LD score regression and high-definition likelihood. Cross-trait pleiotropic analysis was performed using Stratified Pleiotropic Locus Mapping (PLACO) to identify shared loci, followed by Bayesian colocalization to confirm shared causal variants. Gene-level and tissue-specific heritability analyses were conducted, and drug targets were prioritized via summary-based Mendelian randomization (SMR). Finally, immune co-localization and bidirectional Mendelian randomization were employed to elucidate immunological mechanisms and causal relationships. RESULTS: Our analysis identified eight autoimmune diseases with significant genetic correlations to obesity. We discovered 10,324 pleiotropic SNPs, which mapped to 52 independent risk loci, with nine loci confirmed as shared causal variants by colocalization. Gene-level analysis revealed 133 unique pleiotropic genes, including CLN3, SH2B1, and MMEL1, enriched in pathways of hematopoietic cell differentiation and immune homeostasis. Tissue-specific heritability was most prominent in the spleen, whole blood, and EBV-transformed lymphocytes. Immuno-co-localization implicated six IgD+ CD38- %B cell-related traits as key pathological conduits. Bidirectional Mendelian randomization established a causal role of obesity in hypothyroidism, psoriasis, and multiple sclerosis, while revealing an inverse causal association of type 1 diabetes with obesity risk. CONCLUSIONS: This study demonstrates a robust shared genetic foundation between obesity and multiple autoimmune diseases, pinpointing specific pleiotropic loci, genes, and immune cell subsets. Our findings provide a mechanistic framework for their comorbidity and highlight potential targets for therapeutic intervention.

Humans↗

PubMed2026

Consistent and idiosyncratic pleiotropy in shaping genetic correlations.

Pleiotropy, the phenomenon where a single mutation influences multiple phenotypic traits, creates genetic correlations that can constrain evolutionary trajectories. Yet genetic correlations differ in their persistence: some remain stable over long evolutionary timescales, whereas others change rapidly across generations or environments. One explanation is that similar values of genetic correlation, rG, can arise from different pleiotropic architectures: broadly aligned effects across many loci, or disproportionate covariance contributions from a few large effect loci. Motivated by the distinction between vertical and horizontal pleiotropy, here, we develop a bivariate marker effect framework for recombinant mapping populations that separates candidate large covariance contributors from the polygenic background correlation, rD. We define rD as the correlation among marker effects after trimming markers with unusually large covariance contributions. rD is a trait-pair summary of how consistently small and moderate effect markers align across the genome; high rD is expected when many perturbations propagate through shared developmental, physiological, causal, or geometric structure. Applying this framework to high-dimensional yeast single-cell morphology, we show that trait pairs with similar rG can differ substantially in rD, and that a small number of candidate outlier regions can strongly influence some marker effect correlations. We then test whether rD predicts the environmental stability of genetic correlations under geldanamycin-mediated Hsp90 perturbation. Trait pairs with stronger rD show smaller absolute changes in rG. These results suggest that genetic correlations supported by a strong polygenic marker effect background are more environmentally stable than correlations shaped primarily by a few large covariance contributors.

Genetic Pleiotropy↗

PubMed2026

Dissecting the shared genetic architecture between migraine subtypes and cardiovascular diseases: a multi-layered genomic analysis.

BACKGROUND: Epidemiological studies have linked migraine to an increased risk of cardiovascular disease (CVD); however, the shared genetic basis and putative causal relationships between migraine subtypes and cardiovascular traits remain poorly understood. METHODS: Leveraging large-scale GWAS summary statistics for migraine phenotypes (overall migraine, migraine with aura [MA], and migraine without aura [MO]) from FinnGen R12, along with seven cardiovascular diseases from publicly available consortia, we conducted a multi-layered genetic analysis. This integrative framework encompassed genetic correlation [linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL)], cross-trait meta-analysis (CPASSOC and PLACO), Bayesian colocalization, summary-data-based Mendelian randomization (SMR) using GTEx v8 eQTL data, and bidirectional two-sample Mendelian randomization (MR). RESULTS: Significant genetic correlations were identified between migraine and multiple cardiovascular traits, with hypertension and coronary artery disease (CAD) showing the most robust associations. MA exhibited broader genetic overlap with cardiovascular diseases than MO, including a notably stronger correlation with ischemic stroke, whereas MO demonstrated a stronger correlation with hypertension. Cross-trait meta-analysis identified 160 pleiotropic loci across 17 of 21 trait pairs. Colocalization analysis confirmed 32 loci harboring shared causal variants, mapped to 13 candidate genes, of which 7 (PHACTR1, LRP1, SOX7, ABO, FHOD3, MEI1, XKR6) were further validated by SMR as exhibiting tissue-specific regulatory effects. Among these, PHACTR1 displayed the broadest pleiotropic profile across migraine phenotypes and vascular diseases. After MR-PRESSO outlier removal, bidirectional MR identified 10 MR-supported associations, two of which (genetic liability to hypertension on overall migraine, and CAD on MA) survived Bonferroni correction, all free of detectable horizontal pleiotropy. Genetic liability to hypertension was associated with increased migraine risk (OR = 1.90, 95% CI 1.25-2.90, P = 2.64 × 10⁻³), atherosclerotic diseases showed subtype-specific effects (inverse for MO, positive for MA), and, in the reverse direction, migraine was associated with increased ischemic stroke risk. CONCLUSIONS: This study provides a comprehensive and systematic characterization of the shared genetic architecture between migraine subtypes and cardiovascular diseases. By identifying pleiotropic genes and bidirectional putative causal relationships with subtype-specific patterns, our findings carry implications for the development of targeted therapeutics and subtype-specific cardiovascular risk stratification.

Humans↗

PubMed2009

Peptide YY (PYY) gene polymorphisms in the 3'-untranslated and proximal promoter regions regulate cellular gene expression and PYY secretion and metabolic syndrome traits in vivo.

RATIONALE: Obesity is a heritable trait that contributes to hypertension and subsequent cardiorenal disease risk; thus, the investigation of genetic variation that predisposes individuals to obesity is an important goal. Circulating peptide YY (PYY) is known for its appetite and energy expenditure-regulating properties; linkage and association studies have suggested that PYY genetic variation contributes to susceptibility for obesity, rendering PYY an attractive candidate for study of disease risk. DESIGN: To explore whether common genetic variation at the human PYY locus influences plasma PYY or metabolic traits, we systematically resequenced the gene for polymorphism discovery and then genotyped common single-nucleotide polymorphisms across the locus in an extensively phenotyped twin sample to determine associations. Finally, we experimentally validated the marker-on-trait associations using PYY 3'-untranslated region (UTR)/reporter and promoter/reporter analyses in neuroendocrine cells. RESULTS: Four common genetic variants were discovered across the locus, and three were typed in phenotyped twins. Plasma PYY was highly heritable (P < 0.0001), and genetic pleiotropy was noted between plasma PYY and body mass index (BMI) (P = 0.03). A PYY haplotype extending from the proximal promoter (A-23G, rs2070592) to the 3'-UTR (C+1134A, rs162431) predicted not only plasma PYY (P = 0.009) but also other metabolic syndrome traits. Functional studies with transfected luciferase reporters confirmed regulatory roles in altering gene expression for both 3'-UTR C+1134A (P < 0.001) and promoter A-23G (P = 0.0016). CONCLUSIONS: Functional genetic variation at the PYY locus influences multiple heritable metabolic syndrome traits, likely conferring susceptibility to obesity and subsequent cardiorenal disease.

3' Untranslated Regions↗

PubMed2026

Distinct mutational landscapes for germline and somatic cancer variants in forty tumor suppressor genes.

Germline and somatic cancer variants in tumor suppressor genes (TSGs) share loss-of-function mechanisms, but studies of a few genes (DICER1 and CEBPA) have demonstrated differences in variant consequence and location. To systematically assess whether TSGs display distinct mutational patterns, we leveraged large public genetic databases and compared 32,941 high-quality pathogenic/likely pathogenic (P/LP) germline variants in ClinVar, with 12,907 oncogenic/likely oncogenic (O/LO) somatic tumor variants from cBioPortal across 40 TSGs. Only 3,863 (9.2%) variants were shared. Eighteen TSGs showed significantly different distributions of variant occurrences by molecular consequence, replicated with non-overlapping somatic data from the COSMIC database (chi-squared tests, false discovery rate = 5%). DICER1, TP53, and SMAD4 displayed excess somatic missense events, while nine TSGs (e.g., RB1 and APC) contained excess somatic stop-gain events throughout the coding sequence. Analysis by tumor type revealed excess stop-gain events in tissues exposed to environmental mutagens with corresponding mutation signatures. For several TSGs (WT1), germline variants predispose to tumors (Wilms' tumor) distinct from the majority source of somatic data (myeloid leukemia). Germline and somatic events are also distributed unevenly across cDNA locations, with 103 regions of preferential clustering in 39 TSGs (78 somatic and 25 germline). Twenty somatic clusters contained recurring frameshifts in homopolymer runs, many in tumors with microsatellite instability. Germline clusters contain more germline-exclusive variants, some driving non-cancer phenotypes reflecting genetic pleiotropy. Altogether, germline and somatic variants of TSGs represent unique sets with substantially different patterns shaped by selection pressures from gene-specific and somatic mutational mechanisms. Characterizing these distinctions enables more accurate clinical interpretation of TSG variants.

Humans↗

PubMed2025

A genome-wide analysis of the shared genetic risk architecture of complex neurological and psychiatric disorders.

Although neurological and psychiatric disorders have historically been considered to reflect distinct pathogenic entities, recent findings suggest shared pathophysiological mechanisms. However, the extent to which these heritable disorders share genetic influences remains unclear. Here we performed a comprehensive analysis of genome-wide association study data, involving nearly 1 million cases across ten neurological diseases and ten psychiatric disorders, to compare their common genetic signal and biological associations. Using complementary statistical tools, we demonstrate that a large set of common genetic variants impacts the risk of multiple neurological and psychiatric disorders, even in the absence of genetic correlations. Furthermore, genome-wide association studies on psychiatric disorders consistently implicate neuronal biology, whereas neurological diseases are associated with diverse neurobiological processes. Together, this study elucidates the genetic relationship between complex neurological and psychiatric disorders, indicating a larger degree of genetic pleiotropy than previously recognized. The findings have implications for disease classification, precision medicine and clinical practice.

Humans↗

PubMed2026

Characterising the motif composition and allele length distribution of ZFHX3 GGC repeat expansions in amyotrophic lateral sclerosis.

A pathogenic GGC repeat expansion in zinc finger homeobox 3 (ZFHX3), encoding a pure polyglycine (polyG) tract, causes spinocerebellar ataxia type 4 (SCA4). Intermediate expansions of other SCA loci have been implicated in amyotrophic lateral sclerosis (ALS), while repeat motif composition is recognised to influence pathogenicity in neurodegenerative diseases. Given the genetic pleiotropy between ALS and SCA, we evaluated whether ZFHX3 GGC expansions are associated with ALS and characterised repeat motif composition. ZFHX3 GGC repeat sizes were genotyped using ExpansionHunter in short-read whole-genome sequencing data from ALS cases and healthy controls of European ancestry. Repeat sizes were visually inspected using REViewer, and motif configurations were manually derived from a subset. Receiver operating characteristic analysis and Youden's J statistic identified a candidate repeat size threshold. Logistic regression tested associations of repeat length and motif composition with ALS, while regression models assessed clinical phenotypes. Across 5785 ALS cases and 7982 controls, no association was observed between ZFHX3 expansions and ALS risk. Longer alleles showed a nominal association with later disease onset, however this did not remain significant after Bonferroni correction. Among 802 ALS cases and 800 controls, 50 distinct motif compositions were identified, including 11 encoding pure polyG tracts characteristic of pathogenic SCA4 expansions; none were associated with ALS. Although no association with ALS was observed, this study established the dynamic nature of ZFHX3 repeat motif composition and configuration. Variation within and between repeat sizes, including pure polyG repeats, supports consideration of motif composition alongside allele length when evaluating neurodegenerative disease risk.

Journal Article↗

PubMed2025

Exploring the causal relationship between plasma proteins and postherpetic neuralgia: a Mendelian randomization study.

BACKGROUND: The proteome represents a valuable resource for identifying therapeutic targets and clarifying disease mechanisms in neurological disorders. This study investigated potential causal relationships between plasma proteins and postherpetic neuralgia (PHN). METHODS: We conducted a two-sample Mendelian randomization (MR) analysis using genome-wide association study (GWAS) summary statistics from the Decode Genetics dataset (4,907 plasma proteins) and the FinnGen database (490 PHN cases and 435,371 controls). Instrumental variables (IVs) were selected based on relevance, independence, and exclusivity. Causal associations were assessed using inverse-variance weighted (IVW), MR-Egger regression, simple mode, weighted mode, and weighted median methods. Sensitivity analyses, including leave-one-out tests, evaluated result robustness, while colocalization analysis examined shared causal variants between traits. RESULTS: Eight plasma proteins showed significant associations with PHN (PFDR < 0.05). Higher levels of ATRN, PIANP, and CD48 correlated with increased PHN risk, whereas elevated KIR2DL5A, GPI, SEMG2, EIF4B, and HFE2 levels were associated with reduced risk. Sensitivity analyses supported these findings and excluded genetic pleiotropy as a major confounding factor. Colocalization analysis did not detect shared causal variants (PPH4 < 0.8). CONCLUSION: These results suggest a potential causal role for eight plasma proteins in PHN pathogenesis. While these proteins may serve as biomarkers or therapeutic candidates, further validation is required. This study advances understanding of PHN pathophysiology and supports future investigations into diagnostic and therapeutic strategies.

Mendelian randomization↗

PubMed2026

Genetic insights into the relationship between age at menarche and mental health-related phenotypes.

BACKGROUND: Multiple observational studies have reported associations between age at menarche (AAM) and mental health problems, yet their shared genetic architecture remains poorly characterized. METHODS: We leveraged genome-wide association study summary statistics for AAM and 15 mental health-related phenotypes. We conducted a multi-method integrative analysis encompassing linkage disequilibrium score regression, pleiotropic analysis under the composite null hypothesis, functional mapping and annotation, multi-marker analysis of genomic annotation, pathway enrichment, and bidirectional two-sample Mendelian randomization (MR) to explore shared genetic architecture and potential causal relationships. RESULTS: Our study identified significant genetic correlations between AAM and eight mental health-related phenotypes (miserableness, fed-up feelings, nervous feelings, ever thought that life is not worth living, ever self-harmed, depression, ever smoker, and age started smoking in former smokers). A total of 155 pleiotropic loci, 18 colocalized loci (e.g., 6q16.3), and 203 pleiotropic genes (e.g., LIN28B) were identified. These genes are expressed in multiple regions, including the cerebral cortex and hypothalamus, and are involved in various biological processes and signaling pathways. Additionally, MR analysis revealed causal associations between AAM and 5 mental health-related phenotypes (mood swings, miserableness, fed-up feelings, and age at which smokers started smoking in former/current smokers). CONCLUSIONS: Our study revealed extensive genetic associations between AAM and mental health-related phenotypes, and further explored the potential causal relationships between them. These findings enhance our understanding of the relationship from a genetic perspective and establish a foundation for future research to explore the biological pathways and environmental interactions contributing to these associations.

Genome-Wide Association Study↗

PubMed2026

Dissecting pleiotropy between major depressive disorder and physical disease comorbidities.

Major depressive disorder (MDD) is characterized by substantial comorbidity with medical conditions. To achieve better outcomes for patients with MDD, an improved understanding of the mechanisms underlying pervasive comorbidities is required. Here, to this end, we mapped patterns of pleiotropy by defining four clusters of physical diseases (cardiovascular, metabolic, gastrointestinal and immune) and analyzed their genetic relationships with MDD using genomic structural equation modeling. Three disease clusters exhibited independent associations with MDD and accounted for 47% of MDD h2SNP, with the gastrointestinal disease cluster having the strongest association (β = 0.63, s.e. = 0.05, P = 3.04 × 10-30). In addition, we identified independent loci associated with the shared genetic liability between each disease cluster and MDD, revealing different pleiotropic components. Characterization of these loci revealed previously unidentified associations with MDD and physical disease traits, along with unique biological pathways, drug groups, cell types and genes associated with each disease-MDD cluster. Our findings reveal genetic connections implicating the gut-brain axis as a key mechanism underlying the comorbidity of physical diseases in MDD. This work advances our understanding of MDD by highlighting unique and shared genetic components across different disease systems.

Major Depressive Disorder↗

PubMed2025

Possible linking and treatment between Parkinson's disease and inflammatory bowel disease: a study of Mendelian randomization based on gut-brain axis.

BACKGROUND: Mounting evidence suggests that Parkinson's disease (PD) and inflammatory bowel disease (IBD) are closely associated and becoming global health burdens. However, the causal relationships and common pathogeneses between them are uncertain. Furthermore, they are uncurable. Thus, we aimed to identify the causal relationships and novel therapeutic targets shared between them based on their common pathophysiological mechanisms in gut-brain-axis (GBA). METHODS: A meta-analysis on bidirectional Mendelian randomization (MR) utilizing various datasets was performed to estimate their causal relationship. Then, pleiotropic analysis under the composite null hypothesis (PLACO) with functional mapping combined with annotation of genetic associations (FUMA) analysis were conducted to identify pleiotropic genes. Next, blood, brain and intestine expression quantitative trait locus (eQTL) were taken to perform drug-target MR finding common causal genes in two diseases. Colocalization analysis ensured the eQTLs of corresponding gene colocalized with disease. Enrichment analysis and protein‒protein interaction (PPI) network were done to explore common pathogenesis pathways. Genes passed all analysis were regarded as drug targets. RESULTS: Our MR meta-analysis revealed the bidirectional causal relationship between diseases, with combined ORs for PD on IBD, CD, UC (1.050 [95% CI 1.014-1.086], 1.044 [95% CI 0.995-1.095], 1.063 [95% CI 1.016-1.120]); for IBD, CD, UC on PD (1.003 [95% CI 0.973-1.034], 1.035 [95% CI 1.004-1.067], 1.008 [95% CI 0.977-1.040]). Overall, 277, 216 and 201 genes were identified as pleiotropic genes between PD and IBD, CD, UC. Total of 733 genes were classified as tier 3 (found in only one tissue) druggable targets, 57 as tier 2 (found in two tissues, 51 protein-coding genes) and 9 as tier 3 (found in three tissues). Among 60 protein-coding druggable targets over tier 2, 18 overlapped with pleiotropic genes and enriched in mitochondria, antigen presentation, processing and immune cell regulation pathways. Three druggable genes (LRRK2, RAB29 and HLA-DQA2) passed colocalization analysis. LRRK2 and RAB29 were reported to be pleiotropic genes, and RAB29 and HLA-DQA2 were reported for the first time as potential drug targets. CONCLUSIONS: This study established a reliable causal relationship, possible shared drug targets and common pathogenesis pathways of two diseases, which had important implications for intervention and treatment of two diseases simultaneously.

Humans↗

PubMed2025

Exploiting pleiotropy to enhance variant discovery with functional false discovery rates.

The cost of recruiting participants for genome-wide association studies (GWASs) can limit sample sizes and hinder the discovery of genetic variants. Here we introduce the surrogate functional false discovery rate (sfFDR) framework that integrates summary statistics of related traits to increase power. The sfFDR framework provides estimates of FDR quantities such as the functional local FDR and q value, and uses these estimates to derive a functional P value for type I error rate control and a functional local Bayes' factor for post-GWAS analyses. Compared with a standard analysis, sfFDR substantially increased power (equivalent to a 52% increase in sample size) in a study of obesity-related traits from the UK Biobank and discovered eight additional lead SNPs near genes linked to immune-related responses in a rare disease GWAS of eosinophilic granulomatosis with polyangiitis. Collectively, these results highlight the utility of exploiting related traits in both small and large studies.

Humans↗

PubMed2011

Adaptation to climate across the Arabidopsis thaliana genome.

Understanding the genetic bases and modes of adaptation to current climatic conditions is essential to accurately predict responses to future environmental change. We conducted a genome-wide scan to identify climate-adaptive genetic loci and pathways in the plant Arabidopsis thaliana. Amino acid-changing variants were significantly enriched among the loci strongly correlated with climate, suggesting that our scan effectively detects adaptive alleles. Moreover, from our results, we successfully predicted relative fitness among a set of geographically diverse A. thaliana accessions when grown together in a common environment. Our results provide a set of candidates for dissecting the molecular bases of climate adaptations, as well as insights about the prevalence of selective sweeps, which has implications for predicting the rate of adaptation.

Acclimatization↗

PubMed2025

Decoding missense variants pleiotropy in the immune GPCR P2RY8.

G protein-coupled receptors (GPCRs) form the largest family of cell surface receptors and remain a central focus in pharmacology and drug discovery. Despite extensive structural and pharmacological studies, the functional impact of missense variation across GPCRs remains poorly understood, particularly for receptors involved in immune regulation. In this issue of Cell Genomics, LaFlam et al.1 systematically map P2RY8 variant functions using deep mutational scanning (DMS) combined with structural biology approaches, revealing pleiotropy and mechanisms linking GPCR variation to B cell confinement and lymphoma.

Humans↗

PubMed2026

Multi-omics uncovers the pleiotropic genetic mechanisms linking MASLD and cardiometabolic syndromes.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) and cardiovascular-kidney-metabolic (CKM) syndrome are interrelated conditions with shared pathophysiological features; however, the genetic architecture underlying their relationship has not been fully elucidated. Deciphering this shared genetic basis holds promise for advancing mechanistic insights and therapeutic discovery. METHODS: We performed an integrated genome-wide cross-trait analysis using GWAS summary statistics for MASLD and 38 CKM traits. Our analysis estimated genetic correlations, inferred causal relationships, and identified pleiotropic variants. Candidate causal genes and druggable targets were subsequently prioritized through integrating multi-omics data. RESULTS: MASLD exhibited significant genetic correlations with 16 CKM traits, especially metabolic and cardiovascular conditions. Bidirectional causal relationships were observed between MASLD and T2D, adiposity, and lipid traits. We discovered 116 pleiotropic loci, including 65 shared causal variants such as rs429358 near APOE, which exerted influence across multiple traits. Gene-based analyses prioritized 152 unique candidate pleiotropic genes, enriched in lipid and cholesterol metabolism, and highly expressed in the liver, adipose, and immune-related cell types, such as macrophages and endothelial cells. Multi-omics integration validated 131 genes using eQTL and pQTL data from multiple tissues and cohorts. Notably, FTO and APOE emerged as central pleiotropic hubs, and druggability evaluation highlighted APOE, LPL, PPARG, and GPBAR1 as established therapeutic targets for metabolic diseases. CONCLUSION: This study provides a comprehensive map of the shared genetic architecture between MASLD and CKM syndrome, reveals novel causal genes and repurposable drug targets, and offers insights into precision medicine approaches for cardiometabolic and liver diseases.

Humans↗

PubMed2025

Phenotypic pleiotropy of missense variants in human B cell confinement receptor P2RY8.

Missense variants can have pleiotropic effects on protein function, and predicting these effects can be difficult. We performed near-saturation deep mutational scanning of P2RY8, a G protein-coupled receptor that promotes germinal center B cell confinement. We assayed the effect of each variant on surface expression, migration, and proliferation. We delineated variants that affected both expression and function, affected function independently of expression, and discrepantly affected migration and proliferation. We also used cryo-electron microscopy to determine the structure of activated, ligand-bound P2RY8, providing structural insights into the effects of variants on ligand binding and signal transmission. We applied the deep mutational scanning results to both improve computational variant effect predictions and to characterize the phenotype of germline variants and lymphoma-associated variants. Together, our results demonstrate the power of integrating deep mutational scanning, structure determination, and in silico prediction to advance the understanding of a receptor important in human health.

Humans↗

PubMed2026

Eating Disorders and Parkinson's Disease-2: Genetic Epidemiology and Shared Genomics.

OBJECTIVE: Individuals with anorexia nervosa (AN) share premorbid traits with Parkinson's Disease (PD) (e.g., anxiety) and exhibit a two-fold relative risk of a reported family history of PD. Published estimates of intra- and inter-disorder genetic architecture were extracted and compared prior to conducting novel analyses to provide evidence for cross-disorder genetic risk. METHODS: National register or meta-analytic familial, twin, and common variant genome-wide studies were searched; estimates and findings were extracted and compared. Novel cross-disorder conditional and conjunctional false discovery rate analyses were performed. RESULTS: Sibling relative risks and additive genetic estimates of the two disorders were similar. AN had greater common variant heritability than PD whether measured via infinitesimal model (linkage disequilibrium score regression, LDSC) or causal mixture model (MiXeR). AN had greater polygenicity than PD (mean (SD) 2.50E-03 (1.64E-04) versus 2.72E-4 (1.47E-05), p < 0.001), but lower discoverability than PD (4.20E-05 (2.69E-06) versus 1.40E-04 (6.95E-06), p < 0.001). Global genetic correlation was significant (e.g., bivariate LDSC rg = 0.10, p = 0.0033). Novel analyses identified cross-disorder enrichment, and cross-disorder risk at chr3p21.31. CONCLUSIONS: Cross-disorder AN and PD research identified shared risk variants at chr3p21.31, genes and mechanisms (e.g., conditioning, fear, and reward) linked to a shared endophenotype.

Parkinson's disease↗

PubMed2026

Genetic overlap between depression and C-reactive protein levels: Evidence from a cross-trait analysis.

Inflammation and depression have been consistently associated, with elevated C-reactive protein (CRP) levels observed in a significant subset of affected individuals. However, the genetic mechanisms underlying this association remain poorly understood. We integrated results from large-scale genome-wide association studies (GWAS) of depression and CRP levels in a cross-trait analysis specifically focusing on identifying horizontally pleiotropic loci. Identified variants were stratified as concordant versus discordant based on their direction of effects on the two traits and followed up using functional annotation, gene set enrichment, and colocalization analyses. We also explored causal relationships using Mendelian Randomization (MR) analysis with extensive sensitivity analyses, including adjustment for body mass index (BMI). We identified 9 novel loci. Functional analyses revealed that concordant loci were enriched in genes linked to immune and inflammatory processes, while discordant loci mostly mapped to metabolic pathways, including lipid regulation. MR provided strong evidence for body mass index driving a causal relationship between the genetic liability of depression on CRP levels. Our findings suggest that the association between depression and CRP levels is partly driven by shared genetic influences, pointing to different biological pathways depending on whether genetic effects are concordant or discordant. These results underscore the importance of considering effect direction when assessing the genetic overlap between depression and inflammatory processes. In addition, they highlight BMI as a key factor in the causal relationship between depression and systemic inflammation.

C-Reactive Protein↗