Significant transcriptomic overlap between PD and a range of clinically relevant traits is revealed, providing novel insights into disease biology with potential to improve disease prediction.
Abstract
Despite major progress in genomic risk loci identification, biological mechanisms underlying Parkinson's disease (PD) remain incompletely understood and the informativity of polygenic score (PGS)-based prediction remains modest. Polytranscriptomic scores (PTS) - the sum of an individual's observed gene expression weighted by transcriptome-wide association z-scores - combine the stability of genetics with the dynamic biology of gene expression and have the potential to identify associations not captured by genetics alone. We present the first large-scale cross-trait and multi-PTS analysis of PD, calculating ~550 PTS for 100 phenotypes using whole-blood RNA-seq data from three independent clinical cohorts in the Accelerating Medicines Partnership Parkinson's Disease programme (AMP-PD) (N = 2,741; NCASES = 1,644). We identify 26 Bonferroni significant cross-trait PTS associations with PD (p<9x10-5) involving 18 phenotypes and 11 trait categories, including neurodegenerative diseases, respiratory function, sleep and cardiovascular traits. Only one of these associations was observed using corresponding PGS, highlighting the added value of integrating directly measured transcriptomic data. Combining multiple PTS within machine learning multi-PTS models improved prediction of PD case/control status beyond age, sex and PD-PGS in external validation, with a sparse model including an additional seven PTS achieving an AUC of 0.74 [0.70-0.78], representing a 0.09-point improvement. These findings reveal transcriptomic overlap between PD and a range of clinically relevant traits, providing novel insights into disease biology with potential to improve disease prediction.
Analysis of statistical frameworks for multi-omics integration in AD research focuses on approaches that enable causal inference and gene prioritization, with a focus on approaches that enable causal inference and gene prioritization.
Zhuolan Li· Journal of Clinical Technolo...· 0 citations
Although genome-wide association studies have identified thousands of disease-associated loci, the mechanistic understanding and drug target discovery remain challenging, particularly for complex diseases. The multi-signal architecture of complex diseases complicates the interpretation of genetic contributions. To address this challenge, we develop an approach comprising locus-specific stratification (LSS) and gene regulatory prioritization score (GRPS), which uniquely considers multi-signals during fine-mapping and target gene identification. LSS significantly enhances the interpretability of genetic risk associated with complex diseases. For loci associated with serum urate levels, the method identifies candidate causal genes in 34.43% of loci, surpassing the performance of other methods by 5.47% to 25.14%. GRPS considers the regulatory network of LSS-variants comprehensively and successfully nominates under-explored drug targets for hyperuricemia with high confidence such as SLC17A4, which is further validated using epigenetic activation and phenotypic assays. This study introduces an approach to efficiently and comprehensively address the multi-signal challenges in complex diseases. Genetic risk of complex diseases poses unique challenges. Here, the authors develop a locus-specific stratification and gene regulatory prioritisation strategy to address issues arising from multi-signals in complex diseases.
Jing Zhang, Qiao-Qiao Liu, Ye Zhu et al.· Nature Communications· 0 citations
Parkinson's disease (PD) is a progressive neurodegenerative disorder with a complex etiology, in which genetic variability in non-coding regions and epigenetic mechanisms play a central role. This study investigated allele-specific expression (ASE) patterns in peripheral blood RNA-Seq data from 487 individuals with idiopathic PD (iPD) and healthy controls from the PPMI initiative, aiming to characterize disease-associated transcriptional imbalances and their relationship with CpG islands. We identified 31,219 significant variants at heterozygous sites, with 4537 variants unique to the iPD group (14.53% of the total) mapping to 921 genes, a number significantly higher than that observed in controls (73 genes). Functional analysis revealed consistent enrichment in immune pathways (HLA-B antigen presentation, FcγR signaling, Th17 differentiation) and processes involving ubiquitination and protein degradation, suggesting an integrated molecular signature of immune dysregulation and proteostatic stress. Chromosomal distribution showed hotspots on chromosomes 1, 2, 6, 12, and 17, with a predominance of transition variants in CpG-rich regions. However, no direct linear correlation was observed between variant characteristics and CpG island metrics (density, GC content), indicating that regulatory effects may operate independently of local methylation. Protein interaction network analysis highlighted sub-networks featuring hub genes such as PINK1, GBA1, LRRK2, and PRKN, connected to autophagy and immune response pathways. These results support the view that ASE captures a broad regulatory signature in iPD, distinct from the DNA methylation context, and reinforces the role of immunogenetic dysregulation as a primary component of the disease's pathophysiology, offering new targets for mechanistic investigation and peripheral biomarkers.
R. M. Piergiorge, Alan Tardin da Silva, Ronaldo Francisco et al.· Computers in Biology and Med...· 0 citations
Results show that integrating PRS and ProRS improves prediction beyond either score alone across traits and populations and provide a unified genomic-proteomic prediction framework.
W. Wang, J. Williams, M. Gillman et al.· medRxiv· 0 citations
BACKGROUND
Mitochondrial dysfunction has been implicated in Parkinson's disease (PD), but the genetically regulated mitochondrial genes associated with PD risk remain incompletely defined.
METHODS
We conducted a summary-data-based genetic epidemiology study integrating summary-based Mendelian randomization (SMR), Heterogeneity in dependent instruments (HEIDI) filtering, and Bayesian colocalization to prioritize mitochondrial-related molecular features associated with PD risk. Mitochondrial-related genes were defined using MitoCarta3.0. Genetically predicted gene expression and plasma protein abundance were evaluated using expression quantitative trait loci (eQTL) data from eQTLGen and GTEx v8, and protein quantitative trait loci (pQTL) data was assessed using International Parkinson's Disease Genomics Consortium (IPDGC) as the discovery genome-wide association study (GWAS) and FinnGen as the replication dataset. Prespecified QTL analyses were interpreted using FDR correction, HEIDI filtering, and colocalization support. DNA methylation QTL analysis, mitochondrial phenotype MR, and single-nucleus RNA-seq analysis were performed as complementary analyses.
RESULTS
In the primary eQTL analysis, higher genetically predicted TTC19 expression was associated with lower PD risk (OR = 0.80, 95% CI: 0.74-0.87, PPH4 = 0.80), whereas higher MALSU1 expression was associated with increased PD risk (OR = 2.21, 95% CI: 1.59-3.06, PPH4 = 0.96). Both associations survived FDR correction, passed HEIDI filtering, and showed colocalization support. GTEx whole-blood data supported the direction of the TTC19 association. No mitochondrial protein reached significance after FDR correction and colocalization filtering in the primary pQTL analysis. Complementary methylation analysis highlighted cg06270993 as an exploratory regulatory signal for MALSU1.
CONCLUSIONS
This MR-colocalization study prioritizes TTC19 and MALSU1 as genetically supported mitochondrial-related candidate genes associated with PD risk. Further validation is required to define their functional roles in PD pathogenesis.
Yu-Sheng Zhu, Zihan Ye· Clinical neurology and neuro...· 0 citations
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