Jul 2026· Journal of Clinical Technology and Theory· 0 citations
TL;DR
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.
Abstract
Alzheimer's Disease (AD) is a complex neurodegenerative disorder with a strong genetic architecture. Genome-Wide Association Studies (GWAS) have identified numerous susceptibility loci. However, the majority of associated variants reside in non-coding regions, making it difficult to resolve their functional consequences and identify causal genes. To address this limitation, integration of GWAS with expression Quantitative Trait Loci (eQTL) and protein Quantitative Trait Loci (pQTL) data has emerged as a key strategy for linking genetic variation to downstream molecular phenotypes. This review discusses statistical frameworks for multi-omics integration in AD research, with a focus on approaches that enable causal inference and gene prioritization. Major methods include colocalization analysis for detecting shared causal variants, Mendelian Randomization (MR) for assessing putative causal relationships, and Transcriptome-Wide Association Studies (TWAS) for linking genetically predicted gene expression to disease risk. Applications of these frameworks have facilitated the identification of candidate causal genes and proteins, thereby improving the mechanistic interpretation of AD-associated loci. However, challenges remain, including tissue specificity and cell-type specificity, limited ancestral diversity in available datasets, and constraints in causal inference. Emerging single-cell and spatial multi-omics approaches are expected to provide a more detailed characterization of AD-associated molecular mechanisms while supporting therapeutic target discovery.
Genome-wide association studies (GWAS) have revealed extensive polygenic signals and overlapping genetic architectures across human traits, creating a need for resources that connect trait-level genetic relationships with gene-level functional evidence. Here, we developed the Multi-Omics Causal Resource Database (MOCR-DB), an interactive platform that integrates large-scale GWAS summary statistics from UK Biobank, FinnGen, and the COVID-19 Host Genetics Initiative with molecular quantitative trait locus (QTL) datasets. In total, 613 traits with significant heritability were retained and harmonized using the Unified Medical Language System. MOCR-DB integrates phenotype-to-phenotype analyses, including genetic correlation and Mendelian randomization, with phenotype-to-gene analyses based on QTL-informed summary-data-based Mendelian randomization analysis (SMR) within a single searchable and interactive framework. The platform supports exploration of cross-trait genetic correlations, putative causal relationships, and candidate functional gene associations. An AI-assisted module provides concise plain-language summaries to help contextualize statistical findings. As a case study, we examined obesity and COVID-19 severity, where genetically predicted obesity showed a stronger association with critical COVID-19 and lung eQTL-based SMR analyses revealed distinct immune- and neuronal-related molecular patterns across severity groups. MOCR-DB thus provides a unified and accessible resource for investigating shared genetic architectures and prioritized functional gene candidates across complex traits, supporting the generation of reproducible and biologically interpretable hypotheses. The database is publicly available at https://chenhongwei.net/public/MOCRdb/.
Graphical Abstract
Data resources, analytical framework, and interpretation in MOCR-DB
The Multi-Omics Causal Resource Database (MOCR-DB) integrates large-scale GWAS summary statistics and molecular QTL datasets to provide a unified framework for genetic correlation, causal inference, and functional mediation. Data resources include GWAS summary statistics from UK Biobank, FinnGen, and the COVID-19 Host Genetics Initiative, together with 53 xQTL datasets across 49 tissues (eQTL, mQTL, sQTL, and caQTL). The analytical framework combines linkage disequilibrium score regression (LDSC) for estimating heritability and cross-trait genetic correlation, Mendelian randomization (MR) to infer potential causal relationships between traits, and summary-data-based Mendelian randomization (SMR) to identify tissue-specific functional genes. Results are presented through interactive genetic network searches that link diseases, biomarkers, lifestyle factors, and molecular traits via correlation, causality, and functional annotation. An AI-assisted module further facilitates causal and functional interpretation by summarizing complex results from LDSC, MR, and SMR analyses into accessible biological insights. Together, MOCR-DB provides systematic exploration of shared genetic architectures and functional mediators across complex human traits.
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
A brain cell type-resolved genetic prioritization framework for AD is provided, nominating genetically supported candidate druggable targets and linking them to neurodegenerative and immune-related pathways.
Ren-Jun Huang, Li-Qing Guan, Yang Chen et al.· Journal of Alzheimer's Disea...· 0 citations
INTRODUCTION
This study aimed to explore shared genetic architectures underlying Alzheimer's disease (AD) and its known risk factors.
METHODS
Significant common variants between AD and its risk factors were identified using GWAS data. The 1000 Genomes Project genotyping data enabled the detection of linkage disequilibrium (LD) blocks and haplotype structures. Functional impact assessments, protein-protein interaction analyses, pathway mapping and enrichment studies were performed.
RESULTS
Sixteen significant variants across nine genes were associated with AD and at least one risk factor (p ≤ 5 × 10-8). Genes APOE, ABCA1 and TOMM40 showed strong associations with AD (adjusted p = 9.75 × 10-9). High-confidence interactions were identified among these genes, as well as APP and LRP1, within the AD pathway. Variant rs429358 (p ≤ 3 × 10-15) on the APOE gene was linked to AD, metabolic syndrome (MetS), diabetes, waist-to-hip ratio (WHR) and ageing. Variant rs2075650 (p ≤ 6 × 10-9) on TOMM40 correlated AD risk with MetS, WHR and body mass index (BMI). Variants rs483082 (p ≤ 2 × 10-32) and rs71352238 (p ≤ 1 × 10-11) on APOC1 and TOMM40 were associated with AD and MetS. Variants rs4420638 (p ≤ 2 × 10-34) and rs1800978 (p ≤ 2 × 10-9) on APOC1 and ABCA genes were associated with AD and WHR. The rs13237518 (p ≤ 5 × 10-11) was associated with AD risk in diabetic patients. Furthermore, the rs4277405 (p ≤ 9 × 10-20) associated AD with cardiovascular disease (CVD). Haplotypic structures were also identified for all these variants (D' and r2 ≥ 0.8).
DISCUSSION
This study identifies genetic variants and LD blocks on APOE, ABCA1, TOMM40 and APOC1 genes shared between AD and its risk factors, revealing common genetic links and potential shared susceptibility pathways.
Morteza Gholami, A. Ahmadi, Mohammad Amin Akhavan Niaki et al.· Psychogeriatrics· 0 citations
Background Aortic aneurysm (AA) is a life-threatening cardiovascular condition with a strong genetic component, however, its molecular mechanisms remain poorly understood. Although genome-wide association studies (GWAS) have identified numerous risk loci, most prior studies have investigated genetic and metabolic factors separately, leaving the causal pathways from genetic variants to disease largely unexplored. Methods We established an integrative framework combining cross-tissue transcriptome-wide association studies (TWAS) with metabolomic mediation analysis. First, we integrated GWAS data from FinnGen R12 with multi-tissue expression quantitative trait loci (eQTL) data from Genotype-Tissue Expression Project (GTEx) V8, then performed cross-tissue TWAS using the Unified Test for MOlecular SignaTures (UTMOST) and single-tissue validation with the Functional Summary-based Imputation (FUSION) to prioritize susceptibility genes. Second, we applied Mendelian randomization (MR), colocalization, and Fine-mapping Of CaUsal gene Sets (FOCUS) to assess causality and identify high-confidence genes. Third, we performed metabolite mediation analysis to uncover metabolic pathways linking genetic variants to disease risk. Finally, we validated key findings in mouse models of thoracic aortic aneurysm (TAA) and abdominal aortic aneurysm (AAA) using Quantitative Real-Time Reverse Transcription Polymerase Chain Reaction (RT-qPCR) and Western blotting. Results We identified multiple novel susceptibility genes for AA and its subtypes. Key genes included ADH family members (ADH1A, ADH1B, ADH4, ADH6) and ZNF827, which showed cross-subtype associations with strong colocalization evidence in vascular tissues. Metabolite mediation analysis revealed significant pathways involving N-acetylphenylalanine and methionine sulfoxide. Functional enrichment revealed distinct biological mechanisms: AA and AAA were primarily associated with metabolic pathways, whereas TAA-related genes were enriched in developmental and contractile processes. PheWAS indicated no significant off-target associations. Critically, experimental validation in mouse models confirmed significant upregulation of ZNF827 in TAA and ADH6 in AAA at both mRNA and protein levels, corroborating the genetic predictions. Conclusion This integrated cross-omics analysis identifies novel genetic loci and, crucially, uncovers specific nutrient-related metabolic pathways that mediate genetic risk. These findings provide a mechanistic basis for future nutritional and metabolic intervention studies in AA and its subtypes.
Hanxi Wang, Junjie Cheng, Jiali Yao et al.· Frontiers in Nutrition· 0 citations
Background Psoriasis is a chronic immune-mediated inflammatory skin disease driven by complex interactions between genetic susceptibility, immune dysregulation, and keratinocyte dysfunction. Although genome-wide association studies have identified numerous risk loci, most studies have focused on genetic associations rather than causal inference, limiting the identification of effective therapeutic targets. We employed an integrative multi-omics strategy to identify causal genes and potential therapeutic candidates for psoriasis. Methods We integrated psoriasis genome-wide association study (GWAS), expression quantitative trait loci (eQTL), and protein quantitative trait loci (pQTL) datasets to perform two-sample Mendelian Randomization (MR) analyses and prioritize high-confidence causal genes. Most genetic datasets were derived from European-ancestry populations. Single-cell transcriptomic analysis was then used to determine their cell-type-specific expression patterns. Finally, drug-target prediction and molecular docking were applied to identify potential therapeutic compounds. Results Integrative MR analyses indicated that genetically predicted higher BLMH expression and plasma protein levels were significantly associated with reduced psoriasis risk, with colocalization analysis supporting shared causal variants at the BLMH locus. Single-cell transcriptomics further demonstrated keratinocyte-enriched expression of BLMH and marked downregulation in psoriatic lesional skin. Drug screening using DSigDB combined with molecular docking identified tamibarotene as a potential BLMH-binding compound with favorable docking affinity. In vitro experiments showed that tamibarotene treatment was associated with increased BLMH expression and reduced KRT16, IL6, and IL8 expression in the M5-stimulated HaCaT model. Conclusion Our integrative multi-omics analysis nominates BLMH as a keratinocyte-enriched protective candidate gene in psoriasis and suggests tamibarotene as a potential repurposed compound for further investigation. However, the experimental validation remains preliminary, and further mechanistic, in vivo, and translational studies are required to validate these findings.
Rui Shen, Bin-Yi Ran, Jia-Zheng Liu et al.· Journal of Inflammation Rese...· 0 citations
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