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It is estimated that the impact of related genes on the risk of Alzheimer’s disease (AD) is nearly 70%. Identifying candidate causal genes can help treatment and diagnosis. The maturity of sequencing technology and the reduction of cost make genome-wide association study (GWAS) become an important means to find disease-related mutation sites. Becau...
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Objective: To explore potential causal genetic variants and genes underlying the pathogenesis of uterine leiomyomas (ULs).
Methods: We conducted the summary data-based Mendelian randomization (SMR) analyses and performed functional mapping and annotation using FUMA to examine genetic variants and genes that are potentially involved in the pathogeni...
Citations
... Sustaining a healthy brain is imperative for attaining longevity and overall well-being [4]. However, diagnosing and treating brain diseases pose complex challenges [5][6][7][8]. Numerous human brain diseases exhibit significant genetic components [9][10][11]. Identifying gene biomarkers associated with these conditions is crucial for elucidating their pathogenesis and facilitating drug development. ...
... Furthermore, we have chosen four representative brain diseases (Alzheimer's disease, Parkinson's disease, Major depression, and Autism) for comprehensive investigation and discussion. These diseases are well-known for their high prevalence and significant impact on individuals, thus extensively studied by various models [5,7,8,44,68] (Fig. 3F). Notably, all these evaluation metrics surpass those of the previous method [7], demonstrating the excellent performance of M-GBBD. ...
Background
Brain diseases pose a significant threat to human health, and various network-based methods have been proposed for identifying gene biomarkers associated with these diseases. However, the brain is a complex system, and extracting topological semantics from different brain networks is necessary yet challenging to identify pathogenic genes for brain diseases.
Results
In this study, we present a multi-network representation learning framework called M-GBBD for the identification of gene biomarker in brain diseases. Specifically, we collected multi-omics data to construct eleven networks from different perspectives. M-GBBD extracts the spatial distributions of features from these networks and iteratively optimizes them using Kullback–Leibler divergence to fuse the networks into a common semantic space that represents the gene network for the brain. Subsequently, a graph consisting of both gene and large-scale disease proximity networks learns representations through graph convolution techniques and predicts whether a gene is associated which brain diseases while providing associated scores. Experimental results demonstrate that M-GBBD outperforms several baseline methods. Furthermore, our analysis supported by bioinformatics revealed CAMP as a significantly associated gene with Alzheimer's disease identified by M-GBBD.
Conclusion
Collectively, M-GBBD provides valuable insights into identifying gene biomarkers for brain diseases and serves as a promising framework for brain networks representation learning.
... Second, through integration of GWAS findings with meQTLs, and in cases with eQTLs, we highlight multiple examples of specific putative mechanisms underlying GWAS genetic impacts on human phenotypes. Our work is consistent with and extends previous efforts, both disease-specific [61,62] and multitrait [18,37], that integrate different molecular data at the genome-wide level to provide new insights into disease processes and biological pathways. ...
Background
Pinpointing genetic impacts on DNA methylation can improve our understanding of pathways that underlie gene regulation and disease risk.
Results
We report heritability and methylation quantitative trait locus (meQTL) analysis at 724,499 CpGs profiled with the Illumina Infinium MethylationEPIC array in 2358 blood samples from three UK cohorts. Methylation levels at 34.2% of CpGs are affected by SNPs, and 98% of effects are cis-acting or within 1 Mbp of the tested CpG. Our results are consistent with meQTL analyses based on the former Illumina Infinium HumanMethylation450 array. Both SNPs and CpGs with meQTLs are overrepresented in enhancers, which have improved coverage on this platform compared to previous approaches. Co-localisation analyses across genetic effects on DNA methylation and 56 human traits identify 1520 co-localisations across 1325 unique CpGs and 34 phenotypes, including in disease-relevant genes, such as USP1 and DOCK7 (total cholesterol levels), and ICOSLG (inflammatory bowel disease). Enrichment analysis of meQTLs and integration with expression QTLs give insights into mechanisms underlying cis-meQTLs (e.g. through disruption of transcription factor binding sites for CTCF and SMC3) and trans-meQTLs (e.g. through regulating the expression of ACD and SENP7 which can modulate DNA methylation at distal sites).
Conclusions
Our findings improve the characterisation of the mechanisms underlying DNA methylation variability and are informative for prioritisation of GWAS variants for functional follow-ups. The MeQTL EPIC Database and viewer are available online at https://epicmeqtl.kcl.ac.uk.
... ; underlying GWAS genetic impacts on human phenotypes. Our work is consistent with and extends previous efforts, both disease-specific [58,59] and multi-trait [18,34], that integrate different molecular data at the genome-wide level to provide new insights into disease processes and biological pathways. ...
Pinpointing genetic impacts on DNA methylation can improve our understanding of pathways that underlie gene regulation and disease risk. We report heritability and methylation quantitative trait locus (meQTL) analysis at 724,499 CpGs profiled with the Illumina Infinium MethylationEPIC array in 2,358 blood samples from three UK cohorts, with replication. Methylation levels at 34.2% of CpGs were affected by SNPs, and 98% of effects were cis -acting or within 1 Mbp of the tested CpG. Our results are consistent with meQTL analyses based on the former Illumina Infinium HumanMethylation450 array. Both meQTL SNPs and CpGs with meQTLs were overrepresented in enhancers, which have improved coverage on this platform compared to previous approaches. Co-localisation analyses across genetic effects on DNA methylation and 56 human traits identified 1,520 co-localisations across 1,325 unique CpGs and 34 phenotypes, including in disease-relevant genes, such ICOSLG (inflammatory bowel disease), and USP1 and DOCK7 (total cholesterol levels). Enrichment analysis of meQTLs and integration with expression QTLs gave insights into mechanisms underlying cis -meQTLs, for example through disruption of transcription factor binding sites for CTCF and SMC3, and trans -meQTLs, for example through regulating the expression of ACD and SENP7 which can modulate DNA methylation at distal sites. Our findings improve the characterisation of the mechanisms underlying DNA methylation variability and are informative for prioritisation of GWAS variants for functional follow-ups. A results database and viewer are available online.
... This pattern can be identified by examining the correlations between variants regarding the QTL and eQTL variants' effects [141], or p values (on a logarithmic scale) [24,45], or correlations between local genomic estimated breeding values (GEBVs) and gene expression [142]. Other methods used to associate eQTL with GWAS results include transcriptome-wide association scans (TWAS) [143], Mendelian randomisation [144,145], and Bayesian colocalisation methods [142,146]. ...
Simple Summary: Milk and other dairy products are commonly consumed in many parts of the world. Dairy cattle, having millions of milk trait records, make excellent model species for understanding the genetics controlling the production of milk. This manuscript gives a summary of the current understanding of the genetic signals for milk production, in terms of the biological pathways they are involved with, and highlights a number of methods that can be used to identify the genes and variants underlying these signals. Knowledge of these variants will improve the ability of farmers and animal breeding companies to increase the rate of genetic gain for milk traits and enable the use of technologies such as gene editing. Abstract: Milk is a complex liquid, and the concentrations of many of its components are under genetic control. Many genes and pathways are known to regulate milk composition, and the purpose of this review is to highlight how the discoveries of quantitative trait loci (QTL) for milk phenotypes can elucidate these pathways. The main body of this review focuses primarily on QTL discovered in cattle (Bos taurus) as a model species for the biology of lactation, and there are occasional references to sheep genetics. The following section describes a range of techniques that can be used to help identify the causative genes underlying QTL when the underlying mechanism involves the regulation of gene expression. As genotype and phenotype databases continue to grow and diversify, new QTL will continue to be discovered, and although proving the causality of underlying genes and variants remains difficult, these new data sets will further enhance our understanding of the biology of lactation.
... Most of the AD GWAS implicated loci are non-coding [14,29] and choosing the closest gene to an index variant could miss genes that are further away or miss other regulatory mechanisms. Therefore we did not expect to find enrichment of GWAS hits (closest genes) among the differentially expressed genes, although some SNPs have been shown to be directly related to AD [37]. Nevertheless, there was a significant enrichment of differentially expressed genes in the temporal cortex associated with PRS. ...
Polygenic risk scores (PRS) have been widely adopted as a tool for measuring common variant liability and they have been shown to predict lifetime risk of Alzheimer’s disease (AD) development. However, the relationship between PRS and AD pathogenesis is largely unknown. To this end, we performed a differential gene-expression and associated disrupted biological pathway analyses of AD PRS vs. case/controls in human brain-derived cohort sample (cerebellum/temporal cortex; MayoRNAseq). The results highlighted already implicated mechanisms: immune and stress response, lipids, fatty acids and cholesterol metabolisms, endosome and cellular/neuronal death, being disrupted biological pathways in both case/controls and PRS, as well as previously less well characterised processes such as cellular structures, mitochondrial respiration and secretion. Despite heterogeneity in terms of differentially expressed genes in case/controls vs. PRS, there was a consensus of commonly disrupted biological mechanisms. Glia and microglia-related terms were also significantly disrupted, albeit not being the top disrupted Gene Ontology terms. GWAS implicated genes were significantly and in their majority, up-regulated in response to different PRS among the temporal cortex samples, suggesting potential common regulatory mechanisms. Tissue specificity in terms of disrupted biological pathways in temporal cortex vs. cerebellum was observed in relation to PRS, but limited tissue specificity when the datasets were analysed as case/controls. The largely common biological mechanisms between a case/control classification and in association with PRS suggests that PRS stratification can be used for studies where suitable case/control samples are not available or the selection of individuals with high and low PRS in clinical trials.
... Although most mQTLs do not clearly impact human traits 33 , multiple studies have provided evidence that a small fraction of mQTLs are associated with human phenotypes and can point to causal disease-relevant pathways and contexts [18][19][20][21]33,57,58 . To evaluate the impact of mQTLs on traits in a systematic manner, and compare their effects to those of eQTLs, we performed QTL-GWAS colocalization by integrating FDR < 0.05 QTLs with 83 GWAS datasets that had at least one QTL-overlapping GWAS hit (P < 5 × 10 −8 ). ...
... We observed that mQTL colocalizations were more abundant than eQTL colocalizations for almost all (91%) of GWASs (Fig. 4). Alike observations from other studies 20,21,57 , the observed overlap between eQTL-and mQTL-GWAS colocalizations is moderate, with 27% (749/2,734) of GWAS hits colocalizing with both QTL types in the same tissue (e/mQTL-shared), 55% of hits colocalizing with at least one mQTL but with no eQTLs (mQTL-specific) and 18% of hits colocalizing with at least one eQTL but with no mQTLs (eQTL-specific). The larger fraction of mQTL-specific colocalizations is consistent across trait groups (Extended Data Fig. 9a). ...
Studies of DNA methylation (DNAm) in solid human tissues are relatively scarce; tissue-specific characterization of DNAm is needed to understand its role in gene regulation and its relevance to complex traits. We generated array-based DNAm profiles for 987 human samples from the Genotype-Tissue Expression (GTEx) project, representing 9 tissue types and 424 subjects. We characterized methylome and transcriptome correlations (eQTMs), genetic regulation in cis (mQTLs and eQTLs) across tissues and e/mQTLs links to complex traits. We identified mQTLs for 286,152 CpG sites, many of which (>5%) show tissue specificity, and mQTL colocalizations with 2,254 distinct GWAS hits across 83 traits. For 91% of these loci, a candidate gene link was identified by integration of functional maps, including eQTMs, and/or eQTL colocalization, but only 33% of loci involved an eQTL and mQTL present in the same tissue type. With this DNAm-focused integrative analysis, we contribute to the understanding of molecular regulatory mechanisms in human tissues and their impact on complex traits. As part of the enhanced GTEx (eGTEx) project, 987 human samples from 9 tissue types and 424 donors are assayed using DNA methylation microarrays. Colocalization of GWAS variants, eQTLs and mQTLs shows diverse links between genetic variation, molecular phenotypes and complex traits.
... The researchers also replicated their findings against multiple pre-existing datasets. Zhao et al. (103) conducted a meta-analysis on five GWAS datasets, three eQTL datasets and three mQTL datasets, obtained mostly from brain tissue, to analyze AD-related genes. The researchers used Summary data-based Mendelian randomization to compare the different datasets, and found significant concordance between all three types of datasets in terms of AD-related genes identified. ...
Advances and reduction of costs in various sequencing technologies allows for a closer look at variations present in the non-coding regions of the human genome. Correlating non-coding variants with large-scale multi-omics data holds the promise not only of a better understanding of likely causal connections between non-coding DNA and expression of traits, but also identifying potential disease-modifying medicines. Genome-phenome association studies have created large datasets of DNA variants that are associated with multiple traits or diseases, such as Alzheimer's disease; yet, the functional consequences of variants, in particular of non-coding variants, remain largely unknown. Recent advances in functional genomics and computational approaches have led to the identification of potential roles of DNA variants, such as various quantitative trait locus (xQTL) techniques. Multi-omics assays and analytic approaches towards xQTL have identified links between genetic loci and human transcriptomic, epigenomic, proteomic, and metabolomics data. In this review, we first discuss the recent development of xQTL from multi-omics findings. We then highlight multimodal analysis of xQTL and genetic data for identification of risk genes and drug targets using Alzheimer's disease as an example. We finally discuss challenges and future research directions (e.g. artificial intelligence) for annotation of non-coding variants in complex diseases.
... Three genes of these 182 shared genes have recorded association with AD in Malacards (out of 209 genes) [47], DYRK1A [48,49], GPC1 [50][51][52] and PRNP [53][54][55]. Additionally, the POLR2E gene has been detected through another method that integrates GWAS, expression quantitative trait loci, and methylation quantitative trait loci data to identify AD-related genes [56]. ...
Background:
Genome-wide association studies have successfully identified variants associated with multiple conditions. However, generalizing discoveries across diverse populations remains challenging due to large variations in genetic composition. Methods that perform gene expression imputation have attempted to address the transferability of gene discoveries across populations, but with limited success.
Methods:
Here, we introduce a pipeline that combines gene expression imputation with gene module discovery, including a dense gene module search and a gene set variation analysis, to address the transferability issue. Our method feeds association probabilities of imputed gene expression with a selected phenotype into tissue-specific gene-module discovery over protein interaction networks to create higher-level gene modules.
Results:
We demonstrate our method's utility in three case-control studies of Alzheimer's disease (AD) for three different race/ethnic populations (Whites, African descent and Hispanics). We discovered 182 AD-associated genes from gene modules shared between these populations, highlighting new gene modules associated with AD.
Conclusions:
Our innovative framework has the potential to identify robust discoveries across populations based on gene modules, as demonstrated in AD.
... At a single level, such as the protein level, the function of genes in the biological development of lacunar stroke is difficult to explain. More epigenetic investigations, based on mQTL, single-cell sequencing, and whole-genome sequencing, are needed to design tailored therapy regimens and offer a complete understanding of the molecular mechanisms implicated in lacunar stroke [67,68]. Second, the method for detecting Slow Off-rate Modified Aptamers was limited to a subset of proteomes and did not cover the whole proteome. ...
Background
Previous genome-wide association studies (GWAS) have identified numerous risk genes for lacunar stroke, but it is challenging to decipher how they confer risk for the disease. We employed an integrative analytical pipeline to efficiently transform genetic associations to identify novel proteins for lacunar stroke.
Methods
We systematically integrated lacunar stroke genome-wide association study (GWAS) (N=7338) with human brain proteomes (N=376) to perform proteome-wide association studies (PWAS), Mendelian randomization (MR), and Bayesian colocalization. We also used an independent human brain proteomic dataset (N=152) to annotate the new genes.
Results
We found that the protein abundance of seven genes (ICA1L, CAND2, ALDH2, MADD, MRVI1, CSPG4, and PTPN11) in the brain was associated with lacunar stroke. These seven genes were mainly expressed on the surface of glutamatergic neurons, GABAergic neurons, and astrocytes. Three genes (ICA1L, CAND2, ALDH2) were causal in lacunar stroke (P < 0.05/proteins identified for PWAS; posterior probability of hypothesis 4 ≥ 75 % for Bayesian colocalization), and they were linked with lacunar stroke in confirmatory PWAS and independent MR. We also found that ICA1L is related to lacunar stroke at the brain transcriptome level.
Conclusions
Our present proteomic findings have identified ICA1L, CAND2, and ALDH2 as compelling genes that may give key hints for future functional research and possible therapeutic targets for lacunar stroke.
... SMR (Summary-based Mendelian Randomization) software was used to integrate summary-level data from the IGAP GWAS with data from BRAINEAC eQTL studies to identify genes with potential expression levels altered in certain brain regions of AD brains [19]. Individuallevel SNP genotype data from 1000 Genomes European population were used to estimate linkage disequilibrium (LD) block information. ...
Background
Large-scale genome-wide association studies have successfully identified many genetic variants significantly associated with Alzheimer’s disease (AD), such as rs429358, rs11038106, rs723804, rs13591776, and more. The next key step is to understand the function of these SNPs and the downstream biology through which they exert the effect on the development of AD. However, this remains a challenging task due to the tissue-specific nature of transcriptomic and proteomic data and the limited availability of brain tissue.In this paper, instead of using coupled transcriptomic data, we performed an integrative analysis of existing GWAS findings and expression quantitative trait loci (eQTL) results from AD-related brain regions to estimate the transcriptomic alterations in AD brain.
Results
We used summary-based mendelian randomization method along with heterogeneity in dependent instruments method and were able to identify 32 genes with potential altered levels in temporal cortex region. Among these, 10 of them were further validated using real gene expression data collected from temporal cortex region, and 19 SNPs from NECTIN and TOMM40 genes were found associated with multiple temporal cortex imaging phenotype.
Conclusion
Significant pathways from enriched gene networks included neutrophil degranulation, Cell surface interactions at the vascular wall, and Regulation of TP53 activity which are still relatively under explored in Alzheimer’s Disease while also encouraging a necessity to bind further trans-eQTL effects into this integrative analysis.
