A transcriptome-wide association study identifies novel blood-based gene biomarker candidates for Alzheimer’s disease risk

Sun, Yanfa; Zhou, Dan; Rahman, Rezanur; Zhu, Jingjing; Ghoneim, Dalia; Cox, Nancy J.; Beach, Thomas G.; Wu, Chong; Gamazon, Eric R.; Wu, Lang

doi:10.1093/hmg/ddab229

Cited by 12 publications

(4 citation statements)

References 69 publications

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“…17 Notably, we modified the original script of UTMOST by using uniform hyperparameters across different folds to make the hyperparameters directly comparable. 17,25 We confirmed that the modified UTMOST gave an unbiased estimate of prediction performance using empirical datasets. 21 Details of the modification can be found at https://github.…”

Section: Building Normal Prostate Tissue Gene Expression Prediction M...supporting

confidence: 64%

A transcriptome‐wide association study identifies novel candidate susceptibility genes for prostate cancer risk

Liu

Zhu

Zhou

et al. 2021

Intl Journal of Cancer

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A large proportion of heritability for prostate cancer risk remains unknown. Transcriptome‐wide association study combined with validation comparing overall levels will help to identify candidate genes potentially playing a role in prostate cancer development. Using data from the Genotype‐Tissue Expression Project, we built genetic models to predict normal prostate tissue gene expression using the statistical framework PrediXcan, a modified version of the unified test for molecular signatures and Joint‐Tissue Imputation. We applied these prediction models to the genetic data of 79 194 prostate cancer cases and 61 112 controls to investigate the associations of genetically determined gene expression with prostate cancer risk. Focusing on associated genes, we compared their expression in prostate tumor vs normal prostate tissue, compared methylation of CpG sites located at these loci in prostate tumor vs normal tissue, and assessed the correlations between the differentiated genes' expression and the methylation of corresponding CpG sites, by analyzing The Cancer Genome Atlas (TCGA) data. We identified 573 genes showing an association with prostate cancer risk at a false discovery rate (FDR) ≤ 0.05, including 451 novel genes and 122 previously reported genes. Of the 573 genes, 152 showed differential expression in prostate tumor vs normal tissue samples. At loci of 57 genes, 151 CpG sites showed differential methylation in prostate tumor vs normal tissue samples. Of these, 20 CpG sites were correlated with expression of 11 corresponding genes. In this TWAS, we identified novel candidate susceptibility genes for prostate cancer risk, providing new insights into prostate cancer genetics and biology.

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Section: Building Normal Prostate Tissue Gene Expression Prediction M...supporting

confidence: 64%

A transcriptome‐wide association study identifies novel candidate susceptibility genes for prostate cancer risk

Liu

Zhu

Zhou

et al. 2021

Intl Journal of Cancer

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“… Novikova et al (2021) integrated AD GWAS data with myeloid-specific epigenomic and transcriptomic datasets, and identified 11 genes as risk factors for AD to 20 loci. Sun Y. F. et al (2022) utilized two gene expression prediction models of blood to predict meta-GWAS data, determining the expression of 108 genes in blood associated with AD risk, and identified 15 differentially expressed genes (DEGs). Kosoy et al (2022) employed transcriptional and chromatin accessibility analysis on primary human astrocytes derived from 150 donors to identify putative regulatory mechanisms of 21 AD risk loci.…”

Section: Methods Of Identification Of Candidate Biomarkers For Admentioning

confidence: 99%

A review and analysis of key biomarkers in Alzheimer’s disease

Zhang,

Liu,

Zhang

et al. 2024

Front. Neurosci.

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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects over 50 million elderly individuals worldwide. Although the pathogenesis of AD is not fully understood, based on current research, researchers are able to identify potential biomarker genes and proteins that may serve as effective targets against AD. This article aims to present a comprehensive overview of recent advances in AD biomarker identification, with highlights on the use of various algorithms, the exploration of relevant biological processes, and the investigation of shared biomarkers with co-occurring diseases. Additionally, this article includes a statistical analysis of key genes reported in the research literature, and identifies the intersection with AD-related gene sets from databases such as AlzGen, GeneCard, and DisGeNet. For these gene sets, besides enrichment analysis, protein–protein interaction (PPI) networks utilized to identify central genes among the overlapping genes. Enrichment analysis, protein interaction network analysis, and tissue-specific connectedness analysis based on GTEx database performed on multiple groups of overlapping genes. Our work has laid the foundation for a better understanding of the molecular mechanisms of AD and more accurate identification of key AD markers.

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“…Multi-omics analyses are well-established to discover functional genes, drug targets, and biomarkers in various complex diseases, including neurological disorders [22,[31][32][33][34], cancers [35][36][37][38][39], and cardiovascular disease [40]. Multi-omics data integration provides an innovative avenue to bring multi-layer biological information to systematically identify novel insights into the complex pathobiology of diseases [41,42].…”

Section: Introductionmentioning

confidence: 99%

“…TWAS framework integrates eQTLs data with GWAS to reveal associations between genes and traits [13]. TWAS studies determine the association of genetically predicted gene expression levels of genes with complex traits; the TWAS framework is unable to provide the effect of causal strength of the variants and horizontal pleiotropy [22]. Colocalisation is also an integrative gene-prioritisation method that integrates eQTLs data with GWAS signals to identify the co-occurrence of variants between pairs of traits [23].…”

Section: Introductionmentioning

confidence: 99%

Integration of Mendelian randomisation and systems biology models to identify novel blood-based biomarkers for stroke

Islam

Rahman

Khan

et al. 2023

Preprint

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Stroke is the second largest cause of mortality in the world. Genome-wide association studies (GWAS) have identified some genetic variants associated with stroke risk, but their putative functional causal genes are unknown. Hence, we aimed to identify putative functional causal gene biomarkers of stroke risk. We used a summary-based Mendelian randomisation (SMR) approach to identify the pleiotropic associations of genetically regulated traits (i.e., gene expression and DNA methylation) with stroke risk. Using SMR approach, we integrated cis-expression quantitative loci (cis-eQTLs) and cis-methylation quantitative loci (cis-mQTLs) data with GWAS summary statistics of stroke. We also utilised heterogeneity in dependent instruments (HEIDI) test to distinguish pleiotropy from linkage from the observed associations identified through SMR analysis. Our integrative SMR analyses and HEIDI test revealed 45 candidate biomarker genes (FDR < 0.05; PHEIDI>0.01) that were pleiotropically or potentially causally associated with stroke risk. Of those candidate biomarker genes, 10 genes (HTRA1, PMF1, FBN2, C9orf84, COL4A1, BAG4, NEK6, SH2B3, SH3PXD2A, ACAD10) were differentially expressed in genome-wide blood transcriptomics data from stroke and healthy individuals (FDR<0.05). Functional enrichment analysis of the identified candidate biomarker genes revealed gene ontologies and pathways involved in stroke, including cell aging, metal ion binding and oxidative damage. Based on the evidence of genetically regulated expression of genes through SMR and directly measured expression of genes in blood, our integrative analysis suggests ten genes as blood biomarkers of stroke risk. Furthermore, our study provides a better understanding of the influence of DNA methylation on the expression of genes linked to stroke risk.

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A transcriptome-wide association study identifies novel blood-based gene biomarker candidates for Alzheimer’s disease risk

Cited by 12 publications

References 69 publications

A transcriptome‐wide association study identifies novel candidate susceptibility genes for prostate cancer risk

A transcriptome‐wide association study identifies novel candidate susceptibility genes for prostate cancer risk

A review and analysis of key biomarkers in Alzheimer’s disease

Integration of Mendelian randomisation and systems biology models to identify novel blood-based biomarkers for stroke

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