A splicing transcriptome-wide association study identifies novel altered splicing for Alzheimer's disease susceptibility

Sun, Yanfa; Bae, Ye Eun; Zhu, Jingjing; Zhang, Zichen; Zeng, Hua; Yu, Jun; Wu, Chong; Wu, Lang

doi:10.1016/j.nbd.2023.106209

Cited by 9 publications

(4 citation statements)

References 68 publications

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“…A recent study integrating summary-level GWAS and meta-analytic cis -eQTL data found genes NDUFS2 and PRSS36 were related to AD risk [ 45 ]. Recent splicing TWAS analyses found that causal splicing introns of gene WDR33 and LRRC37A4P were associated with AD in multiple brain tissues [ 46 ]. A recent study that integrated eQTL data of blood tissue and GWAS of late-onset AD (LOAD) by a Bayesian statistical method identified risk gene ZNF226 [ 47 ].…”

Section: Resultsmentioning

confidence: 99%

Bayesian genome-wide TWAS with reference transcriptomic data of brain and blood tissues identified 141 risk genes for Alzheimer’s disease dementia

Guo,

Yang

2024

Alz Res Therapy

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Background Transcriptome-wide association study (TWAS) is an influential tool for identifying genes associated with complex diseases whose genetic effects are likely mediated through transcriptome. TWAS utilizes reference genetic and transcriptomic data to estimate effect sizes of genetic variants on gene expression (i.e., effect sizes of a broad sense of expression quantitative trait loci, eQTL). These estimated effect sizes are employed as variant weights in gene-based association tests, facilitating the mapping of risk genes with genome-wide association study (GWAS) data. However, most existing TWAS of Alzheimer's disease (AD) dementia are limited to studying only cis-eQTL proximal to the test gene. To overcome this limitation, we applied the Bayesian Genome-wide TWAS (BGW-TWAS) method to leveraging both cis- and trans- eQTL of brain and blood tissues, in order to enhance mapping risk genes for AD dementia. Methods We first applied BGW-TWAS to the Genotype-Tissue Expression (GTEx) V8 dataset to estimate cis- and trans- eQTL effect sizes of the prefrontal cortex, cortex, and whole blood tissues. Estimated eQTL effect sizes were integrated with the summary data of the most recent GWAS of AD dementia to obtain BGW-TWAS (i.e., gene-based association test) p-values of AD dementia per gene per tissue type. Then we used the aggregated Cauchy association test to combine TWAS p-values across three tissues to obtain omnibus TWAS p-values per gene. Results We identified 85 significant genes in prefrontal cortex, 82 in cortex, and 76 in whole blood that were significantly associated with AD dementia. By combining BGW-TWAS p-values across these three tissues, we obtained 141 significant risk genes including 34 genes primarily due to trans-eQTL and 35 mapped risk genes in GWAS Catalog. With these 141 significant risk genes, we detected functional clusters comprised of both known mapped GWAS risk genes of AD in GWAS Catalog and our identified TWAS risk genes by protein-protein interaction network analysis, as well as several enriched phenotypes related to AD. Conclusion We applied BGW-TWAS and aggregated Cauchy test methods to integrate both cis- and trans- eQTL data of brain and blood tissues with GWAS summary data, identifying 141 TWAS risk genes of AD dementia. These identified risk genes provide novel insights into the underlying biological mechanisms of AD dementia and potential gene targets for therapeutics development.

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Section: Resultsmentioning

confidence: 99%

Bayesian genome-wide TWAS with reference transcriptomic data of brain and blood tissues identified 141 risk genes for Alzheimer’s disease dementia

Guo,

Yang

2024

Alz Res Therapy

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“…Homology considerations suggest that this is likely the case for PPP1R13B as well. Indeed, altered splicing of PPP1R13B has just recently been described [ 26 ] and this might play a predominant role in distinct molecular subgroups in AML as well. In line, a transcriptome analysis in pediatric CBF AML has revealed distinct alternative splicing events which are differentially expressed in the respective subgroups [ 27 ].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of apoptosis stimulating protein of TP53-1 (ASPP1/PPP1R13B) to predict therapy resistance and overall survival in acute myeloid leukemia (AML)

Schittenhelm,

Kaiser,

Győrffy

et al. 2024

Cell Death Dis

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ASPP1 (PPP1R13B) belongs to a family of p53-binding proteins and enhances apoptosis by stimulation of p53-transactivation of selected proapoptotic target genes. It is preferentially expressed in hematopoietic stem cells (HSC) and together with p53 preserves the genomic integrity of the HSC pool. Consequently, dysfunction of ASPP1 has been associated with malignant transformation and development of acute lymphoblastic leukemias and lymphomas - whereas methylation of the promoter region is linked to reduced transcription and ultimately attenuated expression of ASPP1. The role of ASPP1 in AML is not known. We now show that impaired regulation of PPP1R13B contributes to the biology of leukemogenesis and primary therapy resistance in AML. PPP1R13B mRNA expression patterns thereby define a distinct prognostic profile - which is not reflected by the European leukemia net (ELN) risk score. These findings have direct therapeutic implications and we provide a strategy to restore ASPP1 protein levels using hypomethylating agents to sensitize cells towards proapoptotic drugs. Prospective clinical trials are warranted to investigate the role of ASPP1 (PPP1R13B) as a biomarker for risk stratification and as a potential therapeutic target to restore susceptibility to chemotherapy.

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“…LRRC37A and its subtype LRRC37A2 have been identified as pivotal factors in the development of Parkinson's disease [ 38 , 39 ] . Additionally, the LRRC37A4P and LINC02210 subtypes are associated with susceptibility to Alzheimer's disease [ 40 ] . Variants in KANSL1-AS1 and DND1P1 may contribute to an increased risk of anxiety [ 41 ] .…”

Section: Discussionmentioning

confidence: 99%

Mendelian randomization and single-cell analysis reveal relationships between sleep disorders and myocardial infarction

Luo,

Song,

Xiong

et al. 2024

Preprint

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BackgroundMyocardial infarction (MI) and sleep apnoea are common diseases, and epidemiologic evidence suggests that the two are related. Methods Our research relied on genome-wide association summary statistics (GWAS) pertaining to patients diagnosed with sleep apnoea, snoring, insomnia, or narcolepsy, as well as individuals diagnosed with MI. Using a bidirectional Mendelian randomization (MR) approach, we investigated the relationships between MI and sleep apnoea, snoring, insomnia and narcolepsy. In addition, colocalization analysis was used to identify genes whose increased expression may contribute to sleep disorders. Subsequently, we illustrated that the upregulation of these genes serves as a negative prognostic factor for MI through the application of summary-data-based MR (SMR). Furthermore, we analysedsingle-cell data obtained from individuals with MI to elucidate the subcellular localization of these genes and assess the enriched pathways. ResultsBidirectional MR analysis suggested no causal association between sleep apnoea and MI. Snoring, insomnia, and narcolepsy are risk factors for MI. The genes associated with snoring, insomnia, and narcolepsy were all colocalized with eQTLs. SMR demonstrated that these genes are risk factors for MI. Single-cell analysis revealed that these genes were predominantly localized to cardiac endothelial cells and enriched in the Rap1 pathway. Conclusion Our study revealedthat snoring, insomnia, and narcolepsy are risk factors for MI. The primary mechanism could involve sleep disorders inducing alterations in the expression abundance of LINC02210, LRRC37A, LRRC37A2, KANSL1-AS1, and DND1P1, thereby increasing the risk of MI via the Rap1 pathway.

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A splicing transcriptome-wide association study identifies novel altered splicing for Alzheimer's disease susceptibility

Cited by 9 publications

References 68 publications

Bayesian genome-wide TWAS with reference transcriptomic data of brain and blood tissues identified 141 risk genes for Alzheimer’s disease dementia

Bayesian genome-wide TWAS with reference transcriptomic data of brain and blood tissues identified 141 risk genes for Alzheimer’s disease dementia

Evaluation of apoptosis stimulating protein of TP53-1 (ASPP1/PPP1R13B) to predict therapy resistance and overall survival in acute myeloid leukemia (AML)

Mendelian randomization and single-cell analysis reveal relationships between sleep disorders and myocardial infarction

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