On the interpretation of transcriptome-wide association studies

Leeuw, de; Werme,; Savage,; Peyrot,; Posthuma,

doi:10.1101/2021.08.15.456414

Cited by 7 publications

(5 citation statements)

References 60 publications

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“…To dissect whether regulation of gene expression might underlie local r g s between disease traits, we performed local r g analyses using expression quantitative trait loci (eQTLs) from eQTLGen 43 and PsychENCODE 44 , which represent large human blood and brain expression datasets, respectively ( Table 1 ). We used LAVA to study relationships between gene expression and disease traits on account of its ability to model the uncertainty in eQTL effect estimates (unlike the commonly used TWAS framework, which has been shown to have an increased type 1 error rate 45 , as a result of it not accounting for the uncertainty in the estimated genetic component of gene expression). In addition, where three-way relationships were observed between 2 disease traits and an eQTL, we computed partial correlations to determine whether correlations between disease traits could be explained by the eQTL.…”

Section: Resultsmentioning

confidence: 99%

“…We restricted analyses to the 5 LD blocks highlighted in Figure 2 45,893,307), which contained genes of interest to at least one of the disease traits implicated by local ‫ݎ‬ analyses. From these LD blocks of interest, we defined genic regions (gene start and end coordinates ± 100 kb) for all overlapping protein-coding, antisense or lincRNA genes (n = 92).…”

Section: Incorporation Of Gene Expression Traits To Facilitate Functi...mentioning

confidence: 99%

“…LD block locations were in reference to build GRCh37 and are presented in the format: LD block identifier, chromosome:start-end. 45,893,307). From these LD blocks of interest, we defined genic regions for all protein-coding, antisense or lincRNA genes that overlapped an LD block of interest.…”

Section: Defining Genomic Regions For Local Genetic Correlation Analysismentioning

confidence: 99%

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Local genetic correlations exist among neurodegenerative and neuropsychiatric diseases

Wagen

Lona-Durazo

Scholz

et al. 2022

Preprint

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Neurodegenerative diseases overlap neuropathologically and clinically, frequently manifesting with co-pathologies and comorbid symptoms that span multiple domains, including the neuropsychiatric. Genetically, shared risk loci have been identified across neurodegenerative diseases; however, global analyses of genetic correlation (rg) show minimal rg among neurodegenerative diseases or across neurodegenerative and neuropsychiatric diseases. Importantly, local rgs can exist in the absence of global relationships and can offer valuable insight into underlying shared biological mechanisms. To determine whether regions of local rg exist among common neurodegenerative and neuropsychiatric diseases, we applied local analysis of [co]variant annotation (LAVA) to genome-wide association studies of 3 neurodegenerative diseases (Alzheimer's disease, Lewy body dementia and Parkinson's disease) and 3 neuropsychiatric disorders (bipolar disorder, major depressive disorder and schizophrenia). In addition, we used data from blood- and brain-derived expression quantitative trait loci (eQTLs) to facilitate functional interpretation of local rgs between disease traits. We identified several local rgs missed in global analyses, including between (i) all 3 neurodegenerative diseases and schizophrenia and (ii) Alzheimer's and Parkinson's disease. For those local rgs identified in genomic regions containing well-known, disease-implicated genes, such as SNCA, CLU and APOE, incorporation of eQTLs suggested that genetic overlaps between diseases may be driven by more than one gene. Collectively, our data demonstrate that complex genetic relationships exist among neurodegenerative and neuropsychiatric diseases and highlight putative pleiotropic genomic regions and genes, findings that imply sharing of pathogenic processes and the potential existence of common therapeutic targets.

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Section: Resultsmentioning

confidence: 99%

Section: Incorporation Of Gene Expression Traits To Facilitate Functi...mentioning

confidence: 99%

Section: Defining Genomic Regions For Local Genetic Correlation Analysismentioning

confidence: 99%

See 1 more Smart Citation

Local genetic correlations exist among neurodegenerative and neuropsychiatric diseases

Wagen

Lona-Durazo

Scholz

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…Previously, we showed that naïve application of MR without sensitivity analyses may yield over 30% unreliable results 5 . A recent study further suggested that 51% of results from transcriptomewide association studies could not be confirmed by genetic colocalization 49 . Another study showed the importance of distinguishing disease-causing gene expression from diseaseinduced gene expression by evaluating reverse causality using genetic data 28,50 .…”

Section: Discussionmentioning

confidence: 99%

Proteome-wide Mendelian randomization in global biobank meta-analysis reveals multi-ancestry drug targets for common diseases

Zhao

Rasheed

Nøst

et al. 2022

Preprint

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Proteome-wide Mendelian randomization (MR) shows value in prioritizing drug targets in Europeans, but limited data has made identification of causal proteins in other ancestries challenging. Here we present a multi-ancestry proteome-wide MR analysis pipeline based on cross-population data from the Global Biobank Meta-analysis Initiative (GBMI). We estimated the causal effects of 1,545 proteins on eight complex diseases in up to 32,658 individuals of African ancestries and 1.22 million individuals of European ancestries. We identified 45 and seven protein-disease pairs with MR and genetic colocalization evidence in the two ancestries respectively. 15 protein-disease pairs showed evidence of differential effects between males and females. A multi-ancestry MR comparison identified two protein-disease pairs with MR evidence of an effect in both ancestries, seven pairs with European-specific effects and seven with African-specific effects. Integrating these MR signals with observational and clinical trial evidence, we were able to evaluate the efficacy of one existing drug, identify seven drug repurposing opportunities and predict seven novel effects of proteins on diseases. Our results highlight the value of proteome-wide MR in informing the generalisability of drug targets across ancestries and illustrate the value of multi-cohort and biobank meta-analysis of genetic data for drug development.

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“…Therefore, we used all tissue types in GTEx, regardless of disease relevance. In addition, the methodological limitations of TWAS 9,59 might affect drug discovery using GReX.…”

Section: Discussionmentioning

confidence: 99%

A practical guideline of genomics-driven drug discovery in the era of global biobank meta-analysis

Namba

Konuma

et al. 2021

Preprint

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SummaryGenomics-driven drug discovery is indispensable for accelerating the development of novel therapeutic targets. However, the drug discovery framework based on evidence from genome-wide association studies (GWAS) has not been established, especially for cross-population GWAS meta-analysis. Here, we introduce a practical guideline for genomics-driven drug discovery for cross-population meta-analysis, as lessons from the Global Biobank Meta-analysis Initiative (GBMI). Our drug discovery framework encompassed three methodologies and was applied to the 13 common diseases targeted by GBMI (Nmean = 1,329,242). First, we evaluated the overlap enrichment between disease risk genes and the drug-target genes of the disease-relevant medication categories. An omnibus approach integrating the four gene prioritization tools yielded twice the enrichment in the disease-relevant medication categories compared with any single tool, and identified drugs with approved indications for asthma, gout, and venous thromboembolism. Second, we performed an endophenotype Mendelian randomization analysis using protein quantitative trait loci as instrumental variables. After the application of quality controls, including a colocalization analysis, significant causal relationships were estimated for 18 protein–disease pairs, including MAP2K inhibitors for heart failure. Third, we conducted an in silico screening for negative correlations between genetically determined disease case–control gene expression profiles and compound-regulated ones. Significant negative correlations were observed for 31 compound–disease pairs, including a histone deacetylase inhibitor for asthma. Integration of the three methodologies provided a comprehensive catalog of candidate drugs for repositioning, nominating promising drug candidates targeting the genes involved in the coagulation process for venous thromboembolism. Our study highlighted key factors for successful genomics-driven drug discovery using cross-population meta-analysis.

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On the interpretation of transcriptome-wide association studies

Cited by 7 publications

References 60 publications

Local genetic correlations exist among neurodegenerative and neuropsychiatric diseases

Local genetic correlations exist among neurodegenerative and neuropsychiatric diseases

Proteome-wide Mendelian randomization in global biobank meta-analysis reveals multi-ancestry drug targets for common diseases

A practical guideline of genomics-driven drug discovery in the era of global biobank meta-analysis

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