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Modeling prediction error improves power of transcriptome-wide association studies
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What this work says
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What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/gepi.22290openalex-snapshot
Investigation of prediction accuracy and the impact of sample size, ancestry, and tissue in transcriptome‐wide association studies
inferred← citeshttps://doi.org/10.1038/s41588-019-0385-zopenalex-snapshot
Opportunities and challenges for transcriptome-wide association studies
inferred← citeshttps://doi.org/10.1101/219428openalex-snapshot
A Bayesian approach to mediation analysis predicts 206 causal target genes in Alzheimer’s disease
inferred← citeshttps://doi.org/10.1101/529297openalex-snapshot
Leveraging functional annotation to identify genes associated with complex diseases
inferred← citeshttps://doi.org/10.1101/546580openalex-snapshot
Quantifying the impact of genetically regulated expression on complex traits and diseases
inferred← citeshttps://doi.org/10.1371/journal.pcbi.1008315openalex-snapshot
Leveraging functional annotation to identify genes associated with complex diseases
inferred← citeshttps://doi.org/10.15302/j-qb-020-0228openalex-snapshot
Transcriptome wide association studies: general framework and methods
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A Powerful Framework for Integrating eQTL and GWAS Summary Data
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