ArticleBioinformatics (Oxford, England)2016
Using genomic annotations increases statistical power to detect eGenes.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework.Journal of translational medicine · 2024Article
- DeepNull models non-linear covariate effects to improve phenotypic prediction and association power.Nature communications · 2022Article
- Article
- MARS: leveraging allelic heterogeneity to increase power of association testing.Genome biology · 2021Article
- Finding associated variants in genome-wide association studies on multiple traits.Bioinformatics (Oxford, England) · 2018Article
- Cis-SNPs Set Testing and PrediXcan Analysis for Gene Expression Data using Linear Mixed Models.Scientific reports · 2017Article
- Applying meta-analysis to genotype-tissue expression data from multiple tissues to identify eQTLs and increase the number of eGenes.Bioinformatics (Oxford, England) · 2017Article
- Computational Prediction of the Global Functional Genomic Landscape: Applications, Methods, and Challenges.Human heredity · 2016Review
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Authors and funding
7 authors.
Funding
Abstract
motivationExpression quantitative trait loci (eQTLs) are genetic variants that affect gene expression. In eQTL studies, one important task is to find eGenes or genes whose expressions are associated with at least one eQTL. The standard statistical method to determine whether a gene is an eGene requires association testing at all nearby variants and the permutation test to correct for multiple testing. The standard method however does not consider genomic annotation of the variants. In practice, variants near gene transcription start sites (TSSs) or certain histone modifications are likely to regulate gene expression. In this article, we introduce a novel eGene detection method that considers this empirical evidence and thereby increases the statistical power.
resultsWe applied our method to the liver Genotype-Tissue Expression (GTEx) data using distance from TSSs, DNase hypersensitivity sites, and six histone modifications as the genomic annotations for the variants. Each of these annotations helped us detected more candidate eGenes. Distance from TSS appears to be the most important annotation; specifically, using this annotation, our method discovered 50% more candidate eGenes than the standard permutation method. CONTACT: buhm.han@amc.seoul.kr or eeskin@cs.ucla.edu.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.