ArticleBiometrics2012
A statistical framework for eQTL mapping using RNA-seq data.
Article in Biometrics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers.
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Who cites it
87 citing papers in PubMed.
- DetectGxT: detecting gene-by-treatment interactions on molecular count phenotypes accounting for allelic additivity.bioRxiv : the preprint server for biology · 2026Article
- Probing the limits of cis-acting gene regulation using a model of allelic imbalance quantitative trait loci.PLoS genetics · 2025Article
- Probabilistic classification of gene-by-treatment interactions on molecular count phenotypes.PLoS genetics · 2025Article
- Population-level allelic dispersion modeling by maelstRom yields genome-wide maps of allele-specific dysregulation during early carcinogenesis.GigaScience · 2025Article
- A holistic approach to understanding immune-mediated inflammatory diseases: bioinformatic tools to integrate omics data.Computational and structural biotechnology journal · 2024Review
- Computational methods for allele-specific expression in single cells.Trends in genetics : TIG · 2024Review
- eQTL studies: from bulk tissues to single cells.Journal of genetics and genomics = Yi chuan xue bao · 2023Review
- Learning gene networks under SNP perturbation using SNP and allele-specific expression data.bioRxiv : the preprint server for biology · 2023Article
- SEESAW: detecting isoform-level allelic imbalance accounting for inferential uncertainty.Genome biology · 2023Article
- A computational method for cell type-specific expression quantitative trait loci mapping using bulk RNA-seq data.Nature communications · 2023Article
- eQTL Studies: from Bulk Tissues to Single Cells.ArXiv · 2023Article
- Characterization of sequence determinants of enhancer function using natural genetic variation.eLife · 2022Article
- DeCAF: a novel method to identify cell-type specific regulatory variants and their role in cancer risk.Genome biology · 2022Article
- Airpart: interpretable statistical models for analyzing allelic imbalance in single-cell datasets.Bioinformatics (Oxford, England) · 2022Article
- eQTL mapping using allele-specific count data is computationally feasible, powerful, and provides individual-specific estimates of genetic effects.PLoS genetics · 2022Article
- Adaptive eQTLs reveal the evolutionary impacts of pleiotropy and tissue-specificity while contributing to health and disease.HGG advances · 2022Article
- scDALI: modeling allelic heterogeneity in single cells reveals context-specific genetic regulation.Genome biology · 2022Article
- Early Breast Cancer Evolution by Autosomal Broad Copy Number Alterations.International journal of genomics · 2022Article
- eQTLs as causal instruments for the reconstruction of hormone linked gene networks.Frontiers in endocrinology · 2022Review
- Inheritance of Yield Components and Morphological Traits in Avocado cv. Hass From "Criollo" "Elite Trees"Frontiers in plant science · 2022Article
27 more citing papers are in PubMed but not listed here.
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1 author.
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Abstract
RNA-seq may replace gene expression microarrays in the near future. Using RNA-seq, the expression of a gene can be estimated using the total number of sequence reads mapped to that gene, known as the total read count (TReC). Traditional expression quantitative trait locus (eQTL) mapping methods, such as linear regression, can be applied to TReC measurements after they are properly normalized. In this article, we show that eQTL mapping, by directly modeling TReC using discrete distributions, has higher statistical power than the two-step approach: data normalization followed by linear regression. In addition, RNA-seq provides information on allele-specific expression (ASE) that is not available from microarrays. By combining the information from TReC and ASE, we can computationally distinguish cis- and trans-eQTL and further improve the power of cis-eQTL mapping. Both simulation and real data studies confirm the improved power of our new methods. We also discuss the design issues of RNA-seq experiments. Specifically, we show that by combining TReC and ASE measurements, it is possible to minimize cost and retain the statistical power of cis-eQTL mapping by reducing sample size while increasing the number of sequence reads per sample. In addition to RNA-seq data, our method can also be employed to study the genetic basis of other types of sequencing data, such as chromatin immunoprecipitation followed by DNA sequencing data. In this article, we focus on eQTL mapping of a single gene using the association-based method. However, our method establishes a statistical framework for future developments of eQTL mapping methods using RNA-seq data (e.g., linkage-based eQTL mapping), and the joint study of multiple genetic markers and/or multiple genes.
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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.