ArticleNature communications2021
A scalable unified framework of total and allele-specific counts for cis-QTL, fine-mapping, and prediction.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- TIGER: The gene expression regulatory variation landscape of human pancreatic islets.Cell reports · 2021Pooled it
- Machine learning and statistical methods for molecular quantitative trait loci.Nature reviews. Genetics · 2026Review
- Experimental and computational methods for allelic imbalance analysis from single-nucleus RNA-seq data.Genome biology · 2026Article
- Computational methods for allele-specific expression in single cells.Trends in genetics : TIG · 2024Review
- Decoding polygenic diseases: advances in noncoding variant prioritization and validation.Trends in cell biology · 2024Review
- Secreted Protein Profiling of Human Aortic Smooth Muscle Cells Identifies Vascular Disease Associations.Arteriosclerosis, thrombosis, and vascular biology · 2024Article
- Haplotype-aware modeling of cis-regulatory effects highlights the gaps remaining in eQTL data.Nature communications · 2024Article
- MESuSiE enables scalable and powerful multi-ancestry fine-mapping of causal variants in genome-wide association studies.Nature genetics · 2024Article
- eQTL studies: from bulk tissues to single cells.Journal of genetics and genomics = Yi chuan xue bao · 2023Review
- Secreted protein profiling of human aortic smooth muscle cells identifies vascular disease associations.medRxiv : the preprint server for health sciences · 2023Article
- Blood cell traits' GWAS loci colocalization with variation in PU.1 genomic occupancy prioritizes causal noncoding regulatory variants.Cell genomics · 2023Article
- SEESAW: detecting isoform-level allelic imbalance accounting for inferential uncertainty.Genome biology · 2023Article
- Genetic regulation of cell-type specific chromatin accessibility shapes the etiology of brain diseases.bioRxiv : the preprint server for biology · 2023Article
- eQTL Studies: from Bulk Tissues to Single Cells.ArXiv · 2023Article
- Article
- Genetic determinants of chromatin reveal prostate cancer risk mediated by context-dependent gene regulation.Nature genetics · 2022Article
- Allelic imbalance of chromatin accessibility in cancer identifies candidate causal risk variants and their mechanisms.Nature genetics · 2022Article
- eQTL mapping using allele-specific count data is computationally feasible, powerful, and provides individual-specific estimates of genetic effects.PLoS genetics · 2022Article
- A scalable unified framework of total and allele-specific counts for cis-QTL, fine-mapping, and prediction.Nature communications · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Genetic studies of the transcriptome help bridge the gap between genetic variation and phenotypes. To maximize the potential of such studies, efficient methods to identify expression quantitative trait loci (eQTLs) and perform fine-mapping and genetic prediction of gene expression traits are needed. Current methods that leverage both total read counts and allele-specific expression to identify eQTLs are generally computationally intractable for large transcriptomic studies. Here, we describe a unified framework that addresses these needs and is scalable to thousands of samples. Using simulations and data from GTEx, we demonstrate its calibration and performance. For example, mixQTL shows a power gain equivalent to a 29% increase in sample size for genes with sufficient allele-specific read coverage. To showcase the potential of mixQTL, we apply it to 49 GTEx tissues and find 20% additional eQTLs (FDR < 0.05, per tissue) that are significantly more enriched among trait associated variants and candidate cis-regulatory elements comparing to the standard approach.
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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.