ArticleGenome biology2022
PCA outperforms popular hidden variable inference methods for molecular QTL mapping.
Article in Genome biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 1 of them a synthesis that pooled it.
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Who cites it
58 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A meta-analysis of single-nucleus expression quantitative trait loci linking genetic risk to brain disorders.Nature genetics · 2026Pooled it
- A scalable approach to investigating sequence-to-function predictions from personal genomes.Nature methods · 2026Article
- Dual-transcriptomic analysis of human nasal transcriptome and microbiome reveals host-bacteria associations in symptomatic respiratory infection.BMC genomics · 2026Article
- Population-scale detection of methylation outliers from long-read genome sequencing.medRxiv : the preprint server for health sciences · 2026Article
- FastGxC: Fast and powerful context-specific eQTL mapping in bulk and single-cell data.Cell genomics · 2026Article
- Identifying expression and DNA methylation biomarkers for lung adenocarcinoma risk in East Asia.Journal of the National Cancer Institute · 2026Article
- Fine-tuning sequence-to-expression models on personal genome and transcriptome data.Genome biology · 2026Article
- Decoding the enhancer-driven regulatory network for lactoferrin across mammalian species.Zoological research · 2026Article
- USADAE: a deep learning approach to disentangle hidden covariates in RNA-seq data.Briefings in bioinformatics · 2026Article
- Single-cell full-length transcriptome of human lung reveals genetic effects on isoform regulation beyond gene-level expression.bioRxiv : the preprint server for biology · 2026Article
- Lessons from single cell omics: admixed American ancestry and sex confer cardiometabolic disease risk in Mexicans.Genome medicine · 2026Article
- Single-cell lung eQTL dataset of Asian never-smokers highlights the roles of alveolar cells in lung cancer etiology.bioRxiv : the preprint server for biology · 2026Article
- OmiGA for ultra-efficient molecular quantitative trait loci mapping.Nature communications · 2026Article
- Integrated analysis of GWAS and molQTLs reveals cell-specific genetic variants in the porcine immune system.Communications biology · 2026Article
- Integrating multi-omic QTLs and predictive models reveals regulatory architectures at immune related GWAS loci in CD4+ T cells.medRxiv : the preprint server for health sciences · 2026Article
- Molecular QTL are enriched for structural variants in a cattle long-read cohort.Communications biology · 2026Article
- m6AConquer: a consistently quantified and orthogonally validated database for the N6-methyladenosine (m6A) epitranscriptome.Nucleic acids research · 2026Article
- COX7A1-mediated mitochondrial dysfunction can induce ferroptosis in endometrial cancer cells.PloS one · 2026Article
- Article
- distQTL: distribution quantitative trait loci identification by population-scale single-cell data.NAR genomics and bioinformatics · 2025Article
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5 authors.
Funding
Abstract
backgroundEstimating and accounting for hidden variables is widely practiced as an important step in molecular quantitative trait locus (molecular QTL, henceforth "QTL") analysis for improving the power of QTL identification. However, few benchmark studies have been performed to evaluate the efficacy of the various methods developed for this purpose.
resultsHere we benchmark popular hidden variable inference methods including surrogate variable analysis (SVA), probabilistic estimation of expression residuals (PEER), and hidden covariates with prior (HCP) against principal component analysis (PCA)-a well-established dimension reduction and factor discovery method-via 362 synthetic and 110 real data sets. We show that PCA not only underlies the statistical methodology behind the popular methods but is also orders of magnitude faster, better-performing, and much easier to interpret and use.
conclusionsTo help researchers use PCA in their QTL analysis, we provide an R package PCAForQTL along with a detailed guide, both of which are freely available at https://github.com/heatherjzhou/PCAForQTL . We believe that using PCA rather than SVA, PEER, or HCP will substantially improve and simplify hidden variable inference in QTL mapping as well as increase the transparency and reproducibility of QTL research.
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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.