ArticleProceedings of the National Academy of Sciences of the United States of America2025
Computationally efficient whole-genome quantile regression at biobank scale.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Optimizing phenotype scale improves genetic analyses in large-scale biobanks.bioRxiv : the preprint server for biology · 2026Article
- Enhancing polygenic risk prediction by modeling quantile-specific genetic effects.Scientific reports · 2026Article
- Computationally efficient whole-genome quantile regression at biobank scale.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Quantile-specific confounding: correction for subtle population stratification via quantile regression.Genetics · 2025Article
Corrections and comments
- Update of
Authors and funding
8 authors.
Funding
Abstract
Genotype-phenotype associations can be context-dependent and dynamic in nature leading to heterogeneity of genetic effects across different parts of the phenotype distribution. Quantile regression, an alternative to linear regression for continuous phenotypes, is particularly well suited for detecting and characterizing heterogeneous genotype-phenotype associations. Here, we propose a computationally efficient whole-genome quantile regression technique, Regenie.QRS, for biobank-scale genome-wide association studies (GWAS) data with genetic structure. Our approach first estimates the polygenic effect, and then incorporates this effect as an offset in the nonmixed quantile regression model. Our simulations demonstrate robust control of type I error and higher power to detect heterogeneous associations relative to linear regression in GWAS and improved power over the marginal quantile regression tests. We present applications using data from the UK Biobank and the ProgeNIA/SardiNIA project, where we show the advantages of Regenie.QRS in identifying and characterizing heterogeneous genetic effects. To cite just one interesting example, using quantile regression, we are able to show that even though variants at the
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.