ArticleProceedings of the National Academy of Sciences of the United States of America2022
A quantile integral linear model to quantify genetic effects on phenotypic variability.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 35 citations in OpenAlex.
- An introduction to polygenic scores - methodological basics and recent advances.Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2026Article
- Optimizing phenotype scale improves genetic analyses in large-scale biobanks.bioRxiv : the preprint server for biology · 2026Article
- Detecting gene-environment interactions to guide personalized intervention: Boosting distributional regression for polygenic scores.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Enhancing polygenic risk prediction by modeling quantile-specific genetic effects.Scientific reports · 2026Article
- Genetic Impacts on Variability of Body Fat Distribution Uncover Gene-Environment and Gene-Gene Interactions.bioRxiv : the preprint server for biology · 2026Article
- The geometry of G × E: How scaling and endogenous treatment effects shape interaction direction.PLoS genetics · 2026Article
- Polygenic scores capture genetic modification of the adiposity-cardiometabolic risk factor relationship.Cell genomics · 2026Article
- Polygenic modeling of genetic effects on both phenotypic mean and variance: distributional regression for BMI, blood and urine biomarkers in the UK Biobank.Frontiers in bioinformatics · 2026Article
- Distributional genetic effects reveal context-dependent molecular regulation in human brain aging and Alzheimer's disease.Research square · 2025Article
- Quantile-specific confounding: correction for subtle population stratification via quantile regression.Genetics · 2025Article
- Detecting latent interaction effects when analyzing binary traits.PLoS genetics · 2025Article
- PIGEON: a statistical framework for estimating gene-environment interaction for polygenic traits.Nature human behaviour · 2025Article
- A Robust Association Test Leveraging Unknown Genetic Interactions: Application to Cystic Fibrosis Lung Disease.Genetic epidemiology · 2025Article
- Genome-wide analyses of variance in blood cell phenotypes provide new insights into complex trait biology and prediction.Nature communications · 2025Article
- The integration of quantile regression with 3VmrMLM identifies more QTNs and QTN-by-environment interactions using SNP- and haplotype-based markers.Plant communications · 2025Article
- Mining for gene-environment and gene-gene interactions: parametric and non-parametric tests for detecting variance quantitative trait loci.Frontiers in genetics · 2025Article
- Genetic Prediction Modeling in Large Cohort Studies via Boosting Targeted Loss Functions.Statistics in medicine · 2024Article
- Task-Agnostic Machine-Learning-Assisted Inference.Advances in neural information processing systems · 2024Article
- Interaction of family SES with children's genetic propensity for cognitive and noncognitive skills: No evidence of the Scarr-Rowe hypothesis for educational outcomes.Research in social stratification and mobility · 2024Article
- Genome-wide discovery for biomarkers using quantile regression at biobank scale.Nature communications · 2024Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Detecting genetic variants associated with the variance of complex traits, that is, variance quantitative trait loci (vQTLs), can provide crucial insights into the interplay between genes and environments and how they jointly shape human phenotypes in the population. We propose a quantile integral linear model (QUAIL) to estimate genetic effects on trait variability. Through extensive simulations and analyses of real data, we demonstrate that QUAIL provides computationally efficient and statistically powerful vQTL mapping that is robust to non-Gaussian phenotypes and confounding effects on phenotypic variability. Applied to UK Biobank (
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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.