Evidence map›Paper›PMID 40690556›Full record

ArticleGenetics2025

Quantile-specific confounding: correction for subtle population stratification via quantile regression.

Chen Wang, Marco Masala, Edoardo Fiorillo, Marcella Devoto, Francesco Cucca, Iuliana Ionita-Laza

Abstract read
In one paragraph

Article in Genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Computationally efficient whole-genome quantile regression at biobank scale.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Chen WangDepartment of Biostatistics, Columbia University, New York, NY 10027, United States.ORCID 0000-0001-9127-0054
Marco MasalaInstitute for Genetic and Biomedical Research, National Research Council, Monserrato 09042, Italy.ORCID 0000-0001-5556-9964
Edoardo FiorilloInstitute for Genetic and Biomedical Research, National Research Council, Monserrato 09042, Italy.
Marcella DevotoInstitute for Genetic and Biomedical Research, National Research Council, Monserrato 09042, Italy.
Francesco CuccaInstitute for Genetic and Biomedical Research, National Research Council, Monserrato 09042, Italy.
Iuliana Ionita-LazaDepartment of Biostatistics, Columbia University, New York, NY 10027, United States.ORCID 0000-0002-9001-2026

Funding

Novel Statistical methods for DNA Sequencing Data, and applications to Autism.R01MH095797 · NIMH · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI IONITA, IULIANA · 2012 to 2022
$3.1M
Multi-omics approaches for gene discovery in Alzheimer's Disease.RF1AG072272 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI IONITA, IULIANA, WEI, YING · 2021 to 2021
$1.6M
Multi-omics approaches for gene discovery in Alzheimer's Disease.R01AG072272 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI IONITA, IULIANA, WEI, YING · 2024 to 2024
$552k
NIA NIH HHS R01 AG072272NIA NIH HHS RF1 AG072272NIH HHS AG072272NIH HHS MH095797NIMH NIH HHS R01 MH095797
6 · The paper itself

Abstract

Subtle population structure remains a significant concern in genome-wide association studies. Using human height as an example, we show how quantile regression, a natural extension of linear regression, can better correct for subtle population structure due to its inherent ability to adjust for quantile-specific effects of covariates such as principal components. We utilize data from the UK Biobank and the SardiNIA/ProgeNIA project for demonstration.

Indexed as

Body HeightGenetics, PopulationGenome-Wide Association StudyHumansPolymorphism, Single NucleotideRegression AnalysisUnited KingdomGWASpopulation stratificationquantile regressionquantile specific confounding

Identifiers

PMID40690556
PMCPMC12641523

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.