ArticleNational science review2026
Genome-wide association studies and QTL mapping for traits deviating from normal distribution.
Article in National science review, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Crop breeding by design: integrating big data for future food security.National science review · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantitative traits are the targets for genome-wide association studies (GWAS) and quantitative trait locus (QTL) mapping. After adjusting for systematic effects, these traits are assumed to be normally distributed so that typical linear models and linear mixed models (LMMs) can be used to detect markers associated with QTLs. Many traits in crops, animals and humans, however, do not follow the assumed normal distribution and many of them are not even continuously distributed. We developed statistical models and software packages to map QTLs and perform association studies for such non-normal traits under the generalized linear mixed model (GLMM) framework. We developed a pseudo response (PSR) method to estimate the polygenic variance by generating a pseudo-response variable that is treated as a conventional quantitative trait. We then scanned the genome for markers associated with the PSR variable in the usual LMM. The new method is called the pseudo response generalized linear mixed model (PSR-GLMM). We illustrated the method with the purple color trait of rice (binary trait) and a set of simulated non-normal traits. We then applied the method to four datasets: a binary trait from an
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.