ReviewNature reviews. Genetics2026
Machine learning and statistical methods for molecular quantitative trait loci.
Review in Nature reviews. Genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An important goal of biology is to understand how genetic variation translates into molecular and then broader phenotypic variation. Quantitative trait locus (QTL) mapping studies are designed to statistically test the relationship between genetic and phenotypic variation, with molecular QTLs (molQTLs) capturing genetic effects on molecular traits, such as gene expression or chromatin accessibility, as the variable phenotypes of interest. Technological advances have provided molQTL mapping methods with increased molecular phenotypes to test, as well as larger cohorts, the latter of which provides greater statistical power to associate genetic variants with these phenotypes. With these advances, statistical models - and, increasingly, machine learning methods - are being developed to improve standard molQTL mapping approaches, downstream analyses and other aspects of molQTL studies to gain additional insights into the mechanisms connecting genetic and phenotypic variation, especially in a gene regulatory context.
Identifiers
42791384What Socratic 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 Socratic graph.