ReviewCell genomics2026
Polygenic backgrounds influence phenotypic consequences of variants in cells, individuals, and populations.
Review in Cell genomics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Genetic and health determinants of cancer risk in Bangladeshi and Pakistani individuals in the UK.Nature communications · 2026Article
- BRCA1 rs799917 and rs1799966 Variants and Breast Cancer Risk in Nigerian Women: A Case-Control Study With Population-Based Allele Frequency Analysis.Cancer control : journal of the Moffitt Cancer CenterArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Both rare and common genetic variants contribute to human disease, and emerging evidence suggests that they combine additively to influence disease liability. However, the non-linear relationship between disease liability and disease prevalence means that risk variants may have more severe phenotypic consequences in high-risk polygenic backgrounds and minimal impact in low-risk backgrounds, resulting in uneven selection across the population. As a result, selection coefficients may be better modeled as distributions that differ across populations, time, environments, and individuals than as single values. As the number of genes contributing to a trait and epistasis between alleles increases, so does phenotypic variance, pushing more individuals to extreme phenotypes and enhancing negative selection. Because disease-relevant phenotypes may be masked in certain genetic backgrounds, we argue that the polygenic background should be considered when designing experiments to characterize the molecular underpinnings of complex traits.
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.