ReviewNature reviews. Genetics2026
Human genetics across levels of biological organization.
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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genetic variation influences human physiology across biological scales from molecules to cells, tissues, organs and the whole organism. Unravelling how variants and their genetic effects propagate across these levels, through molecular interactions, cellular programmes and tissue architectures, to shape phenotypes remains a central challenge in human genetics. Resolving this challenge requires deciphering the genetic architecture of each biological layer and developing systems-level analyses that aim to integrate across scales. Network-based and computational approaches, including artificial intelligence, offer opportunities to move beyond statistical associations towards a context-aware, mechanistic understanding of the genetics underlying human traits and disease, although integration across layers remains limited. Here we review recent advances in mapping genetic effects across biological scales, from intracellular networks that capture molecular interactions, through single-cell and spatial omics approaches that define cellular and tissue contexts, to population-scale imaging genomics that links genetic variation to organ-level and organismal phenotypes. We discuss emerging strategies and remaining challenges for integrating these layers into mechanistic models of genotype-phenotype relationships.
Identifiers
42493559What 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.