Evidence mapPaperPMID 42493559Full record

ReviewNature reviews. Genetics2026

Human genetics across levels of biological organization.

Diederik S Laman Trip, Pedro Beltrao

Abstract readReview
PubMed Publisher
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Diederik S Laman TripDepartment of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland. lamantrip@imsb.biol.ethz.ch.ORCID http://orcid.org/0000-0001-6635-0626
Pedro BeltraoDepartment of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland. pbeltrao@ethz.ch.ORCID http://orcid.org/0000-0002-2724-7703

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

What Socratic holds

Textmetadata
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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 Socratic graph.