Pulse Brain · Growing Health Evidence Index
Tier 1 — Meta-analysis / systematic reviewPeer-reviewed

Deciphering the impact of genomic variation on function

Writing group (ordered by contribution), J Engreitz, Heather A. Lawson, Harinder Singh, Lea M. Starita, Gary C. Hon, Hannah Carter, Nidhi Sahni, Timothy E. Reddy, Xihong Lin, Yun Li, Nikhil Munshi, Maria H. Chahrour, Alan P. Boyle, Benjamin C. Hitz, A Mortazavi, Mark Craven, Karen L. Mohlke, Luca Pinello, Ting Wang, Anshul Kundaje, Feng Yue, Sarah Cody, Nina Farrell, Michael I. Love, Lara A. Muffley, Michael J. Pazin, Fairlie Reese, Eric Van Buren, Working Group and Focus Group Co-Chairs (alphabetical by last name), Catalog, Kushal K. Dey, Characterization, Martin Kircher, Computational Analysis, Modeling, and Prediction, Jian Ma, Predrag Radivojac, Project Design, Brunilda Balliu, Mapping, Brian A. Williams, Networks, Danwei Huangfu, Standards and Pipelines, Cardiometabolic, Chong Y. Park, Thomas Quertermous, Cellular Programs and Networks, Jishnu Das, Coding Variants, Michael A. Calderwood, Douglas M. Fowler, Marc Vidal, CRISPR, Lucas Ferreira, Defining and Systematizing Function, Sean D. Mooney, Vikas Pejaver, Enumerating Variants, Jingjing Zhao, Evolution, Steven Gazal, Evan Koch, Steven K. Reilly, Shamil Sunyaev, Imaging, Anne E. Carpenter, Immune, Jason D. Buenrostro, Christina S. Leslie, Rachel E. Savage, Impact on Diverse Populations, Stefanija Giric, iPSC, Chongyuan Luo, Kathrin Plath, MPRA, Alejandro Barrera, Max Schubach, Noncoding Variants, Andreas R. Gschwind, Jill E. Moore, Neuro, Nadav Ahituv, Phenotypic Impact and Function, S. Stephen Yi, QTL/Statgen, Ingileif B. Hallgrímsdóttir, Kyle J. Gaulton, Saori Sakaue, Single Cell, Sina Booeshaghi, Eugenio Mattei, Surag Nair, Lior Pachter, Austin T. Wang, Characterization Awards (contact PI, MPIs (alphabetical by last name), other members (alphabetical by last name)), UM1HG011966, Jay Shendure, Vikram Agarwal

Nature · 2024

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Summary

This collaborative, large-scale research working group presents a comprehensive framework for deciphering how genomic variation translates into functional impact across diverse biological contexts. Drawing on multiple complementary experimental platforms—including CRISPR screening, massively parallel reporter assays, quantitative trait locus mapping, and single-cell transcriptomics—the authors synthesise evidence for variant characterisation and predictive modelling of phenotypic consequences. The work establishes standards and integrated approaches for systematically cataloguing the functional landscape of human genetic variation.

UK applicability

The methodological framework and standards proposed are broadly applicable to UK genomic research and personalised medicine initiatives. Findings may inform UK biobanks, NHS genomic medicine programmes, and population health studies seeking to interpret the functional significance of genetic variants in clinical and agricultural contexts.

Key measures

Functional annotations of genomic variants; mapping of genetic variants to cellular and molecular phenotypes; integration of CRISPR, MPRA, QTL, single-cell, imaging, and evolutionary analyses across cardiometabolic, immune, neurological, and other biological systems

Outcomes reported

The study characterised the functional impact of genomic variation across multiple biological systems and tissues, integrating diverse experimental and computational approaches to understand how genetic variants affect phenotypic outcomes.

Theme
Measurement & metrics
Subject
Measurement methods & nutrient profiling
Study type
Systematic Review
Study design
Systematic review and methodology paper
Source type
Peer-reviewed study
Status
Published
Geography
United States
System type
Laboratory / in vitro
DOI
10.1038/s41586-024-07510-0
Catalogue ID
SNmoj1y0mg-my69o0

Topic tags

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