Evidence map›Paper›PMID 41073786›Full record

ArticleNature genetics2025

Population-scale gene-based analysis of whole-genome sequencing provides insights into metabolic health.

Yajie Zhao, Sam Lockhart, Jimmy Liu, Xihao Li, Adrian Cortes, Xing Hua, Eugene J Gardner, Katherine A Kentistou, Marisa Cañadas-Garre, Laurie Fabian and 11 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

21 authors.

Yajie Zhao *MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.ORCID http://orcid.org/0000-0002-2747-0219
Sam Lockhart *Metabolic Research Laboratory, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.ORCID http://orcid.org/0000-0003-2092-4350
Jimmy Liu *Human Genetics and Genomics, GSK, Collegeville, PA, USA.ORCID http://orcid.org/0000-0002-8379-5480
Xihao Li *Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.ORCID http://orcid.org/0000-0001-8151-0106
Adrian CortesHuman Genetics and Genomics, GSK, Stevenage, UK.ORCID http://orcid.org/0000-0002-3490-007X
Xing HuaDivision of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.
Eugene J GardnerMRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.ORCID http://orcid.org/0000-0001-9671-1533
Katherine A KentistouMRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.ORCID http://orcid.org/0000-0002-5816-664X
Marisa Cañadas-GarreMRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Laurie FabianMRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.ORCID http://orcid.org/0009-0007-3145-5357
Karen HoMRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Nicholas TimpsonMRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.ORCID http://orcid.org/0000-0002-7141-9189
Yancy LoHuman Genetics and Genomics, GSK, Collegeville, PA, USA.ORCID http://orcid.org/0000-0003-2843-4882
Jonathan DavitteHuman Genetics and Genomics, GSK, Collegeville, PA, USA.
David B SavageMetabolic Research Laboratory, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.ORCID http://orcid.org/0000-0002-7857-7032
Carolyn Buser-DoepnerHuman Genetics and Genomics, GSK, Collegeville, PA, USA.
Ken K OngMRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.
Haoyu Zhang *Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA.ORCID http://orcid.org/0000-0001-6423-0444
Robert Scott *Human Genetics and Genomics, GSK, Stevenage, UK.ORCID http://orcid.org/0000-0003-3634-3016
Stephen O'Rahilly *Metabolic Research Laboratory, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK.ORCID http://orcid.org/0000-0003-2199-4449
John R B Perry *MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK. John.Perry@mrc-epid.cam.ac.uk.ORCID http://orcid.org/0000-0001-6483-3771

Funding

Wellcome Trust
6 · The paper itself

Abstract

In addition to its coverage of the noncoding genome, whole-genome sequencing (WGS) may better capture the coding genome than exome sequencing. Here we sought to exploit this and identify new rare, protein-coding variants associated with metabolic health in WGS data (n = 708,956) from the UK Biobank and All of Us studies. Identified genes highlight new biological mechanisms, including protein-truncating variants (PTVs) in the DNA double-strand break repair gene RIF1 that have a substantial effect on body mass index (2.66 kg m

Indexed as

Diabetes Mellitus, Type 2Whole Genome SequencingBody Mass IndexFemaleGenetic Predisposition to DiseaseGenome, HumanGenome-Wide Association StudyHumansInsulin Receptor Substrate ProteinsMaleMiddle AgedPolymorphism, Single NucleotideRenal Insufficiency, ChronicInsulin Receptor Substrate Proteins

Identifiers

PMID41073786
PMCPMC12513836

What OpenQuestion holds

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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 OpenQuestion graph.