Evidence map›Paper›PMID 41168175›Full record

SynthesisNature communications2025

Discovery of obesity genes through cross-ancestry analysis.

Deepro Banerjee, Santhosh Girirajan

Abstract readMeta-Analysis
In one paragraph

Synthesis in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Deepro BanerjeeBioinformatics and Genomics Graduate Program, Huck Institutes of the Life Sciences, University Park, PA, USA.ORCID http://orcid.org/0000-0003-4877-0920
Santhosh GirirajanBioinformatics and Genomics Graduate Program, Huck Institutes of the Life Sciences, University Park, PA, USA. sxg47@psu.edu.ORCID http://orcid.org/0000-0003-0598-4945

Funding

Genomic basis of phenotypic variability of genomic disordersR01GM121907 · NIGMS · PENNSYLVANIA STATE UNIVERSITY, THE · PI GIRIRAJAN, SANTHOSH · 2017 to 2025
$5.2M
NIGMS NIH HHS R01 GM121907U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS) GM121907
6 · The paper itself

Abstract

Gene discoveries in obesity have largely relied on homogeneous populations, limiting their generalizability across ancestries. Here, we conduct a gene-based rare variant association study of BMI on 839,110 individuals from six ancestries across two population-scale biobanks. A cross-ancestry meta-analysis identifies 13 genes, including YLPM1, RIF1, GIGYF1, SLC5A3, and GRM7, that confer about three-fold risk for severe obesity, are expressed in the brain and adipose tissue, and are linked to obesity traits such as body-fat percentage. While YLPM1, MC4R, and SLTM show consistent effects, GRM7 and APBA1 show significant ancestral heterogeneity. Polygenic risk additively increases obesity penetrance, and phenome-wide studies reveal additional associations, including YLPM1 with altered mental status. These genes also influence cardiometabolic comorbidities, including GIGYF1 and SLTM towards type 2 diabetes with or without BMI as a mediator, and altered levels of plasma proteins, such as LECT2 and NCAN, which in turn affect BMI. Our findings provide insights into the genetic basis of obesity and its related comorbidities across ancestries and ascertainments.

Indexed as

ObesityAdipose TissueAdultBody Mass IndexDiabetes Mellitus, Type 2FemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMaleMiddle AgedMultifactorial InheritancePolymorphism, Single Nucleotide

Identifiers

PMID41168175
PMCPMC12575707

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.