Evidence map›Paper›PMID 41013211›Full record

ArticleBMC genomic data2025

Multi-omics mediation pipeline reveals differential pathways of maternal SNPs affecting newborn adiposity outcomes.

Nathan P Gill, Alan Kuang, Denise M Scholtens

Abstract read
In one paragraph

Article in BMC genomic data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

3 authors.

Nathan P GillDivision of Biostatistics and Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, USA. nathan.gill@northwestern.edu.
Alan KuangDivision of Biostatistics and Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, USA.
Denise M ScholtensDivision of Biostatistics and Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, USA.

Funding

Hyperglycemia and Adverse Pregnancy Outcome (HAPO)R01HD034243 · NICHD · NORTHWESTERN UNIVERSITY AT CHICAGO · PI METZGER, BOYD E · 1999 to 2010
$16.2M
HAPO: DATA COORDINATING CENTERR01HD034242 · NICHD · NORTHWESTERN UNIVERSITY AT CHICAGO · PI DYER, ALAN R · 1999 to 2007
$3.0M
Predicting Newborn and Childhood Adiposity: An Integrated Omics ApproachR01DK117491 · NIDDK · NORTHWESTERN UNIVERSITY AT CHICAGO · PI LOWE, WILLIAM L, SCHOLTENS, DENISE M · 2018 to 2022
$2.8M
Maternal Obesity and Gestational Diabetes: Impact on MetabolomeR01DK095963 · NIDDK · NORTHWESTERN UNIVERSITY AT CHICAGO · PI LOWE, WILLIAM L · 2013 to 2016
$1.7M
Network Models for MetabolomicsR01LM013444 · NLM · UNIVERSITY OF MASSACHUSETTS AMHERST · PI BALASUBRAMANIAN, RAJI, SCHOLTENS, DENISE M · 2020 to 2023
$1.4M
Eunice Kennedy Shriver National Institute of Child Health and Human Development R01HD34242, R01HD34243NICHD NIH HHS R01 HD034242NICHD NIH HHS R01 HD034243NIDDK NIH HHS R01 DK095963NIDDK NIH HHS R01DK095963, R01DK117491NIDDK NIH HHS R01 DK117491NLM NIH HHS R01 LM013444U.S. National Library of Medicine R01LM013444
6 · The paper itself

Abstract

backgroundA great deal of previous research describes the impact of the maternal metabolic and genetic milieu on newborn adiposity outcomes. However, much of this research does not focus on all aspects of the problem simultaneously. Studies focusing on metabolic factors may not distinguish between maternal and fetal genetic pathways, while studies that do focus on these different genetic pathways may not incorporate metabolic information into effect estimates or variant classifications. In this paper, we introduce a novel multi-omics pipeline for maternal genetic variant selection and mediation effect testing that can handle all these pathways, and use it to investigate broad patterns in the effects of maternal genetic variants on newborn adiposity outcomes.

resultsA Bayesian network model is used to incorporate both metabolomic and genomic data into an initial filter for maternal variants likely to affect newborn adiposity outcomes through a direct maternal genetic effect, an indirect fetal genetic effect, a maternal metabolic effect, or some combination of these pathways. A mediation model is then fit to these candidate variants and associated outcomes to identify which of these pathways, if any, mediate the total effect. We then group maternal genetic variants according to the relative magnitudes of these three effect pathways. In an application to existing mother-newborn data from the HAPO study, we find that of 78 candidate variants, the majority influence newborn birthweight solely through either a direct maternal or indirect fetal genetic effect (37% and 40%, respectively), a smaller number through both of these (14%), relatively few exclusively through the maternal metabolic pathway (6%), and almost none through a combination of the maternal metabolic pathway with either of the two genetic pathways (3%). We also find that these overall patterns of mediation effects are similar across outcomes.

conclusionsOur results reveal broad patterns in the effects of maternal genetic variants on newborn adiposity, and identify both new genetic loci and loci known from previous literature to influence newborn adiposity. These results demonstrate the potential for scientific discovery enabled by our multi-omics mediation pipeline, and the approach is broadly applicable for untangling path-specific contributions in the modern integrated multi-omics landscape.

Indexed as

AdiposityGenomicsPolymorphism, Single NucleotideBayes TheoremBirth WeightFemaleHumansInfant, NewbornMultiomicsPregnancyBayesian networksGeneticsMediation analysisMetabolomics

Identifiers

PMID41013211
PMCPMC12466079

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.