Evidence map›Paper›PMID 37602697›Full record

ArticleGenetics2023

Searching and visualizing genetic associations of pregnancy traits by using GnuMoM2b.

Qi Yan, Rafael F Guerrero, Raiyan R Khan, Andy A Surujnarine, Ronald J Wapner, Matthew W Hahn, Anita Raja, Ansaf Salleb-Aouissi, William A Grobman, Hyagriv Simhan and 7 more

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genetics, 2023. 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
0.3field-weighted citation impact, top 29% of its field
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 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

17 authors at 11 institutions in 1 country.

Qi YanDepartment of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY 10032, USA.ORCID 0000-0002-5236-9673
Rafael F GuerreroDepartment of Computer Science, Indiana University, Bloomington, IN 47405, USA.
Raiyan R KhanDepartment of Computer Science, Columbia University, New York, NY 10032, USA.
Andy A SurujnarineDepartment of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY 10032, USA.
Ronald J WapnerDepartment of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY 10032, USA.
Matthew W HahnDepartment of Computer Science, Indiana University, Bloomington, IN 47405, USA.
Anita RajaDepartment of Computer Science, CUNY Hunter College, New York, NY 10065, USA.
Ansaf Salleb-AouissiDepartment of Computer Science, Columbia University, New York, NY 10032, USA.
William A GrobmanDepartment of Obstetrics and Gynecology, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
Hyagriv SimhanDepartment of Obstetrics, Gynecology and Reproductive Sciences, University of Pittsburgh, Pittsburgh, PA 15213, USA.
Nathan R BlueDepartment of Obstetrics and Gynecology, University of Utah, Salt Lake City, UT 84132, USA.
Robert SilverDepartment of Obstetrics and Gynecology, University of Utah, Salt Lake City, UT 84132, USA.
Judith H ChungDepartment of Obstetrics and Gynecology, University of California, Irvine, Orange, CA 92697, USA.
Uma M ReddyDepartment of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY 10032, USA.
Predrag RadivojacKhoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA.
Itsik Pe'erDepartment of Computer Science, Columbia University, New York, NY 10032, USA.
David M HaasDepartment of Obstetrics and Gynecology, Indiana University, Indianapolis, IN 46202, USA.
Columbia University Irving Medical Center · USColumbia University · USUniversity of Utah · USHunter College · USIndiana University Bloomington · USIndiana University – Purdue University Indianapolis · USNorth Carolina State University · USNortheastern University · USThe Ohio State University · USUniversity of California, Irvine · USUniversity of Pittsburgh · US

Funding

Machine learning approaches towards risk assessment and prediction of adverse pregnancy outcomesR01HD101246 · NICHD · INDIANA UNIVERSITY INDIANAPOLIS · PI HAAS, DAVID M., NATARAJAN, SRIRAAM · 2020 to 2022
$1.4M
SCH: Prediction of Preterm Birth in Nulliparous WomenR01LM013327 · NLM · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI FRIEDMAN, ALEXANDER M, PE'ER, ITSHACK G · 2019 to 2022
$1.2M
NICHD NIH HHS R01 HD101246NIH HHSNLM NIH HHS R01 LM013327
6 · The paper itself

Abstract

Adverse pregnancy outcomes (APOs) are major risk factors for women's health during pregnancy and even in the years after pregnancy. Due to the heterogeneity of APOs, only few genetic associations have been identified. In this report, we conducted genome-wide association studies (GWASs) of 479 traits that are possibly related to APOs using a large and racially diverse study, Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b). To display extensive results, we developed a web-based tool GnuMoM2b (https://gnumom2b.cumcobgyn.org/) for searching, visualizing, and sharing results from a GWAS of 479 pregnancy traits as well as phenome-wide association studies of more than 17 million single nucleotide polymorphisms. The genetic results from 3 ancestries (Europeans, Africans, and Admixed Americans) and meta-analyses are populated in GnuMoM2b. In conclusion, GnuMoM2b is a valuable resource for extraction of pregnancy-related genetic results and shows the potential to facilitate meaningful discoveries.

Indexed as

Genome-Wide Association StudyPhenomicsFemaleHumansPhenotypePolymorphism, Single NucleotidePregnancyRisk Factorsadverse pregnancy outcomesGWASnuMoM2bPheWAS

Identifiers

PMID37602697
PMCPMC10691790
OpenAlexW4386019867

What OpenQuestion holds

Textmetadata
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.