Evidence map›Paper›PMID 37341229›Full record

ArticleGenetic epidemiology2023

A gene-based association test of interactions for maternal-fetal genotypes identifies genes associated with nonsyndromic congenital heart defects.

Manyan Huang, Chen Lyu, Nianjun Liu, Wendy N Nembhard, John S Witte, Charlotte A Hobbs, Ming Li, National Birth Defects Prevention Study

Open access · hybridAbstract read
In one paragraph

Article in Genetic epidemiology, 2023. 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
0.4field-weighted citation impact, top 34% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. 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

8 authors at 5 institutions in 1 country.

Manyan HuangDepartment of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, Indiana, USA.
Chen LyuDepartment of Population Health, New York University Grossman School of Medicine, New York City, New York, USA.
Nianjun LiuDepartment of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, Indiana, USA.
Wendy N NembhardDepartment of Epidemiology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.
John S WitteDepartment of Epidemiology and Population Health, Stanford University, Stanford, California, USA.
Charlotte A HobbsRady Children's Institute for Genomic Medicine, San Diego, California, USA.
Ming LiDepartment of Epidemiology and Biostatistics, Indiana University Bloomington, Bloomington, Indiana, USA.ORCID 0000-0003-0273-6217
National Birth Defects Prevention Study
Indiana University Bloomington · USChildren’s Institute · USNew York University · USStanford Health Care · USUniversity of Arkansas for Medical Sciences · US

Funding

Genomic/Epigenomic Factors and Non-Syndromic Congenital Heart Defect Risk R01HD039054 · NICHD · ARKANSAS CHILDREN'S HOSPITAL RES INST · PI HOBBS, CHARLOTTE A · 2000 to 2016
$13.6M
Random Field Modelling of genetic and epigenetic association underlying congenital heart defects in the presence of disease heterogeneityK01HL140333 · NHLBI · TRUSTEES OF INDIANA UNIVERSITY · PI LI, MING · 2018 to 2021
$724k
Random Field Methods for integrative genomic analysis and high-dimensional risk prediction of congenital heart defectsR56HL164477 · NHLBI · TRUSTEES OF INDIANA UNIVERSITY · PI LI, MING · 2023 to 2023
$488k
Gene-by-Gene and Gene-by-Environment Interactions Underlying Congenital Heart DefectsR03HD092854 · NICHD · TRUSTEES OF INDIANA UNIVERSITY · PI LI, MING · 2018 to 2019
$171k
HSRD VA CDP 13-003NCBDD CDC HHS U01 DD001229NCBDD CDC HHS U01 DD001285NCBDD CDC HHS U01 DD001306NHLBI NIH HHS K01 HL140333NHLBI NIH HHS R56 HL164477NICHD NIH HHS R01 HD039054NICHD NIH HHS R03 HD092854
6 · The paper itself

Abstract

The risk of congenital heart defects (CHDs) may be influenced by maternal genes, fetal genes, and their interactions. Existing methods commonly test the effects of maternal and fetal variants one-at-a-time and may have reduced statistical power to detect genetic variants with low minor allele frequencies. In this article, we propose a gene-based association test of interactions for maternal-fetal genotypes (GATI-MFG) using a case-mother and control-mother design. GATI-MFG can integrate the effects of multiple variants within a gene or genomic region and evaluate the joint effect of maternal and fetal genotypes while allowing for their interactions. In simulation studies, GATI-MFG had improved statistical power over alternative methods, such as the single-variant test and functional data analysis (FDA) under various disease scenarios. We further applied GATI-MFG to a two-phase genome-wide association study of CHDs for the testing of both common variants and rare variants using 947 CHD case mother-infant pairs and 1306 control mother-infant pairs from the National Birth Defects Prevention Study (NBDPS). After Bonferroni adjustment for 23,035 genes, two genes on chromosome 17, TMEM107 (p = 1.64e-06) and CTC1 (p = 2.0e-06), were identified for significant association with CHD in common variants analysis. Gene TMEM107 regulates ciliogenesis and ciliary protein composition and was found to be associated with heterotaxy. Gene CTC1 plays an essential role in protecting telomeres from degradation, which was suggested to be associated with cardiogenesis. Overall, GATI-MFG outperformed the single-variant test and FDA in the simulations, and the results of application to NBDPS samples are consistent with existing literature supporting the association of TMEM107 and CTC1 with CHDs.

Indexed as

Genome-Wide Association StudyHeart Defects, CongenitalCase-Control StudiesFemaleGenotypeHumansModels, GeneticMotherscongenital heart defectsgene-based association testmaternal-fetal genotype interactionrare variants

Identifiers

PMID37341229
PMCPMC11781787
OpenAlexW4381470379

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

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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.