Evidence map›Paper›PMID 37693475›Full record

ArticlemedRxiv : the preprint server for health sciences2023

Direct and INdirect effects analysis of Genetic lOci (DINGO): A software package to increase the power of locus discovery in GWAS meta-analyses of perinatal phenotypes and traits influenced by indirect genetic effects.

Liang-Dar Hwang, Gabriel Cuellar-Partida, Loic Yengo, Jian Zeng, Robin N Beaumont, Rachel M Freathy, Gunn-Helen Moen, Nicole M Warrington, David M Evans

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 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
–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. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Liang-Dar HwangInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.
Gabriel Cuellar-PartidaGilead Sciences, Inc, Forest City, CA, USA.
Loic YengoInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.
Jian ZengInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.
Robin N BeaumontDepartment of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK.
Rachel M FreathyDepartment of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK.
Gunn-Helen MoenInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.
Nicole M WarringtonInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.
David M EvansInstitute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.

Funding

Integrating genomic studies of gestational duration and birth weight to understand maternal and fetal causes of adverse pregnancy outcomes and links with later diseasesR01HD101669 · NICHD · CINCINNATI CHILDRENS HOSP MED CTR · PI FREATHY, RACHEL, JACOBSSON, BO · 2021 to 2024
$2.1M
NICHD NIH HHS R01 HD101669Wellcome Trust
6 · The paper itself

Abstract

Perinatal traits are influenced by genetic variants from both fetal and maternal genomes. Genome-wide association studies (GWAS) of these phenotypes have typically involved separate fetal and maternal scans, however, this approach may be inefficient as it does not utilize the information shared across the individual GWAS. In this manuscript we investigate the performance of three strategies to detect loci in maternal and fetal GWAS of the same trait: (i) the traditional strategy of analysing maternal and fetal GWAS separately; (ii) a novel two degree of freedom test which combines information from maternal and fetal GWAS; and (iii) a novel one degree of freedom test where signals from maternal and fetal GWAS are meta-analysed together conditional on the estimated sample overlap. We demonstrate through a combination of analytical formulae and data simulation that the optimal strategy depends on the extent of sample overlap/relatedness between the maternal and fetal GWAS, the correlation between own and offspring phenotypes, whether loci jointly exhibit fetal and maternal effects, and if so, whether these effects are directionally concordant. We apply our methods to summary results statistics from a recent GWAS meta-analysis of birth weight from deCODE, the UK Biobank and the Early Growth Genetics (EGG) consortium. Both the two degree of freedom (213 loci) and meta-analytic approach (226 loci) dramatically increase the number of robustly associated genetic loci for birth weight relative to separately analysing the scans (183 loci). Our best strategy identifies an additional 62 novel loci compared to the most recent published meta-analysis of birth weight and implicates both known and new biological pathways in the aetiology of the trait. We implement our methods in the online DINGO (

Identifiers

PMID37693475
PMCPMC10491281

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.