Evidence map›Paper›PMID 35666111›Full record

ArticleeLife2022

Quantifying concordant genetic effects of de novo mutations on multiple disorders.

Hanmin Guo, Lin Hou, Yu Shi, Sheng Chih Jin, Xue Zeng, Boyang Li, Richard P Lifton, Martina Brueckner, Hongyu Zhao, Qiongshi Lu

Open access · goldAbstract read
In one paragraph

Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.9field-weighted citation impact, top 23% 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

5 citing papers in PubMed, 5 citations in OpenAlex.

  1. Joint analysis ofNAR genomics and bioinformatics · 2025
    Article
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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

10 authors at 4 institutions in 3 countries.

Hanmin GuoCenter for Statistical Science, Tsinghua University, Beijing, China.ORCID 0000-0001-9022-5307
Lin HouCenter for Statistical Science, Tsinghua University, Beijing, China.
Yu ShiYale School of Management, Yale University, New Haven, United States.
Sheng Chih JinDepartment of Genetics, Washington University in St. Louis, St. Louis, United States.
Xue ZengDepartment of Genetics, Yale University, New Haven, United States.
Boyang LiDepartment of Biostatistics, Yale School of Public Health, New Haven, United States.
Richard P LiftonDepartment of Genetics, Yale University, New Haven, United States.
Martina BruecknerDepartment of Genetics, Yale University, New Haven, United States.
Hongyu ZhaoDepartment of Genetics, Yale University, New Haven, United States.
Qiongshi LuDepartment of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, United States.ORCID 0000-0002-4514-0969
Yale University · USTsinghua University · CNUniversity of Wisconsin–Madison · USWashington University in St. Louis · US

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Genetic determinants of human heterotaxy and aortic arch malformationU01HL098162 · NHLBI · YALE UNIVERSITY · PI BRUECKNER, MARTINA, GRUBER, PETER J · 2009 to 2024
$5.9M
Novel statistical methods and tools to integrate multiple endophenotypes and functional annotation data to study the roles of rare variants in complex human diseases using sequencing dataR01GM134005 · NIGMS · YALE UNIVERSITY · PI WU, BAOLIN, ZHAO, HONGYU · 2020 to 2023
$1.6M
Statistical Methods for Analyzing Birth Defects CohortsR03HD100883 · NICHD · YALE UNIVERSITY · PI ZHAO, HONGYU · 2021 to 2022
$335k
NCATS NIH HHS UL1 TR001863NHLBI NIH HHS U01 HL098162NICHD NIH HHS R03 HD100883NIGMS NIH HHS R01 GM134005
6 · The paper itself

Abstract

Exome sequencing on tens of thousands of parent-proband trios has identified numerous deleterious de novo mutations (DNMs) and implicated risk genes for many disorders. Recent studies have suggested shared genes and pathways are enriched for DNMs across multiple disorders. However, existing analytic strategies only focus on genes that reach statistical significance for multiple disorders and require large trio samples in each study. As a result, these methods are not able to characterize the full landscape of genetic sharing due to polygenicity and incomplete penetrance. In this work, we introduce EncoreDNM, a novel statistical framework to quantify shared genetic effects between two disorders characterized by concordant enrichment of DNMs in the exome. EncoreDNM makes use of exome-wide, summary-level DNM data, including genes that do not reach statistical significance in single-disorder analysis, to evaluate the overall and annotation-partitioned genetic sharing between two disorders. Applying EncoreDNM to DNM data of nine disorders, we identified abundant pairwise enrichment correlations, especially in genes intolerant to pathogenic mutations and genes highly expressed in fetal tissues. These results suggest that EncoreDNM improves current analytic approaches and may have broad applications in DNM studies.

Indexed as

ExomeGenetic Predisposition to DiseaseExome SequencingHumansMutationde novo mutationdevelopmental disordergeneticsgenomicshumanmultiple disorderswhole exome sequencing

Identifiers

PMID35666111
PMCPMC9217133
OpenAlexW4281876029

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.