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Quantifying concordant genetic effects of de novo mutations on multiple disorders

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...

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Autores principales: Guo, Hanmin, Hou, Lin, Shi, Yu, Jin, Sheng Chih, Zeng, Xue, Li, Boyang, Lifton, Richard P, Brueckner, Martina, Zhao, Hongyu, Lu, Qiongshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9217133/
https://www.ncbi.nlm.nih.gov/pubmed/35666111
http://dx.doi.org/10.7554/eLife.75551
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author Guo, Hanmin
Hou, Lin
Shi, Yu
Jin, Sheng Chih
Zeng, Xue
Li, Boyang
Lifton, Richard P
Brueckner, Martina
Zhao, Hongyu
Lu, Qiongshi
author_facet Guo, Hanmin
Hou, Lin
Shi, Yu
Jin, Sheng Chih
Zeng, Xue
Li, Boyang
Lifton, Richard P
Brueckner, Martina
Zhao, Hongyu
Lu, Qiongshi
author_sort Guo, Hanmin
collection PubMed
description 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.
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spelling pubmed-92171332022-06-23 Quantifying concordant genetic effects of de novo mutations on multiple disorders Guo, Hanmin Hou, Lin Shi, Yu Jin, Sheng Chih Zeng, Xue Li, Boyang Lifton, Richard P Brueckner, Martina Zhao, Hongyu Lu, Qiongshi eLife Genetics and Genomics 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. eLife Sciences Publications, Ltd 2022-06-06 /pmc/articles/PMC9217133/ /pubmed/35666111 http://dx.doi.org/10.7554/eLife.75551 Text en © 2022, Guo et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited.
spellingShingle Genetics and Genomics
Guo, Hanmin
Hou, Lin
Shi, Yu
Jin, Sheng Chih
Zeng, Xue
Li, Boyang
Lifton, Richard P
Brueckner, Martina
Zhao, Hongyu
Lu, Qiongshi
Quantifying concordant genetic effects of de novo mutations on multiple disorders
title Quantifying concordant genetic effects of de novo mutations on multiple disorders
title_full Quantifying concordant genetic effects of de novo mutations on multiple disorders
title_fullStr Quantifying concordant genetic effects of de novo mutations on multiple disorders
title_full_unstemmed Quantifying concordant genetic effects of de novo mutations on multiple disorders
title_short Quantifying concordant genetic effects of de novo mutations on multiple disorders
title_sort quantifying concordant genetic effects of de novo mutations on multiple disorders
topic Genetics and Genomics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9217133/
https://www.ncbi.nlm.nih.gov/pubmed/35666111
http://dx.doi.org/10.7554/eLife.75551
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