Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes

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DOI

  • Julia K. Goodrich, Broad Institute
  • ,
  • Moriel Singer-Berk, Broad Institute
  • ,
  • Rachel Son, Broad Institute
  • ,
  • Abigail Sveden, Broad Institute
  • ,
  • Jordan Wood, Broad Institute
  • ,
  • Eleina England, Broad Institute
  • ,
  • Joanne B. Cole, Broad Institute
  • ,
  • Ben Weisburd, Broad Institute
  • ,
  • Nick Watts, Broad Institute
  • ,
  • Lizz Caulkins, Broad Institute
  • ,
  • Peter Dornbos, Broad Institute
  • ,
  • Ryan Koesterer, Broad Institute
  • ,
  • Zachary Zappala, Broad Institute
  • ,
  • Haichen Zhang, University of Maryland, Baltimore
  • ,
  • Kristin A. Maloney, University of Maryland, Baltimore
  • ,
  • Andy Dahl, University of California at Los Angeles
  • ,
  • Carlos A. Aguilar-Salinas, Instituto Nacional de Ciencias Medicas y Nutricion Salvador Zubiran
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  • Gil Atzmon, Yeshiva University, University of Haifa
  • ,
  • Francisco Barajas-Olmos, Instituto Nacional de Medicina Genomica
  • ,
  • Nir Barzilai, Yeshiva University
  • ,
  • John Blangero, University of Texas Rio Grande Valley
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  • Eric Boerwinkle, University of Texas Health Science Center at Houston, Baylor College of Medicine
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  • Lori L. Bonnycastle, National Institutes of Health
  • ,
  • Erwin Bottinger, Icahn School of Medicine at Mount Sinai
  • ,
  • Donald W. Bowden, Wake Forest University
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  • Federico Centeno-Cruz, Instituto Nacional de Medicina Genomica
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  • John C. Chambers, Imperial College London, Nanyang Technological University
  • ,
  • Nathalie Chami, Icahn School of Medicine at Mount Sinai
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  • Edmund Chan, National University of Singapore
  • ,
  • Juliana Chan, Chinese University of Hong Kong
  • ,
  • Ching Yu Cheng, Singapore National Eye Center, National University of Singapore
  • ,
  • Yoon Shin Cho, Hallym University
  • ,
  • Cecilia Contreras-Cubas, Instituto Nacional de Medicina Genomica
  • ,
  • Emilio Córdova, Instituto Nacional de Medicina Genomica
  • ,
  • Adolfo Correa, University of Mississippi
  • ,
  • Ralph A. DeFronzo, University of Texas Health Science Center at San Antonio
  • ,
  • Ravindranath Duggirala, University of Texas Rio Grande Valley
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  • Josée Dupuis, Boston University
  • ,
  • Ma Eugenia Garay-Sevilla, University of Guanjuato. Campus León. León
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  • Humberto García-Ortiz, Instituto Nacional de Medicina Genomica
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  • Christian Gieger, Helmholtz Zentrum München - German Research Center for Environmental Health, German Center for Diabetes Research
  • ,
  • Benjamin Glaser, Hadassah University Medical Centre
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  • Clicerio González-Villalpando, Instituto Nacional de Salud Publica
  • ,
  • Ma Elena Gonzalez, Centro de Estudios en Diabetes, Mexico City
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  • Niels Grarup, Københavns Universitet
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  • Leif Groop, Lund University Diabetes Centre, University of Helsinki
  • ,
  • Myron Gross, University of Minnesota Twin Cities
  • ,
  • Christopher A. Haiman, Keck School of Medicine of USC, Danmark
  • Sohee Han, National Institute of Health
  • ,
  • Craig L. Hanis, University of Texas School Public Health
  • ,
  • Torben Hansen, Københavns Universitet
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  • Nancy L. Heard-Costa, Boston University School of Medicine
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  • Marit E. Jørgensen, Steno Diabetes Center, Syddansk Universitet, University of Greenland
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  • Oluf Pedersen, Københavns Universitet
  • ,
  • Daniel R. Witte
  • AMP-T2D-GENES Consortia

Hundreds of thousands of genetic variants have been reported to cause severe monogenic diseases, but the probability that a variant carrier develops the disease (termed penetrance) is unknown for virtually all of them. Additionally, the clinical utility of common polygenetic variation remains uncertain. Using exome sequencing from 77,184 adult individuals (38,618 multi-ancestral individuals from a type 2 diabetes case-control study and 38,566 participants from the UK Biobank, for whom genotype array data were also available), we apply clinical standard-of-care gene variant curation for eight monogenic metabolic conditions. Rare variants causing monogenic diabetes and dyslipidemias display effect sizes significantly larger than the top 1% of the corresponding polygenic scores. Nevertheless, penetrance estimates for monogenic variant carriers average 60% or lower for most conditions. We assess epidemiologic and genetic factors contributing to risk prediction in monogenic variant carriers, demonstrating that inclusion of polygenic variation significantly improves biomarker estimation for two monogenic dyslipidemias.

OriginalsprogEngelsk
Artikelnummer3505
TidsskriftNature Communications
Vol/bind12
ISSN2041-1723
DOI
StatusUdgivet - dec. 2021

Bibliografisk note

Funding Information:
This work was supported by NIH/NIDDK U01 DK105554 to JCF. This research has been conducted using the UK Biobank Resource under application number 27892. MSU is supported by NIH/NIDDK K23 DK114551. AODL was supported by NIH/NICHD K12 HD052896. MB is supported by NIH/NIDDK DK062370. JCF is also supported by NIH/ NIDDK K24 DK110550. JMM is supported by American Diabetes Association Innovative and Clinical Translational Award 1-19-ICTS-068. Please see Supplementary Information for additional Extended Acknowledgements.

Publisher Copyright:
© 2021, The Author(s).

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

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