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

AMP-T2D-GENES Consortia

doi:10.1038/s41467-021-23556-4

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

AMP-T2D-GENES Consortia

Twin Research & Genetic Epidemiology

Research output: Contribution to journal › Article › peer-review

25 Citations (Scopus)

Abstract

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.

Original language	English
Article number	3505
Journal	Nature Communications
Volume	12
Issue number	1
DOIs	https://doi.org/10.1038/s41467-021-23556-4
Publication status	Published - Dec 2021

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@article{468ff83709924ac882f32b5d97f2bad3,

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

abstract = "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.",

author = "{AMP-T2D-GENES Consortia} and Goodrich, {Julia K.} and Moriel Singer-Berk and Rachel Son and Abigail Sveden and Jordan Wood and Eleina England and Cole, {Joanne B.} and Ben Weisburd and Nick Watts and Lizz Caulkins and Peter Dornbos and Ryan Koesterer and Zachary Zappala and Haichen Zhang and Maloney, {Kristin A.} and Andy Dahl and Aguilar-Salinas, {Carlos A.} and Gil Atzmon and Francisco Barajas-Olmos and Nir Barzilai and John Blangero and Eric Boerwinkle and Bonnycastle, {Lori L.} and Erwin Bottinger and Bowden, {Donald W.} and Federico Centeno-Cruz and Chambers, {John C.} and Nathalie Chami and Edmund Chan and Juliana Chan and Cheng, {Ching Yu} and Cho, {Yoon Shin} and Cecilia Contreras-Cubas and Emilio C{\'o}rdova and Adolfo Correa and DeFronzo, {Ralph A.} and Ravindranath Duggirala and Jos{\'e}e Dupuis and Garay-Sevilla, {Ma Eugenia} and Humberto Garc{\'i}a-Ortiz and Christian Gieger and Benjamin Glaser and Clicerio Gonz{\'a}lez-Villalpando and Gonzalez, {Ma Elena} and Niels Grarup and Leif Groop and Myron Gross and Christopher Haiman and Small, {Kerrin S.} and Spector, {Timothy D.}",

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: {\textcopyright} 2021, The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",

year = "2021",

month = dec,

doi = "10.1038/s41467-021-23556-4",

language = "English",

volume = "12",

journal = "Nature Communications",

issn = "2041-1723",

publisher = "Nature Publishing Group",

number = "1",

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TY - JOUR

T1 - Determinants of penetrance and variable expressivity in monogenic metabolic conditions across 77,184 exomes

AU - AMP-T2D-GENES Consortia

AU - Goodrich, Julia K.

AU - Singer-Berk, Moriel

AU - Son, Rachel

AU - Sveden, Abigail

AU - Wood, Jordan

AU - England, Eleina

AU - Cole, Joanne B.

AU - Weisburd, Ben

AU - Watts, Nick

AU - Caulkins, Lizz

AU - Dornbos, Peter

AU - Koesterer, Ryan

AU - Zappala, Zachary

AU - Zhang, Haichen

AU - Maloney, Kristin A.

AU - Dahl, Andy

AU - Aguilar-Salinas, Carlos A.

AU - Atzmon, Gil

AU - Barajas-Olmos, Francisco

AU - Barzilai, Nir

AU - Blangero, John

AU - Boerwinkle, Eric

AU - Bonnycastle, Lori L.

AU - Bottinger, Erwin

AU - Bowden, Donald W.

AU - Centeno-Cruz, Federico

AU - Chambers, John C.

AU - Chami, Nathalie

AU - Chan, Edmund

AU - Chan, Juliana

AU - Cheng, Ching Yu

AU - Cho, Yoon Shin

AU - Contreras-Cubas, Cecilia

AU - Córdova, Emilio

AU - Correa, Adolfo

AU - DeFronzo, Ralph A.

AU - Duggirala, Ravindranath

AU - Dupuis, Josée

AU - Garay-Sevilla, Ma Eugenia

AU - García-Ortiz, Humberto

AU - Gieger, Christian

AU - Glaser, Benjamin

AU - González-Villalpando, Clicerio

AU - Gonzalez, Ma Elena

AU - Grarup, Niels

AU - Groop, Leif

AU - Gross, Myron

AU - Haiman, Christopher

AU - Small, Kerrin S.

AU - Spector, Timothy D.

N1 - 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.

PY - 2021/12

Y1 - 2021/12

N2 - 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.

AB - 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.

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U2 - 10.1038/s41467-021-23556-4

DO - 10.1038/s41467-021-23556-4

M3 - Article

C2 - 34108472

AN - SCOPUS:85107774343

SN - 2041-1723

VL - 12

JO - Nature Communications

JF - Nature Communications

IS - 1

M1 - 3505

ER -

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

Abstract

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