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

Julia K. Goodrich, Moriel Singer-Berk, Rachel Son, Abigail Sveden, Jordan Wood, Eleina England, Joanne B. Cole, Ben Weisburd, Nick Watts, Lizz Caulkins, Peter Dornbos, Ryan Koesterer, Zachary Zappala, Haichen Zhang, Kristin A. Maloney, Andy Dahl, Carlos A. Aguilar-Salinas, Gil Atzmon, Francisco Barajas-Olmos, Nir BarzilaiJohn Blangero, Eric Boerwinkle, Lori L. Bonnycastle, Erwin Bottinger, Donald W. Bowden, Federico Centeno-Cruz, John C. Chambers, Nathalie Chami, Edmund Chan, Juliana Chan, Ching Yu Cheng, Yoon Shin Cho, Cecilia Contreras-Cubas, Emilio Córdova, Adolfo Correa, Ralph A. DeFronzo, Ravindranath Duggirala, Josée Dupuis, Ma Eugenia Garay-Sevilla, Humberto García-Ortiz, Christian Gieger, Benjamin Glaser, Clicerio González-Villalpando, Ma Elena Gonzalez, Niels Grarup, Leif Groop, Myron Gross, Christopher Haiman, Torben Hansen, Marit E. Jørgensen, AMP-T2D-GENES Consortia

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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 languageEnglish
Article number3505
JournalNature Communications
Volume12
Issue number1
ISSN2041-1723
DOIs
Publication statusPublished - Dec 2021

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© 2021, The Author(s).

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