Polygenic prediction of preeclampsia and gestational hypertension

子痫前期 妊娠高血压 医学 怀孕 子痫 产科 全基因组关联研究 生物信息学 内科学 单核苷酸多态性 生物 遗传学 基因型 基因
作者
Michael C. Honigberg,Buu Truong,Raiyan R. Khan,Brenda Xiao,Laxmi Bhatta,Ha My T. Vy,Rafael F. Guerrero,Art Schuermans,Margaret Sunitha Selvaraj,Aniruddh P. Patel,Satoshi Koyama,So Mi Jemma Cho,Shamsudheen Karuthedath Vellarikkal,Mark Trinder,Sarah Urbut,Kathryn J. Gray,Ben Brumpton,Snehal Patil,Sebastian Zöllner,Mariah C. Antopia
出处
期刊:Nature Medicine [Nature Portfolio]
卷期号:29 (6): 1540-1549 被引量:68
标识
DOI:10.1038/s41591-023-02374-9
摘要

Preeclampsia and gestational hypertension are common pregnancy complications associated with adverse maternal and child outcomes. Current tools for prediction, prevention and treatment are limited. Here we tested the association of maternal DNA sequence variants with preeclampsia in 20,064 cases and 703,117 control individuals and with gestational hypertension in 11,027 cases and 412,788 control individuals across discovery and follow-up cohorts using multi-ancestry meta-analysis. Altogether, we identified 18 independent loci associated with preeclampsia/eclampsia and/or gestational hypertension, 12 of which are new (for example, MTHFR–CLCN6, WNT3A, NPR3, PGR and RGL3), including two loci (PLCE1 and FURIN) identified in the multitrait analysis. Identified loci highlight the role of natriuretic peptide signaling, angiogenesis, renal glomerular function, trophoblast development and immune dysregulation. We derived genome-wide polygenic risk scores that predicted preeclampsia/eclampsia and gestational hypertension in external cohorts, independent of clinical risk factors, and reclassified eligibility for low-dose aspirin to prevent preeclampsia. Collectively, these findings provide mechanistic insights into the hypertensive disorders of pregnancy and have the potential to advance pregnancy risk stratification. A multi-ancestry genetic meta-analysis identifies 12 new loci associated with preeclampsia and gestational hypertension and proposes the integration of polygenic scores and clinical factors for disease prediction
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