JOURNAL ARTICLE

Maternal and fetal origins of offspring blood pressure: statistical analysis using genetic correlation and genetic risk score-based Mendelian randomization.

  • Published In: International Journal of Epidemiology, 2023, v. 52, n. 5. P. 1360 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Jin, Siyi; Wang, Ting; Wenying, Chen; Wu, Yuxuan; Huang, Shuiping; Zeng, Ping 3 of 3

Abstract

This article focuses on investigating the genetic and causal relationship between birthweight and blood pressure, distinguishing maternal-specific and fetal-specific genetic effects. Using large-scale genome-wide association study (GWAS) summary statistics and individual-level data from the UK Biobank, the study found a significant inverse genetic correlation between fetal-specific birthweight and blood pressure traits—systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse pressure (PP)—and identified numerous pleiotropic genes shared between these traits. Mendelian randomization analysis using maternal-specific genetic risk scores (GRS) in mother-offspring pairs revealed a causal negative association of maternal birthweight effects with offspring SBP and PP, supporting the developmental origins of health and disease (DOHaD) hypothesis, while no such causal effect was observed in father-offspring pairs. These findings highlight common genetic components underlying birthweight and blood pressure and provide insights into the early prevention of hypertension.

Additional Information

  • Source:International Journal of Epidemiology. 2023/10, Vol. 52, Issue 5, p1360
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2023
  • ISSN:0300-5771
  • DOI:10.1093/ije/dyad034
  • Accession Number:172824651
  • Copyright Statement:Copyright of International Journal of Epidemiology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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