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Fetal genome predicted birth weight and polycystic ovary syndrome in later life: a Mendelian randomization study

Associations between lower birth weight and higher polycystic ovary syndrome (PCOS) risk have been reported in previous observational studies, however, the causal relationship is still unknown. Based on decomposed fetal and maternal genetic effects on birth weight (n  =  406,063), we conducted a two...

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Autores principales: Liu, Dong, Gan, Yuexin, Zhang, Yue, Cui, Linlin, Tao, Tao, Zhang, Jun, Zhao, Jian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10282929/
https://www.ncbi.nlm.nih.gov/pubmed/37351103
http://dx.doi.org/10.3389/fendo.2023.1140499
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author Liu, Dong
Gan, Yuexin
Zhang, Yue
Cui, Linlin
Tao, Tao
Zhang, Jun
Zhao, Jian
author_facet Liu, Dong
Gan, Yuexin
Zhang, Yue
Cui, Linlin
Tao, Tao
Zhang, Jun
Zhao, Jian
author_sort Liu, Dong
collection PubMed
description Associations between lower birth weight and higher polycystic ovary syndrome (PCOS) risk have been reported in previous observational studies, however, the causal relationship is still unknown. Based on decomposed fetal and maternal genetic effects on birth weight (n  =  406,063), we conducted a two-sample Mendelian randomization (MR) analysis to assess potential causal relationships between fetal genome predicted birth weight and PCOS risk using a large-scale genome-wide association study (GWAS) including 4,138 PCOS cases and 20,129 controls. To further eliminate the maternally transmitted or non-transmitted effects on fetal growth, we performed a secondary MR analysis by utilizing genetic instruments after excluding maternally transmitted or non-transmitted variants, which were identified in another birth weight GWAS (n = 63,365 parent-offspring trios from Icelandic birth register). Linkage disequilibrium score regression (LDSR) analysis was conducted to estimate the genetic correlation. We found little evidence to support a causal effect of fetal genome determined birth weight on the risk of developing PCOS (primary MR analysis, OR: 0.86, 95% CI: 0.52 to 1.43; secondary MR analysis, OR: 0.86, 95% CI: 0.54 to 1.39). In addition, a marginally significant genetic correlation (r(g) = -0.14, se = 0.07) between birth weight and PCOS was revealed via LDSR analysis. Our findings indicated that observed associations between birth weight and future PCOS risk are more likely to be attributable to genetic pleiotropy driven by the fetal genome rather than a causal mechanism.
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spelling pubmed-102829292023-06-22 Fetal genome predicted birth weight and polycystic ovary syndrome in later life: a Mendelian randomization study Liu, Dong Gan, Yuexin Zhang, Yue Cui, Linlin Tao, Tao Zhang, Jun Zhao, Jian Front Endocrinol (Lausanne) Endocrinology Associations between lower birth weight and higher polycystic ovary syndrome (PCOS) risk have been reported in previous observational studies, however, the causal relationship is still unknown. Based on decomposed fetal and maternal genetic effects on birth weight (n  =  406,063), we conducted a two-sample Mendelian randomization (MR) analysis to assess potential causal relationships between fetal genome predicted birth weight and PCOS risk using a large-scale genome-wide association study (GWAS) including 4,138 PCOS cases and 20,129 controls. To further eliminate the maternally transmitted or non-transmitted effects on fetal growth, we performed a secondary MR analysis by utilizing genetic instruments after excluding maternally transmitted or non-transmitted variants, which were identified in another birth weight GWAS (n = 63,365 parent-offspring trios from Icelandic birth register). Linkage disequilibrium score regression (LDSR) analysis was conducted to estimate the genetic correlation. We found little evidence to support a causal effect of fetal genome determined birth weight on the risk of developing PCOS (primary MR analysis, OR: 0.86, 95% CI: 0.52 to 1.43; secondary MR analysis, OR: 0.86, 95% CI: 0.54 to 1.39). In addition, a marginally significant genetic correlation (r(g) = -0.14, se = 0.07) between birth weight and PCOS was revealed via LDSR analysis. Our findings indicated that observed associations between birth weight and future PCOS risk are more likely to be attributable to genetic pleiotropy driven by the fetal genome rather than a causal mechanism. Frontiers Media S.A. 2023-06-07 /pmc/articles/PMC10282929/ /pubmed/37351103 http://dx.doi.org/10.3389/fendo.2023.1140499 Text en Copyright © 2023 Liu, Gan, Zhang, Cui, Tao, Zhang and Zhao https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Endocrinology
Liu, Dong
Gan, Yuexin
Zhang, Yue
Cui, Linlin
Tao, Tao
Zhang, Jun
Zhao, Jian
Fetal genome predicted birth weight and polycystic ovary syndrome in later life: a Mendelian randomization study
title Fetal genome predicted birth weight and polycystic ovary syndrome in later life: a Mendelian randomization study
title_full Fetal genome predicted birth weight and polycystic ovary syndrome in later life: a Mendelian randomization study
title_fullStr Fetal genome predicted birth weight and polycystic ovary syndrome in later life: a Mendelian randomization study
title_full_unstemmed Fetal genome predicted birth weight and polycystic ovary syndrome in later life: a Mendelian randomization study
title_short Fetal genome predicted birth weight and polycystic ovary syndrome in later life: a Mendelian randomization study
title_sort fetal genome predicted birth weight and polycystic ovary syndrome in later life: a mendelian randomization study
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10282929/
https://www.ncbi.nlm.nih.gov/pubmed/37351103
http://dx.doi.org/10.3389/fendo.2023.1140499
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