遗传学洞察人类卵巢老化的年龄特异性生物学机制。
Genetic insights into the age-specific biological mechanisms governing human ovarian aging.
发表日期:2023 Aug 01
作者:
Sven E Ojavee, Liza Darrous, Marion Patxot, Kristi Läll, Krista Fischer, Reedik Mägi, Zoltan Kutalik, Matthew R Robinson
来源:
AMERICAN JOURNAL OF HUMAN GENETICS
摘要:
目前几乎没有证据表明人类表型的遗传基础在不同阶段存在显著差异。然而,时间相关表型的研究还不足,并且可以被视为反映潜在危险因素的表现形式,当数值范围广泛时,该因素在整个生命过程中不太可能保持恒定。在这项研究中,我们发现在英国生物库中与天然绝经年龄(ANM)有关的245个基因全幅显著相关的基因关联中,有74%显示出一种与年龄相关的效应形式。其中19个已经通过我们的建模框架得到复制性的发现,该框架能够确定DNA变异年龄发病关联的时间相关性,而不会造成显著的多重检验负担。在从早期到晚期绝经的范围内,我们发现存在明显不同的潜在生物途径,ANM与健康指标和结果之间的遗传相关性的符号变化,以及推断的因果关系的差异。我们发现,仅有DNA损伤应激反应过程会影响早期绝经的卵巢储备和消耗的情况。通过遗传学介导的ANM延迟与所有年龄组的乳腺癌和平滑肌瘤的相对风险增加以及晚期ANM女性的高胆固醇和心力衰竭相关。这些发现表明,通过适当建模大规模生物库数据,我们能够更好地理解健康指标和结果的遗传风险因素关系的年龄依赖性。版权所有©2023作者。由Elsevier Inc.发布,保留所有权利。
There is currently little evidence that the genetic basis of human phenotype varies significantly across the lifespan. However, time-to-event phenotypes are understudied and can be thought of as reflecting an underlying hazard, which is unlikely to be constant through life when values take a broad range. Here, we find that 74% of 245 genome-wide significant genetic associations with age at natural menopause (ANM) in the UK Biobank show a form of age-specific effect. Nineteen of these replicated discoveries are identified only by our modeling framework, which determines the time dependency of DNA-variant age-at-onset associations without a significant multiple-testing burden. Across the range of early to late menopause, we find evidence for significantly different underlying biological pathways, changes in the signs of genetic correlations of ANM to health indicators and outcomes, and differences in inferred causal relationships. We find that DNA damage response processes only act to shape ovarian reserve and depletion for women of early ANM. Genetically mediated delays in ANM were associated with increased relative risk of breast cancer and leiomyoma at all ages and with high cholesterol and heart failure for late-ANM women. These findings suggest that a better understanding of the age dependency of genetic risk factor relationships among health indicators and outcomes is achievable through appropriate statistical modeling of large-scale biobank data.Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.