Evidence of transgenerational effects on autism spectrum disorder using multigenerational space-time cluster detection.

IF 3 2区 医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Rebecca Richards Steed, Amanda V Bakian, Ken Robert Smith, Neng Wan, Simon Brewer, Richard Medina, James VanDerslice
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引用次数: 3

Abstract

Background: Transgenerational epigenetic risks associated with complex health outcomes, such as autism spectrum disorder (ASD), have attracted increasing attention. Transgenerational environmental risk exposures with potential for epigenetic effects can be effectively identified using space-time clustering. Specifically applied to ancestors of individuals with disease outcomes, space-time clustering characterized for vulnerable developmental stages of growth can provide a measure of relative risk for disease outcomes in descendants.

Objectives: (1) Identify space-time clusters of ancestors with a descendent with a clinical ASD diagnosis and matched controls. (2) Identify developmental windows of ancestors with the highest relative risk for ASD in descendants. (3) Identify how the relative risk may vary through the maternal or paternal line.

Methods: Family pedigrees linked to residential locations of ASD cases in Utah have been used to identify space-time clusters of ancestors. Control family pedigrees of none-cases based on age and sex have been matched to cases 2:1. The data have been categorized by maternal or paternal lineage at birth, childhood, and adolescence. A total of 3957 children, both parents, and maternal and paternal grandparents were identified. Bernoulli space-time binomial relative risk (RR) scan statistic was used to identify clusters. Monte Carlo simulation was used for statistical significance testing.

Results: Twenty statistically significant clusters were identified. Thirteen increased RR (> 1.0) space-time clusters were identified from the maternal and paternal lines at a p-value < 0.05. The paternal grandparents carry the greatest RR (2.86-2.96) during birth and childhood in the 1950's-1960, which represent the smallest size clusters, and occur in urban areas. Additionally, seven statistically significant clusters with RR < 1 were relatively large in area, covering more rural areas of the state.

Conclusion: This study has identified statistically significant space-time clusters during critical developmental windows that are associated with ASD risk in descendants. The geographic space and time clusters family pedigrees with over 3 + generations, which we refer to as a person's geographic legacy, is a powerful tool for studying transgenerational effects that may be epigenetic in nature. Our novel use of space-time clustering can be applied to any disease where family pedigree data is available.

Abstract Image

Abstract Image

Abstract Image

多代时空聚类检测对自闭症谱系障碍跨代影响的证据。
背景:与复杂健康结果相关的跨代表观遗传风险,如自闭症谱系障碍(ASD),已引起越来越多的关注。时空聚类可以有效识别具有表观遗传效应的跨代环境风险暴露。特别适用于具有疾病结果的个体的祖先,以脆弱发育阶段为特征的时空聚类可以为后代的疾病结果提供相对风险的度量。目的:(1)识别具有临床ASD诊断的后代的祖先和匹配对照的时空集群。(2)确定后代中ASD相对风险最高的祖先发育窗口。(3)确定相对风险如何通过母系或父系变化。方法:使用与犹他州ASD病例居住地相关的家庭谱系来识别祖先的时空集群。根据年龄和性别,无病例的对照家系已与病例2:1匹配。这些数据按出生时、童年和青春期的母系或父系进行了分类。共有3957名儿童,包括父母双方以及祖父母。采用伯努利时空二项相对危险度(RR)扫描统计量对聚类进行识别。采用蒙特卡罗模拟进行统计学显著性检验。结果:确定了20个具有统计学意义的聚类。在父系和母系中发现了13个RR(> 1.0)增加的时空簇,p值为p值。结论:本研究发现了在关键发育窗口期与后代ASD风险相关的具有统计学意义的时空簇。3代以上家庭谱系的地理时空集群,即一个人的地理遗产,是研究可能具有表观遗传性质的跨代效应的有力工具。我们对时空聚类的新颖使用可以应用于任何家庭谱系数据可用的疾病。
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来源期刊
International Journal of Health Geographics
International Journal of Health Geographics PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH -
CiteScore
10.20
自引率
2.00%
发文量
17
审稿时长
12 weeks
期刊介绍: A leader among the field, International Journal of Health Geographics is an interdisciplinary, open access journal publishing internationally significant studies of geospatial information systems and science applications in health and healthcare. With an exceptional author satisfaction rate and a quick time to first decision, the journal caters to readers across an array of healthcare disciplines globally. International Journal of Health Geographics welcomes novel studies in the health and healthcare context spanning from spatial data infrastructure and Web geospatial interoperability research, to research into real-time Geographic Information Systems (GIS)-enabled surveillance services, remote sensing applications, spatial epidemiology, spatio-temporal statistics, internet GIS and cyberspace mapping, participatory GIS and citizen sensing, geospatial big data, healthy smart cities and regions, and geospatial Internet of Things and blockchain.
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