overview: Where and when the grandparents and the child’s parents were born may contribute to the increased risk of ASD in offspring.
sauce: University of Utah
When and where are often key clues for epidemiologists, medical sleuths who help solve the underlying mysteries of disease. The technology dates back at least to his 19th-century London, where a doctor named John Snow mapped cholera deaths and traced the origin of the outbreak to his one well in the city. The epidemic ended when the well was closed.
Taking this idea to a new level, health scientists at the University of Utah, using a unique combination of geographic and demographic data, recently discovered when and where the parents and grandparents of children in Utah were born and raised with autism. concluded that it may contribute to an increased risk of descendants.
Scientists believe that this new approach could be used to investigate the temporal and spatial aspects of any disease for which genealogical information is available.
Research published in International Journal of Health Geographicsone of the first to assess the effects of time and space (when, where) across generations on increased risk of autism.
Over time, the findings could lead to the identification of environmental factors, such as exposure to pollutants, that can have destructive effects on genetic information passed on from generation to generation, the researchers say.
“By looking back at families and where and when they lived, we were able to detect clusters of individuals who appeared to be at increased risk for autism among their offspring,” said U of U Health Public. said James VanDerslice, an environmental epidemiologist in the Department of Health. Senior author of the study.
“Knowing that the parents and grandparents of children with autism shared space and time brings us closer to understanding the environmental factors that may have influenced this health condition.”
Transgenerational epidemiological studies are difficult and time-consuming, says Rebecca Richards Steed, the study’s principal investigator and a graduate student in the Department of Geography at the University of Utah. In fact, most of these studies have been done in animals, which reproduce quickly and can be followed for several generations in a shorter period of time than humans.
VanDerslice and Richards-Steed use existing technology in new ways to examine existing data available to parents and grandparents to identify locations and potential risk factors that increase disease risk in the next generation. By specifying the time period, we avoided this drawback.
Researchers used the Utah Registry of Autism and Developmental Disabilities in conjunction with the Utah Population Database (UPDB) to identify parents and grandparents of children with autism born between 1989 and 2014. Did.
UPDB’s birth certificates, driver’s license information, census and medical records helped scientists track when and where these individuals lived. UPDB is one of the few databases in the world that contains this kind of information.
For comparison, we randomly selected parents and grandparents of children not diagnosed with autism from the UPDB database. The individual’s name was withheld from researchers.
In all, VanDerslice and colleagues identified where 7,900 parents and 31,600 grandparents were born and raised. They identified 20 major clusters, or groups, scattered throughout the state. After analysis, 13 of the 20 clusters (9 grandparents, 4 parents) were associated with an increased risk of autism in their children or grandchildren. The odds of becoming autistic were about three times higher than expected.
“What we see is consistent with current scientific understanding that paternal genetics is key to evolutionary change and adaptation,” says Richards Steed. In that case, it is quite possible that signals shaped by environmental experience come from paternal lineages that are passed down in the family.”
Seven clusters, all located in rural areas, had a low risk of association between autism and family history.
“I’m not sure why some rural areas seemed to have what’s called a protective effect,” says Richards-Steed. “It is certainly possible that urban parents and grandparents had different environmental exposures and experiences.”
“Based on our findings, we can say that what we are exposed to now probably affects not only us and our children, but also our children’s children. It means that there is
Going forward, researchers will delve deeper into factors such as lifestyle that may help explain these results.
“Evidence shows that our environment has a deterministic effect on our growth and development, including the germ cells we carry for our next generation,” he said. VanDerslice says.
“Exploring the space and time shared by our ancestors may provide clues about environmental factors that may lead to biological changes that increase the risk of disease in future generations. “
Scientists believe that this new approach could be used to investigate the temporal and spatial aspects of other conditions for which genealogical information is available.
“This idea is not unique to autism,” says Richards-Steed. “This could be applied to any disease and could enhance our ability to understand how the confluence of genetic and environmental factors has long-term effects on family health.”
About this Autism Research News
author: press office
sauce: University of Utah
contact: Press Office – University of Utah
image: image is public domain
Original research: open access.
“Evidence for transgenerational impact on autism spectrum disorders using multigenerational spatiotemporal cluster detection” by Rebecca Richards Steed et al. International Journal of Health Geographics
Evidence for transgenerational impact on autism spectrum disorders using multigenerational spatiotemporal cluster detection
The cross-generational epigenetic risks associated with complex health outcomes such as autism spectrum disorders (ASD) are receiving increasing attention. Exposures to environmental risks across generations with potential epigenetic effects can be effectively identified using spatio-temporal clustering. Spatiotemporal clustering characterized by vulnerable developmental stages of growth, which applies specifically to the ancestors of individuals with disease outcome, can provide a measure of the relative risk of disease outcome in offspring.
(1) identify ancestral spatiotemporal clusters with offspring with controls consistent with a clinical ASD diagnosis; (2) identify the ancestral developmental window with the highest relative risk of her ASD in offspring; (3) identify how relative risk varies by maternal or paternal lineage;
Family trees associated with place of residence of ASD cases in Utah have been used to identify ancestral spatiotemporal clusters. Non-case control pedigrees based on age and sex are matched 2:1 with cases. Data are grouped by maternal or paternal lineage at birth, childhood, and adolescence. A total of 3957 children of parents, maternal and paternal grandparents were identified. We identified clusters using Bernoulli space-time binomial relative risk (RR) scan statistics. Monte Carlo simulations were used for statistical significance testing.
Twenty statistically significant clusters were identified. Thirteen increased RR (> 1.0) space-time clusters were identified from maternal and paternal lines with p-value < 0.05. The paternal grandparent had the largest RR (2.86–2.96) at birth and childhood from the 1950s to her 1960s, representing the smallest size cluster and occurring in urban areas. In addition, the seven statistically significant clusters with RR < 1 are relatively large in area and cover more rural areas of the state.
In this study, we identified statistically significant spatiotemporal clusters during key developmental periods associated with ASD risk in offspring. Geographic space and time clusters of family genealogies span three or more generations, which we call individuals. geographical heritage, is a powerful tool for studying transgenerational effects that may be epigenetic in nature. The novel use of spatio-temporal clustering can be applied to any disease for which genealogical data are available.