Observational methods for human studies of transgenerational effects.

There are substantial challenges in studying human transgenerational epigenetic outcomes resulting from environmental conditions. The task requires specialized methods and tools that incorporate specific knowledge of multigenerational relationship combinations of probands and their ancestors, phenotype data for individuals, environmental information of ancestors and their descendants, which can span historical to present datasets, and informative environmental data that chronologically aligns with ancestors and descendants over space and time. As a result, there are few epidemiologic studies of potential transgenerational effects in human populations, thus limiting the knowledge of ancestral environmental conditions and the potential impacts we face with modern human health outcomes. In an effort to overcome some of the challenges in studying human transgenerational effects, we present two transgenerational study designs: transgenerational space-time cluster detection and transgenerational case-control study design. Like other epidemiological methods, these methods determine whether there are statistical associations between phenotypic outcomes (e.g., adverse health outcomes) among probands and the shared environments and environmental factors facing their ancestors. When the ancestor is a paternal grandparent, a statistically significant association provides some evidence that a transgenerational inheritable factor may be involved. Such results may generate useful hypotheses that can be explored using epigenomic data to establish conclusive evidence of transgenerational heritable effects. Both methods are proband-centric: They are designed around the phenotype of interest in the proband generation for case selection and family pedigree creation. In the examples provided, we incorporate at least three generations of paternal lineage in both methods to observe a potential transgenerational effect.

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

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.

Geographic Proximity of Family Members and Healthcare Utilization After Complex Surgical Procedures.

OBJECTIVE: We sought to determine the relationship between a patient's proximal familial social support, defined as the geographic proximity of family members, and healthcare utilization after complex cardiovascular and oncologic procedures. BACKGROUND: Social support mechanisms are increasingly identified as modifiable risk factors for healthcare utilization. METHODS: We performed a retrospective cohort study of 60,895 patients undergoing complex cardiovascular procedures or oncologic procedures. We defined healthcare utilization outcomes as 30-day all-cause readmission unplanned readmission, nonindex hospital readmission, index hospital length of stay, and home discharge disposition. For each patient, we aggregated the number of first-degree relatives (FDR) living within 30 miles of the patient's home address at the time of the surgical procedure into the following categories: 0 to 1, 2 to 3, 4 to 5, 6+ FDRs. We developed hierarchical multivariable regression models to determine the relationship between the number of FDR living within 30 miles of the patient and the healthcare utilization outcomes. RESULTS: Compared with patients with 0 to 1 FDRs, patients with 6+ FDRs living in close proximity had significantly lower rates of all-cause readmission (12.1% vs 13.5%, P <0.001), unplanned readmission (10.9% vs 12.0%, P =0.001), nonindex readmission (2.6% vs 3.2%, P =0.003); higher rates of home discharge (88.0% vs 85.3%, P <0.001); and shorter length of stay (7.3 vs 7.5 days, P =0.02). After multivariable adjustment, a larger number of FDRs living within 30 miles of the patient was significantly associated with a lower likelihood of all-cause readmission ( P <0.001 for trend), 30-day unplanned readmission ( P <0.001), nonindex readmission ( P <0.001); higher likelihood of home discharge ( P <0.001); and shorter index length of stay ( P <0.001). CONCLUSIONS: The geographic proximity of family members is significantly associated with decreased healthcare utilization after complex cardiovascular and oncologic surgical procedures.

Spatial clusters of cancer incidence: analyzing 1940 census data linked to 1966-2017 cancer records.

PURPOSE: A life course perspective to cancer incidence is important for understanding effects of the environment during early life on later cancer risk. We assessed spatial clusters of cancer incidence based on early life location defined as 1940 US Census Enumeration District (ED). METHODS: A cohort of 260,585 individuals aged 0-40 years in 1940 was selected. Individuals were followed from 1940 to cancer diagnosis, death, or last residence in Utah. We geocoded ED centroids in Utah for the 1940 Census. Spatial scan statistics with purely spatial elliptic scanning window were used to identify spatial clusters of EDs with excess cancer rates across 26 cancer types, assuming a discrete Poisson model. RESULTS: Cancer was diagnosed in 66,904 (25.67%) individuals during follow-up across 892 EDs. Average follow-up was 50.9 years. We detected 15 clusters of excess risk for bladder, breast, cervix, colon, lung, melanoma, oral, ovary, prostate, and soft tissue cancers. An urban area had dense overlap of multiple cancer types, including two EDs at increased risk for five cancer types each. CONCLUSIONS: Early environments may contribute to cancer risk later in life. Life course perspectives applied to the study of cancer incidence can provide insights for increasing understanding of cancer etiology.