Despite an emerging consensus that many diseases are influenced by multiple gene-gene and gene-environment interactions, little is known about how genetic and environmental factors interact to influence outcomes in children. In addition, although psychosocial theory focuses on how socio-economic characteristics influence the health status of children, we have little information about how social factors interact with genetic and environmental factors to affect child well-being. Thus, methods to address the simultaneous and combined influence of social, environmental, and genetic factors are imperative. The mission of Duke's Center for Geospatial Medicine is to develop systematic, spatially-based methods for analyzing the pathways through which the environment, genetic, and psychosocial domains jointly shape child health and well-being. Specific goals of this Center are to: 1) develop an interdisciplinary center that supports research on how genetic, environmental, and social aspects of vulnerability combine to affect children's health and well-being; 2) promote interdisciplinary research interactions among programs in biomedicine, environmental health, statistics, and social sciences; 3) advance new methodologies for incorporating innovative spatial analysis into health research; 4) develop new and creative analytic approaches that address spatial/temporal variation, multiple comparisons, confounding, and effect modification; 5) train young scholars in the resulting methods. We will leverage active partnerships among the Nicholas School of the Environment, the Duke University Medical Center, and Trinity College of Arts and Sciences. We will link social, environmental, and genetic vulnerability using advanced spatial statistical approaches in combination with techniques from genetics and genomics. Using neural tube defects as a prototype health endpoint, we will develop a generalized framework for applying these methods to a wide variety of health endpoints, including autism, asthma, ADHD, and obesity.

Agency
National Institute of Health (NIH)
Institute
National Center for Research Resources (NCRR)
Type
Exploratory Grants (P20)
Project #
5P20RR020782-02
Application #
7171446
Study Section
Special Emphasis Panel (ZRR1-BT-8 (01))
Project Start
2005-08-01
Project End
2006-07-31
Budget Start
2005-08-01
Budget End
2006-07-31
Support Year
2
Fiscal Year
2005
Total Cost
$594,485
Indirect Cost
Name
Duke University
Department
Type
Schools of Earth Sciences/Natur
DUNS #
044387793
City
Durham
State
NC
Country
United States
Zip Code
27705
Grotegut, Chad A; Ngan, Emily; Garrett, Melanie E et al. (2017) The association of single-nucleotide polymorphisms in the oxytocin receptor and G protein-coupled receptor kinase 6 (GRK6) genes with oxytocin dosing requirements and labor outcomes. Am J Obstet Gynecol 217:367.e1-367.e9
Zhu, Bin; Ashley-Koch, Allison E; Dunson, David B (2013) Generalized admixture mapping for complex traits. G3 (Bethesda) 3:1165-75
Zhu, Bin; Dunson, David B; Ashley-Koch, Allison E (2012) Adverse subpopulation regression for multivariate outcomes with high-dimensional predictors. Stat Med 31:4102-13
Swamy, Geeta K; Edwards, Sharon; Gelfand, Alan et al. (2012) Maternal age, birth order, and race: differential effects on birthweight. J Epidemiol Community Health 66:136-42
Swamy, Geeta K; Garrett, Melanie E; Miranda, Marie Lynn et al. (2011) Maternal vitamin D receptor genetic variation contributes to infant birthweight among black mothers. Am J Med Genet A 155A:1264-71
Tassone, Eric C; Miranda, Marie Lynn; Gelfand, Alan E (2010) Disaggregated spatial modelling for areal unit categorical data. J R Stat Soc Ser C Appl Stat 59:175-190
Berrocal, Veronica J; Gelfand, Alan E; Holland, David M (2010) A Spatio-Temporal Downscaler for Output From Numerical Models. J Agric Biol Environ Stat 15:176-197
Gray, Simone C; Edwards, Sharon E; Miranda, Marie Lynn (2010) Assessing exposure metrics for PM and birth weight models. J Expo Sci Environ Epidemiol 20:469-77
Boyles, Abee L; Billups, Ashley V; Deak, Kristen L et al. (2006) Neural tube defects and folate pathway genes: family-based association tests of gene-gene and gene-environment interactions. Environ Health Perspect 114:1547-52