We develop novel statistical techniques for nonparametric Bayes analysis of high-dimensional covariate data, directly motivated by the largest population-based study ever conducted on the causes of birth defects. The methods we develop will enable borrowing of information and shrinkage across high-dimensional environmental, biomedical, pharmacological, and sociodemographic risk factors (and interactions among them) and across a multitude of birth defects, many of which are too rare to be studied in isolation. Using a hierarchical structure directly motivated by embryonic development, the borrowing of information can be informed by our knowledge of mechanistic development of the embryo. These novel methods may significantly impact the study of rare congenital malformations. The methods to be developed have broad application in public health and medicine, where exposures or characteristics of interest may be great in number and interactions are important, such as the examination high-dimensional gene by environment and gene-gene interactions.

Public Health Relevance

This project addresses a critical need of finding clues to the etiology and pathogenesis of congenital mal- formations, using data from the largest population-based study ever conducted on the causes of birth defects. While birth defects are the leading cause of infant mortality, the leading cause of death among children aged 1-4, and the fifth-ranked cause of premature mortality in the United States, many individual defects are too rare to be studied comprehensively, even in studies that are very large. Our new statistical methods for sparse shrinkage incorporate current knowledge of embryonic development and allow some borrowing of information across differ- ent birth defects while keeping each defect as a separate entity of interest in the statistical model. These novel methods will allow investigators to investigate the simultaneous influence of multiple exposures and combinations of exposures on multiple outcomes.

National Institute of Health (NIH)
National Institute of Environmental Health Sciences (NIEHS)
Research Project (R01)
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Biostatistical Methods and Research Design Study Section (BMRD)
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Dilworth, Caroline H
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University of North Carolina Chapel Hill
Biostatistics & Other Math Sci
Schools of Public Health
Chapel Hill
United States
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Rappazzo, Kristen M; Warren, Joshua L; Meyer, Robert E et al. (2016) Maternal residential exposure to agricultural pesticides and birth defects in a 2003 to 2005 North Carolina birth cohort. Birth Defects Res A Clin Mol Teratol 106:240-9
Warren, Joshua L; Stingone, Jeanette A; Herring, Amy H et al. (2016) Bayesian multinomial probit modeling of daily windows of susceptibility for maternal PM2.5 exposure and congenital heart defects. Stat Med 35:2786-801
Berchuck, Samuel I; Warren, Joshua L; Herring, Amy H et al. (2016) Spatially Modelling the Association Between Access to Recreational Facilities and Exercise: The 'Multi-Ethnic Study of Atherosclerosis'. J R Stat Soc Ser A Stat Soc 179:293-310
Buckley, Jessie P; Engel, Stephanie M; Braun, Joseph M et al. (2016) Prenatal Phthalate Exposures and Body Mass Index Among 4- to 7-Year-old Children: A Pooled Analysis. Epidemiology 27:449-58
Zhou, Jing; Herring, Amy H; Bhattacharya, Anirban et al. (2016) Nonparametric Bayes modeling for case control studies with many predictors. Biometrics 72:184-92
Nethery, Rachel C; Warren, Joshua L; Herring, Amy H et al. (2015) A common spatial factor analysis model for measured neighborhood-level characteristics: The Multi-Ethnic Study of Atherosclerosis. Health Place 36:35-46
Long, D Leann; Preisser, John S; Herring, Amy H et al. (2015) A Marginalized Zero-inflated Poisson Regression Model with Random Effects. J R Stat Soc Ser C Appl Stat 64:815-830
Warren, Joshua L; Luben, Thomas J; Sanders, Alison P et al. (2014) An evaluation of metrics for assessing maternal exposure to agricultural pesticides. J Expo Sci Environ Epidemiol 24:497-503
Wheeler, Matthew W; Dunson, David B; Pandalai, Sudha P et al. (2014) Mechanistic Hierarchical Gaussian Processes. J Am Stat Assoc 109:894-904
Long, D Leann; Preisser, John S; Herring, Amy H et al. (2014) A marginalized zero-inflated Poisson regression model with overall exposure effects. Stat Med 33:5151-65

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