William Gauderman, PhD

Professor of Population and Public Health Sciences

 

Health Equity Interests

I develop novel statistical methods and apply them to discover novel risk factors for cancer, respiratory disease, and other complex health outcomes.

Biography

Dr. Gauderman's research falls into three areas:  

1) Statistical methods:  He has developed novel statistical methods for applications in genetic epidemiology over the past 30 years.  He has focused on methods that unite information from both genetic and environmental sources, with particular emphasis on gene-environment (GxE) interactions.  These have included methods applicable to pedigree studies, candidate gene studies, and genomewide association studies (GWAS).  Across these topic areas, he and his trainees have developed more efficient (statistically more powerful) methods for detecting GxE interactions and have demonstrated that incorporating GxE interactions into an analysis can increase power to detect a novel gene.   

2)  Software development:   He has always felt that the development of software is an important way to translate new statistical methods into a format that can be utilized by others in the analysis of their data.  This is particularly true for methods that involve complex calculations (e.g. analysis of pedigrees), non-standard models (e.g. 2-step methods for GxE analysis), or large databases (e.g. genomewide association studies).  He has developed three distinct software packages over his career:  1) The Genetic Analysis Package (GAP), which implements novel methods developed for segregation and linkage analysis of pedigrees;  2) Quanto, which implements sample size and power calculations for genetic epidemiology studies; and 3) GxEscanR, which implements methods developed for genomewide GxE scans.  

3) Applied data analysis:  He has dedicated a significant portion of his time to the analysis of real data, with the goal of publishing findings in a substantive medical/biomedical journal.  His work has included the investigation of how air pollution in southern California affects children’s respiratory health, work stemming from his involvement in the Children's Health Study (CHS).  In 2004, he led a paper in NEJM showing that children in communities with poor air quality have reduced lung function development during their important adolescent growth period.  He followed this with a paper in Lancet in 2007 demonstrating that in addition to regional air quality, living close to a busy freeway has an additional negative impact on adolescent lung development.  Since the 1990's, pollutant levels in southern California have declined by as much as 50% for several of the main criteria pollutants, including nitrogen dioxide (NO2) and particulate matter (PM).  He led another NEJM paper in 2015 demonstrating that these improvements in air quality are associated with substantial improvements in children's adolescent lung development.  Related to his work in air pollution epidemiology, he served on the U.S. EPA's clean air scientific advisory committee (CASAC, ozone review panel).  He has also testified at federal, state, and local venues related to air quality issues and has responded to numerous requests for interviews by television, radio, web, and newspaper sources related to each of the three papers described above.  He also has a longstanding interest in cancer epidemiology and is currently co-PI of a large study aimed at identifying GxE interactions for colorectal cancer, a project that includes over 100,000 study subjects.  The methods and software he has developed are currently being used to scan the genome for GxE interactions with several factors known to influence colorectal cancer risk, including smoking, red meat consumption, alcohol, aspirin, and obesity.

Research Interests

  • Population Characteristics
  • Chronic Conditions
  • Morbidity and mortality
  • Risk Factors
  • Preventive Medicine
  • Adolescents
  • Cancer
  • Lifestyle
  • Molecular Epidemiology
  • Genetic
  • Genetics
  • Cancer Epidemiology
  • Epidemiological Factors
  • Exposures
  • Lifestyle
  • Air Pollution
  • Exposure Assessment Methods
  • environmental
  • Exposure Modeling
  • respiratory
  • Health Endpoints
  • Environmental Omics
  • adolescence
  • Statistical Methods
  • Multilevel Modeling
  • Data Sciences
  • Genomic Data Sciences
  • Public Health Data Sciences

Courses Taught

  • Directed Research
  • Master's Thesis
  • Master's Thesis
  • Master's Thesis
  • Research
  • Doctoral Dissertation
  • Doctoral Dissertation
  • Doctoral Dissertation
  • Doctoral Dissertation
  • Doctoral Dissertation