Elizabeth Hauser, PhD
Principal Investigator
Professor of Biostatistics & Bioinformatics
Member of Duke Molecular Physiology Institute
Contact Information

Carmichael Building


Elizabeth Hauser, Professor in the Department of Biostatistics and Bioinformatics with secondary appointments in Medicine, Statistical Science, and Nursing, is a Statistical Geneticist with graduate degrees in Biostatistics and Epidemiology. Her research interests include statistical methods development for the analysis of complex genetic traits, genetic analysis of family data, identification of gene-environment interactions, and integrated analysis of metabolomics and genomic data. She has worked on studies of cardiovascular disease, diabetes, kidney disease, aging, and cancer.

MHS, Epidemiology/Genetic Epidemiology, The Johns Hopkins University School of Hygiene and Public Health, Baltimore, MD
MS, Biostatistics, University of Michigan, Ann Arbor, MI
PhD, Biostatistics/Statistical Genetics, University of Michigan, Ann Arbor, MI 


Dr. Elizabeth Hauser is a Statistical Geneticist and Genetic Epidemiologist who has worked in the area of human genetics for over 30 years.  Her research interests range from statistical methods development and study design to applications of genetic analysis methods to a wide range of diseases and conditions.  She collaborates with other investigators to include new methods of genomic analysis in applied gene identification studies.

Cardiovascular Disease: 
Dr. Hauser’s first project at Duke University was a family study of early-onset cardiovascular disease, called GENECARD, in collaboration with Dr. William Kraus and a large international consortium of academic researchers and researchers from Glaxo-Smith-Kline.  The GENECARD study collected over 1000 families (1) and performed genome-wide linkage studies (2), ultimately identifying and replicating several genes associated with coronary artery disease including the genes KALRN (3), GATA2 (4), LSAMP (5), FAM5C (6) , NPY (7), ALOX5AP (8), NC (9), and PLA2G7 (10).  We are fortunate to have also been able to assemble a second dataset, CATHGEN, including nearly 10,000 samples from the Duke Cardiac Catheterization Lab that was successfully used to perform genetic association tests as replication of GENECARD findings.  We are also examining the impact of risk factors for cardiovascular disease and gene-by-environment interactions on cardiovascular disease risk, including lipid levels, smoking (11), exercise (12), air pollution (13,14), psychosocial stress (15,16,17) and age (18,19). The studies in gene-by-air pollution interactions have expanded our research networks to include researchers at the Environmental Protection Agency interested in the effects of air pollution on cardiovascular health. The studies in the gene-by-psychosocial stress interactions are in collaboration with Dr. Redford Williams and researchers at the Behavioral Medicine Research Institute. Our research programs in the genetics of cardiovascular disease and gene-environment interactions have augmented the available CATHGEN dataset with gene expression, metabolomics, epigenetic and sequence data.

A key focus of our cardiovascular genetics research has been in the relationship between age and cardiovascular disease.  For Dr. Hauser this has grown into a more general focus on aging with her involvement in the Duke Older Americans Independence Center (OAIC) or Pepper Center.  As part of the Pepper Center, Dr. Hauser has participated in genetic studies of osteoarthritis (20), survivorship in cardiovascular disease (21,22) and an epigenetic study of smooth-muscle cell aging that identified the COL15A1 gene (23), as well as a genome-wide association study of over 2300 centenarians from a population based survey of healthy aging in China (24).

Studies at the Durham Veterans affairs(VA):
For the past 10 years Dr. Hauser has collaborated with investigators in the Durham VA Cooperative Studies Program Epidemiology Center (CSPEC). The Cooperative Studies Program is organized out of the VA Office of Research and Development to perform multi-site studies of medical conditions of importance to Veterans, many of which are also common conditions. CSP has several centers across the US affiliated with major academic medical centers. The CSP portfolio includes a wide range of studies. Particularly exciting among these is the Million Veteran Program (MVP), a VA-wide collaborative study to enroll one million Veterans in a biorepository linked to the VA electronic health record. As of January 2021 the number of Veterans enrolled was nearly 750,000 with over 600,000 with genome-wide genotyping data. The size of MVP has required new approaches to every aspect of the molecular and statistical approach (25,26). Dr. Hauser is working on genetic studies of suicide and Gulf War Veterans illness.

Another area of work at the VA is the study of the genomics of colon cancer precursors through study of a longitudinal cohort study of safety and efficacy of colonoscopy begun in the early 1990s, directed by Drs. Dawn Provenzale and David Lieberman. The team has recently completed the 10-year follow-up analysis of the dataset, initiating analysis with joint longitudinal statistical models that take both longitudinal colonoscopy experience and mortality into account (27). Dr. Hauser is leading the development of genetic and genomic analysis of samples for CSP #380, planning GWAS, sequence and methylation analyses (28).

Statistical Methods Development:
As a biostatistician and statistical geneticist, Dr. Hauser is interested in developing methods to improve assessment of genetic models for complex diseases. The software programs listed below are available under the LINKS tab. These methodological studies often include extensive statistical simulation studies, for which Dr. Hauser and colleagues have created the SIMLA software package (293031). Dr. Hauser and colleagues have developed the Ordered-Subset Analysis (OSA) programs for linkage analysis (32), family-based association analysis (33), case-control studies (34), and sequence analysis. The methods and related simulation studies address the pressing need for statistical methods that can be used for gene identification in genetic models of complex disease that include genetic heterogeneity and gene-by-environment interaction. Dr. Hauser is also interested in developing methods and software for integrated systems biology analysis by collaborating with bioinformaticians and computational biologists (35,36,37,38). Finally, as the Section Chair of Computational Biology in the DMPI, Dr. Hauser is committed to methods development for reproducible research and excellence in research informatics.



Software Programs

If you are interested in obtaining any of these packages, please contact Dr. Hauser directly.

    In the presence of genetic heterogeneity, APL-OSA can identify a genetically homozygous subset of families based on a trait-related covariate.
    The Combined Likelihood Ratio Test is an extension of the original likelihood ratio test (LRT) proposed by Weinberg, et. al. (1999) to test child genotype risk, maternal genotype effects and parent-of-origin effects. (Windows only.)
    CNVAnalyst is a Java GUI for statistical, bioinformatic, and visual analyses of CNVs. CNVAnalyst assists in the choice of the optimal CNV calling method and its parameters for each CNV dataset.
    The OSA program is the product of collaboration between the University of Michigan and the DMPI. OSA tests for linkage in heterogeneous data sets by taking covariate data into account. Co-variates may include linkage evidence at other genes, environmental exposures, or biological trait values such as cholesterol, age at onset, etc. (Solaris and Linux only.)
    The OSACC program was designed to evaluate evidence for association in the presence of genetic heterogeneity.
  • SIBLINK V 3.0
    The SIBLINK program performs linkage analysis on affected sib-pairs.
    SIMLA is a SIMuLAtion program that generates data sets of families for use in linkage and association studies.
    SIMLA_3.3 with GUI adds a graphical frontend to SIMLA3.2 (included) to assist users in creating a control file.
    SIMLAPLOT is a tool designed to help visualize the joint effect of genes and continuous environmental covariates on simulation models.


APL provides a novel test for association in the presence of linkage using general pedigree data.

CAPL, the Combined Association in the Presence of Linkage test is a powerful association method that can accommodate family and case-control data and account for population stratification. It allows for missing parental genotypes and multiple affected siblings in nuclear families.

The Pedigree Disequilibrium Test (PDT) analysis program allows the user to test for linkage and association in general pedigree data. In addition to allele- and genotype-specific analysis of individual markers, PDT version 5.1 adds the ability to perform genotype-specific analysis over marker sets.

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