E. Andres Houseman, ScD
Adjunct Associate Professor
Dr. Andres Houseman's research involves latent variable methods, model-based clustering, and high-dimensional data analysis. Recent work has focused on computationally efficient methods for epigenomics research.
BiographyDr. Andres Houseman is a biostatistician with diverse interests, including molecular epidemiology, biomarker discovery, and environmental exposure assessment. Much of his statistical research involves latent variable methods, model-based clustering, and high-dimensional data analysis. Recent work has focused on computationally efficient methods for epigenomics research.
Research DescriptionMany current projects involve statistical methods for epigenetics, an exciting field of genome-wide biological inquiry. Epigenetics is the study of heritable changes in gene function not involving changes in DNA sequence, the best examples of which are DNA methylation and histone modification. Because these processes help to regulate gene expression, but also may be affected by the expression of certain genes, miRNAs, and other molecular factors, their genome-wide study necessarily requires methods for integrative genomics, and thus provides a rich opportunity for challenging statistical work. In addition, because environmental factors can influence epigenetic processes, epigenetics may play a central role in gene-environment interactions, and thus provides additional interesting opportunities for epidemiologic research.
The literature of DNA methylation emphasizes the "methylator phenotype", whose statistical description essentially involves clustering or latent class modeling. Recent statistical research suggests that, in the context of epigenetics, model-based clustering may outperform metric-based and other nonparametric approaches. My work has focused on finding computationally efficient solutions to model-based clustering problems in the context of DNA methylation, and the integration of DNA methylation data with other genomic data types.
Grants and AwardsHoward Hughes Medical Institute Predoctoral Fellowship (2000)
AffiliationsHarvard School of Public Health, Department of Biostatistics (Adjunct Assistant Professor)
Dana-Farber/Harvard Cancer Center (Associate Member)
International Biometrics Society (Member)
Funded ResearchR01 CA121147 (Kelsey) NIH/NIEHS
The Molecular Epidemiology of Bladder Cancer
R01 CA126939 (Kelsey) NIH/NCI
The Epidemiology of Molecular Alterations in Mesothelioma
R01 CA075971 (Betensky) NIH/NCI
Statistical Methods for Analysis of Failure Time Data
P01 CA134294 (Ryan/Lin) NCI
Statistical Informatics for Cancer Research
P30 ES000002 (Dockery) NIH/NIEHS
Kresge Center for Environmental Health
UL1 RR025758 (Nadler) NIH/NCRR
Harvard Clinical and Translational Science Center (Biostatistics Node/HSPH)