Combining Disease Models to Test for Gene–Environment Interaction in Nuclear Families

Authors

  • Thomas J. Hoffmann,

    Corresponding author
    1. Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, U.S.A.
    2. Department of Epidemiology and Biostatistics and Institute of Human Genetics, University of California San Francisco, San Francisco, California 94143, U.S.A.
    Search for more papers by this author
  • Stijn Vansteelandt,

    1. Department of Applied Mathematics and Computer Sciences, Ghent University, Ghent, Belgium
    Search for more papers by this author
  • Christoph Lange,

    1. Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, U.S.A.
    2. Channing Laboratory and Division of Pulmonary and Critical Care Medicine, Department of Medicine Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115, U.S.A.
    Search for more papers by this author
  • Edwin K. Silverman,

    1. Channing Laboratory and Division of Pulmonary and Critical Care Medicine, Department of Medicine Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115, U.S.A.
    Search for more papers by this author
  • Dawn L. DeMeo,

    1. Channing Laboratory and Division of Pulmonary and Critical Care Medicine, Department of Medicine Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115, U.S.A.
    Search for more papers by this author
  • Nan M. Laird

    1. Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, U.S.A.
    Search for more papers by this author

email: tjh@post.harvard.edu

Abstract

Summary It is useful to have robust gene–environment interaction tests that can utilize a variety of family structures in an efficient way. This article focuses on tests for gene–environment interaction in the presence of main genetic and environmental effects. The objective is to develop powerful tests that can combine trio data with parental genotypes and discordant sibships when parents' genotypes are missing. We first make a modest improvement on a method for discordant sibs (discordant on phenotype), but the approach does not allow one to use families when all offspring are affected, e.g., trios. We then make a modest improvement on a Mendelian transmission-based approach that is inefficient when discordant sibs are available, but can be applied to any nuclear family. Finally, we propose a hybrid approach that utilizes the most efficient method for a specific family type, then combines over families. We utilize this hybrid approach to analyze a chronic obstructive pulmonary disorder dataset to test for gene–environment interaction in the Serpine2 gene with smoking. The methods are freely available in the R package fbati.

Ancillary