title: A general method for combining different family-based rare-variant tests of association to improve power and robustness of a wide range of genetic architectures.
abstract: Current rare-variant, gene-based tests of association often suffer from a lack of statistical power to detect genotype-phenotype associations as a result of a lack of prior knowledge of genetic disease models combined with limited observations of extremely rare causal variants in population-based samples. The use of pedigree data, in which rare variants are often more highly concentrated than in population-based data, has been proposed as 1 possible method for enhancing power. Methods for combining multiple gene-based tests of association into a single summarypvalue are a robust approach to different genetic architectures when little a priori knowledge is available about the underlying genetic disease model. To date, however, little consideration has been given to combining gene-based tests of association for the analysis of pedigree data. We propose a flexible framework for combining any number of family-based rare-variant tests of association into a single summary statistic and for assessing the significance of that statistic. We show that this approach maintains type I error and improves the robustness, to different genetic architectures, of the statistical power of family- and gene-based rare-variant tests through application to simulated phenotype data from Genetic Analysis Workshop 19.
authors.affiliation: Department of Statistics, Harvard University, Cambridge, MA 02138 USA.
authors.forename: Alden
authors.initials: A
authors.lastname: Green
authors.affiliation: Department of Mathematics and Statistics, Carleton College, Northfield, MN 55057 USA.
authors.forename: Kaitlyn
authors.initials: K
authors.lastname: Cook
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA 98195 USA.
authors.forename: Kelsey
authors.initials: K
authors.lastname: Grinde
authors.affiliation: Department of Statistics, University of Connecticut, Storrs, CT 06269 USA.
authors.forename: Alessandra
authors.initials: A
authors.lastname: Valcarcel
authors.affiliation: Department of Mathematics, Statistics, and Computer Science, Dordt College, Sioux Center, IA 51250 USA.
authors.forename: Nathan
authors.initials: N
authors.lastname: Tintle