Document Type
Conference Proceeding
Publication Date
1-1-2016
Publication Title
BMC Proceedings
Publication Title
BMC Proceedings
Volume
10
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 summary p value 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.
Recommended Citation
Green, Alden; Cook, Kaitlyn; Grinde, Kelsey; Valcarcel, Alessandra; and Tintle, Nathan, "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" (2016). Statistical and Data Sciences: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/sds_facpubs/69
Version
Version of Record