Green, AldenCook, KaitlynGrinde, KelseyValcarcel, AlessandraTintle, Nathan2017-01-032016Green, Alden, Kaitlyn Cook, Kelsey Grinde, Alessandra Valcarcel, and Nathan Tintle. 2016. “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.” BMC Proceedings 10 (Suppl 7): 165-170. doi:10.1186/s12919-016-0024-y. http://dx.doi.org/10.1186/s12919-016-0024-y.1753-6561http://nrs.harvard.edu/urn-3:HUL.InstRepos:29739165Current 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.en-USJournal Article2017-01-0310.1186/s12919-016-0024-y