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

التفاصيل البيبلوغرافية
العنوان: 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
المؤلفون: Nathan L. Tintle, Alden Green, Kaitlyn Cook, Kelsey Grinde, Alessandra M. Valcarcel
المصدر: BMC Proceedings
بيانات النشر: Springer Science and Business Media LLC, 2016.
سنة النشر: 2016
مصطلحات موضوعية: 0301 basic medicine, education.field_of_study, Population, Robustness (evolution), General Medicine, 030105 genetics & heredity, Biology, computer.software_genre, General Biochemistry, Genetics and Molecular Biology, Statistical power, Genetic architecture, 03 medical and health sciences, Proceedings, 030104 developmental biology, A priori and a posteriori, Data mining, p-value, education, computer, Statistic, Type I and type II errors
الوصف: 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.
تدمد: 1753-6561
URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f4c9a2807ed97f5a543d4713f7bf141c
https://doi.org/10.1186/s12919-016-0024-y
حقوق: OPEN
رقم الأكسشن: edsair.doi.dedup.....f4c9a2807ed97f5a543d4713f7bf141c
قاعدة البيانات: OpenAIRE