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Rare-Variant Kernel Machine Test for Longitudinal Data from Population and Family Samples
Author(s) -
Qi Yan,
Daniel E. Weeks,
Hemant K. Tiwari,
Nengjun Yi,
Kui Zhang,
Guimin Gao,
WanYu Lin,
XiangYang Lou,
Wei Chen,
Nianjun Liu
Publication year - 2015
Publication title -
human heredity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.423
H-Index - 62
eISSN - 1423-0062
pISSN - 0001-5652
DOI - 10.1159/000445057
Subject(s) - kernel (algebra) , population , longitudinal data , test (biology) , biology , statistics , genetics , computer science , mathematics , demography , combinatorics , data mining , sociology , paleontology
The kernel machine (KM) test reportedly performs well in the set-based association test of rare variants. Many studies have been conducted to measure phenotypes at multiple time points, but the standard KM methodology has only been available for phenotypes at a single time point. In addition, family-based designs have been widely used in genetic association studies; therefore, the data analysis method used must appropriately handle familial relatedness. A rare-variant test does not currently exist for longitudinal data from family samples. Therefore, in this paper, we aim to introduce an association test for rare variants, which includes multiple longitudinal phenotype measurements for either population or family samples.

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