Contract grant sponsor: NIH; Contract grant numbers: R01 DK078616; U01 DK85526; K24 DK080140; Contract grant sponsor: National Heart, Lung, and Blood Institute (NHLBI); Contract grant number: N01-HC-25195; Contract grant sponsor: Affymetrix, Inc.; Contract grant number: N02-HL-6–4278.
Sequence Kernel Association Test for Quantitative Traits in Family Samples
Article first published online: 26 DEC 2012
© 2012 WILEY PERIODICALS, INC.
Volume 37, Issue 2, pages 196–204, February 2013
How to Cite
Chen, H., Meigs, J. B. and Dupuis, J. (2013), Sequence Kernel Association Test for Quantitative Traits in Family Samples. Genet. Epidemiol., 37: 196–204. doi: 10.1002/gepi.21703
- Issue published online: 10 JAN 2013
- Article first published online: 26 DEC 2012
- Manuscript Accepted: 22 NOV 2012
- Manuscript Revised: 12 NOV 2012
- Manuscript Received: 15 AUG 2012
- NIH. Grant Numbers: R01 DK078616, U01 DK85526, K24 DK080140
- National Heart, Lung, and Blood Institute (NHLBI). Grant Number: N01-HC-25195
- Affymetrix, Inc.. Grant Number: N02-HL-6–4278
- rare variant analysis;
- quantitative traits;
- family samples;
- linear mixed effects model
A large number of rare genetic variants have been discovered with the development in sequencing technology and the lowering of sequencing costs. Rare variant analysis may help identify novel genes associated with diseases and quantitative traits, adding to our knowledge of explaining heritability of these phenotypes. Many statistical methods for rare variant analysis have been developed in recent years, but some of them require the strong assumption that all rare variants in the analysis share the same direction of effect, and others requiring permutation to calculate the P-values are computer intensive. Among these methods, the sequence kernel association test (SKAT) is a powerful method under many different scenarios. It does not require any assumption on the directionality of effects, and statistical significance is computed analytically. In this paper, we extend SKAT to be applicable to family data. The family-based SKAT (famSKAT) has a different test statistic and null distribution compared to SKAT, but is equivalent to SKAT when there is no familial correlation. Our simulation studies show that SKAT has inflated type I error if familial correlation is inappropriately ignored, but has appropriate type I error if applied to a single individual per family to obtain an unrelated subset. In contrast, famSKAT has the correct type I error when analyzing correlated observations, and it has higher power than competing methods in many different scenarios. We illustrate our approach to analyze the association of rare genetic variants using glycemic traits from the Framingham Heart Study.