Adjusted Sequence Kernel Association Test for Rare Variants Controlling for Cryptic and Family Relatedness. Issue 4 (25th March 2013)

• Record Type: Journal Article
• Authors: Oualkacha, Karim; Dastani, Zari; Li, Rui; Cingolani, Pablo E.; Spector, Timothy D.; Hammond, Christopher J.; Richards, J. Brent; Ciampi, Antonio; Greenwood, Celia M. T.
• Is Part Of: Genetic epidemiology. Volume 37:Issue 4(2013)
• Page Start: 366
• Page End: 376
• Publication Date: 2013-03-25
• Subjects: Genetic epidemiology -- Periodicals; Heredity -- Periodicals; Medical geography -- Periodicals; 614
• DOI: 10.1002/gepi.21725
• Languages: English
• ISSNs: 0741-0395

Abstract

<abstract abstract-type="main"> <title>ABSTRACT</title> <p>Recent progress in sequencing technologies makes it possible to identify rare and unique variants that may be associated with complex traits. However, the results of such efforts depend crucially on the use of efficient statistical methods and study designs. Although family‐based designs might enrich a data set for familial rare disease variants, most existing rare variant association approaches assume independence of all individuals. We introduce here a framework for association testing of rare variants in family‐based designs. This framework is an adaptation of the sequence kernel association test (SKAT) which allows us to control for family structure. Our adjusted SKAT (ASKAT) combines the SKAT approach and the factored spectrally transformed linear mixed models (FaST‐LMMs) algorithm to capture family effects based on a LMM incorporating the realized proportion of the genome that is identical by descent between pairs of individuals, and using restricted maximum likelihood methods for estimation. In simulation studies, we evaluated type I error and power of this proposed method and we showed that regardless of the level of the trait heritability, our approach has good control of type I error and good power. Since our approach uses FaST‐LMM to calculate variance components for the proposed mixed model, ASKAT is reasonably fast and can analyze hundreds of thousands of markers. Data from the UK twins consortium are presented to illustrate the ASKAT methodology.</p> </abstract>