A generalized least-squares framework for rare-variant analysis in family data

BMC Proceedings

Proceedings

Jerome I Rotter,¹  Xiuqing Guo¹  Dalin Li² 

[1] David Geffen School of Medicine, University of California Los Angeles, 90095, Los Angeles, CA, USA;Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute and Department of Pediatrics, Harbor-UCLA Medical Center, 90502, Torrance, CA, USA;Medical Genetics Institute, Cedars-Sinai Medical Center, 90048, Los Angeles, CA, USA;David Geffen School of Medicine, University of California Los Angeles, 90095, Los Angeles, CA, USA;

关键词: Rare Variant;    Kinship Matrix;    Generalize Little Square;    Family Data;    Genetic Analysis Workshop;   

DOI  :  10.1186/1753-6561-8-S1-S28

来源: Springer

【 摘 要 】

Rare variants may, in part, explain some of the hereditability missing in current genome-wide association studies. Many gene-based rare-variant analysis approaches proposed in recent years are aimed at population-based samples, although analysis strategies for family-based samples are clearly warranted since the family-based design has the potential to enhance our ability to enrich for rare causal variants. We have recently developed the generalized least squares, sequence kernel association test, or GLS-SKAT, approach for the rare-variant analyses in family samples, in which the kinship matrix that was computed from the high dimension genetic data was used to decorrelate the family structure. We then applied the SKAT-O approach for gene-/region-based inference in the decorrelated data. In this study, we applied this GLS-SKAT method to the systolic blood pressure data in the simulated family sample distributed by the Genetic Analysis Workshop 18. We compared the GLS-SKAT approach to the rare-variant analysis approach implemented in family-based association test-v1 and demonstrated that the GLS-SKAT approach provides superior power and good control of type I error rate.

【 授权许可】

Unknown   
© Li et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.