Abstract
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.
Highlights
Rare variants may, in part, explain some of the missing heritability in current genome-wide association studies [1]
We recently developed an analysis strategy based on generalized least squares (GLS) [11] for family-based rare-variant association analysis, in which we first use the kinship matrix to decorrelate the family-based data, apply a SKAT-O [9] approach to the decorrelated data, which we term the GLS-SKAT approach (Li D, personal communications, 2013)
We applied the GLS-SKAT method to analyze the simulated systolic blood pressure (SBP) data in the family sample provided by the Genetic Analysis Workshop 18 (GAW18) to examine its performance
Summary
In part, explain some of the missing heritability in current genome-wide association studies [1]. Many rare-variant analysis approaches have been proposed in recent years [2,3,4,5,6,7,8,9]; most are aimed at population-based case-control samples. We recently developed an analysis strategy based on generalized least squares (GLS) [11] for family-based rare-variant association analysis, in which we first use the kinship matrix to decorrelate the family-based data, apply a SKAT-O [9] approach to the decorrelated data, which we term the GLS-SKAT approach (Li D, personal communications, 2013). We applied the GLS-SKAT method to analyze the simulated systolic blood pressure (SBP) data in the family sample provided by the Genetic Analysis Workshop 18 (GAW18) to examine its performance
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