biostudies-literatureChen HNational Heart, Lung, and Blood Institute (NHLBI)NIDDK NIH HHSNHLBI NIH HHSNIH awardsAffymetrix, Inc191-7https://www.ebi.ac.uk/biostudies/studies/S-EPMC4158946biostudies-literatureUnknown38(3)Rare variant tests have been of great interest in testing genetic associations with diseases and disease-related quantitative traits in recent years. Among these tests, the sequence kernel association test (SKAT) is an omnibus test for effects of rare genetic variants, in a linear or logistic regression framework. It is often described as a variance component test treating the genotypic effects as random. When the linear kernel is used, its test statistic can be expressed as a weighted sum of single-marker score test statistics. In this paper, we extend the test to survival phenotypes in a Cox regression framework. Because of the anticonservative small-sample performance of the score test in a Cox model, we substitute signed square-root likelihood ratio statistics for the score statistics, and confirm that the small-sample control of type I error is greatly improved. This test can also be applied in meta-analysis. We show in our simulation studies that this test has superior statistical power except in a few specific scenarios, as compared to burden tests in a Cox model. We also present results in an application to time-to-obesity using genotypes from Framingham Heart Study SNP Health Association Resource.Genetic epidemiologySequence kernel association test for survival traits.PMC4158946N01 HC025195N02-HL-6-4278N01-HC-25195N02 HL64278R01DK078616K24 DK080140R01 DK078616N01HC25195U01 DK085526U01 DK85526Brody JHeard-Costa NLCupples LADupuis JLumley TFox CSChen HfalseSequence kernel association test for survival traits.Rare variant tests have been of great interest in testing genetic associations with diseases and disease-related quantitative traits in recent years. Among these tests, the sequence kernel association test (SKAT) is an omnibus test for effects of rare genetic variants, in a linear or logistic regression framework. It is often described as a variance component test treating the genotypic effects as random. When the linear kernel is used, its test statistic can be expressed as a weighted sum of single-marker score test statistics. In this paper, we extend the test to survival phenotypes in a Cox regression framework. Because of the anticonservative small-sample performance of the score test in a Cox model, we substitute signed square-root likelihood ratio statistics for the score statistics, and confirm that the small-sample control of type I error is greatly improved. This test can also be applied in meta-analysis. We show in our simulation studies that this test has superior statistical power except in a few specific scenarios, as compared to burden tests in a Cox model. We also present results in an application to time-to-obesity using genotypes from Framingham Heart Study SNP Health Association Resource.2014-01-01T00:00:00Z2014 Apr2025-06-28T03:05:12.242Z2025-06-28T03:05:12.242ZS-EPMC41589462446452110.1002/gepi.21791