{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Chen H"],"funding":["National Heart, Lung, and Blood Institute (NHLBI)","NIDDK NIH HHS","NHLBI NIH HHS","NIH awards","Affymetrix, Inc"],"pagination":["191-7"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC4158946"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["38(3)"],"pubmed_abstract":["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."],"journal":["Genetic epidemiology"],"pubmed_title":["Sequence kernel association test for survival traits."],"pmcid":["PMC4158946"],"funding_grant_id":["N01 HC025195","N02-HL-6-4278","N01-HC-25195","N02 HL64278","R01DK078616","K24 DK080140","R01 DK078616","N01HC25195","U01 DK085526","U01 DK85526"],"pubmed_authors":["Brody J","Heard-Costa NL","Cupples LA","Dupuis J","Lumley T","Fox CS","Chen H"],"additional_accession":[]},"is_claimable":false,"name":"Sequence kernel association test for survival traits.","description":"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.","dates":{"release":"2014-01-01T00:00:00Z","publication":"2014 Apr","modification":"2025-06-28T03:05:12.242Z","creation":"2025-06-28T03:05:12.242Z"},"accession":"S-EPMC4158946","cross_references":{"pubmed":["24464521"],"doi":["10.1002/gepi.21791"]}}