Sequence 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.
Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America; Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of America.
Han
H
Chen
Thomas
T
Lumley
Jennifer
J
Brody
Nancy L
NL
Heard-Costa
Caroline S
CS
Fox
L Adrienne
LA
Cupples
Josée
J
Dupuis
D015331:Cohort Studies; D056726:Genetic Association Studies; D005838:Genotype; D006321:Heart; D006801:Humans; D016015:Logistic Models; D008957:Models, Genetic; D009765:Obesity / Q000235:genetics; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D016016:Proportional Hazards Models; D012107:Research Design; D012984:Software; D013534:Survival; D016019:Survival Analysis; D013997:Time Factors
Cox proportional hazard model; likelihood ratio test; rare variant analysis; variance component test