Greater power and computational efficiency for kernel-based association testing of sets of genetic variants

@inproceedings{Lippert2014GreaterPA,
  title={Greater power and computational efficiency for kernel-based association testing of sets of genetic variants},
  author={Christoph Lippert and Jing Xiang and Danilo Horta and Christian Widmer and Carl Myers Kadie and David Heckerman and Jennifer Listgarten},
  booktitle={Bioinformatics},
  year={2014}
}
MOTIVATION Set-based variance component tests have been identified as a way to increase power in association studies by aggregating weak individual effects. However, the choice of test statistic has been largely ignored even though it may play an important role in obtaining optimal power. We compared a standard statistical test-a score test-with a recently developed likelihood ratio (LR) test. Further, when correction for hidden structure is needed, or gene-gene interactions are sought, state… CONTINUE READING
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