Generalized fiducial methods for testing quantitative trait locus effects in genetic backcross studies

Pengcheng Ren, Guanfu Liu, Xiaolong Pu, Yan Li

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we propose generalized fiducial methods and construct four generalized p-values to test the existence of quantitative trait locus effects under phenotype distributions from a location-scale family. Compared with the likelihood ratio test based on simulation studies, our methods perform better at controlling type I errors while retaining comparable power in cases with small or moderate sample sizes. The four generalized fiducial methods support varied scenarios: two of them are more aggressive and powerful, whereas the other two appear more conservative and robust. A real data example involving mouse blood pressure is used to illustrate our proposed methods.

Original languageEnglish
Pages (from-to)148-160
Number of pages13
JournalStatistical Theory and Related Fields
Volume6
Issue number2
DOIs
StatePublished - 2022

Keywords

  • Generalized fiducial inference
  • Gibbs algorithm
  • likelihood ratio test
  • mixture model
  • quantitative trait locus

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