A powerful association test of multiple genetic variants using a random-effects model

Kuang Fu Cheng, J. Y. Lee, Wei Zheng, Chun Li

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


There is an emerging interest in sequencing-based association studies of multiple rare variants. Most association tests suggested in the literature involve collapsing rare variants with or without weighting. Recently, a variance-component score test [sequence kernel association test (SKAT)] was proposed to address the limitations of collapsing method. Although SKAT was shown to outperform most of the alternative tests, its applications and power might be restricted and influenced by missing genotypes. In this paper, we suggest a new method based on testing whether the fraction of causal variants in a region is zero. The new association test, T REM, is derived from a random-effects model and allows for missing genotypes, and the choice of weighting function is not required when common and rare variants are analyzed simultaneously. We performed simulations to study the type I error rates and power of four competing tests under various conditions on the sample size, genotype missing rate, variant frequency, effect directionality, and the number of non-causal rare variant and/or causal common variant. The simulation results showed that T REM was a valid test and less sensitive to the inclusion of non-causal rare variants and/or low effect common variants or to the presence of missing genotypes. When the effects were more consistent in the same direction, T REM also had better power performance. Finally, an application to the Shanghai Breast Cancer Study showed that rare causal variants at the FGFR2 gene were detected by T REM and SKAT, but T REM produced more consistent results for different sets of rare and common variants.

Original languageEnglish
Pages (from-to)1816-1827
Number of pages12
JournalStatistics in Medicine
Issue number11
Publication statusPublished - May 20 2014


  • Association test
  • Random-effects model
  • Rare variant
  • Sequencing-based study

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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