Linear mixed models for association analysis of quantitative traits with next‐generation sequencing data Academic Article uri icon

  •  
  • Overview
  •  
  • Research
  •  
  • Identity
  •  
  • Additional Document Info
  •  
  • View All
  •  

abstract

  • We develop linear mixed models (LMMs) and functional linear mixed models (FLMMs) for gene-based tests of association between a quantitative trait and genetic variants on pedigrees. The effects of a major gene are modeled as a fixed effect, the contributions of polygenes are modeled as a random effect, and the correlations of pedigree members are modeled via inbreeding/kinship coefficients. F -statistics and χ 2 likelihood ratio test (LRT) statistics based on the LMMs and FLMMs are constructed to test for association. We show empirically that the F -distributed statistics provide a good control of the type I error rate. The F -test statistics of the LMMs have similar or higher power than the FLMMs, kernel-based famSKAT (family-based sequence kernel association test), and burden test famBT (family-based burden test). The F -statistics of the FLMMs perform well when analyzing a combination of rare and common variants. For small samples, the LRT statistics of the FLMMs control the type I error rate well at the nominal levels α = 0.01 and 0.05 . For moderate/large samples, the LRT statistics of the FLMMs control the type I error rates well. The LRT statistics of the LMMs can lead to inflated type I error rates. The proposed models are useful in whole genome and whole exome association studies of complex traits.

authors

  • Chiu, Chi‐yang
  • Yuan, Fang
  • Zhang, Bing‐song
  • Yuan, Ao
  • Li, Xin
  • Fang, Hong‐Bin
  • Lange, Kenneth
  • Weeks, Daniel E
  • Wilson, Alexander F
  • Bailey‐Wilson, Joan E
  • Musolf, Anthony M
  • Stambolian, Dwight
  • Lakhal‐Chaieb, M'Hamed Lajmi
  • Cook, Richard
  • McMahon, Francis J
  • Amos, Christopher I
  • Xiong, Momiao
  • Fan, Ruzong

publication date

  • March 2019