ON HIGH-DIMENSIONAL MISSPECIFIED MIXED MODEL ANALYSIS IN GENOME-WIDE ASSOCIATION STUDY

• 成果类型: Article
• 署名作者: Jiang, Jiming; Li, Cong; Paul, Debashis; Yang, Can; Zhao, Hongyu
• 署名单位: University of California System; University of California Davis; Takeda Pharmaceutical Company Ltd; Takeda Pharmaceuticals International, Inc.; Hong Kong Baptist University; Yale University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/15-AOS1421
• 发表日期: 2016
• 页码: 2127-2160
• 关键词: heritability; PROPORTION; limit; loci

摘要

We study behavior of the restricted maximum likelihood (REML) estimator under a misspecified linear mixed model (LMM) that has received much attention in recent genome-wide association studies. The asymptotic analysis establishes consistency of the REML estimator of the variance of the errors in the LMM, and convergence in probability of the REML estimator of the variance of the random effects in the LMM to a certain limit, which is equal to the true variance of the random effects multiplied by the limiting proportion of the nonzero random effects present in the LMM. The asymptotic results also establish convergence rate (in probability) of the REML estimators as well as a result regarding convergence of the asymptotic conditional variance of the REML estimator. The asymptotic results are fully supported by the results of empirical studies, which include extensive simulation studies that compare the performance of the REML estimator (under the misspecified LMM) with other existing methods, and real data applications (only one example is presented) that have important genetic implications.