Activity Number:
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535
- Contributed Poster Presentations: Section on Statistics in Genomics and Genetics
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #330241
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Title:
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Inferences in High-Dimensional Misspecified Mixed Model Analysis for GWAS
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Author(s):
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Cecilia Dao* and Jiming Jiang and Debashis Paul and Hongyu Zhao
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Companies:
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Yale Univ and University of California, Davis and UC Davis and Yale
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Keywords:
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asymptotic variance;
misspecified LMM;
REML;
heritability confidence interval
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Abstract:
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We explore inferences for the restricted maximum likelihood (REML) estimator of the variance components under a misspecified linear mixed model (MLMM) often used in genome-wide association studies (GWAS). Through some asymptotic analysis, we propose suitable variance estimators and large sample confidence intervals for the REML estimators of the variance components and heritability of a trait under a MLMM. Extensive simulation studies show that the proposed variance estimators and confidence intervals perform well in large samples based on the percentage of relative bias and empirical coverage probabilities. We compare our method with the standard way to estimate standard errors of REML variance components and confidence interval construction along with some recently proposed methods, and show that our method is more accurate.
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Authors who are presenting talks have a * after their name.