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Abstract:
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Linear mixed model (LMM) is among the most commonly used tools for genetic association studies. However, the standard method for estimating variance components in LMMs -- the restricted maximum likelihood estimation method (REML) -- suffers from several important drawbacks. Here, we present an alternative framework, which we refer to as MQS, for variance component estimation in LMMs. Our method is based on the method of moments (MoM) and the minimal norm quadratic unbiased estimation (MINQUE) criteria, and brings two seemly unrelated methods -- the renowned Haseman-Elston (HE) regression and the recent LD score regression (LDSC) -- into the same, unified framework. A key feature of our method is that it can effectively use only a small random subset of individuals for computation while still produce estimates that are almost as accurate as if the full data were used. As a result, our method produces unbiased and accurate estimates with calibrated standard errors, while is computationally efficient for large data sets. Using simulations and applications to 33 phenotypes from 7 real data sets, we illustrate the benefits of our method for estimating and partitioning chip heritability.
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