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Mixed Models for Meta-Analysis and Sequencing

By Brendan Bulik-Sullivan

Posted 29 May 2015
bioRxiv DOI: 10.1101/020115

Mixed models are an effective statistical method for increasing power and avoiding confounding in genetic association studies. Existing mixed model methods have been designed for ``pooled'' studies where all individual-level genotype and phenotype data are simultaneously visible to a single analyst. Many studies follow a ``meta-analysis'' design, wherein a large number of independent cohorts share only summary statistics with a central meta-analysis group, and no one person can view individual-level data for more than a small fraction of the total sample. When using linear regression for GWAS, there is no difference in power between pooled studies and meta-analyses \cite{lin2010meta}; however, we show that when using mixed models, standard meta-analysis is much less powerful than mixed model association on a pooled study of equal size. We describe a method that allows meta-analyses to capture almost all of the power available to mixed model association on a pooled study without sharing individual-level genotype data. The added computational cost and analytical complexity of this method is minimal, but the increase in power can be large: based on the predictive performance of polygenic scoring reported in \cite{wood2014defining} and \cite{locke2015genetic}, we estimate that the next height and BMI studies could see increases in effective sample size of $\approx$15\% and $\approx$8\%, respectively. Last, we describe how a related technique can be used to increase power in sequencing, targeted sequencing and exome array studies. Note that these techniques are presently only applicable to randomly ascertained studies and will sometimes result in loss of power in ascertained case/control studies. We are developing similar methods for case/control studies, but this is more complicated.

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