Mixed Models for Meta-Analysis and Sequencing

BioRxiv : the Preprint Server for Biology
Brendan K Bulik-Sullivan


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{wood2014definin...Continue Reading

Related Concepts

Genome-Wide Association Study
Meta-Analysis (Publications)
Meta Analysis (Statistical Procedure)
Nucleic Acid Sequencing
Genetic Association Studies
Whole Exome Sequencing
Body Mass Index

Related Feeds

BioRxiv & MedRxiv Preprints

BioRxiv and MedRxiv are the preprint servers for biology and health sciences respectively, operated by Cold Spring Harbor Laboratory. Here are the latest preprint articles (which are not peer-reviewed) from BioRxiv and MedRxiv.