@article {415,
	title = {Data integration methods for phenotype harmonization in multi-cohort genome-wide association studies with behavioral outcomes},
	journal = {Frontiers in Genetics},
	volume = {10},
	year = {2019},
	pages = {1227},
	abstract = {<p>Parallel meta-analysis is a popular approach for increasing the power to detect genetic effects in genome-wide association studies across multiple cohorts. Consortia studying the genetics of behavioral phenotypes are oftentimes faced with systematic differences in phenotype measurement across cohorts, introducing heterogeneity into the meta-analysis and reducing statistical power. This study investigated integrative data analysis (IDA) as an approach for jointly modeling the phenotype across multiple datasets. We put forth a bi-factor integration model (BFIM) that provides a single common phenotype score and accounts for sources of study-specific variability in the phenotype. In order to capitalize on this modeling strategy, a phenotype reference panel was utilized as a supplemental sample with complete data on all behavioral measures. A simulation study showed that a mega-analysis of genetic variant effects in a BFIM were more powerful than meta-analysis of genetic effects on a cohort-specific sum score of items. Saving the factor scores from the BFIM and using those as the outcome in meta-analysis was also more powerful than the sum score in most simulation conditions, but a small degree of bias was introduced by this approach. The reference panel was necessary to realize these power gains. An empirical demonstration used the BFIM to harmonize aggression scores in 9-year old children across the Netherlands Twin Register and the Child and Adolescent Twin Study in Sweden, providing a template for application of the BFIM to a range of different phenotypes. A supplemental data collection in the Netherlands Twin Register served as a reference panel for phenotype modeling across both cohorts. Our results indicate that model-based harmonization for the study of complex traits is a useful step within genetic consortia.</p>
},
	keywords = {consortia, data integration, genome-wide association studies, latent variable modeling, phenotype harmonization},
	doi = {10.3389/fgene.2019.01227},
	author = {Luningham, Justin M and McArtor, Daniel B and Hendriks, Anne M and van Beijsterveldt, Catharina E M and Lichtenstein, Paul and Lundstr{\"o}m, Sebastian and Larsson, Henrik and Bartels, Meike and Boomsma, Dorret I and Lubke, Gitta H}
}
