Data Integration Methods for Phenotype Harmonization in Multi-Cohort Genome-Wide Association Studies With Behavioral Outcomes

Justin M Luningham; Daniel B McArtor; Anne M Hendriks; Catharina E M van Beijsterveldt; Paul Lichtenstein; Sebastian Lundström; Henrik Larsson; Meike Bartels; Dorret I Boomsma; Gitta H Lubke

doi:10.3389/fgene.2019.01227

Data Integration Methods for Phenotype Harmonization in Multi-Cohort Genome-Wide Association Studies With Behavioral Outcomes

Front Genet. 2019 Dec 10:10:1227. doi: 10.3389/fgene.2019.01227. eCollection 2019.

Authors

Justin M Luningham¹, Daniel B McArtor¹, Anne M Hendriks^{2

3}, Catharina E M van Beijsterveldt^{2

3}, Paul Lichtenstein⁴, Sebastian Lundström⁵, Henrik Larsson^{4

6}, Meike Bartels^{2

3

7}, Dorret I Boomsma^{2

3

7}, Gitta H Lubke¹

Affiliations

¹ Department of Psychology, University of Notre Dame, Notre Dame, IN, United States.
² Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
³ Faculty of Behavioural and Movement Sciences, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.
⁴ Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
⁵ Gillberg Neuropsychiatry Centre, University of Gothenburg, Gothenburg, Sweden.
⁶ School of Medical Sciences, Örebro University, Örebro, Sweden.
⁷ Amsterdam Neuroscience, VU University Amsterdam, Amsterdam, Netherlands.

Abstract

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.

Keywords: consortia; data integration; genome-wide association studies; latent variable modeling; phenotype harmonization.