Table 1 Glossary.

Genetics

Heritability

The proportion of variance of a phenotype that is attributable to genetic factors.

Genetic correlation

The degree to which two phenotypes are influenced by the same genetic variation.

GWAS

Genome-wide association study: mass-univariate analysis to relate common variation over the entire length of the DNA to a phenotype of interest.

SNP

Single nucleotide polymorphism: a (common) genetic variation in the DNA sequence where different alleles (nucleotides) can exist in the population.

Polygenic

Influenced by many genetic variants (i.e., hundreds, or thousands of genes), as opposed to monogenic (influenced by a single gene, or single genetic variant).

Mendelian Randomisation

Hypothesis-driven method aimed at inferring causality from (cross-sectional) associations between a genetic variant and two or more phenotypes. E.g. to test whether a modifiable behavioural or neural trait potentially mediates the effect of a genetic variant on a disease [95].

LD-score regression

Linkage-disequilibrium score regression: method to calculate genetic correlations on the basis of GWAS output (i.e., “summary statistics”), given the relationship of the statistics to each variant’s linkage disequilibrium pattern [25]

MRI

Magnetic Resonance Imaging

Functional MRI

MRI acquisition method to estimate regional brain activation based on local blood-oxygen level dependent (BOLD) signal.

Diffusion MRI

MRI acquisition method to measure microstructural tissue properties based on direction and amount of diffusion of water molecules. Most often used for investigating white matter fibres.

Functional connectivity

The degree to which two or more brain regions show similar activation patterns over time, based on the correlation or mutual dependence of their BOLD time-series.

PCA

Principal Component Analysis: data-driven data reduction method to extract maximally uncorrelated components (i.e., “factors”) from many variables.

ICA

Independent Component Analysis: data-driven data reduction method and source identification method, which extracts maximally independent components (i.e., “factors”) from many variables.

CCA

Canonical Correlation Analysis: method to extract modes (here:“factors”) across two or more sets of variables (e.g., MRI and behavioural variables), such that the variables within a mode are maximally correlated.

SEM

Structural Equation Modelling: data reduction method to fit a priori factor structures to data and extract these factors. Can be confirmatory (1 model is tested) or exploratory (multiple a priori models are tested and compared).