Genetic Correlation Between Brain Imaging Phenotypes and Externalizing Behavior: A Large-Scale LDSC Analysis of UK Biobank IDPs

Abstract

Externalizing has been associated with differences in brain structure and function; however, it remains unclear whether these associations reflect shared common-variant genetic influences. Cross-trait linkage disequilibrium score regression was used to estimate genome-wide genetic correlations between externalizing GWAS results and 3,935 brain imaging-derived phenotypes from the UK Biobank BIG40 resource. Imaging phenotypes covered structural MRI, diffusion MRI, susceptibility-weighted imaging, resting-state functional MRI, and task functional MRI. Analyses were included in the primary dataset when the imaging phenotype had positive SNP heritability, a heritability Z statistic of at least 1.96, a mean GWAS chi-square statistic of at least 1.02, at least 200,000 regression SNPs, and a complete LDSC result without a fatal error. Technical imaging quality-control phenotypes were excluded from biological inference. Individual results were corrected using the Benjamini-Hochberg false discovery rate procedure. Aggregated Cauchy association tests were used to evaluate evidence across all imaging phenotypes and within predefined imaging categories. Power, simultaneous confidence bounds, and alternative quality-control definitions were examined in sensitivity analyses. Of 3,935 imaging phenotypes, 3,716 produced estimable genetic correlations, 2,980 met the primary LDSC quality-control criteria, and 2,967 were biological imaging phenotypes. No individual phenotype survived false discovery rate correction; the smallest unadjusted P value was 0.0005, and the minimum adjusted q value was 0.486. The distribution of genetic correlations was centered near zero, with a median genetic correlation of 0.0014 and a median absolute correlation of 0.0338. There was no aggregate evidence across all biological imaging phenotypes using ACAT (P = 0.302), and no predefined imaging category survived correction. The median minimum detectable genetic correlation at 80% power was 0.216. Bonferroni-adjusted simultaneous confidence intervals were contained within [-0.30, 0.30] for 80.0% of phenotypes in the primary analysis and 88.0% under stringent heritability quality control. Broad and stringent sensitivity analyses produced the same overall conclusions. In this study, no statistically robust evidence of global genetic correlations between externalizing and individual UK Biobank brain imaging phenotypes was found. Small, localized, mixed-direction, or developmentally specific genetic effects remain possible.