Meta-analyses of genome-wide linkage scans of anxiety-related phenotypes.
Webb BT., Guo AY., Maher BS., Zhao Z., van den Oord EJ., Kendler KS., Riley BP., Gillespie NA., Prescott CA., Middeldorp CM., Willemsen G., de Geus EJ., Hottenga JJ., Boomsma DI., Slagboom EP., Wray NR., Montgomery GW., Martin NG., Wright MJ., Heath AC., Madden PA., Gelernter J., Knowles JA., Hamilton SP., Weissman MM., Fyer AJ., Huezo-Diaz P., McGuffin P., Farmer A., Craig IW., Lewis C., Sham P., Crowe RR., Flint J., Hettema JM.
Genetic factors underlying trait neuroticism, reflecting a tendency towards negative affective states, may overlap genetic susceptibility for anxiety disorders and help explain the extensive comorbidity amongst internalizing disorders. Genome-wide linkage (GWL) data from several studies of neuroticism and anxiety disorders have been published, providing an opportunity to test such hypotheses and identify genomic regions that harbor genes common to these phenotypes. In all, 11 independent GWL studies of either neuroticism (n=8) or anxiety disorders (n=3) were collected, which comprised of 5341 families with 15 529 individuals. The rank-based genome scan meta-analysis (GSMA) approach was used to analyze each trait separately and combined, and global correlations between results were examined. False discovery rate (FDR) analysis was performed to test for enrichment of significant effects. Using 10 cM intervals, bins nominally significant for both GSMA statistics, P(SR) and P(OR), were found on chromosomes 9, 11, 12, and 14 for neuroticism and on chromosomes 1, 5, 15, and 16 for anxiety disorders. Genome-wide, the results for the two phenotypes were significantly correlated, and a combined analysis identified additional nominally significant bins. Although none reached genome-wide significance, an excess of significant P(SR)P-values were observed, with 12 bins falling under a FDR threshold of 0.50. As demonstrated by our identification of multiple, consistent signals across the genome, meta-analytically combining existing GWL data is a valuable approach to narrowing down regions relevant for anxiety-related phenotypes. This may prove useful for prioritizing emerging genome-wide association data for anxiety disorders.