Abstract
Genome-wide association studies (GWAS) of psychological traits are generally conducted on (dichotomized) sums of items or symptoms (e.g., case-control status), and not on the individual items or symptoms themselves. We conduct large-scale GWAS on 12 neuroticism items and observe notable and replicable variation in genetic signal between items. Within samples, genetic correlations among the items range between 0.38 and 0.91 (mean rg = .63), indicating genetic heterogeneity in the full item set. Meta-analyzing the two samples, we identify 255 genome-wide significant independent genomic regions, of which 138 are item-specific. Genetic analyses and genetic correlations with 33 external traits support genetic differences between the items. Hierarchical clustering analysis identifies two genetically homogeneous item clusters denoted depressed affect and worry. We conclude that the items used to measure neuroticism are genetically heterogeneous, and that biological understanding can be gained by studying them in genetically more homogeneous clusters.
Highlights
Genome-wide association studies (GWAS) of psychological traits are generally conducted on sums of items or symptoms, and not on the individual items or symptoms themselves
For the sum-score we identified 36 genomewide significant (GWS) exonic nonsynonymous single-nucleotide polymorphisms (SNPs) (ExNS), of which 29 overlapped with the 134 ExNS SNPs identified in item-level analyses (Supplementary Data 48). 10 of these overlapping ExNS SNPs are located in a well-known inversion on chromosome 173
We have shown that items used to measure the personality trait neuroticism are genetically heterogeneous, with genetic overlap often being only moderate, and we replicated this in a second sample
Summary
Genome-wide association studies (GWAS) of psychological traits are generally conducted on (dichotomized) sums of items or symptoms (e.g., case-control status), and not on the individual items or symptoms themselves. We conclude that the items used to measure neuroticism are genetically heterogeneous, and that biological understanding can be gained by studying them in genetically more homogeneous clusters. In the context of gene-finding studies, power to detect associated variants is potentially lost when the summed items or symptoms are biologically heterogeneous. The use of composite scores in gene-finding studies directs the focus of analysis to those variants that affect the majority of aggregated items, i.e., ‘global variants’. One can investigate the genetic homogeneity of items or symptoms underlying a sum-score or a case–control status by studying the genetics of the individual items. We use sample 2 to replicate basic findings of sample 1, and meta-analyze the results of both samples to compare item-specific genetic signals to the signal obtained in genetic analysis of the sum-score. We identified two genetically distinct item clusters, which may prove useful targets of investigation in future genetic analyses
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