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- W4387496989 abstract "Genetic research has shown that the current classification of psychiatric disorders does not reflect the genetic architecture of psychopathology. Psychiatric diagnoses are highly genetically correlated, suggesting a genetically driven general factor of psychopathology, called p. Given the observed genetic influence of p on psychiatric disorders, controlling for p could better capture the specific genetic effects associated with individual psychiatric disorders. Using publicly available summary statistics from genome-wide association (GWA) studies of 11 major psychiatric disorders (Grotzinger et al., 2022), we investigated genetic specificity across these disorders by controlling for p using Genomic Structural Equation Modelling (Grotzinger et al., 2019) in a two-step procedure which first created a genetic p-factor of psychopathology and then subtracted (residualized) genetic effects associated with this latent p factor from the genetic effects associated with specific psychiatric disorders (GWAS-by-subtraction, Demange et al., 2021). This allowed us to control for the effect of p in each disorder in order to identify, for each disorder, genetic effects that are independent of p. We then characterised these latent disorder-specific factors (non-p), starting from estimating their SNP heritability, which we compared to heritabilities from the uncorrected disorders. We also examined the genetic correlations between the 11 disorders before and after residualizing for p. We found that the SNP heritabilities of the disorder specific latent factors are more heterogeneous because they are independent of the general influence of p. In terms of genetic correlations before and after partialling out the genetic effects of the p-factor, we found three different patterns of change, including 1) small reduction in genetic correlations, 2) substantial reduction in genetic correlations, and 3) reversed genetic correlations. Our results highlight the importance of considering both generality and specificity in psychopathology. Therefore, depending on the specific purpose of a genetic study, researchers can consider partialling out p-related genetic variance. Our results also showed that partialling out p-related genetic variance will have impact on the results of secondary analyses that use GWAS summary statistics, such as polygenic scores. Partialling out some of the genetic variance may reduce the predictive power of polygenic scores, but the remaining genetic variance will be more specific to the phenotype of interest, which may increase the potential research or clinical utility of polygenic scores." @default.
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- W4387496989 date "2023-10-01" @default.
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- W4387496989 title "15. CONTROLLING FOR THE GENERAL PSYCHOPATHOLOGY FACTOR P IN GENOMIC RESEARCH ON PSYCHIATRIC DISORDERS (PART 1)" @default.
- W4387496989 doi "https://doi.org/10.1016/j.euroneuro.2023.08.126" @default.
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