Controlling for the general psychopathology factor p in genomic research on psychiatric disorders (part 2)

Given the ubiquitously observed genetic influence of a general factor of psychopathology (p) on psychiatric disorders, isolating p would allow us to better capture the specific genetic effects associated with individual psychiatric disorders. As described in Part 1 of this word (Keser et al.), we applied GWAS-by-Subtraction to isolate common genetic effects across 11 psychiatric disorders from genetic effects that are specific to each disorder. We will utilize the LDSC in Genomic Structural Equation Modeling to conduct genetic correlations with publicly available GWAS summary statistics for various biobehavioral traits. Those traits will be from the following categories: socioeconomics, health and disease, anthropomorphic, personality, cognitive, risky behaviour, physical activity, and metabolic traits. Additionally, I intend to conduct functional annotations and gene-set analyses to gain insights into the underlying biological mechanisms linked to these disorders. The work is currently underway, and preliminary results have indicated that the genetic factors specific to each disease exhibit distinct genetic correlations both among themselves and with the biobehavioral outcomes, when compared to the correlations obtained without correcting for the general factor of psychopathology (p). In the next phase, we will employ FUMA to delve into the gene aetiology of the disease-specific genetic factors, further investigating their underlying mechanisms and potential implications. The outcomes of this project will have significant implications in the field of psychiatric genetics, emphasizing the importance of considering both generality and specificity in psychopathology. By demonstrating the distinct genetic correlations and outcomes associated with disease-specific factors (Non-Ps), our findings will shed light on the complex nature of psychiatric conditions and pave the way for new avenues of research. One such application is the use of non-p summary statistics in the creation of polygenic scores, which can capture the specificity of genetic factors in psychiatric disorders. This approach has the potential to enhance our understanding of the genetic architecture and gene-environment interplay underlying these conditions.