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• W4387501995 abstract "Deciphering the intricate genetic architecture of brain phenotypes and psychiatric disorders is a complex task. To tackle this, we employed genomic Principal Component Analysis(PCA) and Independent Component Analysis (ICA). These methods are used to reduce the dimensionality of the large univariate Genome-Wide Association Study (GWAS) data set into fewer, more interpretable components that capture covariation patterns across observations. PCA captures the maximum amount of shared genetic variance per imaging-derived phenotype (IDP) in as few components as possible, while ICA identifies statistically independent sources within the 'mixed' signal, making it more suitable for recovering distinct sources of signal from noisy data. The decomposed data results in a smaller set of multivariate principal and independent components, which represent covariance and dependence in the genetic effects. These components provide a way to disentangle the complex web of genetic interaction effects by grouping SNP effects into covarying clusters. The components derived from this process are considered a hidden representation of the genetic architecture subserving over 2000 brain IDPs. They capture shared signal from IDPs affected by similar genetic effects in the same component, enhancing the signal-to-noise ratio and making the data more interpretable. This data-driven approach does not rely on predefined gene-sets or molecular pathways, instead presenting novel gene-sets based on genomic ICA and PCA for downstream analysis. To delve deeper into the genetic underpinnings of psychiatric disorders, we applied the MAGMA (Multi-marker Analysis of GenoMic Annotation) tool. We tested if the schizophrenia GWAS statistics from the Psychiatric Genomics Consortium were enriched for ten PCs and ten ICs using gene-property tests in MAGMA on the log(P) values of the components. Our findings revealed that four PCs (PC2, p=0.03; PC4, p=0.03; PC6, p=0.04; PC8, p=0.04) and three ICs (IC1, p=0.03; IC2, p=0.005; IC7, p=0.001) were significantly enriched for Schizophrenia, even after correcting for multiple comparison, underscoring the potential of our approach to illuminate the genetic architecture of psychiatric disorders and brain phenotypes. These results mark a significant step forward in our understanding of the genetic basis of psychiatric disorders and provide a robust foundation for future research in this area. Our approach demonstrates the power and potential of data-driven methods in psychiatric genetics research, paving the way for more nuanced and comprehensive investigations in the future." @default.
• W4387501995 created "2023-10-11" @default.
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• W4387501995 date "2023-10-01" @default.
• W4387501995 modified "2023-10-12" @default.
• W4387501995 title "F83. GENETIC OVERLAP OF SCHIZOPHRENIA WITH PRINCIPAL AND INDEPENDENT GENOMIC COMPONENTS OF BRAIN STRUCTURE AND FUNCTION" @default.
• W4387501995 doi "https://doi.org/10.1016/j.euroneuro.2023.08.465" @default.
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