medRxivGenetic & Genomic MedicinePreprint
Pathway-Specific Polygenic Scores Improve Cross-Ancestry Prediction of Psychosis and Clinical Outcomes
Justin D. TubbsiD, Perry B.M. LeungiD, Yuanxin ZhongiD, Na ZhaniD, Tomy C.K. Hui, Karen K. Y. Ho, Karen S. Y. Hung, Eric F.C. CheungiD, Hon‐Cheong SoiD, Simon S. Y. LuiiD, Pak Chung ShamiD
Abstract
Psychotic disorders are debilitating conditions with disproportionately high public health burden. Genetic studies indicate high heritability, but current polygenic scores (PGS) account for only a fraction of variance in psychosis risk. PGS often show poor portability across ancestries, performing significantly worse in non-European populations. Pathway-specific PGS (pPGS), which restrict PGS to genomic locations within distinct biological units, could lead to increased mechanistic understanding of pathways that lead to risk and improve cross-ancestry prediction by reducing noise in genetic predictors. This study examined the predictive power of genome-wide PGS and nine pathway-specific pPGS in a unique Chinese-ancestry sample of deeply-phenotyped psychosis patients and non-psychiatric controls. We found strong evidence for the involvement of schizophrenia-associated risk variants within "nervous system development" (p=2.5e-4) and "regulation of neuron differentiation" pathways (p=3.0e-4) in predicting risk for psychosis. We also found the "ion channel complex" pPGS, with weights derived from GWAS of bipolar disorder, to be strongly associated with the number of inpatient psychiatry admissions a patient experiences (p=1.5e-3) and account for a majority of the signal in the overall bipolar PGS. Importantly, although the schizophrenia genome-wide PGS alone explained only 3.7% of the variance in liability to psychosis in this Chinese ancestry sample, the addition of the schizophrenia-weighted pPGS for "nervous system development" and "regulation of neuron differentiation" increased the variance explained to 6.9%, which is on-par with the predictive power of PGS in European ancestry samples. Thus, not only can pPGS provide greater insight into mechanisms underlying genetic risk for disease and clinical outcomes, but may also improve cross-ancestry risk prediction accuracy.
- organism: human
- assay: GWAS summary statistics
- research focus: polygenic risk
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: benchmarking
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 7
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