Poster Presentation GENEMAPPERS 2026

Testing the performance of polygenic scores for multiple traits to explain cerebral palsy risk in two independent cohorts (#70)

Jodi T Thomas 1 2 , Alexander SF Berry 3 , Matthew T Oetjens 3 , Jesia G Berry 4 , Alastair H MacLennan 4 , Scott D Gordon 1 , Andrew T Hale 5 6 , Catherine M Olsen 7 , David C Whiteman 7 , Rebecca I Torene 3 , David H Ledbetter 8 , Nicholas G Martin 1 , Clare L van Eyk 4 , Jozef Gecz 4 9 , Scott M Myers 3 , Brittany L Mitchell 1 2 10 , Mark A Corbett 4
  1. Brain and Mental Health Program, QIMR Berghofer, Brisbane, QLD, Australia
  2. School of Biomedical Sciences, The University of Queensland, Brisbane, QLD, Australia
  3. Department of Developmental Medicine, Geisinger College of Health Sciences, Lewisburg, PA, USA
  4. Adelaide Medical School and Robinson Research Institute, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA, Australia
  5. Department of Neurosurgery, The University of Alabama at Birmingham, Birmingham, AL, USA
  6. Neuroscience Institute, University of Cape Town, Cape Town, South Africa
  7. Population Health Program, QIMR Berghofer, Brisbane, QLD, Australia
  8. Institute for Pediatric Rare Diseases, College of Medicine, Florida State University, Tallahassee, FL, USA
  9. South Australian Health and Medical Research Institute, Adelaide, SA, Australia
  10. School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia

Cerebral palsy (CP) is a complex neurodevelopmental disorder with both environmental and genetic contributors. Rare genetic variants explain a substantial proportion of CP, but the contribution of common variants remains unclear. We evaluated whether polygenic scores for CP and related traits explain CP risk. We analysed two independent target cohorts: a case-control cohort including people with a confirmed clinical diagnosis of CP from the Australian CP Biobank and population-based controls; and MyCode, a United States healthcare cohort with CP status identified by electronic health records. Only participants of European genetic ancestry were included. CP polygenic scores were constructed using a discovery genome-wide association meta-analysis of Finnish and UK cohorts (ncases=624, ncontrols=495,687), and applied to the target cohorts for out-of-sample prediction. Additional polygenic scores were generated for seven CP-related traits. Predictive performance was assessed using logistic regression, area under the receiver operating characteristic curve, and variance in CP liability explained. The Australian cohort included 525 cases and 20,410 controls, and MyCode 322 cases and 1,610 age-matched controls. The combined model of all eight polygenic scores significantly discriminated CP status, explaining 1·3% of CP liability in the Australian cohort (90% CI lower bound 0·82%, padj<0·0001), and 0·78% in MyCode (90% CI lower bound 0·35%, padj<0·0001). CP-specific polygenic scores demonstrated minimal predictive signal, likely reflecting limited GWAS power. Polygenic scores for known CP risk factors (birth weight, gestational duration, stroke) showed modest predictive performance, with some cohort differences. Results were similar when the Australian cohort was stratified by monogenic CP diagnosis. Our findings demonstrate a measurable polygenic contribution to CP and shared genetic influences with risk factors, including those traditionally considered environmental, and comorbidities. Common variants appear to contribute broadly to CP susceptibility, highlighting a multifactorial risk landscape relevant for earlier diagnosis and intervention.