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• W4387496632 abstract "ADHD is a neurodevelopmental disorder with genetic and environmental risk factors. Twin studies estimate the heritability to be 70-80% but the largest GWAS of ADHD to date estimated the SNP heritability to be 14%. This difference in heritability may be due to non-polygenic effects. However, there may be additional polygenic effects that were not detected in this study. ADHD is genetically correlated with several comorbid/co-occurring psychiatric conditions and lifestyle traits. We hypothesized that inclusion of polygenic scores (PGS) for these additional conditions, which have different sample characteristics (such as method of ascertainment, ancestral background, and number of participants), can leverage the genetic information that is common to both ADHD and the co-occurring condition, and increase the variance explained in an ADHD-related trait. Participants were recruited for the Spit for Science study, a community-based cohort of children and adolescents collected in 2009 (Spit1) and 2019 (Spit2) in Toronto. PGS were calculated with the PRS-PCA extension to PRSice-2 in 5067 individuals from Spit1 with European ancestry, using GWAS summary statistics for ADHD, autism spectrum disorder (ASD), educational attainment (EA), major depressive disorder (MDD) and executive function. Stepwise linear regression was used to assess the association between quantitative ADHD traits (the SWAN questionnaire: Strengths and Weaknesses of Attention-Deficit/Hyperactivity symptoms and Normal behaviour) and the mental health PGSs, selecting the most parsimonious model based on the Bayesian information criterion. A replication analysis was performed in 1300 individuals from Spit2. SWAN scores were pre-adjusted for age, sex and respondent (self or parent). Four ancestry principal components were included as covariates in all models. SWAN was significantly associated with the ADHD PGS (p=1.54 × 10-14, adjusted R2=0.013) when alone in the model. After including additional PGS in the analysis, the most parsimonious model included PGS for ADHD (p=2.03 × 10-8) and EA (p=2.00 × 10-10), with an adjusted R2 of 0.021. The other mental health PGS did not contribute to the final model. Genetic risk for ADHD was positively correlated with SWAN scores, and PGS for EA was negatively correlated with SWAN scores. The genetic correlation between the GWAS summary statistics for ADHD and EA was -0.53. Results were replicated in the Spit2 sample. ADHD PGS was significantly associated with ADHD traits, explaining 1.3% of the variance in a population-based sample. Adding the EA PGS improved model fit, and increased the variance explained to 2.1%, leading us to believe this score may help improve prediction of ADHD traits. Given the larger sample size for the EA GWAS, it likely had higher power to detect variants that are common to both traits. Additionally, the ADHD GWAS was based primarily on individuals of Danish ancestry, while the educational attainment study mostly included individuals from the United Kingdom, so slight differences in allele frequencies may result in different variants included in the genetic score. However, MDD PGS, which is genetically correlated with ADHD and is also well powered, did not improve the ADHD trait variance explained, suggesting the size of the base GWAS cannot solely explain the differences in results. Our results suggest that inclusion of PGS from related/comorbid conditions might improve prediction of other complex mental health conditions." @default.
• W4387496632 created "2023-10-11" @default.
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• W4387496632 date "2023-10-01" @default.
• W4387496632 modified "2023-10-12" @default.
• W4387496632 title "W58. MULTI-PRS APPROACH INCREASES VARIANCE EXPLAINED IN ADHD" @default.
• W4387496632 doi "https://doi.org/10.1016/j.euroneuro.2023.08.246" @default.
• W4387496632 hasPublicationYear "2023" @default.
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