Polygenic Risk of Mental Disorders and Subject-Specific School Grades

Oskar Hougaard Jefsen,Katrine Holde，Preben Bo Mortensen,Liselotte Vogdrup Petersen

BIOLOGICAL PSYCHIATRY（2024）

Aarhus Univ Hosp | Aarhus Univ | Lundbeck Fdn Initiat Integrat Psychiat Res | Univ North Carolina Chapel Hill

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Abstract

BackgroundEducation is essential for socioeconomic security and long-term mental health; however, mental disorders are often detrimental to the educational trajectory. Genetic correlations between mental disorders and educational attainment do not always align with corresponding phenotypic associations, implying heterogeneity in the genetic overlap.MethodsWe unraveled this heterogeneity by investigating associations between polygenic risk scores for 6 mental disorders and fine-grained school outcomes: school grades in language and mathematics in ninth grade and high school, as well as educational attainment by age 25, using nationwide-representative data from established cohorts (N = 79,489).ResultsHigh polygenic liability of attention-deficit/hyperactivity disorder was associated with lower grades in language and mathematics, whereas high polygenic risk of anorexia nervosa or bipolar disorder was associated with higher grades in language and mathematics. Associations between polygenic risk and school grades were mixed for schizophrenia and major depressive disorder and neutral for autism spectrum disorder.ConclusionsPolygenic risk scores for mental disorders are differentially associated with language and mathematics school grades.

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Key words

Educational attainment,Language,Mathematics,Mental disorders,Polygenic risk scores,School performance

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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