Progress in neuro-psychopharmacology & biological psychiatry · 2025
Multigene overlap analysis of bipolar disorder subtypes and educational attainment.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Zhang Jianfei, Wang Wanqi, Peng Yanmin
Аннотация
OBJECTIVE: Bipolar disorder subtypes (BIP-I and BIP-II) differ in clinical presentation and genetic basis, yet their patterns of genetic association with educational attainment (EA) remain poorly understood. This study investigated the genetic overlap between BIP subtypes and EA, along with their underlying molecular mechanisms.
METHODS: Using genome-wide association study (GWAS) data for BIP-I (n = 25,060), BIP-II (n = 6781), and EA (n = 765,283), we estimated genetic overlap using bivariate causal mixed models (MiXeR) and identified shared gene loci through the joint false discovery rate (conjFDR) method.
RESULTS: MiXeR analysis revealed approximately 7.4 K single nucleotide polymorphisms (SNPs) shared between BIP-I and EA, accounting for 97.4 % of SNPs influencing BIP-I and 56.5 % of those affecting EA. ConjFDR identified 264 loci commonly associated with BIP-I and EA, including 168 novel loci for both traits. Among the 312 lead SNPs at these loci, 219 exhibited consistent effects, while 93 demonstrated opposing effects. In contrast, only two loci were co-associated between BIP-II and EA. Functional annotation and enrichment analyses showed that most loci shared by BIP-I and EA were located in intronic and intergenic regions, with associated genes enriched in processes such as protein binding and nervous system development.
CONCLUSIONS: This study highlights the distinct degrees and patterns of genetic association between BIP subtypes and EA, offering insights into the heterogeneity of BIP and a potential genetic basis for clinical subtyping and personalized treatment strategies.
METHODS: Using genome-wide association study (GWAS) data for BIP-I (n = 25,060), BIP-II (n = 6781), and EA (n = 765,283), we estimated genetic overlap using bivariate causal mixed models (MiXeR) and identified shared gene loci through the joint false discovery rate (conjFDR) method.
RESULTS: MiXeR analysis revealed approximately 7.4 K single nucleotide polymorphisms (SNPs) shared between BIP-I and EA, accounting for 97.4 % of SNPs influencing BIP-I and 56.5 % of those affecting EA. ConjFDR identified 264 loci commonly associated with BIP-I and EA, including 168 novel loci for both traits. Among the 312 lead SNPs at these loci, 219 exhibited consistent effects, while 93 demonstrated opposing effects. In contrast, only two loci were co-associated between BIP-II and EA. Functional annotation and enrichment analyses showed that most loci shared by BIP-I and EA were located in intronic and intergenic regions, with associated genes enriched in processes such as protein binding and nervous system development.
CONCLUSIONS: This study highlights the distinct degrees and patterns of genetic association between BIP subtypes and EA, offering insights into the heterogeneity of BIP and a potential genetic basis for clinical subtyping and personalized treatment strategies.