Examining Differences in the Genetic and Functional Architecture of Attention-Deficit/Hyperactivity Disorder Diagnosed in Childhood and Adulthood.

Background: Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder with diagnostic criteria requiring symptoms to begin in childhood. We investigated whether individuals diagnosed as children differ from those diagnosed in adulthood with respect to shared and unique architecture at the genome-wide and gene expression level of analysis. Methods: We used genomic structural equation modeling (SEM) to investigate differences in genetic correlations (rg) of childhood-diagnosed (ncases = 14,878) and adulthood-diagnosed (ncases = 6961) ADHD with 98 behavioral, psychiatric, cognitive, and health outcomes. We went on to apply transcriptome-wide SEM to identify functional annotations and patterns of gene expression associated with genetic risk sharing or divergence across the ADHD subgroups. Results: Compared with the childhood subgroup, adulthood-diagnosed ADHD exhibited a significantly larger negative rg with educational attainment, the noncognitive skills of educational attainment, and age at first sexual intercourse. We observed a larger positive rg for adulthood-diagnosed ADHD with major depression, suicidal ideation, and a latent internalizing factor. At the gene expression level, transcriptome-wide SEM analyses revealed 22 genes that were significantly associated with shared genetic risk across the subtypes that reflected a mixture of coding and noncoding genes and included 15 novel genes relative to the ADHD subgroups. Conclusions: This study demonstrated that ADHD diagnosed later in life shows much stronger genetic overlap with internalizing disorders and related traits. This may indicate the potential clinical relevance of distinguishing these subgroups or increased misdiagnosis for those diagnosed later in life. Top transcriptome-wide SEM results implicated genes related to neuronal function and clinical characteristics (e.g., sleep).

It is unclear whether individuals who are diagnosed with attention-deficit/hyperactivity disorder (ADHD) as children differ from those who are diagnosed in adulthood with respect to their genetic architecture. We found that adulthood-diagnosed ADHD is much more genetically similar than ADHD diagnosed in childhood to disorders in the internalizing space, such as depression and suicidality. Differences between the distinct age groups at diagnosis highlight the importance of distinguishing these subgroups in a clinical and treatment setting.

Genomic SEM applied to explore etiological divergences in bipolar subtypes.

BACKGROUND: Bipolar disorder (BD) is an overarching diagnostic class defined by the presence of at least one prior manic episode (BD I) or both a prior hypomanic episode and a prior depressive episode (BD II). Traditionally, BD II has been conceptualized as a less severe presentation of BD I, however, extant literature to investigate this claim has been mixed. METHODS: We apply genomic structural equation modeling (Genomic SEM) to investigate divergent genetic pathways across BD's two major subtypes using the most recent GWAS summary statistics from the PGC. We begin by identifying divergences in genetic correlations across 98 external traits using a Bonferroni-corrected threshold. We also use a theoretically informed follow-up model to examine the extent to which the genetic variance in each subtype is explained by schizophrenia and major depression. Lastly, transcriptome-wide SEM (T-SEM) was used to identify neuronal gene expression patterns associated with BD subtypes. RESULTS: BD II was characterized by significantly larger genetic overlap across non-psychiatric medical and internalizing traits (e.g. heart disease, neuroticism, insomnia), while stronger associations for BD I were absent. Consistent with these findings, follow-up modeling revealed a substantial major depression component for BD II. T-SEM results revealed 35 unique genes associated with shared risk across BD subtypes. CONCLUSIONS: Divergent patterns of genetic relationships across external traits provide support for the distinction of the bipolar subtypes. However, our results also challenge the illness severity conceptualization of BD given stronger genetic overlap across BD II and a range of clinically relevant traits and disorders.

Genomic SEM Applied to Explore Etiological Divergences in Bipolar Subtypes.

Background: Bipolar Disorder (BD) is an overarching diagnostic class defined by the presence of at least one prior manic episode (BD I) or both a prior hypomanic episode and a prior depressive episode (BD II). Traditionally, BD II has been conceptualized as a less severe presentation of BD I, however, extant literature to investigate this claim has been mixed. Methods: We apply Genomic Structural Equation Modeling (Genomic SEM) to investigate divergent genetic pathways across BD's two major subtypes using the most recent GWAS summary statistics from the PGC. We begin by identifying divergences in genetic correlations across 89 external traits using a Bonferroni corrected threshold. We also use a theoretically informed follow-up model to examine the extent to which the genetic variance in each subtype is explained by schizophrenia and major depression. Lastly, Transcriptome-wide SEM (T-SEM) was used to identify gene expression patterns associated with the BD subtypes. Results: BD II was characterized by significantly larger genetic overlap with internalizing traits (e.g., neuroticism, insomnia, physical inactivity), while significantly stronger associations for BD I were limited. Consistent with these findings, the follow-up model revealed a much larger major depression component for BD II. T-SEM results revealed 41 unique genes associated with risk pathways across BD subtypes. Conclusions: Divergent patterns of genetic relationships across external traits provide support for the distinction of the bipolar subtypes. However, our results also challenge the illness severity conceptualization of BD given stronger genetic overlap across BD II and a range of clinically relevant traits and disorders.