Sleep Health at the Genomic Level: Six Distinct Factors and Their Relationships With Psychopathology

Morrison, Clyde A.; Winiger, Evan A.; Rieselbach, Maya M.; Vetter, Céline; Wright, Kenneth P.; LeBourgeois, Monique K.; Friedman, Naomi P.

doi:10.1016/j.bpsgos.2022.07.002

Cited by 5 publications

(6 citation statements)

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“…We report a systematic study of genetic correlations between 24 chronic pain conditions in a 2-factor model [127] and 11 psychiatric conditions in a 4-factor model [86], both at the individual condition and factor levels. This analysis enabled us to observe trans-diagnostic genetic risks that bring attention to shared biological mechanisms for conditions previously held to be distinct and treated as such in the clinical setting.…”

Section: Discussionmentioning

confidence: 99%

“…In addition to estimating factor loadings, it is possible to estimate inter-factor correlations, which is the main goal of this analysis for the present study. We used a 24-condition two-factor pain model [127] and an 11-condition four-factor psychiatric model [86] to specify a combined model with all six factors allowed to correlate. Given that Genomic SEM takes as input a matrix of genetic correlations, we estimated all pairwise genetic correlations for the complete set of 35 conditions (plus three neuroticism scores for subsequent analyses) in LDSC (Section 2.5).…”

Section: Structural Equation Modellingmentioning

confidence: 99%

“…As done for the original constituent models, we specified all 24 pain conditions to load onto the General Pain factor, and 11 of these conditions to load onto the Musculoskeletal factor, [127]. For the psychiatric portion, we specified ADHD, ALC, CIGS, and CUD to load onto the Externalizing factor, SCZ and BP onto the Psychotic Thought factor, OCD and AN onto the Compulsive Thought factor, and MDD, ANX, and PTSD onto the Internalizing factor [86]. Given that the model as described above did not have optimal fit, we examined residual covariances for outliers, i.e.…”

Section: Structural Equation Modellingmentioning

confidence: 99%

“…We combined 24 pain conditions from our previously published pain model, Figure 1a [127], 11 psychiatric conditions from a published psychiatric model (Figure 1b [86]), and neuroticism (full scale and two subscales), and we calculated their pairwise genetic correlations. In the correlation matrix (Figure 1c) the pain conditions are ordered by loading onto the General Pain factor (F1 in Figure 1a), descending from top to bottom.…”

Section: Pain-psychiatric Correlationsmentioning

confidence: 99%

“…an additional musculoskeletal pain factor [127]. Likewise, a recent publication proposed a model of 4 correlated genetic factors (Externalizing, Internalizing, Compulsive Thought, and Psychotic Thought) [86] explaining the relationships among psychiatric disorders that form several subdomains of general psychopathology [18]. The factors in these models indicate clusters of conditions that may share genetically-driven pathophysiology.…”

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confidence: 99%

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Genetic relationships between chronic pain, psychopathologies, and neuroticism

Zorina-Lichtenwalter

Bango

Čeko

et al. 2023

Preprint

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Chronic pain and psychiatric conditions have consistently demonstrated substantial overlap in risk factors, epidemiology, and effective treatments. Previous work has identified cross-condition latent factors underlying shared genetic risk for several distinct psychiatric conditions and pain conditions. Here, we sought to examine the relationships between these latent genetic factors to determine biological mechanisms common to both pain and psychiatric conditions. We combined two previously published genetic structural equation models. The first model consisted of 24 pain conditions and their two latent factors: General and Musculoskeletal pain-specific. The second model consisted of 11 psychiatric conditions and their four latent factors: Externalizing, Internalizing, Compulsive Thought, and Psychotic Thought. The combined model of six factors and 35 conditions allowed us to estimate correlations between all factors and between conditions of one domain (pain) and factors of the other (psychiatric). We then added three measures of neuroticism (depressive affect subscale, worrying subscale, and total neuroticism score) to this model to examine correlations with all conditions and factors and test for possible explanation of pain-mental disorder relationships by neuroticism. We found that genetic associations between pain and psychiatric conditions were selective to the General Pain factor (and not Musculoskeletal) and Internalizing and Externalizing, but not Thought disorder factors. Neuroticism was associated with pain conditions to the extent that they loaded onto the General Pain factor (i.e., were associated with other pain conditions). Neuroticism also explained a substantial proportion of shared genetic variance between General Pain and Externalizing and between General Pain and Internalizing factors. Overall, the genetic risks shared among chronic pain and psychiatric conditions and neuroticism suggest shared biological mechanisms and underscore the importance of clinical assessment and treatment programs that leverage these commonalities.

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Section: Discussionmentioning

confidence: 99%

Section: Structural Equation Modellingmentioning

confidence: 99%

Section: Structural Equation Modellingmentioning

confidence: 99%

Section: Pain-psychiatric Correlationsmentioning

confidence: 99%

mentioning

confidence: 99%

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Genetic relationships between chronic pain, psychopathologies, and neuroticism

Zorina-Lichtenwalter

Bango

Čeko

et al. 2023

Preprint

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Genomic structural equation modeling reveals latent phenotypes in the human cortex with distinct genetic architecture

Morey,

Zheng,

Bayly

et al. 2024

Transl Psychiatry

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Genetic contributions to human cortical structure manifest pervasive pleiotropy. This pleiotropy may be harnessed to identify unique genetically-informed parcellations of the cortex that are neurobiologically distinct from functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to map the genetic architecture of cortical surface area (SA) and cortical thickness (CT) for 34 brain regions recently reported in the ENIGMA cortical GWAS. Genomic SEM uses the empirical genetic covariance estimated from GWAS summary statistics with LD score regression (LDSC) to discover factors underlying genetic covariance, which we are denoting genetically informed brain networks (GIBNs). Genomic SEM can fit a multivariate GWAS from summary statistics for each of the GIBNs, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA identified 6 GIBNs and CT identified 4 GIBNs, although sensitivity analyses indicated that other structures were plausible. The multivariate GWASs of the GIBNs identified 74 genome-wide significant (GWS) loci (p < 5 × 10−8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of GIBN GWASs found that SA-derived GIBNs had a positive genetic correlation with bipolar disorder (BPD), and cannabis use disorder, indicating genetic predisposition to a larger SA in the specific GIBN is associated with greater genetic risk of these disorders. A negative genetic correlation was observed between attention deficit hyperactivity disorder (ADHD) and major depressive disorder (MDD). CT GIBNs displayed a negative genetic correlation with alcohol dependence. Even though we observed model instability in our application of genomic SEM to high-dimensional data, jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across neuroimaging phenotypes offers new insights into the genetics of cortical structure and relationships to psychopathology.

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Multivariate genome-wide association study of sleep health demonstrates unity and diversity

Morrison,

Winiger,

Wright

et al. 2023

Sleep

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There has been a recent push to focus sleep research less on disordered sleep and more on the dimensional sleep health. Sleep health incorporates several dimensions of sleep: chronotype, efficiency, daytime alertness, duration, regularity, and satisfaction with sleep. A previous study demonstrated sleep health domains correlate only moderately with each other at the genomic level (|rGs| = 0.11–0.51) and show unique relationships with psychiatric domains (controlling for shared variances, duration, alertness, and non-insomnia independently related to a factor for internalizing psychopathology). Of the domains assessed, circadian preference was the least genetically correlated with all other facets of sleep health. This pattern is important because it suggests sleep health should be considered a multifaceted construct rather than a unitary construct. Prior genome-wide association studies (GWASs) have vastly increased our knowledge of the biological underpinnings of specific sleep traits but have only focused on univariate analyses. We present the first multivariate GWAS of sleep and circadian health (multivariate circadian preference, efficiency, and alertness factors, and three single-indicator factors of insomnia, duration, and regularity) using genomic structural equation modeling. We replicated loci found in prior sleep GWASs, but also discovered “novel” loci for each factor and found little evidence for genomic heterogeneity. While we saw overlapping genomic enrichment in subcortical brain regions and shared associations with external traits, much of the genetic architecture (loci, mapped genes, and enriched pathways) was diverse among sleep domains. These results confirm sleep health as a family of correlated but genetically distinct domains, which has important health implications.

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Sleep Health at the Genomic Level: Six Distinct Factors and Their Relationships With Psychopathology

Cited by 5 publications

References 50 publications

Genetic relationships between chronic pain, psychopathologies, and neuroticism

Genetic relationships between chronic pain, psychopathologies, and neuroticism

Genomic structural equation modeling reveals latent phenotypes in the human cortex with distinct genetic architecture

Multivariate genome-wide association study of sleep health demonstrates unity and diversity

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