Genetic influences on eight psychiatric disorders based on family data of 4 408 646 full and half-siblings, and genetic data of 333 748 cases and controls

Pettersson, Erik; Lichtenstein, Paul; Larsson, Henrik; Song, Jie; Agrawal, Arpana; Børglum, Anders D.; Bulik, Cynthia M.; Daly, Mark J.; Davis, Lea K.; Demontis, Ditte; Edenberg, Howard J.; Grove, Jakob; Gelernter, Joel; Neale, Benjamin M.; Pardiñas, Antonio F.; Stahl, Eli A.; Walters, James; Walters, Raymond; Sullivan, Patrick F.; Posthuma, Daniëlle; Polderman, Tinca J. C.

doi:10.1017/s0033291718002039

Cited by 123 publications

(82 citation statements)

References 42 publications

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“…Schizophrenia (SZ) and bipolar (BD) spectrum disorders are severe mental disorders with partly overlapping clinical characteristics and pathophysiology. Both are highly heritable (1) with a substantial neurodevelopmental aetiology (2,3). Along with evidence of accelerated age-related brain changes in adult patients with SZ (4-6), the neurodevelopmental origin supports a dynamic lifespan perspective in which genetic and biological factors interact with age-related environmental and physiological processes.…”

Section: Introductionmentioning

confidence: 92%

Brain age prediction reveals aberrant brain white matter in schizophrenia and bipolar disorder: A multi-sample diffusion tensor imaging study

Tønnesen

Kaufmann

Lange

et al. 2019

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Word count abstract: 249/250 | Main text: 3810/4000 Number of Figures 2 | Tables: 1 | Supplementary information: 1 document Abstract (249/250 words)Background: Schizophrenia (SZ) and bipolar disorders (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, BD, and healthy controls across 10 cohorts.Methods: We trained six cross-validated models using different combinations of DTI data from 927 healthy controls (HC, 18-94 years), and applied the models to the test sets including 648 SZ (18-66 years) patients, 185 BD patients (18-64 years), and 990 HC (17-68 years), estimating brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results.Results: 10-fold cross-validation revealed high accuracy for all models. Compared to controls, the model including all feature sets significantly over-estimated the age of patients with SZ (d=-.29) and BD (d=.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy (FA) based models showed larger group differences than the models based on other DTI-derived metrics.Conclusions: Brain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners.

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Section: Introductionmentioning

confidence: 92%

Brain age prediction reveals aberrant brain white matter in schizophrenia and bipolar disorder: A multi-sample diffusion tensor imaging study

Tønnesen

Kaufmann

Lange

et al. 2019

Preprint

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“…Some of the most prominent studies on the p factor have not included autism thus far, most likely because of their focus on adult psychiatry (e.g. Caspi et al., ; Lahey, Krueger, Rathouz, Waldman, & Zald, ; Pettersson, Lichtenstein, Larsson, Song, & Polderman, ). It is an interesting and arguably still somewhat open question: how is autism connected to the psychopathology p factor model?…”

Section: It Is Not Yet Clear How Neurodevelopmental Conditions Are Inmentioning

confidence: 99%

“…In behaviour genetics, structural equation models have been used for decades to explore the structure of psychopathology and to test models of co‐occurrence between disorders or their related traits (Lahey et al., ). A general genetic factor that influences eight major psychiatric disorders using full‐ and half‐sibling data from Swedish national registers has been reported (Pettersson et al., ). Complementary to these findings from family data, similar conclusions were reached with three other methods that employed measured genotypes (Selzam, Coleman, Caspi, Moffitt, & Plomin, ).…”

Section: It Is Full Steam Ahead For Genetic Research On the P Factormentioning

confidence: 99%

Editorial: The psychopathology p factor: will it revolutionise the science and practice of child and adolescent psychiatry?

Ronald

2019

Child Psychology Psychiatry

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The psychopathology p factor has emerged from a series of strong empirical studies, largely in the adult psychiatry literature. Here, some of the recent findings relating to the p factor in children and adolescents are considered and the implications for child and adolescent psychiatry are discussed. Is it essential to covary for ‘p’ when we study specific domains of psychopathology? Do neurodevelopmental conditions make up part of the psychopathology p factor? How do we treat the ‘p factor’ in clinics? This editorial considers some of the contributions from this issue of Journal of Child Psychology and Psychiatry together with the wider literature that speak to these issues.

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“…It is well established that genetics play a significant role in the etiology of ASD, with increased familial risk in families already affected with ASD [Colvert et al, 2015;Petersen et al, 2018;Risch et al, 2014;Sandin et al, 2014]. As a result, numerous studies on the early development of ASD have employed prospective designs utilizing samples of "high-risk" infant siblings of children diagnosed with ASD (ASD-sibs), given their increased risk of developing this condition [Constantino, Zhang, Frazier, Abbacchi, & Law, 2010;Silverman et al, 2002;Spiker, Lotspeich, Dimiceli, Myers, & Risch, 2002].…”

Section: Introductionmentioning

confidence: 99%

Cognitive and behavioral differences in toddlers with autism spectrum disorder from multiplex and simplex families

et al. 2019

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Prospective, longitudinal designs utilizing “high‐risk” infant siblings of children diagnosed with Autism Spectrum Disorder (ASD‐sibs) have provided unique and valuable insights regarding the early ASD phenotype. However, it remains unclear whether these cases are representative of all children with ASD. The objective in the present study was to investigate whether the early development of toddlers with ASD from multiplex (MPX) families, who have an affected older sibling, is similar or different to toddlers with ASD from simplex (SPX) families, where there is no affected sibling. A further aim was to examine patterns of association between autism symptom severity and cognitive functioning within each group to inform possible mechanisms for group similarities/differences. Behavioral and cognitive assessment data from a sample of toddlers with ASD was utilized, comprising 45 MPX, 127 first‐born SPX, and 72 later‐born SPX toddlers. Participants in the MPX group had significantly higher developmental quotients on the Mullen Scales of Early Learning compared to those in the SPX groups, who did not differ from each other. However, all three groups were similar on their autism severity scores (measured using the Autism Diagnostic Observation Schedule and the Autism Diagnostic Interview), and the pattern of relationships between cognitive ability and autism symptom severity. The results suggest that caution be exercised in generalizing findings from ASD‐sib samples to other samples of children with ASD. The higher cognitive abilities in the MPX group, in addition to biological differences, may also be an outcome of family environmental factors, which deserves further investigation. Autism Research 2019, 12: 682–693. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. Lay Summary We sought to establish whether toddlers with autism from families where there is more than one affected child, called multiplex families, are different to children from simplex families, where there is only one affected child, and no other members within the immediate family with an autism diagnosis. We found that while toddlers from multiplex families were similar to those from simplex families in their autism symptoms, they were more developmentally advanced than children in the latter group.

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Genetic influences on eight psychiatric disorders based on family data of 4 408 646 full and half-siblings, and genetic data of 333 748 cases and controls

Cited by 123 publications

References 42 publications

Brain age prediction reveals aberrant brain white matter in schizophrenia and bipolar disorder: A multi-sample diffusion tensor imaging study

Brain age prediction reveals aberrant brain white matter in schizophrenia and bipolar disorder: A multi-sample diffusion tensor imaging study

Editorial: The psychopathology p factor: will it revolutionise the science and practice of child and adolescent psychiatry?

Cognitive and behavioral differences in toddlers with autism spectrum disorder from multiplex and simplex families

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