Individualizing Recurrence Risks for Severe Mental Illness: Epidemiologic and Molecular Genetic Approaches
Notice bibliographique
Résumé
The meta-analysis conducted by Rasic et al.1 expands on established empiric recurrence risks for severe mental illness (SMI; schizophrenia, bipolar disorder, and major depressive disorder) in offspring of affected individuals and represents a valuable addition to the existing literature. The advantages (and caveats) of these new findings for application in clinical practice deserve special consideration, particularly in light of recent molecular genetic discoveries. Concern about the heritability of SMI is common.2–4 Genetic counseling may be a therapeutic intervention capable of addressing and attenuating such concerns.2,3,5 The results of this meta-analysis will be of assistance in addressing direct questions from patients and family members with respect to a broader phenotype than heretofore possible with the long-standing individual risk estimates for SMI. For schizophrenia, the benefits of forewarning could include a sense of relief for those found to be at a risk that they perceive to be low,2,3 and an increased likelihood of early diagnosis and a shortened duration of untreated psychosis (with concomitant benefits with respect to functional outcomes)6 for those at higher risk.7 These new risk estimates could have been useful in our studies of genetic counseling, which were recently published in the Bulletin.2,3 There remain many limitations inherent in the existing risk estimates available for patients with SMI and their family members, however. Empiric recurrence risks are averaged, aggregate numbers computed from families with widely disparate histories, and may be relatively meaningless on an individual level. Clinical heterogeneity is one important confounder. Familial recurrence risks of SMI in the case of depression, eg, might depend on factors such as psychosis, recurrence, electroconvulsive therapy/other biological treatments, and hospitalizations; there are also known cohort effects for depression.8 In another example, by definition all affected parents are reproductively fit, hence the smaller number of parents with schizophrenia in this meta-analysis, and the potential enrichment within parents with schizophrenia for women and higher functioning individuals. The context of the high prevalence in the general population of many of the psychiatric conditions studied in offspring of individuals with SMI is not reflected in their relative risk estimates and should be provided as background in any counseling. Family history is important on both sides of the family, and as briefly acknowledged by the authors, assortative mating could play a major role in their findings. Finally, there also remains little to no ability to adjust recurrence risk estimates to account for additional nonparent relatives with SMI or spectrum conditions. Psychiatrists and other mental healthcare providers should therefore exercise caution when quoting recurrence risks in clinical practice and consult with/refer to genetic counselors or other genetics professionals as needed. In regard to the meta-analysis by Rasic et al.1 and the studies considered therein, what additional insights are provided by recent molecular genetic studies of SMI? First, not mentioned by the authors are the main clinically relevant molecular findings of recent years, ie, large rare copy number variants (CNVs). The variable expression of large rare CNVs has revealed a genetically related spectrum of disease that embraces conditions outside of those usually considered part of the schizophrenia spectrum. This molecular genetic spectrum includes developmental delay/intellectual disability, autism spectrum disorder (ASD), epilepsy, and congenital anomalies.9 As the authors point out for ASD,1 limited familial recurrence risk data have been collected with regard to this other spectrum. The finding of shared underlying susceptibilities for multiple neuropsychiatric and developmental disorders is consistent with discoveries in other fields, eg, cancer and autoimmune conditions.10,11 These other fields provide a glimpse of what might be possible with respect to diagnostics and interventions in the coming years for psychiatry, as a consequence of an improved understanding of etiopathogenesis. Second, presymptomatic risk prediction in schizophrenia is likely to be fundamentally linked to the underlying genetic architecture.7 Polygenic scores12 or other methods of universal risk prediction based on common sequence variants (single nucleotide polymorphisms) may be incremental and of small effect. In contrast, the identification of specific clinically informative genetic variants may be rare events but of large effect. In the latter case, the 22q11.2 deletions found in up to 1 in 100 individuals with schizophrenia provide a model for what might be increasingly possible in clinical practice: the ability to stratify and modify recurrence risk estimates on an individual basis using the results of clinically available molecular genetic tests (figure 1). Eventually, general risk stratification may be possible with other large rare CNVs that underlie emerging genetic subtypes of schizophrenia.7,13 For these and other reasons, individuals with such clinically significant CNVs should be excluded from studies of general genetic counseling for schizophrenia.2,3 Discovery of a 22q11.2 deletion facilitates recurrence risk stratification for severe mental illness (SMI) in offspring. The 22q11.2 deletion syndrome (22q11.2DS) is associated with a 20%–25% lifetime risk for schizophrenia.14 Knowledge of an affected individual’s 22q11.2 deletion bifurcates the schizophrenia recurrence risk scenario for their offspring (Scenario 2). Transmission of the 22q11.2 deletion would imply a recurrence risk for schizophrenia of approximately 20%–25% in an offspring, whereas a failure to transmit the 22q11.2 deletion would theoretically decrease that risk to the standard population risk estimate (~1%); offspring indicated by an arrow in each case. With no knowledge of the 22q11.2 deletion status, an offspring would have an a priori “averaged” (though individually far less informative) recurrence risk for schizophrenia that is comparable with the standard empiric recurrence risk of 13% (Scenario 1). Recurrence risks for the other neuropsychiatric disorders associated with 22q11.2DS would be similarly affected.14 Of note, knowledge of an individual’s 22q11.2 deletion would also modify the schizophrenia recurrence risk scenario for siblings and thus for nieces/nephews. If the 22q11.2 deletion occurred as a new mutation in the affected individual or was otherwise not inherited by the sibling, the recurrence risk for schizophrenia would be significantly lower than the 20%–25% risk for schizophrenia if the 22q11.2 deletion was also inherited by the sibling. The potential impact of (1) sex differences in reproductive fitness,15 (2) assortative mating, (3) additional information bestowed by multiparity, (4) imprinting and parent-of-origin effects, (5) germline mosaicism, (6) schizophrenia modifier alleles, and (7) other potential confounders are not considered here. Empiric data are needed to confirm these recurrence risk predictions. For the foreseeable future, the family history and empiric recurrence risk estimates will remain a cornerstone of disease prediction.16 The results of the meta-analysis by Rasic and colleagues1 therefore represent an important contribution and will help to inform genetic counseling. Diagnosis and treatment of schizophrenia and other SMI remain primarily a reactive process, even in the presence of major risk factors such as a family history of SMI in a first-degree relative. Accurate prediction of risk remains a major goal in anticipation of future preventative or palliative presymptomatic measures. Future studies that combine epidemiologic and molecular genetic approaches will bring us closer to the ideal: a coherent, overarching risk prediction model for schizophrenia and related SMI that incorporates complete family history and personal genotype information. G.C. is supported by MD/PhD Studentships from the Canadian Institutes of Health Research and McLaughlin Centre. A.S.B. holds the Canada Research Chair in Schizophrenia Genetics and Genomic Disorders, and the Dalglish Chair in 22q11.2 Deletion Syndrome. The authors have declared that there are no conflicts of interest in relation to the subject of this study
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,077 | 0,100 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,002 |
| Méta-épidémiologie (sens large) | 0,007 | 0,022 |
| Bibliométrie | 0,010 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».