Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Parkinson's disease mild cognitive impairment: application and validation of the criteria

Dementia in Parkinson's disease (PD) is a serious health issue and a major concern for many patients. In most cases mild cognitive impairment (MCI) is considered a transitional stage between normal cognitive functioning and dementia which is of potential importance in the early identification of patients at risk for dementia. Recently, the Movement Disorder Society (MDS) proposed diagnostic criteria for MCI in PD (PD-MCI). These criteria comprise two operationalizations: Level I (based on an abbreviated assessment) and Level II (based on comprehensive neuropsychological evaluation permitting MCI subtyping). These criteria need to be validated. This paper describes a project aiming to validate the MDS PD-MCI criteria by pooling and analyzing cross-sectional and longitudinal neuropsychological databases comprising ≥5,500 PD patients and ≥1,700 controls. After applying the MDS PD-MCI Level I and Level II criteria, rates of conversion to PD-dementia and predictive variables for conversion to PD-dementia will be established. This study will also assist in identifying whether revisions of the PD-MCI criteria are required

Association of cerebrospinal fluid β-amyloid 1-42, t-tau, p-tau ₁₈₁, and α-synuclein levels with clinical features of drug-naive patients with early parkinson disease

Importance: We observed a significant correlation between cerebrospinal fluid (CSF) levels of tau proteins and α-synuclein, but not β-amyloid 1-42 (Aβ1-42), and lower concentration of CSF biomarkers, as compared with healthy controls, in a cohort of entirely untreated patients with Parkinson disease (PD) at the earliest stage of the disease studied so far. Objective: To evaluate the baseline characteristics and relationship to clinical features of CSF biomarkers (Aβ1-42, total tau [T-tau], tau phosphorylated at threonine 181 [P-tau₁₈₁], and α-synuclein) in drug-naive patients with early PD and demographically matched healthy controls enrolled in the Parkinson's Progression Markers Initiative (PPMI) study. Design, Setting, and Participants: Cross-sectional study of the initial 102 research volunteers (63 patients with PD and 39 healthy controls) of the PPMI cohort. Main Outcomes and Measures: The CSF biomarkers were measured by INNO-BIA AlzBio3 immunoassay (Aβ1-42, T-tau, and P-tau₁₈₁; Innogenetics Inc) or by enzyme-linked immunosorbent assay (α-synuclein). Clinical features including diagnosis, demographic characteristics, motor, neuropsychiatric, and cognitive assessments, and DaTscan were systematically assessed according to the PPMI study protocol. Results: Slightly, but significantly, lower levels of Aβ1-42, T-tau, P-tau₁₈₁, α-synuclein, and T-tau/Aβ1-42 were seen in subjects with PD compared with healthy controls but with a marked overlap between groups. Using multivariate regression analysis, we found that lower Aβ1-42 and P-tau₁₈₁ levels were associated with PD diagnosis and that decreased CSF T-tau and α-synuclein were associated with increased motor severity. Notably, when we classified patients with PD by their motor phenotypes, lower CSF Aβ1-42 and P-tau₁₈₁ concentrations were associated with the postural instability-gait disturbance-dominant phenotype but not with the tremor-dominant or intermediate phenotype. Finally, we found a significant correlation of the levels of α-synuclein with the levels of T-tau and P-tau₁₈₁. Conclusions and Relevance: In this first report of CSF biomarkers in PPMI study subjects, we found that measures of CSF Aβ1-42, T-tau, P-tau₁₈₁, and α-synuclein have prognostic and diagnostic potential in early-stage PD. Further investigations using the entire PPMI cohort will test the predictive performance of CSF biomarkers for PD progression.11 page(s

Analysis of Shared Heritability in Common Disorders of the Brain

Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology