Effects of polygenic risk for major mental disorders and cross-disorder on cortical complexity

Abstract Background MRI-derived cortical folding measures are an indicator of largely genetically driven early developmental processes. However, the effects of genetic risk for major mental disorders on early brain development are not well understood. Methods We extracted cortical complexity values from structural MRI data of 580 healthy participants using the CAT12 toolbox. Polygenic risk scores (PRS) for schizophrenia, bipolar disorder, major depression, and cross-disorder (incorporating cumulative genetic risk for depression, schizophrenia, bipolar disorder, autism spectrum disorder, and attention-deficit hyperactivity disorder) were computed and used in separate general linear models with cortical complexity as the regressand. In brain regions that showed a significant association between polygenic risk for mental disorders and cortical complexity, volume of interest (VOI)/region of interest (ROI) analyses were conducted to investigate additional changes in their volume and cortical thickness. Results The PRS for depression was associated with cortical complexity in the right orbitofrontal cortex (right hemisphere: p = 0.006). A subsequent VOI/ROI analysis showed no association between polygenic risk for depression and either grey matter volume or cortical thickness. We found no associations between cortical complexity and polygenic risk for either schizophrenia, bipolar disorder or psychiatric cross-disorder when correcting for multiple testing. Conclusions Changes in cortical complexity associated with polygenic risk for depression might facilitate well-established volume changes in orbitofrontal cortices in depression. Despite the absence of psychopathology, changed cortical complexity that parallels polygenic risk for depression might also change reward systems, which are also structurally affected in patients with depressive syndrome.

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Neural correlates of polygenic risk score for autism spectrum disorders in general population

Brain Communications ◽

10.1093/braincomms/fcaa092 ◽

2020 ◽

Vol 2 (2) ◽

Author(s):

Budhachandra Khundrakpam ◽

Uku Vainik ◽

Jinnan Gong ◽

Noor Al-Sharif ◽

Neha Bhutani ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

White Matter ◽

General Population ◽

Cortical Thickness ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Risk Scores ◽

Polygenic Risk ◽

Cortical Regions ◽

White Matter Connectivity

Abstract Autism spectrum disorder is a highly prevalent and highly heritable neurodevelopmental condition, but studies have mostly taken traditional categorical diagnosis approach (yes/no for autism spectrum disorder). In contrast, an emerging notion suggests a continuum model of autism spectrum disorder with a normal distribution of autistic tendencies in the general population, where a full diagnosis is at the severe tail of the distribution. We set out to investigate such a viewpoint by investigating the interaction of polygenic risk scores for autism spectrum disorder and Age2 on neuroimaging measures (cortical thickness and white matter connectivity) in a general population (n = 391, with age ranging from 3 to 21 years from the Pediatric Imaging, Neurocognition and Genetics study). We observed that children with higher polygenic risk for autism spectrum disorder exhibited greater cortical thickness for a large age span starting from 3 years up to ∼14 years in several cortical regions localized in bilateral precentral gyri and the left hemispheric postcentral gyrus and precuneus. In an independent case–control dataset from the Autism Brain Imaging Data Exchange (n = 560), we observed a similar pattern: children with autism spectrum disorder exhibited greater cortical thickness starting from 6 years onwards till ∼14 years in wide-spread cortical regions including (the ones identified using the general population). We also observed statistically significant regional overlap between the two maps, suggesting that some of the cortical abnormalities associated with autism spectrum disorder overlapped with brain changes associated with genetic vulnerability for autism spectrum disorder in healthy individuals. Lastly, we observed that white matter connectivity between the frontal and parietal regions showed significant association with polygenic risk for autism spectrum disorder, indicating that not only the brain structure, but the white matter connectivity might also show a predisposition for the risk of autism spectrum disorder. Our findings showed that the fronto-parietal thickness and connectivity are dimensionally related to genetic risk for autism spectrum disorder in general population and are also part of the cortical abnormalities associated with autism spectrum disorder. This highlights the necessity of considering continuum models in studying the aetiology of autism spectrum disorder using polygenic risk scores and multimodal neuroimaging.

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Abnormalities in left inferior frontal gyral thickness and parahippocampal gyral volume in young people at high genetic risk for bipolar disorder

Psychological Medicine ◽

10.1017/s0033291716000507 ◽

2016 ◽

Vol 46 (10) ◽

pp. 2083-2096 ◽

Cited By ~ 9

Author(s):

G. Roberts ◽

R. Lenroot ◽

A. Frankland ◽

P. K. Yeung ◽

N. Gale ◽

...

Keyword(s):

At Risk ◽

Bipolar Disorder ◽

Surface Area ◽

Cortical Thickness ◽

Genetic Risk ◽

Inferior Frontal Gyrus ◽

Parahippocampal Gyrus ◽

Grey Matter Volume ◽

Degree Relative ◽

Matter Volume

BackgroundFronto-limbic structural brain abnormalities have been reported in patients with bipolar disorder (BD), but findings in individuals at increased genetic risk of developing BD have been inconsistent. We conducted a study in adolescents and young adults (12–30 years) comparing measures of fronto-limbic cortical and subcortical brain structure between individuals at increased familial risk of BD (at risk; AR), subjects with BD and controls (CON). We separately examined cortical volume, thickness and surface area as these have distinct neurodevelopmental origins and thus may reflect differential effects of genetic risk.MethodWe compared fronto-limbic measures of grey and white matter volume, cortical thickness and surface area in 72 unaffected-risk individuals with at least one first-degree relative with bipolar disorder (AR), 38 BD subjects and 72 participants with no family history of mental illness (CON).ResultsThe AR group had significantly reduced cortical thickness in the left pars orbitalis of the inferior frontal gyrus (IFG) compared with the CON group, and significantly increased left parahippocampal gyral volume compared with those with BD.ConclusionsThe finding of reduced cortical thickness of the left pars orbitalis in AR subjects is consistent with other evidence supporting the IFG as a key region associated with genetic liability for BD. The greater volume of the left parahippocampal gyrus in those at high risk is in line with some prior reports of regional increases in grey matter volume in at-risk subjects. Assessing multiple complementary morphometric measures may assist in the better understanding of abnormal developmental processes in BD.

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Genetic risk for bipolar disorder and schizophrenia predicts structure and function of the ventromedial prefrontal cortex

Journal of Psychiatry and Neuroscience ◽

10.1503/jpn.200165 ◽

2021 ◽

Vol 46 (4) ◽

pp. E441-E450

Author(s):

Christoph Abé ◽

Predrag Petrovic ◽

William Ossler ◽

William H. Thompson ◽

Benny Liberg ◽

...

Keyword(s):

Bipolar Disorder ◽

Prefrontal Cortex ◽

Genetic Risk ◽

Psychotic Disorders ◽

Structure And Function ◽

Ventromedial Prefrontal Cortex ◽

Risk Scores ◽

Polygenic Risk ◽

And Function ◽

Structural Brain

Background: Bipolar disorder is highly heritable and polygenic. The polygenic risk for bipolar disorder overlaps with that of schizophrenia, and polygenic scores are normally distributed in the population. Bipolar disorder has been associated with structural brain abnormalities, but it is unknown how these are linked to genetic risk factors for psychotic disorders. Methods: We tested whether polygenic risk scores for bipolar disorder and schizophrenia predict structural brain alterations in 98 patients with bipolar disorder and 81 healthy controls. We derived brain cortical thickness, surface area and volume from structural MRI scans. In post-hoc analyses, we correlated polygenic risk with functional hub strength, derived from resting-state functional MRI and brain connectomics. Results: Higher polygenic risk scores for both bipolar disorder and schizophrenia were associated with a thinner ventromedial prefrontal cortex (vmPFC). We found these associations in the combined group, and separately in patients and drug-naive controls. Polygenic risk for bipolar disorder was correlated with the functional hub strength of the vmPFC within the default mode network. Limitations: Polygenic risk is a cumulative measure of genomic burden. Detailed genetic mechanisms underlying brain alterations and their cognitive consequences still need to be determined. Conclusion: Our multimodal neuroimaging study linked genomic burden and brain endophenotype by demonstrating an association between polygenic risk scores for bipolar disorder and schizophrenia and the structure and function of the vmPFC. Our findings suggest that genetic factors might confer risk for psychotic disorders by influencing the integrity of the vmPFC, a brain region involved in self-referential processes and emotional regulation. Our study may also provide an imaging–genetics vulnerability marker that can be used to help identify individuals at risk for developing bipolar disorder.

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Early manifestations of genetic risk for neurodevelopmental disorders

10.31234/osf.io/qbvw8 ◽

2020 ◽

Author(s):

Ragna Bugge Askeland ◽

Laurie John Hannigan ◽

Helga Ask ◽

Ziada Ayorech ◽

Martin Tesli ◽

...

Keyword(s):

Social Communication ◽

Neurodevelopmental Disorders ◽

Genetic Risk ◽

Repetitive Behavior ◽

Autism Spectrum ◽

Risk Scores ◽

Linear Regression Models ◽

Polygenic Risk ◽

Language Difficulties ◽

Motor Difficulties

Background Attention deficit/hyperactivity disorder (ADHD), autism spectrum disorder (autism), and schizophrenia, are highly heritable neurodevelopmental disorders, affecting the lives of many individuals. To improve prevention and treatment, it is important to increase our understanding of how the polygenic risk for neurodevelopmental disorders manifests during childhood in boys and girls.Method Polygenic risk scores (PRS) for ADHD, autism and schizophrenia were calculated in a sub-sample of 15 205 children from the Norwegian Mother, Father and Child Cohort Study (MoBa). Mother-reported traits of repetitive behavior, social communication, language and motor difficulties, hyperactivity and inattention were measured in children at 6 and 18 months, 3, 5 and 8 years. Linear regression models in a multi-group framework were used to investigate associations between the three PRS and dimensional trait measures in MoBa, and sex was used as a grouping variable. Results Before the age of 2, PRS associations were found with language difficulties, inattention and hyperactivity, mostly increasing in strength up to 8 years. PRS for autism was associated with motor difficulties as early as 6 months. By 8 years, all measured neurodevelopmental traits had shown some association with at least one neurodevelopmental PRS. In general, genetic risk manifested similarly in boys and girls. Associations were, however, stronger in girls for language difficulties and ADHD PRS at 8 years. The association between schizophrenia PRS and inattention was found in girls only at 18 months. For autism PRS, associations with social communication difficulties at 18 months, and motor difficulties at 5 years were observed in boys only. Conclusions Genetic risk for neurodevelopmental disorders manifests early in childhood and broadly across behavioral measures of neurodevelopment. Manifestations may be sex specific in some domains, but these findings need to be replicated.

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INTERACTIVE EFFECTS OF FAMILY HISTORY, POLYGENIC RISK AND AGE ON CORTICAL THICKNESS IN YOUNG PEOPLE AT HIGH GENETIC RISK OF BIPOLAR DISORDER

European Neuropsychopharmacology ◽

10.1016/j.euroneuro.2017.08.256 ◽

2019 ◽

Vol 29 ◽

pp. S924

Author(s):

Rhoshel K. Lenroot ◽

Bronwyn Overs ◽

Gloria Roberts ◽

Andrew Frankland ◽

Florence Levy ◽

...

Keyword(s):

Bipolar Disorder ◽

Family History ◽

Young People ◽

Cortical Thickness ◽

Genetic Risk ◽

Interactive Effects ◽

Polygenic Risk ◽

High Genetic Risk

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F31. POLYGENIC RISK SCORES AND EARLY RISK ENDOPHENOTYPES IN CHILDREN AT GENETIC RISK OF SCHIZOPHRENIA AND BIPOLAR DISORDER: IMPLICATIONS FOR THE DEFINITION OF THE CHILDHOOD RISK STATUS

Schizophrenia Bulletin ◽

10.1093/schbul/sby017.562 ◽

2018 ◽

Vol 44 (suppl_1) ◽

pp. S230-S231

Author(s):

Thomas Paccalet ◽

Alexandre Bureau ◽

Elsa Gilbert ◽

Nicolas Berthelot ◽

Pierre Marquet ◽

...

Keyword(s):

Bipolar Disorder ◽

Genetic Risk ◽

Risk Scores ◽

Risk Status ◽

Polygenic Risk ◽

Early Risk ◽

Definition Of

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The use of polygenic risk scores to identify phenotypes associated with genetic risk of bipolar disorder and depression: A systematic review

Journal of Affective Disorders ◽

10.1016/j.jad.2018.02.005 ◽

2018 ◽

Vol 234 ◽

pp. 148-155 ◽

Cited By ~ 42

Author(s):

Sumit Mistry ◽

Judith R. Harrison ◽

Daniel J. Smith ◽

Valentina Escott-Price ◽

Stanley Zammit

Keyword(s):

Systematic Review ◽

Bipolar Disorder ◽

Genetic Risk ◽

Risk Scores ◽

Polygenic Risk

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The patterns of family genetic risk scores for eleven major psychiatric and substance use disorders in a Swedish national sample

Translational Psychiatry ◽

10.1038/s41398-021-01454-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Kenneth S. Kendler ◽

Henrik Ohlsson ◽

Jan Sundquist ◽

Kristina Sundquist

Keyword(s):

Genetic Risk ◽

Molecular Genetic ◽

National Sample ◽

Autism Spectrum ◽

Risk Scores ◽

Pedigree Data ◽

Obsessive Compulsive ◽

Swedish Population ◽

Compulsive Disorder ◽

Genetic Risk Scores

AbstractTo clarify the structure of genetic risks for 11 major psychiatric disorders, we calculated, from morbidity risks for disorders in 1st–5th degree relatives controlling for cohabitation effects, in the Swedish population born between 1932 and 1995 (n = 5,830,014), the family genetic risk scores (FGRS) for major depression (MD), anxiety disorders (AD), obsessive-compulsive disorder (OCD), bipolar disorder (BD), schizophrenia (SZ), bulimia (BUL), anorexia nervosa (AN), alcohol use disorder (AUD), drug use disorder (DUD), ADHD, and autism-spectrum disorder (ASD). For all affected individuals, we calculated their mean standardized FGRS for each disorder. The patterns of FGRS were quite similar for MD and AD, and for AUD and DUD, but substantially less similar for BUL and AN, BD and SZ, and ADHD and ASD. While OCD had high levels of FGRS for MD and AD, the overall FGRS profile differed considerably from MD and AD. ADHD FGRS scores were substantially elevated in AUD and DUD. FGRS scores for BD, OCD, AN, ASD, ADHD, and especially SZ were relatively disorder-specific while genetic risk for MD and AD had more generalized effects. The levels of FGRS for BMI, coronary artery disease, and educational attainment across our disorders replicated prior associations found using molecular genetic methods. All diagnostic categories examined had elevated FGRS for many disorders producing, for each condition, an informative FGRS profile. Using a novel method which approximates, from pedigree data, aggregate genetic risk, we have replicated and extended prior insights into the structure of genetic risk factors for key psychiatric illnesses.

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Identifying individuals with high risk of Alzheimer’s disease using polygenic risk scores

Nature Communications ◽

10.1038/s41467-021-24082-z ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Ganna Leonenko ◽

Emily Baker ◽

Joshua Stevenson-Hoare ◽

Annerieke Sierksma ◽

Mark Fiers ◽

...

Keyword(s):

High Risk ◽

Risk Score ◽

Genetic Risk ◽

Prediction Accuracy ◽

Genetic Risk Score ◽

Low Risk ◽

Risk Scores ◽

Sample Mean ◽

Polygenic Risk ◽

Reliable Identification

AbstractPolygenic Risk Scores (PRS) for AD offer unique possibilities for reliable identification of individuals at high and low risk of AD. However, there is little agreement in the field as to what approach should be used for genetic risk score calculations, how to model the effect of APOE, what the optimal p-value threshold (pT) for SNP selection is and how to compare scores between studies and methods. We show that the best prediction accuracy is achieved with a model with two predictors (APOE and PRS excluding APOE region) with pT<0.1 for SNP selection. Prediction accuracy in a sample across different PRS approaches is similar, but individuals’ scores and their associated ranking differ. We show that standardising PRS against the population mean, as opposed to the sample mean, makes the individuals’ scores comparable between studies. Our work highlights the best strategies for polygenic profiling when assessing individuals for AD risk.

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