Polygenic risk for schizophrenia and subcortical brain anatomy in the UK Biobank cohort

Abstract Research has shown differences in subcortical brain volumes between participants with schizophrenia and healthy controls. However, none of these differences have been found to associate with schizophrenia polygenic risk. Here, in a large sample (n = 14,701) of unaffected participants from the UK Biobank, we test whether schizophrenia polygenic risk scores (PRS) limited to specific gene-sets predict subcortical brain volumes. We compare associations with schizophrenia PRS at the whole genome level (‘genomic’, including all SNPs associated with the disorder at a p-value threshold < 0.05) with ‘genic’ PRS (based on SNPs in the vicinity of known genes), ‘intergenic’ PRS (based on the remaining SNPs), and genic PRS limited to SNPs within 7 gene-sets previously found to be enriched for genetic association with schizophrenia (‘abnormal behaviour,’ ‘abnormal long-term potentiation,’ ‘abnormal nervous system electrophysiology,’ ‘FMRP targets,’ ‘5HT2C channels,’ ‘CaV2 channels’ and ‘loss-of-function intolerant genes’). We observe a negative association between the ‘abnormal behaviour’ gene-set PRS and volume of the right thalamus that survived correction for multiple testing (ß = −0.031, pFDR = 0.005) and was robust to different schizophrenia PRS p-value thresholds. In contrast, the only association with genomic PRS surviving correction for multiple testing was for right pallidum, which was observed using a schizophrenia PRS p-value threshold < 0.01 (ß = −0.032, p = 0.0003, pFDR = 0.02), but not when using other PRS P-value thresholds. We conclude that schizophrenia PRS limited to functional gene sets may provide a better means of capturing differences in subcortical brain volume than whole genome PRS approaches.

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Schizophrenia-associated genomic copy number variants and subcortical brain volumes in the UK Biobank

Molecular Psychiatry ◽

10.1038/s41380-019-0355-y ◽

2019 ◽

Vol 25 (4) ◽

pp. 854-862 ◽

Cited By ~ 16

Author(s):

Anthony Warland ◽

Kimberley M. Kendall ◽

Elliott Rees ◽

George Kirov ◽

Xavier Caseras

Keyword(s):

Copy Number ◽

Copy Number Variants ◽

Uk Biobank ◽

Brain Volumes ◽

Genomic Copy Number ◽

Subcortical Brain ◽

Genomic Copy ◽

The Uk

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Association of Whole-Genome and NETRIN1 Signaling Pathway–Derived Polygenic Risk Scores for Major Depressive Disorder and White Matter Microstructure in the UK Biobank

Biological Psychiatry Cognitive Neuroscience and Neuroimaging ◽

10.1016/j.bpsc.2018.07.006 ◽

2019 ◽

Vol 4 (1) ◽

pp. 91-100 ◽

Cited By ~ 4

Author(s):

Miruna C. Barbu ◽

Yanni Zeng ◽

Xueyi Shen ◽

Simon R. Cox ◽

Toni-Kim Clarke ◽

...

Keyword(s):

Major Depressive Disorder ◽

White Matter ◽

Depressive Disorder ◽

Risk Scores ◽

Whole Genome ◽

Uk Biobank ◽

Major Depressive ◽

Polygenic Risk ◽

White Matter Microstructure ◽

The Uk

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Association of polygenic risk for major psychiatric illness with subcortical volumes and white matter integrity in UK Biobank

10.1101/080283 ◽

2016 ◽

Cited By ~ 1

Author(s):

LM Reus ◽

X Shen ◽

J Gibson ◽

E Wigmore ◽

L Ligthart ◽

...

Keyword(s):

White Matter ◽

Psychiatric Disorders ◽

Brain Regions ◽

Uk Biobank ◽

Imaging Data ◽

Brain Volumes ◽

Polygenic Risk ◽

Subcortical Brain ◽

The Impact ◽

Subcortical Volumes

AbstractMajor depressive disorder (MDD), schizophrenia (SCZ) and bipolar disorder (BP) are common, disabling and heritable psychiatric diseases with a complex overlapping polygenic architecture. Individuals with these disorders, as well as their unaffected relatives, show widespread structural differences in corticostriatal and limbic networks. Structural variation in many of these brain regions is also heritable and polygenic but whether their genetic architecture overlaps with major psychiatric disorders is unknown. We sought to address this issue by examining the impact of polygenic risk of MDD, SCZ, and BP on subcortical brain volumes and white matter (WM) microstructure in a large single sample of neuroimaging data; the UK Biobank Imaging study. The first release of UK Biobank imaging data compromised participants with overlapping genetic data and subcortical volumes (N = 978) and WM measures (N = 816). Our, findings however, indicated no statistically significant associations between either subcortical volumes or WM microstructure, and polygenic risk for MDD, SCZ or BP. In the current study, we found little or no evidence for genetic overlap between major psychiatric disorders and structural brain measures. These findings suggest that subcortical brain volumes and WM microstructure may not be closely linked to the genetic mechanisms of major psychiatric disorders.

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Schizophrenia-associated genomic copy number variants and subcortical brain volumes in the UK Biobank

10.1101/374678 ◽

2018 ◽

Cited By ~ 1

Author(s):

Anthony Warland ◽

Kimberley M Kendall ◽

Elliott Rees ◽

George Kirov ◽

Xavier Caseras

Keyword(s):

Copy Number ◽

Copy Number Variants ◽

Risk Groups ◽

Clinical Samples ◽

Uk Biobank ◽

Subcortical Structures ◽

Brain Volumes ◽

Subcortical Brain ◽

Heritable Disorder ◽

The Uk

AbstractSchizophrenia is a highly heritable disorder for which anatomical brain alterations have been repeatedly reported in clinical samples. Unaffected at-risk groups have also been studied in an attempt to identify brain changes that do not reflect reverse causation or treatment effects. However, no robust associations have been observed between neuroanatomical phenotypes and known genetic risk factors for schizophrenia. We tested subcortical brain volume differences between 49 unaffected participants carrying at least one of the 12 copy number variants associated with schizophrenia in UK Biobank and 9,063 individuals who did not carry any of the 93 copy number variants reported to be pathogenic. Our results show that CNV carriers have reduced volume in some of the subcortical structures previously shown to be reduced in schizophrenia. Moreover, these associations were partially accounted for by the association between pathogenic copy number variants and cognitive impairment, which is one of the features of schizophrenia.

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Analysis of genetic dominance in the UK Biobank

10.1101/2021.08.15.456387 ◽

2021 ◽

Author(s):

Duncan S Palmer ◽

Wei Zhou ◽

Liam Abbott ◽

Nik Baya ◽

Claire Churchhouse ◽

...

Keyword(s):

Complex Traits ◽

Multiple Testing ◽

Model Organisms ◽

Systematic Evaluation ◽

Hair Color ◽

Phenotypic Variance ◽

Additive Effects ◽

Uk Biobank ◽

Genome Wide ◽

The Uk

In classical statistical genetic theory, a dominance effect is defined as the deviation from a purely additive genetic effect for a biallelic variant. Dominance effects are well documented in model organisms. However, evidence in humans is limited to a handful of traits, particularly those with strong single locus effects such as hair color. We carried out the largest systematic evaluation of dominance effects on phenotypic variance in the UK Biobank. We curated and tested over 1,000 phenotypes for dominance effects through GWAS scans, identifying 175 loci at genome-wide significance correcting for multiple testing (P < 4.7 × 10-11). Power to detect non-additive loci is much lower than power to detect additive effects for complex traits: based on the relative effect sizes at genome-wide significant additive loci, we estimate a factor of 20-30 increase in sample size will be necessary to capture clear evidence of dominance similar to those currently observed for additive effects. However, these localised dominance hits do not extend to a significant aggregate contribution to phenotypic variance genome-wide. By deriving a version of LD-score regression to detect dominance effects tagged by common variation genome-wide (minor allele frequency > 0.05), we found no strong evidence of a contribution to phenotypic variance when accounting for multiple testing. Across the 267 continuous and 793 binary traits the median contribution was 5.73 × 10-4, with unbiased point estimates ranging from -0.261 to 0.131. Finally, we introduce dominance fine-mapping to explore whether the more rapid decay of dominance LD can be leveraged to find causal variants. These results provide the most comprehensive assessment of dominance trait variation in humans to date.

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Association of accelerometer-derived sleep measures with lifetime psychiatric diagnoses: A cross-sectional study of 89,205 participants from the UK Biobank

PLoS Medicine ◽

10.1371/journal.pmed.1003782 ◽

2021 ◽

Vol 18 (10) ◽

pp. e1003782

Author(s):

Michael Wainberg ◽

Samuel E. Jones ◽

Lindsay Melhuish Beaupre ◽

Sean L. Hill ◽

Daniel Felsky ◽

...

Keyword(s):

Bipolar Disorder ◽

Sleep Duration ◽

Sleep Efficiency ◽

Risk Scores ◽

Psychiatric Diagnoses ◽

Uk Biobank ◽

Major Depressive ◽

Cross Sectional ◽

Polygenic Risk ◽

The Uk

Background Sleep problems are both symptoms of and modifiable risk factors for many psychiatric disorders. Wrist-worn accelerometers enable objective measurement of sleep at scale. Here, we aimed to examine the association of accelerometer-derived sleep measures with psychiatric diagnoses and polygenic risk scores in a large community-based cohort. Methods and findings In this post hoc cross-sectional analysis of the UK Biobank cohort, 10 interpretable sleep measures—bedtime, wake-up time, sleep duration, wake after sleep onset, sleep efficiency, number of awakenings, duration of longest sleep bout, number of naps, and variability in bedtime and sleep duration—were derived from 7-day accelerometry recordings across 89,205 participants (aged 43 to 79, 56% female, 97% self-reported white) taken between 2013 and 2015. These measures were examined for association with lifetime inpatient diagnoses of major depressive disorder, anxiety disorders, bipolar disorder/mania, and schizophrenia spectrum disorders from any time before the date of accelerometry, as well as polygenic risk scores for major depression, bipolar disorder, and schizophrenia. Covariates consisted of age and season at the time of the accelerometry recording, sex, Townsend deprivation index (an indicator of socioeconomic status), and the top 10 genotype principal components. We found that sleep pattern differences were ubiquitous across diagnoses: each diagnosis was associated with a median of 8.5 of the 10 accelerometer-derived sleep measures, with measures of sleep quality (for instance, sleep efficiency) generally more affected than mere sleep duration. Effect sizes were generally small: for instance, the largest magnitude effect size across the 4 diagnoses was β = −0.11 (95% confidence interval −0.13 to −0.10, p = 3 × 10−56, FDR = 6 × 10−55) for the association between lifetime inpatient major depressive disorder diagnosis and sleep efficiency. Associations largely replicated across ancestries and sexes, and accelerometry-derived measures were concordant with self-reported sleep properties. Limitations include the use of accelerometer-based sleep measurement and the time lag between psychiatric diagnoses and accelerometry. Conclusions In this study, we observed that sleep pattern differences are a transdiagnostic feature of individuals with lifetime mental illness, suggesting that they should be considered regardless of diagnosis. Accelerometry provides a scalable way to objectively measure sleep properties in psychiatric clinical research and practice, even across tens of thousands of individuals.

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Analysis of 200,000 exome-sequenced UK Biobank subjects fails to identify genes influencing probability of developing a mood disorder resulting in psychiatric referral

10.1101/2021.01.07.20249042 ◽

2021 ◽

Author(s):

David Curtis

Keyword(s):

Affective Disorder ◽

Multiple Testing ◽

Major Effect ◽

Self Report ◽

Uk Biobank ◽

Recurrent Depression ◽

Functional Variants ◽

Gene Sets ◽

Or Gene ◽

Genetic Contributions

AbstractBackgroundDepression is moderately heritable but there is no common genetic variant which has a major effect on susceptibility. A previous analysis of 50,000 subjects failed to implicate any genes or sets of genes associated with risk of affective disorder requiring specialist treatment. A large exome-sequenced dataset is now available.MethodsData from 200,632 exome-sequenced UK Biobank participants was analysed. Subjects were treated as cases if they had reported having seen a psychiatrist for “nerves, anxiety, tension or depression”. Gene-wise weighted burden analysis was performed to see if there were any genes or sets of genes for which there was an excess of rare, functional variants in cases.ResultsThere were 22,886 cases and 176,486 controls. There were 22,642 informative genes but no gene or gene set produced a statistically significant result after correction for multiple testing. None of the genes or gene sets with the lowest p values appeared to be a biologically plausible candidate.LimitationsThe phenotype is based on self-report and the cases are likely to somewhat heterogeneous. Likewise, it is expected that some of the subjects classed as controls will in fact have suffered from depression or some other psychiatric diagnosis.ConclusionsThe results conform exactly with the expectation under the null hypothesis. It seems unlikely that the use of common, poorly defined phenotypes will produce useful advances in understanding genetic contributions to affective disorder and it might be preferable to focus instead on obtaining large exome-sequenced samples of conditions such as bipolar 1 disorder and severe, recurrent depression.

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Abstract P147: Genome Wide Assessment of Shared Genetic Architecture Between Rheumatoid Arthritis and Cardiovascular Diseases Using the UK Biobank Data

Circulation ◽

10.1161/circ.141.suppl_1.p147 ◽

2020 ◽

Vol 141 (Suppl_1) ◽

Author(s):

Yanjun Guo ◽

Wonil Chung ◽

Zhilei Shan ◽

Liming Liang

Keyword(s):

Cardiovascular Diseases ◽

Genetic Correlation ◽

Multiple Testing ◽

Mendelian Randomization ◽

Meta Analysis ◽

Uk Biobank ◽

Increased Risk ◽

The Uk ◽

Cardiovascular Traits ◽

Shared Genetic

Background: Patients with RA have a 2-10 folds increased risk of cardiovascular diseases (CVD) and CVD accounts for almost 50% of the excess mortality in patients with RA when compared with general population, but the mechanisms underlying such associations are largely unknown. Methods: We examined the genetic correlation, causality, and shared genetic variants between RA (Ncase=6,756, Ncontrol=452,476) and CVD (Ncase=44,246, Ncontrol=414,986) using LD Score regression (LDSC), generalized summary-data-based Mendelian Randomization (GSMR), and cross-trait meta-analysis in the UK Biobank Data. Results: In the present study, RA was significantly genetically correlated with MI, angina, CHD, and CVD after correcting for multiple testing (Rg ranges from 0.40 to 0.43, P<0.05/5). Interestingly, when stratified by frequent usage of aspirin and paracetamol, we observed increased genetic correlation between RA and CVD for participants without aspirin usage ( Rg increased to 0.54 [95%CI: 0.54, 0.78] for angina; P value=6.69х10 -6 ), and for participants with usage of paracetamol ( Rg increased to 0.75 [95%CI: 0.20, 1.29] for MI; P value=8.90х10 -3 ). Cross-trait meta-analysis identified 9 independent loci that were shared between RA and at least one of the genetically correlated CVD traits including PTPN22 at chr1p13.2 , BCL2L11 at chr2q13 , and CCR3 at chr3p21.31 ( P single trait <1х10 -3 and P meta <5х10 -8 ) highlighting potential shared etiology between them which include accelerating atherosclerosis and upregulating oxidative stress and vascular permeability. Finally, Mendelian randomization analyses observed inconsistent instrumental effects and were unable to conclude the causality and directionality between RA and CVD. Conclusion: Our results supported positive genetic correlation between RA and multiple cardiovascular traits, and frequent usage of aspirin and paracetamol may modify their associations, but instrumental analyses were unable to conclude the causality and directionality between them.

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