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

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.

scholarly journals Analysis of 50,000 exome-sequenced UK Biobank subjects fails to identify genes influencing probability of psychiatric referral

2020 ◽  
Author(s):  
David Curtis
Keyword(s):  
Multiple Testing ◽  
Rare Variants ◽  
Sequence Data ◽  
Statistical Significance ◽  
Major Effect ◽  
Self Report ◽  
Uk Biobank ◽  
Functional Variants ◽  
Exome Sequence Data ◽  
Exome Sequence

Background Depression is moderately heritable but there is no common genetic variant which has a major effect on susceptibility. It is possible that some very rare variants could have substantial effect sizes and these could be identified from exome sequence data. Methods Data from 50,000 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. Results There were 5,872 cases and 43,862 controls. There were 22,028 informative genes but none produced a statistically significant result after correction for multiple testing. Of the 25 genes individually significant at p<0.001 none appeared to be a biologically plausible candidate. No set of genes achieved statistical significance after correction for multiple testing and those with the lowest p values again did not appear to be biologically plausible candidates. Limitations The phenotype is based on self-report and the cases are likely to somewhat heterogeneous. The number of cases is on the low side for a study of exome sequence data. Conclusions The results conform exactly with the expectation under the null hypothesis. It seems unlikely that depression genetics research will produce findings that might have a substantial clinical impact until far larger samples become available.

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

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Steluta Grama ◽  
Isabella Willcocks ◽  
John J. Hubert ◽  
Antonio F. Pardiñas ◽  
Sophie E. Legge ◽  
...  
Keyword(s):  
Multiple Testing ◽  
Specific Gene ◽  
Whole Genome ◽  
Uk Biobank ◽  
Abnormal Behaviour ◽  
Brain Volumes ◽  
Polygenic Risk ◽  
Subcortical Brain ◽  
Gene Sets ◽  
The Uk

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.

scholarly journals Longitudinal investigation into childhood-and adolescence-onset depression: Psychiatric outcome in early adulthood

2006 ◽  
Vol 188 (3) ◽  
pp. 216-222 ◽  
Author(s):  
Valerie Dunn ◽  
Ian M. Goodyer
Keyword(s):  
Affective Disorder ◽  
Clinical Sample ◽  
Early Adulthood ◽  
Self Report ◽  
Clinical Samples ◽  
Childhood And Adolescence ◽  
Recurrent Depression ◽  
Severely Mentally Ill ◽  
Persistent Depression ◽  
Psychiatric Outcome

BackgroundMajor depression in childhood or adolescence increases the risk of affective disorder in adulthood. The precise nature and course of the subsequent disorder remain unclear.AimsTo investigate long-term psychiatric outcome of school-age depression in community and clinic samples.MethodA group of 113 young adults were followed up after a mean of 7.8 years (s.e.=15).ResultsGroups with persistent and recurrent depression were identified. Recurrence of affective disorder was similar in clinic and community groups. The clinic group had significantly longer index episodes; these were predicted by an early psychiatric history, longer episode duration before treatment and greater impairment. Being female, having higher self-report depression scores and comorbidity at index episode predicted earlier recurrence. Males were more likely to have persistent depression.ConclusionsPrognosis is similar in young people with depression from community and clinical samples. Boys from a clinical sample are at higher risk than girls of becoming persistently and severely mentally ill.

scholarly journals Weighted burden analysis in 200 000 exome-sequenced UK Biobank subjects characterises effects of rare genetic variants on BMI

2021 ◽  
Author(s):  
David Curtis
Keyword(s):  
Multiple Testing ◽  
Rare Variants ◽  
Sequence Data ◽  
Statistical Significance ◽  
Positive Sign ◽  
P Value ◽  
Uk Biobank ◽  
Functional Variants ◽  
Rare Genetic Variants ◽  
The Uk

AbstractIntroductionA number of genes have been identified in which rare variants can cause obesity. Here we analyse a sample of exome sequenced subjects from UK Biobank using BMI as a phenotype.MethodsThere were 199,807 exome sequenced subjects for whom BMI was recorded. Weighted burden analysis of rare, functional variants was carried out, incorporating population principal components and sex as covariates. For selected genes, additional analyses were carried out to clarify the contribution of different categories of variant. Statistical significance was summarised as the signed log 10 of the p value (SLP), given a positive sign if the weighted burden score was positively correlated with BMI.ResultsTwo genes were exome-wide significant, MC4R (SLP = 15.79) and PCSK1 (SLP = 6.61). In MC4R, disruptive variants were associated with an increase in BMI of 2.72 units and probably damaging nonsynonymous variants with an increase of 2.02 units. In PCSK1, disruptive variants were associated with a BMI increase of 2.29 and protein-altering variants with an increase of 0.34. Results for other genes were not formally significant after correction for multiple testing, although SIRT1, ZBED6 and NPC2 were noted to be of potential interest.ConclusionBecause the UK Biobank consists of a self-selected sample of relatively healthy volunteers, the effect sizes noted may be underestimates. The results demonstrate the effects of very rare variants on BMI and suggest that other genes and variants will be definitively implicated when the sequence data for additional subjects becomes available.This research has been conducted using the UK Biobank Resource.

Seasonal Affective Disorder in Australia: Is Photoperiod Critical?

1997 ◽  
Vol 31 (2) ◽  
pp. 279-284 ◽  
Author(s):  
Gregory W. Murray ◽  
David A. Hay
Keyword(s):  
Affective Disorder ◽  
Seasonal Affective Disorder ◽  
Survey Design ◽  
Seasonal Pattern ◽  
General Health Questionnaire ◽  
Epidemiological Survey ◽  
Self Report ◽  
Recurrent Depression ◽  
Sampling Frame ◽  
Assessment Questionnaire

Objectives: Seasonal affective disorder (SAD) is a variant of recurrent depression in which episodes are linked to a particular season, typically winter. SAD is understood as the extreme end of a continuum of seasonality in the general population. Photoperiod (the timing and duration of daylight) has been assumed to be aetiologically critical. The present research used a survey design to investigate the assumed centrality of photoperiod for SAD/seasonality in Australia. Two hypotheses were tested: that self-reported seasonality does not increase further from the equator and that seasonality does not stand alone from non-seasonal neurotic complaints. Method: The sampling frame used was adult females on the Australian Twin Registry roll. A sample of 526 women residing across the latitudes of Australia responded to a survey based around the Seasonal Pattern Assessment Questionnaire (SPAQ). The SPAQ asks respondents to retrospectively report on season-related changes in mood and behaviour. The survey also contained three questionnaire measures of neurotic symptoms of anxiety and depression: the General Health Questionnaire (GHQ), the Community Epidemiological Survey for Depression (CES-D) and the State-Trait Anxiety Inventory—Trait (STAI-T). Results: Self-reported seasonality did not correlate with latitude (r=0.01, NS). On the other hand, a substantial relationship was found between seasonality and each of the measures of non-seasonal complaints: GHQ (r=0.35, p<0.001); CES-D (r=0.35, p<0.001); and STAI-T (r=0.30, p<0.001). Conclusions: Within the limitations of a design based on retrospective self-report, the findings of the present study suggest that the diathesis for SAD/seasonality may not be photoperiod-specific. At least in Australia, there is provisional support for the proposal that human seasonality may have a broader psychological component. The findings are discussed in terms of established research into normal mood, trait personality and non-seasonal depression.

scholarly journals 699 Sleep Health Traits and COVID-19: Mortality Risk from the UK Biobank

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A273-A273
Author(s):  
Xi Zheng ◽  
Ma Cherrysse Ulsa ◽  
Peng Li ◽  
Lei Gao ◽  
Kun Hu
Keyword(s):  
Risk Factors ◽  
Sleep Apnea ◽  
Sleep Duration ◽  
Sex Education ◽  
Mortality Risk ◽  
Self Report ◽  
Severe Illness ◽  
Uk Biobank ◽  
Sleep Health ◽  
The Uk

Abstract Introduction While there is emerging evidence for acute sleep disruption in the aftermath of coronavirus disease 2019 (COVID-19), it is unknown whether sleep traits contribute to mortality risk. In this study, we tested whether earlier-life sleep duration, chronotype, insomnia, napping or sleep apnea were associated with increased 30-day COVID-19 mortality. Methods We included 34,711 participants from the UK Biobank, who presented for COVID-19 testing between March and October 2020 (mean age at diagnosis: 69.4±8.3; range 50.2–84.6). Self-reported sleep duration (less than 6h/6-9h/more than 9h), chronotype (“morning”/”intermediate”/”evening”), daytime dozing (often/rarely), insomnia (often/rarely), napping (often/rarely) and presence of sleep apnea (ICD-10 or self-report) were obtained between 2006 and 2010. Multivariate logistic regression models were used to adjust for age, sex, education, socioeconomic status, and relevant risk factors (BMI, hypertension, diabetes, respiratory diseases, smoking, and alcohol). Results The mean time between sleep measures and COVID-19 testing was 11.6±0.9 years. Overall, 5,066 (14.6%) were positive. In those who were positive, 355 (7.0%) died within 30 days (median = 8) after diagnosis. Long sleepers (&gt;9h vs. 6-9h) [20/103 (19.4%) vs. 300/4,573 (6.6%); OR 2.09, 95% 1.19–3.64, p=0.009), often daytime dozers (OR 1.68, 95% 1.04–2.72, p=0.03), and nappers (OR 1.52, 95% 1.04–2.23, p=0.03) were at greater odds of mortality. Prior diagnosis of sleep apnea also saw a two-fold increased odds (OR 2.07, 95% CI: 1.25–3.44 p=0.005). No associations were seen for short sleepers, chronotype or insomnia with COVID-19 mortality. Conclusion Data across all current waves of infection show that prior sleep traits/disturbances, in particular long sleep duration, daytime dozing, napping and sleep apnea, are associated with increased 30-day mortality after COVID-19, independent of health-related risk factors. While sleep health traits may reflect unmeasured poor health, further work is warranted to examine the exact underlying mechanisms, and to test whether sleep health optimization offers resilience to severe illness from COVID-19. Support (if any) NIH [T32GM007592 and R03AG067985 to L.G. RF1AG059867, RF1AG064312, to K.H.], the BrightFocus Foundation A2020886S to P.L. and the Foundation of Anesthesia Education and Research MRTG-02-15-2020 to L.G.

scholarly journals Peripheral Blood Gene Expression and IgG Glycosylation Profiles as Markers of Tocilizumab Treatment in Rheumatoid Arthritis

2012 ◽  
Vol 39 (5) ◽  
pp. 916-928 ◽  
Author(s):  
BERTALAN MESKO ◽  
SZILARD POLISKA ◽  
SZILVIA SZAMOSI ◽  
ZOLTAN SZEKANECZ ◽  
JANOS PODANI ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Peripheral Blood ◽  
Multiple Testing ◽  
Mononuclear Cells ◽  
Expression Profiles ◽  
Quantitative Polymerase Chain Reaction ◽  
Response To Treatment ◽  
Peripheral Blood Mononuclear ◽  
Gene Sets

Objective.Tocilizumab, a humanized anti-interleukin-6 receptor monoclonal antibody, has recently been approved as a biological therapy for rheumatoid arthritis (RA) and other diseases. It is not known if there are characteristic changes in gene expression and immunoglobulin G glycosylation during therapy or in response to treatment.Methods.Global gene expression profiles from peripheral blood mononuclear cells of 13 patients with RA and active disease at Week 0 (baseline) and Week 4 following treatment were obtained together with clinical measures, serum cytokine levels using ELISA, and the degree of galactosylation of the IgG N-glycan chains. Gene sets separating responders and nonresponders were tested using canonical variates analysis. This approach also revealed important gene groups and pathways that differentiate responders from nonresponders.Results.Fifty-nine genes showed significant differences between baseline and Week 4 and thus correlated with treatment. Significantly, 4 genes determined responders after correction for multiple testing. Ten of the 12 genes with the most significant changes were validated using real-time quantitative polymerase chain reaction. An increase in the terminal galactose content of N-linked glycans of IgG was observed in responders versus nonresponders, as well as in treated samples versus samples obtained at baseline.Conclusion.As a preliminary report, gene expression changes as a result of tocilizumab therapy in RA were examined, and gene sets discriminating between responders and nonresponders were found and validated. A significant increase in the degree of galactosylation of IgG N-glycans in patients with RA treated with tocilizumab was documented.

scholarly journals Analysis of genetic dominance in the UK Biobank

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.

scholarly journals Microarray Data Analysis: From Hypotheses to Conclusions Using Gene Expression Data

2004 ◽  
Vol 26 (5-6) ◽  
pp. 279-290
Author(s):  
Nicola J. Armstrong ◽  
Mark A. van de Wiel
Keyword(s):  
Microarray Data ◽  
Biological Networks ◽  
Multiple Testing ◽  
Gene Selection ◽  
Statistical Testing ◽  
Microarray Data Analysis ◽  
Gene Sets ◽  
New Development ◽  
Statistical Issues ◽  
Critical Interpretation

We review several commonly used methods for the design and analysis of microarray data. To begin with, some experimental design issues are addressed. Several approaches for pre‐processing the data (filtering and normalization) before the statistical analysis stage are then discussed. A common first step in this type of analysis is gene selection based on statistical testing. Two approaches, permutation and model‐based methods are explained and we emphasize the need to correct for multiple testing. Moreover, powerful approaches based on gene sets are mentioned. Clustering of either genes or samples is frequently performed when analyzing microarray data. We summarize the basics of both supervised and unsupervised clustering (classification). The latter may be of use for creating diagnostic arrays, for example. Construction of biological networks, such as pathways, is a statistically challenging but complex task that is a relatively new development and hence mentioned only briefly. We finish with some remarks on literature and software. The emphasis in this paper is on the philosophy behind several statistical issues and on a critical interpretation of microarray related analysis methods.

scholarly journals Detecting retinal neurodegeneration in people with diabetes: Findings from the UK Biobank

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257836
Author(s):  
Roomasa Channa ◽  
Kyungmoo Lee ◽  
Kristen A. Staggers ◽  
Nitish Mehta ◽  
Sidra Zafar ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Glycated Hemoglobin ◽  
Retinal Thickness ◽  
Self Report ◽  
Inner Plexiform Layer ◽  
Uk Biobank ◽  
Fiber Layer ◽  
Cross Sectional ◽  
Retinal Neurodegeneration ◽  
The Uk

Importance Efforts are underway to incorporate retinal neurodegeneration in the diabetic retinopathy severity scale. However, there is no established measure to quantify diabetic retinal neurodegeneration (DRN). Objective We compared total retinal, macular retinal nerve fiber layer (mRNFL) and ganglion cell-inner plexiform layer (GC-IPL) thickness among participants with and without diabetes (DM) in a population-based cohort. Design/setting/participants Cross-sectional analysis, using the UK Biobank data resource. Separate general linear mixed models (GLMM) were created using DM and glycated hemoglobin as predictor variables for retinal thickness. Sub-analyses included comparing thickness measurements for patients with no/mild diabetic retinopathy (DR) and evaluating factors associated with retinal thickness in participants with and without diabetes. Factors found to be significantly associated with DM or thickness were included in a multiple GLMM. Exposure Diagnosis of DM was determined via self-report of diagnosis, medication use, DM-related complications or glycated hemoglobin level of ≥ 6.5%. Main outcomes and measures Total retinal, mRNFL and GC-IPL thickness. Results 74,422 participants (69,985 with no DM; 4,437 with DM) were included. Median age was 59 years, 46% were men and 92% were white. Participants with DM had lower total retinal thickness (-4.57 μm, 95% CI: -5.00, -4.14; p<0.001), GC-IPL thickness (-1.73 μm, 95% CI: -1.86, -1.59; p<0.001) and mRNFL thickness (-0.68 μm, 95% CI: -0.81, -0.54; p<0.001) compared to those without DM. After adjusting for co-variates, in the GLMM, total retinal thickness was 1.99 um lower (95% CI: -2.47, -1.50; p<0.001) and GC-IPL was 1.02 μm lower (95% CI: -1.18, -0.87; p<0.001) among those with DM compared to without. mRNFL was no longer significantly different (p = 0.369). GC-IPL remained significantly lower, after adjusting for co-variates, among those with DM compared to those without DM when including only participants with no/mild DR (-0.80 μm, 95% CI: -0.98, -0.62; p<0.001). Total retinal thickness decreased 0.40 μm (95% CI: -0.61, -0.20; p<0.001), mRNFL thickness increased 0.20 μm (95% CI: 0.14, 0.27; p<0.001) and GC-IPL decreased 0.26 μm (95% CI: -0.33, -0.20; p<0.001) per unit increase in A1c after adjusting for co-variates. Among participants with diabetes, age, DR grade, ethnicity, body mass index, glaucoma, spherical equivalent, and visual acuity were significantly associated with GC-IPL thickness. Conclusion GC-IPL was thinner among participants with DM, compared to without DM. This difference persisted after adjusting for confounding variables and when considering only those with no/mild DR. This confirms that GC-IPL thinning occurs early in DM and can serve as a useful marker of DRN.

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