scholarly journals Effects of pathogenic CNVs on biochemical markers: a study on the UK Biobank

2019 ◽  
Author(s):  
Matthew Bracher-Smith ◽  
Kimberley M Kendall ◽  
Elliott Rees ◽  
Mark Einon ◽  
Michael C O’Donovan ◽  
...  
Keyword(s):  
Statistical Power ◽  
Linear Regression Analysis ◽  
Copy Number Variants ◽  
Uk Biobank ◽  
Rare Cnvs ◽  
Medical Disorders ◽  
Affymetrix Microarrays ◽  
Increase Risk ◽  
Pathogenic Cnvs ◽  
The Uk

ABSTRACTBackgroundPathogenic copy number variants (CNVs) increase risk for medical disorders, even among carriers free from neurodevelopmental disorders. The UK Biobank recruited half a million adults who provided samples for biochemical and haematology tests which have recently been released. We wanted to assess how the presence of pathogenic CNVs affects these biochemical test results.MethodsWe called all CNVs from the Affymetrix microarrays and selected a set of 54 CNVs implicated as pathogenic (including their reciprocal deletions/duplications) and present in five or more persons. We used linear regression analysis to establish their association with 28 biochemical and 23 haematology tests.ResultsWe analysed 421k participants who passed our CNV quality control filters and self-reported as white British or Irish descent. There were 268 associations between CNVs and biomarkers that were significant at a false discovery rate of 0.05. Deletions at 16p11.2 had the highest number of significant associations, but several rare CNVs had higher effect sizes indicating that the lack of significance was likely due to the reduced statistical power for rarer events. The distribution of values can be visualised on our interactive website: http://kirov.psycm.cf.ac.uk/.ConclusionsCarriers of many pathogenic CNVs have changes in biochemical and haematology tests, and many of those are associated with adverse health consequences. These changes did not always correlate with increases in diagnosed medical disorders in this population. Carriers should have regular blood tests in order to identify and treat adverse medical consequences early. Levels of cholesterol and related lipids were unexpectedly lower in carriers of CNVs associated with increased weight gain, most likely due to the use of statins by such people.

scholarly journals Effects of pathogenic CNVs on physical traits in participants of the UK Biobank

2018 ◽  
Author(s):  
David Owen ◽  
Mathew Bracher-Smith ◽  
Kimberley Kendall ◽  
Elliott Rees ◽  
Mark Einon ◽  
...  
Keyword(s):  
Linear Regression Analysis ◽  
Copy Number Variants ◽  
Mirror Image ◽  
Therapeutic Interventions ◽  
Uk Biobank ◽  
Adverse Health Outcomes ◽  
Medical Disorders ◽  
Physical Measurements ◽  
Physical Traits ◽  
The Uk

ABSTRACTBackgroundCopy number variants (CNVs) have been shown to increase risk for physical anomalies, developmental, psychiatric and medical disorders. Some of them have been associated with changes in weight, height, and other physical traits. As most studies have been performed on children and young people, these effects of CNVs in adulthood are not well established.MethodsThe UK Biobank recruited half a million adults who provided a variety of physical measurements. We called all CNVs from the Affymetrix microarrays and selected a set of 54 CNVs implicated as pathogenic (including their reciprocal deletions/duplications) and that were present in five or more persons. Linear regression analysis was used to establish their association with 16 physical traits, relevant to human health.Results396,725 participants of white British or Irish descent (excluding first-degree relatives) passed our quality control filters. There were 214 CNV/trait associations significant at a false discovery rate of 0.1, most of them novel. These traits are associated with adverse health outcomes: e.g. increased weight, waist-to-hip ratio, pulse rate and body fat composition. Deletions at 16p11.2, 16p12.1, NRXN1 and duplications at 16p13.11 and 22q11.2 produced the highest numbers of significant associations. CNVs at 1q21.1, 2q13, 16p11.2, 16p11.2 distal, 16p12.1, 17p12 and 17q12 demonstrated one or more mirror image effects of deletions versus duplications.ConclusionsCarriers of many CNVs should be monitored for physical traits that increase morbidity and mortality. Genes within these CNVs can give insights into biological processes and therapeutic interventions.

scholarly journals Estimating the effective sample size in association studies of quantitative traits

2021 ◽  
Author(s):  
Andrey Ziyatdinov ◽  
Jihye Kim ◽  
Dmitry Prokopenko ◽  
Florian Privé ◽  
Fabien Laporte ◽  
...  
Keyword(s):  
Statistical Power ◽  
Quantitative Traits ◽  
Mixed Model ◽  
Association Studies ◽  
Effective Sample Size ◽  
Environment Interaction ◽  
Uk Biobank ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
The Uk

Abstract The effective sample size (ESS) is a metric used to summarize in a single term the amount of correlation in a sample. It is of particular interest when predicting the statistical power of genome-wide association studies (GWAS) based on linear mixed models. Here, we introduce an analytical form of the ESS for mixed-model GWAS of quantitative traits and relate it to empirical estimators recently proposed. Using our framework, we derived approximations of the ESS for analyses of related and unrelated samples and for both marginal genetic and gene-environment interaction tests. We conducted simulations to validate our approximations and to provide a quantitative perspective on the statistical power of various scenarios, including power loss due to family relatedness and power gains due to conditioning on the polygenic signal. Our analyses also demonstrate that the power of gene-environment interaction GWAS in related individuals strongly depends on the family structure and exposure distribution. Finally, we performed a series of mixed-model GWAS on data from the UK Biobank and confirmed the simulation results. We notably found that the expected power drop due to family relatedness in the UK Biobank is negligible.

scholarly journals Polygenic risk for schizophrenia and season of birth within the UK Biobank cohort

2018 ◽  
Vol 49 (15) ◽  
pp. 2499-2504 ◽  
Author(s):  
Valentina Escott-Price ◽  
Daniel J. Smith ◽  
Kimberley Kendall ◽  
Joey Ward ◽  
George Kirov ◽  
...  
Keyword(s):  
Environmental Exposure ◽  
Copy Number Variants ◽  
Season Of Birth ◽  
Uk Biobank ◽  
Month Of Birth ◽  
Risk Alleles ◽  
Gene Environment ◽  
Increased Risk ◽  
The Uk ◽  
Pathogenic Process

AbstractBackgroundThere is strong evidence that people born in winter and in spring have a small increased risk of schizophrenia. As this ‘season of birth’ effect underpins some of the most influential hypotheses concerning potentially modifiable risk exposures, it is important to exclude other possible explanations for the phenomenon.MethodsHere we sought to determine whether the season of birth effect reflects gene-environment confounding rather than a pathogenic process indexing environmental exposure. We directly measured, in 136 538 participants from the UK Biobank (UKBB), the burdens of common schizophrenia risk alleles and of copy number variants known to increase the risk for the disorder, and tested whether these were correlated with a season of birth.ResultsNeither genetic measure was associated with season or month of birth within the UKBB sample.ConclusionsAs our study was highly powered to detect small effects, we conclude that the season of birth effect in schizophrenia reflects a true pathogenic effect of environmental exposure.

scholarly journals Altered Cortical Brain Structure and Increased Risk for Disease Seen Decades After Perinatal Exposure to Maternal Smoking: A Study of 9000 Adults in the UK Biobank

2019 ◽  
Vol 29 (12) ◽  
pp. 5217-5233 ◽  
Author(s):  
Lauren E Salminen ◽  
Rand R Wilcox ◽  
Alyssa H Zhu ◽  
Brandalyn C Riedel ◽  
Christopher R K Ching ◽  
...  
Keyword(s):  
Brain Structure ◽  
Brain Mri ◽  
Population Based ◽  
Smoke Exposure ◽  
Sensitivity Analyses ◽  
Uk Biobank ◽  
Increased Risk ◽  
Increase Risk ◽  
Sensory Cortices ◽  
The Uk

Abstract Secondhand smoke exposure is a major public health risk that is especially harmful to the developing brain, but it is unclear if early exposure affects brain structure during middle age and older adulthood. Here we analyzed brain MRI data from the UK Biobank in a population-based sample of individuals (ages 44–80) who were exposed (n = 2510) or unexposed (n = 6079) to smoking around birth. We used robust statistical models, including quantile regressions, to test the effect of perinatal smoke exposure (PSE) on cortical surface area (SA), thickness, and subcortical volumes. We hypothesized that PSE would be associated with cortical disruption in primary sensory areas compared to unexposed (PSE−) adults. After adjusting for multiple comparisons, SA was significantly lower in the pericalcarine (PCAL), inferior parietal (IPL), and regions of the temporal and frontal cortex of PSE+ adults; these abnormalities were associated with increased risk for several diseases, including circulatory and endocrine conditions. Sensitivity analyses conducted in a hold-out group of healthy participants (exposed, n = 109, unexposed, n = 315) replicated the effect of PSE on SA in the PCAL and IPL. Collectively our results show a negative, long term effect of PSE on sensory cortices that may increase risk for disease later in life.

scholarly journals Trio-Based Low-Pass Genome Sequencing Reveals Characteristics and Significance of Rare Copy Number Variants in Prenatal Diagnosis

2021 ◽  
Vol 12 ◽  
Author(s):  
Matthew Hoi Kin Chau ◽  
Jicheng Qian ◽  
Zihan Chen ◽  
Ying Li ◽  
Yu Zheng ◽  
...  
Keyword(s):  
Prenatal Diagnosis ◽  
Genome Sequencing ◽  
Copy Number ◽  
De Novo ◽  
Copy Number Variants ◽  
Recurrence Risk ◽  
Rare Cnvs ◽  
Pathogenic Cnvs ◽  
Low Pass ◽  
Clinically Significant

Background: Low-pass genome sequencing (GS) detects clinically significant copy number variants (CNVs) in prenatal diagnosis. However, detection at improved resolutions leads to an increase in the number of CNVs identified, increasing the difficulty of clinical interpretation and management.Methods: Trio-based low-pass GS was performed in 315 pregnancies undergoing invasive testing. Rare CNVs detected in the fetuses were investigated. The characteristics of rare CNVs were described and compared to curated CNVs in other studies.Results: A total of 603 rare CNVs, namely, 597 constitutional and 6 mosaic CNVs, were detected in 272 fetuses (272/315, 86.3%), providing 1.9 rare CNVs per fetus (603/315). Most CNVs were smaller than 1 Mb (562/603, 93.2%), while 1% (6/603) were mosaic. Forty-six de novo (7.6%, 46/603) CNVs were detected in 11.4% (36/315) of the cases. Eighty-four CNVs (74 fetuses, 23.5%) involved disease-causing genes of which the mode of inheritance was crucial for interpretation and assessment of recurrence risk. Overall, 31 pathogenic/likely pathogenic CNVs were detected, among which 25.8% (8/31) were small (<100 kb; n = 3) or mosaic CNVs (n = 5).Conclusion: We examined the landscape of rare CNVs with parental inheritance assignment and demonstrated that they occur frequently in prenatal diagnosis. This information has clinical implications regarding genetic counseling and consideration for trio-based CNV analysis.

scholarly journals Bayesian analysis of genetic association across tree-structured routine healthcare data in the UK Biobank

2017 ◽  
Author(s):  
Adrian Cortes ◽  
Calliope A. Dendrou ◽  
Allan Motyer ◽  
Luke Jostins ◽  
Damjan Vukcevic ◽  
...  
Keyword(s):  
Bayesian Analysis ◽  
Statistical Power ◽  
Heterogeneous Data ◽  
Uk Biobank ◽  
Genetic Associations ◽  
Critical Question ◽  
Healthcare Data ◽  
Immune Mediated ◽  
The Hierarchical Structure ◽  
The Uk

Genetic discovery from the multitude of phenotypes extractable from routine healthcare data has the ability to radically transform our understanding of the human phenome, thereby accelerating progress towards precision medicine. However, a critical question when analysing high-dimensional and heterogeneous data is how to interrogate increasingly specific subphenotypes whilst retaining statistical power to detect genetic associations. Here we develop and employ a novel Bayesian analysis framework that exploits the hierarchical structure of diagnosis classifications to jointly analyse genetic variants against UK Biobank healthcare phenotypes. Our method displays a more than 20% increase in power to detect genetic effects over other approaches, such that we uncover the broader burden of genetic variation: we identify associations with over 2,000 diagnostic terms. We find novel associations with common immune-mediated diseases (IMD), we reveal the extent of genetic sharing between specific IMDs, and we expose differences in disease perception or diagnosis with potential clinical implications.

scholarly journals Panomics: New Databases for Advancing Cardiology

2021 ◽  
Vol 8 ◽  
Author(s):  
Dara Vakili ◽  
Dina Radenkovic ◽  
Shreya Chawla ◽  
Deepak L. Bhatt
Keyword(s):  
Statistical Power ◽  
Biological Variability ◽  
Uk Biobank ◽  
Inclusion Criteria ◽  
Biological Mechanisms ◽  
Noisy Signals ◽  
Clinical Measurements ◽  
The Uk ◽  
Study Designs

The multifactorial nature of cardiology makes it challenging to separate noisy signals from confounders and real markers or drivers of disease. Panomics, the combination of various omic methods, provides the deepest insights into the underlying biological mechanisms to develop tools for personalized medicine under a systems biology approach. Questions remain about current findings and anticipated developments of omics. Here, we search for omic databases, investigate the types of data they provide, and give some examples of panomic applications in health care. We identified 104 omic databases, of which 72 met the inclusion criteria: genomic and clinical measurements on a subset of the database population plus one or more omic datasets. Of those, 65 were methylomic, 59 transcriptomic, 41 proteomic, 42 metabolomic, and 22 microbiomic databases. Larger database sample sizes and longer follow-up are often better suited for panomic analyses due to statistical power calculations. They are often more complete, which is important when dealing with large biological variability. Thus, the UK BioBank rises as the most comprehensive panomic resource, at present, but certain study designs may benefit from other databases.

scholarly journals Multiple linear regression allows weighted burden analysis of rare coding variants in an ethnically heterogeneous population

2020 ◽  
Author(s):  
David Curtis
Keyword(s):  
Linear Regression ◽  
Principal Components ◽  
Rare Variants ◽  
Linear Regression Analysis ◽  
Uk Biobank ◽  
Case Control Studies ◽  
Test Statistic ◽  
Functional Variants ◽  
The Uk ◽  
Coding Variants

AbstractWeighted burden analysis has been used in exome-sequenced case-control studies to identify genes in which there is an excess of rare and/or functional variants associated with phenotype. Implementation in a ridge regression framework allows simultaneous analysis of all variants along with relevant covariates such as population principal components. In order to apply the approach to a quantitative phenotype, a weighted burden score is derived for each subject and included in a linear regression analysis. The weighting scheme is adjusted in order to apply differential weights to rare and very rare variants and a score is derived based on both the frequency and predicted effect of each variant. When applied to an ethnically heterogeneous dataset consisting of 49,790 exome-sequenced UK Biobank subjects and using BMI as the phenotype the method produces a very inflated test statistic. However this is almost completely corrected by including 20 population principal components as covariates. When this is done the top 30 genes include a few which are quite plausibly associated with the phenotype, including LYPLAL1 and NSDHL. This approach offers a way to carry out gene-based analyses of rare variants identified by exome sequencing in heterogeneous datasets without requiring that data from ethnic minority subjects be discarded. This research has been conducted using the UK Biobank Resource.

scholarly journals Gene-Gene Interaction Detection with Deep Learning

2021 ◽  
Author(s):  
Tianyu Cui ◽  
Khaoula El Mekkaoui ◽  
Jaakko Reinvall ◽  
Aki S. Havulinna ◽  
Pekka Marttinen ◽  
...  
Keyword(s):  
Statistical Power ◽  
Gene Interaction ◽  
Uk Biobank ◽  
New Approach ◽  
Interaction Detection ◽  
Complex Interactions ◽  
Current Interaction ◽  
The Uk ◽  
Combine Information ◽  
Hidden Nodes

ABSTRACTWe do not know the extent to which genetic interactions affect the observed phenotype in diseases, because the current interaction detection approaches are limited: they only consider interactions between the top SNPs of each gene, and only simple forms of interaction. We introduce methods for increasing the statistical power of interaction detection by taking into account all SNPs and complex interactions between them, beyond only the currently considered multiplicative relationships. In brief, the relation between SNPs and a phenotype is captured by a gene interaction neural network (NN), and the interactions are quantified by the Shapley score between hidden nodes, which are gene representations that optimally combine information from all SNPs in the gene. Additionally, we design a new permutation procedure tailored for NNs to assess the significance of interactions. The new approach outperformed existing alternatives on simulated datasets, and in a cholesterol study on the UK Biobank it detected six interactions which replicated on an independent FINRISK dataset, four of them novel findings.

scholarly journals Identification of new risk factors for rolandic epilepsy: CNV at Xp22.31 and alterations at cholinergic synapses

2018 ◽  
Vol 55 (9) ◽  
pp. 607-616 ◽  
Author(s):  
Laura Addis ◽  
William Sproviero ◽  
Sanjeev V Thomas ◽  
Roberto H Caraballo ◽  
Stephen J Newhouse ◽  
...  
Keyword(s):  
Mammalian Target Of Rapamycin ◽  
Rolandic Epilepsy ◽  
Rare Cnvs ◽  
Neuronal Signalling ◽  
Cholinergic Synapses ◽  
Full Picture ◽  
Pathogenic Cnvs ◽  
The Usa ◽  
Significant Enrichment ◽  
The Uk

BackgroundRolandic epilepsy (RE) is the most common genetic childhood epilepsy, consisting of focal, nocturnal seizures and frequent neurodevelopmental impairments in speech, language, literacy and attention. A complex genetic aetiology is presumed in most, with monogenic mutations in GRIN2A accounting for >5% of cases.ObjectiveTo identify rare, causal CNV in patients with RE.MethodsWe used high-density SNP arrays to analyse the presence of rare CNVs in 186 patients with RE from the UK, the USA, Sardinia, Argentina and Kerala, India.ResultsWe identified 84 patients with one or more rare CNVs, and, within this group, 14 (7.5%) with recurrent risk factor CNVs and 15 (8.0%) with likely pathogenic CNVs. Nine patients carried recurrent hotspot CNVs including at 16p13.11 and 1p36, with the most striking finding that four individuals (three from Sardinia) carried a duplication, and one a deletion, at Xp22.31. Five patients with RE carried a rare CNV that disrupted genes associated with other epilepsies (KCTD7, ARHGEF15, CACNA2D1, GRIN2A and ARHGEF4), and 17 cases carried CNVs that disrupted genes associated with other neurological conditions or that are involved in neuronal signalling/development. Network analysis of disrupted genes with high brain expression identified significant enrichment in pathways of the cholinergic synapse, guanine-exchange factor activation and the mammalian target of rapamycin.ConclusionOur results provide a CNV profile of an ethnically diverse cohort of patients with RE, uncovering new areas of research focus, and emphasise the importance of studying non-western European populations in oligogenic disorders to uncover a full picture of risk variation.

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