Complex-Traits Genetics Virtual Lab: A community-driven web platform for post-GWAS analyses

Cuéllar-Partida, Gabriel; Lundberg, Mischa; Campos-González, Adrian I.; Kho, Pik Fang; D’Urso, Shannon; Hwang, Liang-Dar; Ngo, Trung Thanh; Rentería, Miguel E.

doi:10.1101/518027

Cited by 98 publications

(99 citation statements)

References 36 publications

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“…A Generalised Summary-data-based Mendelian Randomization (GSMR) test was used as an additional sensitivity analysis to detect pleiotropic effects, but was applied only on the datasets that reported the effect allele frequency 37,40 . We used the platform Complex Trait Genetics Virtual Lab (CTG-VL, available on: https://vl.genoma.io/) to run the statistical package GSMR 58 . This program implements a method that uses summary data from GWAS to distinguish causal or pleiotropic associations 59 .…”

Section: Bidirectional Two Sample Mendelian Randomization (Tsmr) Analmentioning

confidence: 99%

Sleep-related traits and attention-deficit/hyperactivity disorder comorbidity: shared genetic risk factors, molecular mechanisms, and causal effects

Carpena

Bonilla

Matijasevich

et al. 2020

Preprint

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Study Objectives: To evaluate the level of shared genetic components between attention-deficit/hyperactivity disorder (ADHD) and sleep phenotype, common pathways between them and a possible causal relationship between traits. Methods: We used summary statistics of the largest genome-wide association studies available for ADHD and sleep-related phenotypes including insomnia, napping, daytime dozing, snoring, ease getting up, daytime sleepiness, sleep duration and chronotype. We estimated the genomic correlation between ADHD and sleep-related traits using cross-trait LD-score regression and investigated potential common mechanisms using gene-based cross-trait metanalyses and functional enrichment analyses. The causal effect between the sleep related traits and ADHD was estimated with two sample Mendelian randomization (TSMR), using the Inverse Variance Weighted method as the main estimator. Results: Positive genomic correlation between insomnia, daytime napping, daytime dozing, snoring, daytime sleepiness, short and long sleep duration, and ADHD were observed. Insomnia, sleep duration, daytime sleepiness, and snoring shared genes with ADHD, which were involved in neurobiological functions and regulatory signaling pathways. The TSMR approach supported a causal effect of insomnia, daytime napping, and short sleep duration on ADHD, and of ADHD on long sleep duration and chronotype. Conclusion: Our findings suggest that the comorbidity between sleep phenotypes and ADHD may be mediated by common genetic factors with an important role on neuronal signaling pathways. In addition, it may also exist a causal effect of sleep disturbances and short sleep duration on ADHD, reinforcing the role of these sleep phenotypes as predictors or early markers of ADHD.

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Section: Bidirectional Two Sample Mendelian Randomization (Tsmr) Analmentioning

confidence: 99%

Sleep-related traits and attention-deficit/hyperactivity disorder comorbidity: shared genetic risk factors, molecular mechanisms, and causal effects

Carpena

Bonilla

Matijasevich

et al. 2020

Preprint

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“…We used the platform Complex Trait Genetics Virtual Lab (CTG-VL, https://genoma.io/) [13] to run the statistical package SMR [14][15][16]. This program implements a method that uses summary data from GWAS, mQTL or eQTL studies to distinguish causal or pleiotropic associations between DNAm and gene expression, or between either of these and a phenotype, from a situation where the traits are caused by different variants that are in strong LD (see Figure 1b of reference [14]).…”

Section: Summary Data-based Mendelian Randomization (Smr) Of Whole Blmentioning

confidence: 99%

Investigating DNA methylation as a potential mediator between pigmentation genes, pigmentary traits and skin cancer

Bonilla

Bertoni

Min

et al. 2020

Preprint

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BackgroundIncidence rates for melanoma and non-melanoma skin cancer (NMSC), which includes basal cell carcinoma (BCC) and squamous cell carcinoma (SCC), have been steadily increasing in all populations. Populations of European ancestry exhibit the highest rates and therefore, have been widely studied. Pigmentation characteristics are well-known risk factors for skin cancer, particularly fair skin, red hair, blue eyes and the inability to tan. Polymorphisms in established pigmentation-related genes have been associated with these traits and with an increased risk of malignancy. However, the functional relationship between genetic variation and disease is still unclear, with the exception of red hair colour variants in the melanocortin 1 receptor (MC1R) gene. ObjectivesThe aim of this study was to explore the possibility that non-coding pigmentation SNPs are associated with pigmentary traits and skin cancer via DNA methylation (DNAm). Methods and ResultsUsing a meta-GWAS of whole blood DNAm from 36 European cohorts (N=27,750; the Genetics of DNA Methylation Consortium, GoDMC), we found that 19 out of 27 pigmentation-associated SNPs distributed within 10 genes (ASIP, BNC2, IRF4, HERC2, MC1R, OCA2, SLC24A4, SLC24A5, SLC45A2, TYR) were associated with 391 DNAm sites across 30 genomic regions. We selected 25 DNAm sites for further analysis.We examined the effect of the chosen DNAm sites on pigmentation traits, sun exposure phenotypes, and skin cancer, and on gene expression in whole blood. We found an association of decreased DNAm at cg07402062 with red hair in the Avon Longitudinal Study of Parents and Children (ALSPAC), and a strong positive association of DNAm at this and correlated sites with higher expression of SPIRE2. Additionally, we investigated the association of gene expression in skin with pigmentation traits and skin cancer. The expression of ASIP, FAM83C, NCOA6, CDK10, and EXOC2 was associated with hair colour, whilst that of ASIP and CDK10 also had an effect on melanoma and BCC. ConclusionsOur results indicate that DNAm and expression of genes in the 16q24.3 and 20q11.22 regions, deserve to be further investigated as potential mediators of the relationship between genetic variants, pigmentation/sun exposure phenotypes, and some types of skin cancer.We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses.

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“…All traits conducted in studies of European ancestry in CTG-VL catalogue were selected. CTG-VL is a curated resource of genome-wide association (GWA) summary statistics and downstream analysis 23 . The complete list of GWA summary statistics and references are available in CTG-VL.…”

Section: Pipeline Stages and Implementationmentioning

confidence: 99%

“…LCV uses information aggregated across the whole genome to infer potentially causal relationships between complex human traits and diseases 21 . In conjunction with large-scale genetic association studies made possible by resources such as UK Biobank 22 and automated pipelines for quality control and analysis such as the Complex-Traits Genetics Virtual Lab (CTG-VL) 23 , this method now provides an opportunity to obtain information on potentially causal relationships efficiently and at phenome-wide scale. Here we introduce CTG-VL's newly implemented capability to perform a phenome-wide scan across hundreds of traits using the LCV method and the visualization of the results using causal architecture plots.…”

Section: Introductionmentioning

confidence: 99%

Inference and visualization of phenome-wide causal relationships using genetic data: an application to dental caries and periodontitis

Haworth

Kho

Holgerson

et al. 2019

Preprint

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BackgroundHypothesis-free Mendelian randomization studies provide a way to assess the causal relevance of a trait across the human phenome but can be limited by statistical power or complicated by horizontal pleiotropy. The recently described latent causal variable (LCV) approach provides an alternative method for causal inference which might be useful in hypothesis-free experiments. MethodsWe developed an automated pipeline for phenome-wide tests using the LCV approach including steps to estimate partial genetic causality, filter to a meaningful set of estimates, apply correction for multiple testing and then present the findings in a graphical summary termed a causal architecture plot. We apply this process to body mass index and lipid traits as exemplars of traits where there is strong prior expectation for causal effects and dental caries and periodontitis as exemplars of traits where there is a need for causal inference. ResultsThe results for lipids and BMI suggest that these traits are best viewed as creating consequences on a multitude of traits and conditions, thus providing additional evidence that supports viewing these traits as targets for interventions to improve health. On the other hand, caries and periodontitis are best viewed as a downstream consequence of other traits and diseases rather than a cause of ill health. ConclusionsThe automated process is available as part of the MASSIVE pipeline from the Complex-Traits Genetics Virtual Lab (https://vl.genoma.io) and results are available in (https://view.genoma.io). We propose causal architecture plots based on phenome-wide partial genetic causality estimates as a way visualizing the overall causal map of the human phenome. Key messages1) The latent causal variable approach uses summary statistics from genome-wide association studies to estimate a parameter termed genetic causality proportion.2) Systematic estimation of genetic causality proportion for many pairs of traits provides an alternative method for phenome-wide causal inference with some theoretical and practical advantages compared to phenome-wide Mendelian randomization.3) Using this approach, we confirm that lipid traits are an upstream risk factor for other traits and diseases, and we identify that dental diseases are predominantly a downstream consequence of other traits rather than a cause of poor systemic health.4) The method assumes no bidirectional causality and no confounding by environmental correlates of genotypes, so care is needed when these assumptions are not met. 5)We developed an automated and accessible pipeline for estimating phenome-wide causal relationships and generating interactive visual summaries.

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Complex-Traits Genetics Virtual Lab: A community-driven web platform for post-GWAS analyses

Cited by 98 publications

References 36 publications

Sleep-related traits and attention-deficit/hyperactivity disorder comorbidity: shared genetic risk factors, molecular mechanisms, and causal effects

Sleep-related traits and attention-deficit/hyperactivity disorder comorbidity: shared genetic risk factors, molecular mechanisms, and causal effects

Investigating DNA methylation as a potential mediator between pigmentation genes, pigmentary traits and skin cancer

Inference and visualization of phenome-wide causal relationships using genetic data: an application to dental caries and periodontitis

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