PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies

Hall, Molly A.; Wallace, John R.; Lucas, Anastasia; Kim, Dokyoon; Basile, Anna O.; Verma, Shefali S.; McCarty, CA; Brilliant, Murray H.; Peissig, Peggy L.; Kitchner, Terrie; Verma, Anurag; Pendergrass, Sarah A.; Dudek, Scott M.; Moore, Jason H.; Ritchie, Marylyn D.

doi:10.1038/s41467-017-00802-2

Cited by 37 publications

(32 citation statements)

References 66 publications

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“…At the heart of poor reproducibility are three main issues: lack of 1) standardized infrastructure (i.e., software to perform QC protocols), 2) clear documentation of QC and analysis protocols, and 3) standardization of QC protocols (Peng, 2015). SNP QC has become standardized in terms of protocols (Laurie et al, 2010;Turner et al, 2011;Zuvich et al, 2011;Verma et al, 2014; Ellingson and Fardo, 2016) and infrastructure (Purcell et al, 2007;Zheng et al, 2012;Hall et al, 2017), and it is commonplace for these to be well-documented in publications employing SNP data. Exposome QC protocol and infrastructure development, on the other hand, have previously received little attention (Engel et al, 2013;Zhu et al, 2013;Emwas et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…Though the backbone of EWAS is regression, a traditional and established statistical method, the series of data preprocessing steps the user takes to reach the analysis stage has not been standardized. Where GWAS shines is its standardized genomic quality control (QC) pipelines (Lemke et al, 2010;Turner et al, 2011;Ellingson and Fardo, 2016;MacArthur et al, 2017), made easily executable by a variety of popular and sophisticated platforms, such as PLINK (Purcell et al, 2007), PLATO (Hall et al, 2017;Ritchie Lab and Geisginger Health Systems, 2017), and GCTA (Yang et al, 2011). EWAS, however, falls behind GWAS in this regard.…”

Section: Introductionmentioning

confidence: 99%

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CLARITE Facilitates the Quality Control and Analysis Process for EWAS of Metabolic-Related Traits

et al. 2019

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While genome-wide association studies are an established method of identifying genetic variants associated with disease, environment-wide association studies (EWAS) highlight the contribution of nongenetic components to complex phenotypes. However, the lack of high-throughput quality control (QC) pipelines for EWAS data lends itself to analysis plans where the data are cleaned after a first-pass analysis, which can lead to bias, or are cleaned manually, which is arduous and susceptible to user error. We offer a novel software, CLeaning to Analysis: Reproducibility-based Interface for Traits and Exposures (CLARITE), as a tool to efficiently clean environmental data, perform regression analysis, and visualize results on a single platform through user-guided automation. It exists as both an R package and a Python package. Though CLARITE focuses on EWAS, it is intended to also improve the QC process for phenotypes and clinical lab measures for a variety of downstream analyses, including phenome-wide association studies and geneenvironment interaction studies. With the goal of demonstrating the utility of CLARITE, we performed a novel EWAS in the National Health and Nutrition Examination Survey (NHANES) (N overall Discovery=9063, N overall Replication=9874) for body mass index (BMI) and over 300 environment variables post-QC, adjusting for sex, age, race, socioeconomic status, and survey year. The analysis used survey weights along with cluster and strata information in order to account for the complex survey design. Sixteen BMI results replicated at a Bonferroni corrected p < 0.05. The top replicating results were serum levels of g-tocopherol (vitamin E) (Discovery Bonferroni p: 8.67x10-12 , Replication Bonferroni p: 2.70x10-9) and iron (Discovery Bonferroni p: 1.09x10-8 , Replication Bonferroni p: 1.73x10-10). Results of this EWAS are important to consider for metabolic trait analysis, as BMI is tightly associated with these phenotypes. As such, exposures predictive of BMI may be useful for covariate and/or interaction assessment of metabolic-related traits. CLARITE allows improved data quality for EWAS, gene-environment interactions, and phenome-wide association studies by establishing a high-throughput quality control infrastructure. Thus, CLARITE is recommended for studying the environmental factors underlying complex disease.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CLARITE Facilitates the Quality Control and Analysis Process for EWAS of Metabolic-Related Traits

et al. 2019

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“…Association studies were performed using the genotype and phenotype data prepared as described above using the PLatform for the Analysis, Translation, and Organization of large-scale data (PLATO) 92,93 , a standalone program developed by the Ritchie lab for the performance of phenome-wide linear/logistic regression of genetic variants against participant phenotypes. Linear regression, assuming an additive genetic model, was performed across all 16,874 ciliary genetic variants and the 12 phenotypes listed above.…”

Section: Discovery Association Studiesmentioning

confidence: 99%

Mendelian pathway analysis of laboratory traits reveals distinct roles for ciliary subcompartments in common disease pathogenesis

Drivas

Lucas

Zhang

et al. 2020

Preprint

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SummaryRare monogenic disorders of the primary cilium, termed ciliopathies, are characterized by extreme presentations of otherwise-common diseases, such as diabetes, hepatic fibrosis, and kidney failure. However, despite a revolution in our understanding of the cilium’s role in rare disease pathogenesis, the organelle’s contribution to common disease remains largely unknown. We hypothesized that common genetic variants affecting Mendelian ciliopathy genes might also contribute to common complex diseases pathogenesis more generally. To address this question, we performed association studies of 16,875 common genetic variants across 122 well-characterized ciliary genes with 12 quantitative laboratory traits characteristic of ciliopathy syndromes in 378,213 European-ancestry individuals in the UK BioBank. We incorporated tissue-specific gene expression analysis, expression quantitative trait loci (eQTL) and Mendelian disease information into our analysis, and replicated findings in meta-analysis to increase our confidence in observed associations between ciliary genes and human phenotypes. 73 statistically-significant gene-trait associations were identified across 34 of the 122 ciliary genes that we examined (including 8 novel, replicating associations). With few exceptions, these ciliary genes were found to be widely expressed in human tissues relevant to the phenotypes being studied, and our eQTL analysis revealed strong evidence for correlation between ciliary gene expression levels and patient phenotypes. Perhaps most interestingly our analysis identified different ciliary subcompartments as being specifically associated with distinct sets of patient phenotypes, offering a number of testable hypotheses regarding the cilium’s role in common complex disease. Taken together, our data demonstrate the utility of a Mendelian pathway-based approach to genomic association studies, and challenge the widely-held belief that the cilium is an organelle important mainly in development and in rare syndromic disease pathogenesis. The continued application of techniques similar to those described here to other phenotypes/Mendelian diseases is likely to yield many additional fascinating associations that will begin to integrate the fields of common and rare disease genetics, and provide insight into the pathophysiology of human diseases of immense public health burden.Contacttheodore.drivas@gmail.com

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“…We tested for single-tissue gene-trait associations by performing association tests on imputed GReX and ACTG baseline lab traits using PLATO [58,59] Colors represent different TWAS methods and y-axis is the false positive rate of tissues among statistically significant results. Single-tissue TWAS wrongly identified 5% and 77% traitirrelevant tissues for tissue-specific and broadly expressed genes, respectively.…”

Section: Statistical Analysis For Gene-level Associationsmentioning

confidence: 99%

Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults

Veturi

Verma

et al. 2020

Preprint

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As a type of relatively new methodology, the transcriptome-wide association study (TWAS) has gained interest due to capacity for gene-level association testing. However, the development of TWAS has outpaced statistical evaluation of TWAS gene prioritization performance. Current TWAS methods vary in underlying biological assumptions about tissue specificity of transcriptional regulatory mechanisms. In a previous study from our group, this may have affected whether TWAS methods better identified associations in single tissues versus multiple tissues. We therefore designed simulation analyses to examine how the interplay between particular TWAS methods and tissue specificity of gene expression affects power and type I error rates for gene prioritization. We found that cross-tissue identification of expression quantitative trait loci (eQTLs) improved TWAS power. Single-tissue TWAS (i.e., PrediXcan) had robust power to identify genes expressed in single tissues, but, had high false positive rates for genes that are expressed in multiple tissues. Cross-tissue TWAS (i.e., UTMOST) had overall equal or greater power and controlled type I error rates for genes expressed in multiple tissues. Based on these simulation results, we applied a tissue specificity-aware TWAS (TSA-TWAS) analytic framework to look for gene-based associations with pre-treatment laboratory values from AIDS Clinical Trial Group (ACTG) studies. We replicated several proof-of-concept transcriptionally regulated gene-trait associations, including UGT1A1 (encoding bilirubin uridine diphosphate glucuronosyl transferase enzyme) and total bilirubin levels (p = 3.59×10−12), and CETP (cholesteryl ester transfer protein) with high-density lipoprotein cholesterol (p = 4.49×10−12). We also identified several novel genes associated with metabolic and virologic traits, as well as pleiotropic genes that linked plasma viral load, absolute basophil count, and/or triglyceride levels. By highlighting the advantages of different TWAS methods, our simulation study promotes a tissue specificity-aware TWAS analytic framework that revealed novel aspects of HIV-related traits.publicly available.

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PLATO software provides analytic framework for investigating complexity beyond genome-wide association studies

Cited by 37 publications

References 66 publications

CLARITE Facilitates the Quality Control and Analysis Process for EWAS of Metabolic-Related Traits

CLARITE Facilitates the Quality Control and Analysis Process for EWAS of Metabolic-Related Traits

Mendelian pathway analysis of laboratory traits reveals distinct roles for ciliary subcompartments in common disease pathogenesis

Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults

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