Each function in EasyOmics is independent, allowing users to input data, adjust parameters, run analyses, and visualize results with simple point-and-click actions.
The sidebar contains a menu to select the corresponding page, a selection list for analysis functions, a file upload panel, a parameter control panel, and an action button. The main panel visualizes the analysis output and displays feedback.
This significantly simplifies population-scale omics data analysis, making it more convenient for biologists to perform a series of omics analyses.
In EasyOmics workflow, GWAs function can perform association analysis between genotype and phenotype and find QTLs with significant association. Omics QTL function treats omic data as molecular phenotype and tests the association with genotypic data. MR function integrates the QTLs and OmicQTLs to perform causal inference. OmicsWAS tests the association between phenotypic and omics data.
Introduction
A graphical interface for population-scale omics data association, integration and visualization
EasyOmics is an R Shiny application with a graphic user interface (GUI) application that integrates the Omic data for GWAS analysis. It is a user-friendly application that allows users to perform association analysis locally without any coding.
Moreover, it simplifies data compatibility issues across various analysis tools, requiring only VCF, GFF, and phenotype TXT files.
The analysis parameters of invoked tools are automatically set to default values, which reduces the complexity of parameter setting for users.
Parameters also could be adjusted by users in the “Other Parameters” input text box of every analysis function. The setted parameters are the main invoked softwares’ parameters. Please see “Citation” for the main invoked softwares’ references.
| Function | Description |
|---|---|
| Data Matching | Preparing input files for subsequent analysis. |
| Phenotype Analysis | Providing critical insights into the input data characteristics and facilitates the detection of outlier values. |
| GWAS | Testing the significance of associations between each SNP and the phenotype using a linear mixed model. |
| COJO | Fine mapping of GWAS result and identify secondary association signals. |
| Locus Zoom | Displaying the significance, linkage, and nearby genes of SNPs in specific chromosome regions. |
| Omic QTL | Employing linear models for association analysis of omics data and genotype data. |
| Two Traits MR | Exploring causal relationships between two traits. |
| SMR | Exploring causal relationships between trait and omic molecular trait. |
| OmicWAS | Testing the associations between omic data and complex traits. |
File Stream of Workflow
This is the file stream of the workflow in EasyOmics. Only four input files need users to prepare. The phenotype data (txt), genotype data (vcf), genome annotation data(gff), and omic data(txt). The analysis workflow could be seperated into three parts by the dashed line. The first part just need the user prepareed files. The second parts need the output files of the first part. The third part need the output files of the second part.
Check the diagram by function is a good way to understand the input data and output data. For example, in Locus Zoom function, there are four arrows indicate the input data: Matched phe.txt and Matched vcf.vcf from Data Matching, GWA.mlma from GWAS function, and GFF.gff file from user.
Citation
If you use EasyOmics, please cite:
EasyOmics:
Han, Y., Du, Q., Dai, Y., Gu, S., Liu, W., et al., 2024. EasyOmics-A graphical interface for population-scale omics data association , integration and visualization. bioRxiv. https://doi.org/10.1101/2024.02.20.581292
The corressponding references of the main invoked softwares are as follows, and listed in the “Analysis” page of this tutorial.
Chen, Z.L., Meng, J.M., Cao, Y., Yin, J.L., Fang, R.Q., et al., 2019. A high-speed search engine pLink 2 with systematic evaluation for proteome-scale identification of cross-linked peptides. Nat. Commun. 10. https://doi.org/10.1038/s41467-019-11337-z
Dong, S.-S., He, W.-M., Ji, J.-J., Zhang, C., Guo, Y., et al., 2021. LDBlockShow: a fast and convenient tool for visualizing linkage disequilibrium and haplotype blocks based on variant call format files. Brief. Bioinform. 22, bbaa227. https://doi.org/10.1093/bib/bbaa227
Huang, M., Liu, X., Zhou, Y., Summers, R. M., and Zhang, Z. (2019). BLINK: A package for the next level of genome-wide association studies with both individuals and markers in the millions. Gigascience. 8, 1–12. https://doi.org/10.1093/gigascience/giy154
Shabalin, A.A., 2012. Matrix eQTL: Ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358. https://doi.org/10.1093/bioinformatics/bts163
Yang, J., Ferreira, T., Morris, A.P., Medland, S.E., Madden, P.A.F., et al., 2012. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375. https://doi.org/10.1038/ng.2213
Yang, J., Lee, S.H., Goddard, M.E., Visscher, P.M., 2011. GCTA: A tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82. https://doi.org/10.1016/j.ajhg.2010.11.011
Zhang, F., Chen, W., Zhu, Z., Zhang, Q., Nabais, M.F., et al., 2019. OSCA: A tool for omic-data-based complex trait analysis. Genome Biol. 20, 1–13. https://doi.org/10.1186/s13059-019-1718-z
Zhu, Z., Zheng, Z., Zhang, F., Wu, Y., Trzaskowski, M., et al., 2018. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. 9. https://doi.org/10.1038/s41467-017-02317-2
Zhu, Z., Zhang, F., Hu, H., Bakshi, A., Robinson, M.R., et al., 2016. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487. https://doi.org/10.1038/ng.3538