Five Critical Genes Related to Seven COVID-19 Subtypes: A Data Science Discovery

Zhang, Zhengjun

doi:10.6339/21-JDS1005

Journal of Data Science

Five Critical Genes Related to Seven COVID-19 Subtypes: A Data Science Discovery

Volume 19, Issue 1 (2021), pp. 142–150

Zhengjun Zhang

https://doi.org/10.6339/21-JDS1005

Pub. online: 3 February 2021 Type: Data Science In Action

Received
1 December 2020

Accepted
1 January 2021

Published
3 February 2021

Abstract

Since the first confirmed case of COVID-19 was identified in December 2019, the total COVID-19 patients are up to 80,675,745, and the number of deaths is 1,764,185 as of December 27, 2020. The problem is that researchers are still learning about it, and new variants of SARS-CoV-2 are not stopping. For medical treatment, essential and informative genes can lead to accurate tests of whether an individual has contracted COVID-19 and help develop highly efficient vaccines, antiviral drugs, and treatments. As a result, identifying critical genes related to COVID-19 has been an urgent task for medical researchers. We conducted a competing risk analysis using the max-linear logistic regression model to analyze 126 blood samples from COVID-19-positive and COVID-19-negative patients. Our research led to a competing COVID-19 risk classifier derived from 19,472 genes and their differential expression values. The final classifier model only involves five critical genes, ABCB6, KIAA1614, MND1, SMG1, RIPK3, which led to 100% sensitivity and 100% specificity of the 126 samples. Given their 100% accuracy in predicting COVID-19 positive or negative status, these five genes can be critical in developing proper, focused, and accurate COVID-19 testing procedures, guiding the second-generation vaccine development, studying antiviral drugs and treatments. It is expected that these five genes can motivate numerous new COVID-19 researches.

Supplementary material

Supplementary Material

Outcome Table 1 is in a supplementary file available online. A Matlab^® demo code for solving Equation (4) is also available.

References

Andersen K, Rambaut A, Lipkin W, et al. (2020). The proximal origin of SARS-COV-2. Nature Medicine, 26: 450–452.

Cao W, Zhang Z (2020). New extreme value theory for maxima of maxima. Statistical Theory and Related Fields. Forthcoming, https://doi.org/10.1080/24754269.2020.1846115.

Cui Q, Xu Y, Zhang Z, Chan V (2020). Max-linear regression models with regularization. Journal of Econometrics. Forthcoming, https://doi.org/10.1016/j.jeconom.2020.07.017.

Cui Q, Zhang Z (2018). Max-linear competing factor models. Journal of Business & Economic Statistics, 36(1): 62–74.

Fan J, Li R, Zhang CH, Zou H (2020). Statistical Foundations of Data Science. Chapman and Hall/CRC.

Guglielmi G (2020). Fast coronavirus tests: What they can and can’t do. Nature, 585: 496–498.

Lu R, Zhao X, Li J, Niu P, Yang B, Wu H (2020). Genomic characterisation and epidemiology of 2019 novel coronavirus: Implications for virus origins and receptor binding. The Lancet, 395: 565–574.

Malinowski A, Schlather M, Zhang Z (2016). Intrinsically weighted means and non-ergodic marked point processes. Annals of the Institute of Statistical Mathematics, 68(1): 1–24.

Mick E, Kamm J, Pisco A, Ratnasiri K, et al. (2020). Upper airway gene expression reveals suppressed immune responses to SARS-COV-2 compared with other respiratory viruses. Nature Communications, 11: 5854.

Overmyer KA, Shishkova E, Miller IJ, Balnis J, Bernstein MN, Peters-Clarke TM, et al. (2020). Large-scale multi-omic analysis of COVID-19 severity. Cell Systems, 12(1): 23–40. https://doi.org/10.1016/j.cels.2020.10.003.

Rowland C (2020). Doctors and nurses want more data before championing vaccines to end the pandemic: Health systems are launching bids to assure their medical workers that vaccines will be safe and effective. CNN, November 21, 2020 at 6:00 a.m. CST.

Teng HY, Zhang Z (2020). Absolute and relative treatment effects in clinical trials: Models and applications in COVID-19 treatments. Manuscript submitted, University of Wisconsin.

The-RECOVERY-Collaborative-Group (2020). Effect of hydroxychloroquine in hospitalized patients with COVID-19. The New England Journal of Medicine, 383(21): 2030–2040.

The-Severe-Covid-19-GWAS-Group (2020). Genomewide association study of severe COVID-19 with respiratory failure. The New England Journal of Medicine, 383(16): 1522–1534. PMID: 32558485.

Xie Y, Zhang Z, Rathouz PJ, Barrett B (2019). Multivariate semi-continuous proportionally constrained two-part fixed effects models and applications. Statistical Methods in Medical Research, 28: 3516–3533.

Xu Y (2019). Regression models with max-linear structure, PhD Dissertation, University of Wisconsin.

Yu WB, Tang GD, Zhang L, Corlett RT (2020). Decoding the evolution and transmissions of the novel pneumonia coronavirus (SARS-CoV-2/HCoV-19) using whole genomic data. Zoology Research, 41(3): 247–257.

Zhang R, Tie X, Qi Z, Bevins NB, et al. (2020). Diagnosis of coronavirus disease 2019 pneumonia by using chest radiography: Value of artificial intelligence. Radiology, 298(2): E88–E97. Published Online: September 24, 2020.

Zhang Z (2005). A new class of tail-dependent time series models and its applications in financial time series. Advances in Econometrics, 20(B): 323–358.

Zhang Z (2008). Quotient correlation: A sample based alternative to Pearson’s correlation. The Annals of Statistics, 36(2): 1007–1030.

Zhang Z (2020). On studying extreme values and systematic risks with nonlinear time series models and tail dependence measures (with discussions). Statistical Theory and Related Fields, Forthcoming, https://doi.org/10.1080/24754269.2020.1856590.

Zhang Z, Qi Y, Ma X (2011). Asymptotic independence of correlation coefficients with application to testing hypothesis of independence. Electronic Journal of Statistics, 5: 342–372.

Zhang Z, Zhang C, Cui Q (2017). Random threshold driven tail dependence measures with application to precipitation data analysis. Statistica Sinica, 27(2): 685–709.

This is a free to read article.

Keywords

classification competing risk COVID-19 test COVID-19 treatment COVID-19 vaccine gene-gene interaction

Funding

The work was partially supported by NSF-DMS-2012298 (NSF).

Metrics

since February 2021

5225 Article info views	0 Full article views
1090 PDF downloads	648 XML downloads

RSS

Authors

Abstract

Supplementary material

References

Export citation

Copy and paste formatted citation

Download citation in file