Genetic Pleiotropy Test by Quasi p-Value with Application to Typhoon Data in China

doi:10.1007/s11424-022-0287-5

Journal of Systems Science and Complexity ›› 2022, Vol. 35 ›› Issue (4): 1557-1572.DOI: 10.1007/s11424-022-0287-5

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Genetic Pleiotropy Test by Quasi p-Value with Application to Typhoon Data in China

WU Qiang¹, ZHONG Shaojun², TONG Xingwei¹

1. School of Statistics, Beijing Normal University, Beijing 100875, China;
2. School of Mathematics and Statistics, Hubei University of Science and Technology, Hubei 437000, China

Received:2020-11-15 Revised:2021-05-14 Online:2022-08-25 Published:2022-08-02
Contact: ZHONG Shaojun,Email:celons@163.com
Supported by:
This paper was supported by National Key Research and Development Program of China under Grant No. 2017YFA0604903, the National Natural Science Foundation of China under Grant No. 11971064 and the Ph.D. start-up fund of Hubei University of Science and Technology under Grant No. BK201813.

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WU Qiang, ZHONG Shaojun, TONG Xingwei. Genetic Pleiotropy Test by Quasi p-Value with Application to Typhoon Data in China[J]. Journal of Systems Science and Complexity, 2022, 35(4): 1557-1572.

To test genetic pleiotropy, the main difficulty lies in the failure to find a test statistic and calculate its p-value for determining whether to reject the null hypothesis or not. To deal with this issue, the authors propose a quasi p-value, which plays the similar role as the usual p-value in genetic pleiotropy test. In the formula of the quasi p-value, the main task is to determine the weights. In this paper, the authors present two weighted methods based on the Bayesian rule and extend the proposed methods to study a single binary trait using a data-driven EM algorithm. Extensive simulation studies are conducted for the assessment of the two proposed methods and illustrate that the proposed methods improve the performance of power by comparing with the two-stage method. In addition, the authors apply the proposed methods to the data of tropical storms that occurred on the mainland of China since 1949, investigating the relationship between the landing site and predictive features of tropical storms, and showing that the landing site has a large influence on at least two features of typhoon.

Key words: Bayesian rule, data-driven EM algorithm, genetic pleiotropy, quasi p-value

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