Method of data generation for affected grandparent-grandchild pairs and its applications

Shizhe Liu; Liang Tong; Ying Zhou

doi:10.1117/12.2686040

28 July 2023 Method of data generation for affected grandparent-grandchild pairs and its applications

Shizhe Liu, Liang Tong, Ying Zhou

Proceedings Volume 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023); 1275639 (2023) https://doi.org/10.1117/12.2686040
Event: 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), 2023, Tangshan, China

Abstract

With the development of genome-wide association analysis and sequencing techniques, lots of rare and common variants associated with complex traits or diseases have been detected. Besides, in recent years, the research based on family data has attracted wide attention, but most of the research only consider the data of unrelated individuals and siblings, and rarely consider the data of distant relatives like grandparent-grandchild pairs, uncle-nephew pairs, cousin pairs, and so on. In this paper, we propose an effective method for generating affected grandparent-grandchild pair data (called GAGP). Based on association analysis, we use a large number of simulation experiments to evaluate the effectiveness and application of the new sampling method. The simulation results show that in all cases, the new method is valid and obtains good test results compared with other methods, which indicates that the method has better performance. The new method is implemented by the software R.

Citation Download Citation

Shizhe Liu, Liang Tong, and Ying Zhou "Method of data generation for affected grandparent-grandchild pairs and its applications", Proc. SPIE 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023), 1275639 (28 July 2023); https://doi.org/10.1117/12.2686040

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