Application and Challenges of Statistical Methods in Biological Genetics

Abstract

Humans are curious about genes, from plants to animals, from breeding to diseases. For centuries, it has been considered a genetic disease. With the development of medicine, people have also realized that many diseases are heritable. With the birth of modern statistics, humans have created many models. This article focuses on the application of statistical methods in biological genetics. This paper introduces the principles and their applications of Least Absolute Shrinkage and Selection Operator Regression, the Chen-Stein Method, and Logical Regression model in different branches, such as gene set selection. These models can effectively tackle the problem of reproducibility in genetics to a certain extent when used correctly. In addition, they offer an effective means of data analysis in genetics field. Although the three models have their weaknesses, such as the use and selection of a priori, it is reasonable to believe that with the continuous improvement of the models by mathematicians, they can have better prospects.