The Significant Effects of Threshold Selection for Advancing Nitrogen Use Efficiency in Whole Genome of Bread Wheat

Currently in wheat breeding, genome wide association studies (GWAS) have successfully revealed the genetic basis of complex traits such as nitrogen use efficiency (NUE) and its biological processes. In the GWAS model, thresholding is common strategy to indicate deviation of expected range of p-value(s), and it can be used to find the distribution of true positive associations under or over of test statistics. Therefore, the threshold plays a critical role to identify reliable and significant associations in wide genome, while the proportion of false positive results is relatively low. The problem of multiple comparisons arises when a statistical analysis involves multiple simultaneous statistical tests, each of them has the potential to be a discovery. There are several ways to address this problem, including the family-wise error rate and false discovery rate (FDR), raw and adjusted p-value(s), consideration of threshold coherence and consonance, and the properties of proportional hypothesis tests in the threshold definition. We encountered some limitations in the definition of FDR threshold, particularly in the upper bounds of linear and nonlinear approaches. We emphasize that empirical null distributions based on permutation test can be useful when the assumption of linear or parametric FDR approaches do not hold. Nevertheless, we believe that it is necessary to utilize modern statistical optimization techniques to evaluate the stability and performance of our results and to select significant FDR threshold. By incorporating the neural network algorithm, it is possible to improve the reliability of FDR threshold and increase the probability of identifying true genetic associations while minimizing the risk of false positives in GWAS results.