E-index Method for Identifying Dynamic Growth Traits of Poplar

Abstract

To detect the mechanism of growth, volume is important to uncover the genetic basis of dynamic complex quantitative traits. Unfortunately, it is difficult to construct the unique simple growth curve to accurately describe the growth process of all trees by the conventional GWAS based on the functional mapping method, which reduces the power of statistics for the growth model. To address this issue, this work adopted a novel approach about the Earliness degree index (E-index). First, it adopted the method of spline interpolation to fit the growth data to acquire the growth curves for each tree. Second, an innovative calculation model based on E-index was used to measure the earliness degree for each growth curve and to identify the potential relationship between QTL effects and traits by a series of hypothesis tests. Besides, a permutation test could be used to estimate the threshold for p values and to screen out significant QTLs from SNPs related to the growth process. To verify the validity and practicability of our model, we applied this method on the data about the volumes of 64 poplar trees chosen randomly from the progeny of two poplar species I-69 and I-45 with 156362 single nucleotide polymorphisms (SNPs). Through the E-index method, 13 significant markers were identified for testcross and 10 for intercross related to the growth process. Overall, this study could help elucidate the underlying genetic mechanisms of complex dynamic traits and perform marker-assisted selection in poplar.