FASTmrEMMA is a new algorithm that can approximate the estimation of QTN variance (see Materials and Methods). Thus, we need to know whether this approximation has a significant effect on the estimate of QTN variance. Tableau To answer this question, four flowering time traits in Arabidopsis [29] were analyzed by FASTmrEMMA and an exact method implemented by PROC MIXED in SAS. The estimates for QTN variance are listed in Figure 1 and Table S1. As a result, the relative error between the two methods ranged from 0.0 to 24.09%, and the average was 1.60%, indicating no effect on the QTN variance estimate using FASTmrEMMA under the conditions of this simulation.
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When the SNP effect is viewed as random, three variance components will be estimated. Generally, the polygenic variance is larger than zero while variance components for most SNPs are zero because these markers are not associated with the trait of interest. In other words, as in most mixed model approaches, variance components in FASTmrEMMA are also estimated under the assumption that one variance component is zero.