title: A complete solution for dissecting pure main and epistatic effects of QTL in triple testcross design.
abstract: Epistasis plays an important role in genetics, evolution and crop breeding. To detect the epistasis, triple test cross (TTC) design had been developed several decades ago. Classical procedures for the TTC design use only linear transformations Z(1), Z(2) and Z(3), calculated from the TTC family means of quantitative trait, to infer the nature of the collective additive, dominance and epistatic effects of all the genes. Although several quantitative trait loci (QTL) mapping approaches in the TTC design have been developed, these approaches do not provide a complete solution for dissecting pure main and epistatic effects. In this study, therefore, we developed a two-step approach to estimate all pure main and epistatic effects in the F(2)-based TTC design under the F(2) and F(∞) metric models. In the first step, with Z(1) and Z(2) the augmented main and epistatic effects in the full genetic model that simultaneously considered all putative QTL on the whole genome were estimated using empirical Bayes approach, and with Z(3) three pure epistatic effects were obtained using two-dimensional genome scans. In the second step, the three pure epistatic effects obtained in the first step were integrated with the augmented epistatic and main effects for the further estimation of all other pure effects. A series of Monte Carlo simulation experiments has been carried out to confirm the proposed method. The results from simulation experiments show that: 1) the newly defined genetic parameters could be rightly identified with satisfactory statistical power and precision; 2) the F(2)-based TTC design was superior to the F(2) and F(2:3) designs; 3) with Z(1) and Z(2) the statistical powers for the detection of augmented epistatic effects were substantively affected by the signs of pure epistatic effects; and 4) with Z(3) the estimation of pure epistatic effects required large sample size and family replication number. The extension of the proposed method in this study to other base populations was further discussed.
authors.affiliation: Section on Statistical Genomics, State Key Laboratory of Crop Genetics and Germplasm Enhancement/Chinese National Center for Soybean Improvement, College of Agriculture, Nanjing Agricultural University, Nanjing, Jiangsu, China.
authors.forename: Xiao-Hong
authors.initials: XH
authors.lastname: He
authors.forename: Yuan-Ming
authors.initials: YM
authors.lastname: Zhang
mesh_terms: D001947:Breeding / Q000379:methods*; D044127:Epigenesis, Genetic / Q000235:genetics*; D005819:Genetic Markers / Q000235:genetics; D016678:Genome / Q000235:genetics; D008957:Models, Genetic; D009010:Monte Carlo Method; D040641:Quantitative Trait Loci / Q000235:genetics*
chemical_list: D005819:Genetic Markers