In this study 90 individuals of recombinant inbred lines (RILs) were developed by crossing subspecies of japonica rice cultivar, ‘ Nagdong’ and an indica type cultivar, ‘ Cheongcheong’ . These individuals were used to identify the QUANTITATIVE TRAIT LOCI of panicle TRAITs using SSR markers. A genetic linkage map was constructed using one hundred fifty four simple sequence repeat (SSR) primers covering distance of 1973. 6 cM of the whole genome with mean distance of 13. 9 cM among markers. QTLs were mapped using composite interval mapping method, nineteen QTLs were recognized for the panicle TRAITs on chromosomes 4, 5, 6, 8, 10, 11, 12 with individual QTL explained 8. 8% to 37. 9% of phenotypic variation. Two pleiotropic effects LOCI were found on chromosomes 4 and 6. These QTLs affecting leaf TRAITs, panicle TRAITs and panicle branch TRAITs would be beneficial to high-yield rice improvement.

Understanding the genomics aspect of curve variable allows for the combination of genomic regions of such model-based variables from multiple measurements into a few biologically meaningful variables. With this motivation, the aim of the current study was a model-based QUANTITATIVE TRAIT LOCI (QTL) detection for growth curve variables in Ghezel fat-tailed sheep. We tested the following items during research: 1) Determining the best nonlinear growth models using six nonlinear equations (Von Bertalanffy, Gompertz, Logistic, Richards, Weibull and Brody) according to 24905 obtained data sets collected from the Ghezel Sheep Breeding Center, Iran, during the 1994-2013 period,2) Conducted partial genome scan to identify significant QTl controlling best growth model parameters in Ghezel sheep using three half-sib families (Family size=25-50) and 8 microsatellite markers distributed on ovine chromosome 1. In addition, QTL effects for two paternal half-sibs using two models, individual families and across families were calculated. Molecular data were analyzed using SAS and GridQTL programs. Observed results demonstrated the Brody model was the best growth model for growth data according to the lower values of RMSE, AIC and BIC and generally greater values of R2adj than other models. Thus, Brody model parameters (A, B, and C) were sub-jected to further QTL analysis. Also, our observation identified one significant QTL between the markers INRA11-CSSM004 associated with Brody model A variable (maturity) located in 123 CM in chromosome 1 (P<0. 01). Analyses using more families and advance massive genotyping tools will be useful to confirm or to reject these findings.