Selection Strategies for Increased Growth Rate in Japanese Quails

In this study, selection strategies were simulated to find optimal selection strategy in Japanese quails. Breeding goal was consisted of body weight and egg weight traits. Effects of using genetic markers related to carcass weight were investigated by five different selection strategies. In this case, breeding goal included body weight, egg weight and carcass weight. Deterministic simulation, based on single stage selection and discrete generation, was used for predicting genetic gain and rate of inbreeding. In the first (base) program, phenotypic information was available on body and egg weights. In the second program, information on body and egg weights along with carcass weight were included in selection index and in the third program, with taking into account the indirect carcass measurements in selection index, including breast weight and back weight, genetic gain was increased for body weight compared with two previous mentioned programs. Genetic gain for body weight was decreased by including indirect carcass measurements in selection index. In marker assisted selection programs, QTL information that described 5, 10, 20 and 50% of genetic variation in carcass weight was also included in selection index. In the next step, within the first breeding program which assumed QTL described 5% of genetic variance for carcass weight, genetic progress of carcass weight was increased 3.1% in relation to base program. This increasing trend was observed for other cases with different values of genetic variance described by assumed QTL. In the fourth program which assumed QTL described 50% of genetic variance for carcass weight, this increase reached to 42%. Genetic response for carcass weight was increased appropriately with considering different values of genetic variance due to QTL. Therefore, the use of QTL information resulted in increase in the accuracy of breeding values and it could be possible to select genetically superior candidates with greater reliability values.