Major Genes Detection for Some Economically Important Traits in Iranian Native Chicken Using Statistical Methods.

Exact methods for major gene detection have been based on using molecular information. Special mating designs with high costing are needed for production of this type information that, without basic research for optimization of production and using them, it would be very challenging and difficult for developing country such as Iran. So, before applying these methods it would be better to, by using statistical methods, segregation of these genes is detected. Some of detected major genes that are using in developed countries animal breeding programs were detected firstly by statistical methods. In this research, using statistical methods, major genes that affecting four economically important recorded traits in two breeding centers, including age at first laying, body weight, egg weight and number of laid eggs, were detected. With deleting outlier and Box-Cox transformation on adjusted data, false positive results were controlled. Normality test on adjusted records of four traits in two centers confirmed that major genes are segregating for these traits. Bartlett test for homogeneity of variance in paternal half-sib families indicated that these variances were not homogeny and χ2 statistics were significant (P<0.01) for all traits. Fain test also confirmed the curvilinear relationship between means and variances of half-sib families and both of these two methods detected segregation of major genes for recorded traits. Mixture test as the most powerful method among all statistical methods for major gene detection in animal population, detected that the best distribution for most of traits is mixed distribution and this is due to of major gene segregation that affecting these traits and different means of major gene locus's genotypes.