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Selection for disease resistance related traits is a tool of choice for evidencing and exploring genetic variability and studying underlying resistance mechanisms. In this framework, chickens originating from a base population, homozygote for the B19 major histocompatibility complex (MHC) were divergently selected for either progression or regression of tumors induced at 4 weeks of age by a SR-D strain of Rous sarcoma virus (RSV). The first generation of selection was based on a progeny test and subsequent selections were performed on full-sibs. Data of 18 generations including a total of 2010 birds measured were analyzed for the tumor profile index (TPI), a synthetic criterion of resistance derived from recording the volume of the tumors and mortality. Response to selection and heritability of TPI were estimated using a restricted maximum likelihood method with an animal model. Significant progress was shown in both directions: the lines differing significantly for TPI and mortality becoming null in the "regressor" line. Heritability of TPI was estimated as 0.49 ± 0.05 and 0.53 ± 0.06 within the progressor and regressor lines respectively, and 0.46 ± 0.03 when estimated over lines. Preliminary results showed within the progressor line a possible association between one Rfp-Y type and the growth of tumors.

The chicken major histocompatibility complex ( MHC) genes are organized into two genetically independent clusters which both possess class I and class IIbeta genes: the classical B complex and the Restriction fragment pattern- Y ( Rfp-Y) complex. In this study, we have examined the role of Rfp-Y genes in transplantation immunity. For this we used three sublines, B19H1, B19H2 and B19H3, derived from a line fixed for B19. Southern blots, PCR-SSCP assays using primers specific for Rfp-Y genes, and Rfp-Y class I allele-specific sequencing show that the polymorphisms observed in B19H1, B19H2 and B19H3 are due to the presence of three different Rfp-Y haplotypes. The Rfp-Y class I ( YF) alleles in these three haplotypes are highly polymorphic, and RT-PCR shows that at least two YF loci are expressed in each subline. The three sublines show Rfp-Y-directed alloreactivity in that Rfp-Y-incompatible skin grafts are rejected within 15 days, a rate intermediate between that seen in B-incompatible rejection (7 days) and that observed for grafts within the sublines (20 days). We conclude that Rfp-Y has an intermediate role in allograft rejection, likely to be attributable to polymorphism at the class I loci within this region.

White Leghorn chicks homozygous for B 1 9 MHC haplotype were selected for 18 generations on tumor regression after inoculation in the wing web with an SR-D strain of Rous sarcoma virus (RSV) at 4 wk of age. Each chick wasassigned a tumor profile index (TPI) based on age at death and size of the tumor. During 18 generations, 2,010 birds were divergently selected on TPI for either progression or regression of the tumor (P and R lines). A Brody growth curve was fitted for each bird. Brody function parameters included the asymptotic tumor volume (A), the factor for increased growth in progression phase (K 1 ), the factor for decreased growth in regression phase (K 2 ), age at maximum volume (T m a x ), and maximum volume of the tumor (V m a x ). Tumor growth curves were found to be different according to line, sex, and restriction fragment pattern Y complex Rfp-Y MHC haplotype (Yw* 1 5 , Yw* 1 6 , and Yw* 1 7 ). Within the P line, birds from the Yw* 1 6 haplotype reached V m a x at an earlier age than Yw* 1 5 and Yw* 1 7 , but with a lower V m a x value. Within the R line, tumor growth curves of birds from Yw* 1 6 and Yw* 1 7 haplotypes were similar. Rank correlations between the different parameters and TPI were low (between -0.26 and 0.36). Heritability estimated by the sire component was high for V m a x (0.73). Heritabilities of T m a x and K 2 were moderate (0.20 to 0.23 for T m a x and 0.18 to 0.21 for K 2 ) allowing these traits to be used as selection criteria. Heritabilities of A and K 1 were lower than 0.12. Modeling the growth curve should contribute to better distinction between progressors and regressors.