Why Do We Need to Conserve What We Have? A Post-Genome Sequencing Perspective on Existing Chicken Strains ,

Genetic diversity and population structure in lines of chickens divergently selected for high and low 8-week body weight

A long-term selection experiment for high or low 8-wk BW in White Plymouth Rock chickens was conducted to study effects of selection on BW and correlated characters. Two lines [high (HWS), low (LWS) weight] were established and have undergone 48 generations of selection. The lines were managed to curtail inbreeding and to maintain similar population structures; such is necessary for equitable comparison of selection response between lines. Our objective was to test the success of that breeding strategy by characterizing genetic diversity and inbreeding in these lines. A pedigree of 5,998 individuals was assembled, with 68 founders, 2,962 HWS chickens, and 2,968 LWS chickens. Inbreeding coefficients (F) were calculated for each line. Maximum F was 0.53 and 0.61, mean F was 0.26 (SD 0.15) and 0.30 (SD 0.17), and change in F was 1.3 and 1.6% per generation in LWS and HWS lines, respectively. The effective population size was 38.3 in LWS and 32.1 in HWS lines. The effective number of founders was 15.7 in both lines, and the effective number of ancestors was 17.5 and 15.5 in LWS and HWS lines, respectively. Thirty ancestors accounted for 90% of the genetic makeup of both lines. Seven male and eight female founders still contributed to both lines at generation 48, although some contributed more to one line than the other. Family sizes were similar for males and females of each line, with males having larger family sizes with greater variance. Accumulated inbreeding was high and effective population size was low, as expected in closed lines. Effective number of founders was relatively low compared with actual number of founders, indicating some contributed more than others to the last generation. Family size statistics indicated that fewer males than females were used, leading to the observed levels of inbreeding. Given their similarity in genetic diversity and family size, it can be concluded that breeding decisions throughout the project resulted in similar population structures in the lines.

Detection of agriculturally important QTLs in chickens and analysis of the factors affecting genotyping strategy

Three single cross populations were generated in order to analyze factors affecting the ability to detect true linkage with minimum false positive or false negative associations, and to detect associations between markers and quantitative traits. The three populations are: (1) a broiler x broiler cross of a single sire and 34 dams, resulting in 266 progeny; (2) a broiler x broiler cross of a single sire and 41 dams resulting in 360 progeny; and (3) a broiler x layer cross of a single sire with 56 dams resulting in 1180 progeny. Based on these three resource populations we show that: a) gradient selective genotyping was more effective than the random selective genotyping; b) selective genotyping was significant at a selected proportion less than 62% of the cumulative truncation point; c) as few as 10% of selected individuals (5% of each of the two tails) were sufficient to show significant association between markers and phenotypes; d) a gradient slices approach was more powerful than using replicates of the extreme groups; and e) in resource populations resulting from crosses between lines of different backgrounds, most of the microsatellite markers used are polymorphic. We also used simulation to test factors affecting power to detect true associations between markers and traits that are hard to detect in experimental resource populations. Using defined populations in the simulation, we concluded that the following guidelines provide reliable detection of linked QTLs: 1) the resource population size should be larger than 100; 2) a QTL effect larger than 0.4 SD is detectable with a reasonable number of markers (>100) and resource population size (>200 subjects); 3) the DNA pool from each tail of the trait distribution should contain at least 10% of the resource family; 4) each of the two DNA pools should include more than 35 individuals. Some of these guidelines that were deduced from the simulation analysis have been confirmed in the experimental part of this study.