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.
ASJC Scopus subject areas
- Molecular Biology