title: Testing for segregation distortion in genetic scoring data from backcross or doubled haploid populations.
abstract: It is important that breeders have the means to assess genetic scoring data for segregation distortion because of its probable effect on the design of efficient breeding strategies. Scoring data is usually assessed for segregation distortion by separate nonindependent chi2 tests at each locus in a set of marker loci. This analysis gives the loci most affected by selection if it exists, but it cannot give a statistically correct test for the presence or absence of selection in a linkage group as a whole. I have used a combined test based on the statistic, which is the most significant P-value from the above tests, called the single locus test. I have also derived mathematically a new combined statistical test, the overall test, for segregation distortion that requires genetic scoring data for a single linkage group. This test also takes genetic linkage into account. Using a range of marker densities and population sizes, simulations were carried out, to compare the power of these two statistical tests to detect the effect of selection at one or two loci. The single locus test was always found to be more powerful than the overall test, but the single locus test required a more complicated P-value correction. For the single locus test, approximate correction factors for the P-values are given for a range of marker densities and genetic lengths.
authors.affiliation: Molecular Genetics, Agriculture and Agri-Food Canada, Saskatoon Research Centre, 107 Science Place, Saskatoon, SK, Canada S7N 0X2. nixonj@agr.gc.ca
authors.forename: J
authors.initials: J
authors.lastname: Nixon
mesh_terms: D016009:Chi-Square Distribution; D002874:Chromosome Mapping; D020090:Chromosome Segregation / Q000235:genetics*; D003198:Computer Simulation; D005819:Genetic Markers; D005828:Genetics, Population; D006238:Haploidy; D008957:Models, Genetic; D010944:Plants / Q000235:genetics*; D015203:Reproducibility of Results
chemical_list: D005819:Genetic Markers