Detection of marker-QTL associations by studying change in marker frequencies with selection

• Published in: Theoretical and applied genetics 2007-02, Vol.114 (4), p.669-681
• Main Authors: Gallais, A, Moreau, L, Charcosset, A
• Format: Article
• Language: English
• Subjects: Analysis of Variance; artificial selection; Biological and medical sciences; Breeding - methods; Classical genetics, quantitative genetics, hybrids; Computer Simulation; corn; Fundamental and applied biological sciences. Psychology; Gene Frequency; genetic markers; Genetic Markers - genetics; Genetics of eukaryotes. Biological and molecular evolution; Genotype; Models, Genetic; Plants - genetics; population size; Pteridophyta, spermatophyta; Quantitative Trait Loci; Selection, Genetic; variance; Vegetals; Zea mays
• ISSN: 0040-5752; 1432-2242
• DOI: 10.1007/s00122-006-0467-z

The value of selective genotyping for the detection of QTL has already been studied from a theoretical point of view but with the assumption of a negligible contribution [graphic removed] of the QTL to the phenotypic variance. For predicting change in gene frequency, we show that this assumption is only valid for [graphic removed] less than 0.05 and for a proportion selected higher than 1%. Therefore, we develop a study of the optimization of selective genotyping without assumption on QTL effect, with selection either of both tails (bidirectional genotyping or BSG) or only one tail (unidirectional genotyping or USG). For a given population size of phenotyped plants the optimal proportion selected for selective genotyping is around 30% for each tail. For the same investment as in ANOVA, by investing more in phenotyping than in genotyping when the cost ratio of genotyping to phenotyping is higher than 1, the optimal proportion selected appears to be between 10 and 20% for each tail. It is mainly affected by the cost ratio and decreases when the cost ratio increases. At this optimum, BSG is competitive with ANOVA, or even more powerful, when the cost ratio is higher than 1. USG can also be competitive when the cost ratio is higher than 2. Using experimental data from two populations of about 300 F4 inbred families of maize, it was verified that BSG at the optimum gives the same results as ANOVA or is better whereas USG is less powerful or equivalent.