title: Efficiency of generations for estimating marker-associated QTL effects by multiple regression.
abstract: Knowledge about the efficiency of generations for estimating marker-associated QTLs is needed for selection. The objective of this paper is to develop a theory to compare the efficiency of segregating generations and testcrosses from the cross of two inbred lines differing in value for a quantitative trait (P1 x P2) for estimating additive, dominance and heterotic effects of QTLs by stepwise regression. An equation that predicts the smallest gene effect in genetic standard deviation units that can be detected with 50% chance at a significance level as a function of the heritability (h2) and the recombination frequency (r) of markers was developed for the segregating generations and testcrosses. For estimating additive effects, the most efficient generation was the doubled-haploid (DH) lines; the most inefficient was the North Carolina Design III (NCD III), followed by selfed backcrosses (SB); the selfed families from F2 individual plants (F2:3 lines) are inferior to the recombinant inbreds (RI) for low r, but are better than RI for high h2 and r. Dominance effects are less efficiently estimated than additive effects. The NCD III is better than the SB and the F2:3 lines for detecting dominance effects. The RI and DH do not estimate dominance effects. The differential heterotic QTL effects of lines P1 and P2 when crossed with tester T can be estimated by evaluating testcrosses of individual F2 plants (F2T), recombinant inbreds (RIT) and double-haploid lines (DHT). The DHT is superior to the other generations. The F2T is better than the RIT for r > or = 0.20, but inferior for r < or = 0.1 or low heritability.
authors.affiliation: Centro de Investigaciones Agrarias de Mabegondo, La Coruña, Spain.
authors.forename: J
authors.initials: J
authors.lastname: Moreno-Gonzalez
mesh_terms: D003433:Crosses, Genetic; D005819:Genetic Markers; D008957:Models, Genetic; D010944:Plants / Q000235:genetics*; D012044:Regression Analysis; D012641:Selection, Genetic
chemical_list: D005819:Genetic Markers