Consistency between marker- and genealogy-based heritability estimates in an experimental stand of Prosopis alba (Leguminosae)

• Autores: Bessega, C.; Saidman, B.O.; Darquier, M.R.; Ewens, M.; Sánchez, L.; Rozenberg, P.; Vilardi, J.C.
• Año de publicación: 2009
• Idioma: inglés
• Tipo de recurso: artículo
• Fuente: Am. J. Bot. 2009;96(2):458-465
• Materia: Fabaceae; Heritability; Leguminosae; Molecular markers; Prosopis alba; Quantitative traits; arid region; estimation method; experimental study; forest resource; genealogy; genetic marker; heritability; legume; life cycle; profitability; relatedness; resource management; semiarid region; Fabaceae; Prosopis; Prosopis alba
• Institución: Universidad Nacional de Buenos Aires. Facultad de Ciencias Exactas y Naturales

Prosopis represents a valuable forest resource in arid and semiarid regions. Management of promising species requires information about genetic parameters, mainly the heritability (h2) of quantitative profitable traits. This parameter is traditionally estimated from progeny tests or half-sib analysis conducted in experimental stands. Such an approach estimates h 2 from the ratio of between-family/total phenotypic variance. These analyses are difficult to apply to natural populations of species with a long life cycle, overlapping generations, and a mixed mating system, without genealogical information. A promising alternative is the use of molecular marker information to infer relatedness between individuals and to estimate h 2 from the regression of phenotypic similarity on inferred relatedness. In the current study we compared h2 of 13 quantitative traits estimated by these two methods in an experimental stand of P. alba, where genealogical information was available. We inferred pairwise relatedness by Ritland's method using six microsatellite loci. Relatedness and heritability estimates from molecular information were highly correlated to the values obtained from genealogical data. Although Ritland's method yields lower h 2 estimates and tends to overestimate genetic correlations between traits, this approach is useful to predict the expected relative gain of different quantitative traits under selection without genealogical information.
Fil:Bessega, C. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil:Saidman, B.O. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil:Darquier, M.R. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.
Fil:Vilardi, J.C. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina.