Effect of population structure corrections on the results of association mapping tests in complex maize diversity panels
Mezmouk, Sofiane | Dubreuil, Pierre | Bosio, Mickael | Decousset, Laurent | Charcosset, Alain, A. | Praud, Sébastien | Mangin, Brigitte, B. | Unité de Biométrie et Intelligence Artificielle (ancêtre de MIAT) (UBIA) ; Institut National de la Recherche Agronomique (INRA) | Genetics and Genomics in Cereals ; BIOGEMMA | Génétique Quantitative et Evolution - Le Moulon (Génétique Végétale) (GQE-Le Moulon) ; Institut National de la Recherche Agronomique (INRA)-Université Paris-Sud - Paris 11 (UP11)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS) | French "Association Nationale de la Recherche et de la Technologie'' (ANRT)
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Показать больше [+] Меньше [-]Английский. Association mapping of sequence polymorphisms underlying the phenotypic variability of quantitative agronomical traits is now a widely used method in plant genetics. However, due to the common presence of a complex genetic structure within the plant diversity panels, spurious associations are expected to be highly frequent. Several methods have thus been suggested to control for panel structure. They mainly rely on ad hoc criteria for selecting the number of ancestral groups; which is often not evident for the complex panels that are commonly used in maize. It was thus necessary to evaluate the effect of the selected structure models on the association mapping results. A real maize data set (342 maize inbred lines and 12,000 SNPs) was used for this study. The panel structure was estimated using both Bayesian and dimensional reduction methods, considering an increasing number of ancestral groups. Effect on association tests depends in particular on the number of ancestral groups and on the trait analyzed. The results also show that using a high number of ancestral groups leads to an over-corrected model in which all causal loci vanish. Finally the results of all models tested were combined in a meta-analysis approach. In this way, robust associations were highlighted for each analyzed trait.
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