Poster: Comparative Genomics

Abs # 923: A comparative analysis of QTLs for root traits in maize and rice: results and perspectives for QTL cloning using rice as a model

Presenter:	Tuberosa, Roberto , tuberosa@agrsci.unibo.it
Authors	Tuberosa, Roberto  (A)   Salvi, Silvio  (A)   Giuliani, Silvia  (A)   Bellotti, Massimo  (A)   Sanguineti, Maria Corinna  (A)   Stefanelli, Sandra  (A)   Landi, Pierangelo  (A)
Affiliations:	(A): Department of Agroenvironmental Sciences and Technology

Because of the large resources necessary to clone a QTL, it is advisable to focus on major QTLs affecting the trait of interest in different crosses and across species. Our long-term goal is to identify major QTLs controlling root traits in maize and clone the genes underlying such QTLs using rice as a model species. As a first step, we have analysed QTL data for root traits in maize and rice to identify the syntenic regions more valuable for a cloning approach. In maize, 4 populations have been investigated for QTLs for root traits (Tuberosa et al., 2002, Annals of Botany 89: 941-963). Several chromosome regions affected root traits in 2 or 3 populations. The most important QTLs were on chromosome bins 1.03, 1.06, 1.08, 2.04, 7.02, 8.06 and 10.04. Exploiting syntenic information, we have compared the maize QTL data with those for over 400 QTLs for root traits measured in 7 rice populations. The maize bins most frequently identified were: 2.04, 5.03, 8.05 and 5.04. We have used NILs developed for a major QTL (root-ABA1) in bin 2.04 affecting root traits and leaf ABA to produce a mapping population suitable for a positional cloning approach, presently the main strategy for QTL cloning. The rice sequence and synteny relationships allow us to use the markers closest to the QTL to cross-reference the genetic map to the rice sequence. The rice genome will then provide candidate genes and new markers to increase the mapping resolution, strongly reducing the need for contiged BAC/YAC libraries. Although QTL cloning remains a highly resource-demanding undertaking, its integration with genomics approaches will play an increasingly important role for harnessing the favourable allelic variation at loci affecting agronomically valuable traits.