Use of Diversity Arrays Technology (DArT) for detection of QTL underlying plant architecture and yield-related traits in cassava

Abstract

ABSTRACT Cassava (Manihot esculenta Crantz) is an important starchy root crop representing food security for more than one billion people in the world. Plant architecture (PA) is a key factor underlying yield, and understanding the physiological basis of PA may lead to improved yield plants. In this study, we identified genomic regions controlling several traits associated with plant architecture and productivity in Thai cassava cultivars. The analysis was conducted in an F1 population derived from a cross between “Huay Bong 60” (female parent) and “Hanatee” (male parent). A genetic linkage analysis was undertaken with 167 conventional DArT markers using JoinMap 3.0 software. Plant architecture and productivity were evaluated over four years at the Rayong Field Crops Research Center, Thailand. DArT markers associated with five traits of plant architecture (number of mainstems per plant (NMain), number of apices per plant (NAp), plant height (PH), first branching height (BH), branching level (BL)) as well as two productivity measurements harvest index (HI) and fresh root yield (FRY) were identified by simple interval mapping (SIM) using MapQTL 4.0. Small effects of 22 plant architecture quantitative trait loci (QTL) were detected, with phenotypic contributions varying from 5.1 to 10.9%. Small effects of four QTL of FRY and five QTL of HI were also detected. Additionally, PH showed a significant correlation with FRY. However, this study has limitations from the low density of the markers, therefore additional markers would be necessary for an efficient genetic linkage map and QTL detection.