QTL mapping for yield components and agronomic traits in a Brazilian soybean population

Josiane Isabela Da Silva Rodrigues,Adésio Ferreira,Newton Deniz Piovesan,Maurilio Alves Moreira,Fábio Demolinari De Miranda,Everaldo Gonçalves De Barros,Cosme Damião Cruz,Marcia Flores Da Silva Ferreira

doi:10.1590/1984-70332016v16n4a41

Abstract

Abstract The objective of this work was to map QTL for agronomic traits in a Brazilian soybean population. For this, 207 F2:3 progenies from the cross CS3035PTA276-1-5-2 x UFVS2012 were genotyped and cultivated in Vicosa-MG, using randomized block design with three replications. QTL detection was carried out by linear regression and composite interval mapping. Thirty molecular markers linked to QTL were detected by linear regression for the total of nine agronomic traits. QTL for SWP (seed weight per plant), W100S (weight of 100 seeds), NPP (number of pods per plant), and NSP (number of seeds per plant) were detected by composite interval mapping. Four QTL with additive effect are promising for marker-assisted selection (MAS). Particularly, the markers Satt155 and Satt300 could be useful in simultaneous selection for greater SWP, NPP, and NSP.

Highlights

Soybean is by far the main export product in Brazil, and it is grown almost everywhere in the country
In the 2014/2015 season, its production accounted for 96.243 million tons, which corresponds to 46% of total grain yield in the country (CONAB 2015)
In addition to the low variability in the improved germplasm (Hyten et al 2006), other factors hinder the selection of productive cultivars, such as the environmental influence (Ainsworth et al 2012), which reduces the efficiency of selection of superior genotypes, and the existing negative correlation between grain yield and protein content (Popovic et al 2012), since another purpose of breeding programs is the increase in the protein content of the grain

Summary

Introduction

Soybean is by far the main export product in Brazil, and it is grown almost everywhere in the country. In addition to the low variability in the improved germplasm (Hyten et al 2006), other factors hinder the selection of productive cultivars, such as the environmental influence (Ainsworth et al 2012), which reduces the efficiency of selection of superior genotypes, and the existing negative correlation between grain yield and protein content (Popovic et al 2012), since another purpose of breeding programs is the increase in the protein content of the grain Facing these difficulties, the knowledge of the genetic control involved with grain yield may direct more effective strategies for selection, such as marker-assisted selection (MAS)

Objectives

Methods

Results