Identification of quantitative trait loci for agronomically important traits and their association with genic-microsatellite markers in sorghum

Abstract

The identification of quantitative trait loci (QTLs) affecting agronomically important traits enable to understand their underlying genetic mechanisms and genetic basis of their complex interactions. The aim of the present study was to detect QTLs for 12 agronomic traits related to staygreen, plant early development, grain yield and its components, and some growth characters by analyzing replicated phenotypic datasets from three crop seasons, using the population of 168 F(7) RILs of the cross 296B x IS18551. In addition, we report mapping of a subset of genic-microsatellite markers. A linkage map was constructed with 152 marker loci comprising 149 microsatellites (100 genomic- and 49 genic-microsatellites) and three morphological markers. QTL analysis was performed by using MQM approach. Forty-nine QTLs were detected, across environments or in individual environments, with 1-9 QTLs for each trait. Individual QTL accounted for 5.2-50.4% of phenotypic variance. Several genomic regions affected multiple traits, suggesting the phenomenon of pleiotropy or tight linkage. Stable QTLs were identified for studied traits across different environments, and genetic backgrounds by comparing the QTLs in the study with previously reported QTLs in sorghum. Of the 49 mapped genic-markers, 18 were detected associating either closely or exactly as the QTL positions of agronomic traits. EST marker Dsenhsbm19, coding for a key regulator (EIL-1) of ethylene biosynthesis, was identified co-located with the QTLs for plant early development and staygreen trait, a probable candidate gene for these traits. Similarly, such exact co-locations between EST markers and QTLs were observed in four other instances. Collectively, the QTLs/markers identified in the study are likely candidates for improving the sorghum performance through MAS and map-based gene isolations.

Variation in flowering time in sorghum core collection and mapping of QTLs controlling flowering time by association analysis

“…This QTL was detected under 11 h of photoperiod and appeared to be responsible for the control of the photoperiod sensitivity in sorghum. In same way QTL detected on chromosome 5 has the same position as previously reported by Srinivas et al (2009) and can be considered as QTL controlling photoperiod sensitivity in sorghum. The QTL detected on chromosome 1 only under short daylength appears to be newly mapped in sorghum.…”

Quantitative trait loci associated to agronomic traits and yield components in a <i>Sorghum bicolor</i> L. Moench RIL population cultivated under pre-flowering drought and well-watered conditions

“…In this experiment, we identified major QTL groups on SBI-06 between marker sPbn-4036 and sPbn-6445 and on SBI-10 between the marker sPbn-9999 and sPbn-3832. Clustering of QTL for agronomic traits on a particular marker locus was reported by [16] on SBI-01 and SBI-06. Overlapping of QTL for multiple traits on the same chromosome region could be as a result of influence by the same set of genes (pleiotropy), genetic trade-offs among multiple traits or the effect of certain physiological or developmental measures on complex traits [30].…”

“…QTL associated with post-flowering drought resistance have been identified in sorghum using restricted fragment length polymorphism (RFLP) markers [3,6,[10][11][12][13][14] and random amplified polymorphic DNA (RAPD) markers [10,13]. EST-derived microsatellite markers, which may improve marker-assisted selection possibilities, were developed [15] and drought stress EST-derived microsatellite loci were mapped by [16]. DArT markers, which were used in the present study, are array based and analyses can be carried out on a minimal DNA sample requirement without prior DNA sequence information.…”

A Sorghum Mutant Resource as an Efficient Platform for Gene Discovery in Grasses

“…We investigated whether our sequenced mutant library could help validate genes located in QTL regions. To this end, we focused on 13 seed size QTL intervals reported in four mapping studies (Paterson et al, 1995;Feltus et al, 2006;Srinivas et al, 2009;Zhang et al, 2015). Four of these 13 genomic regions had been identified in both QTL and GWAS studies.…”