Genetic Dissection of Yield and Its Component Traits Using High-Density Composite Map of Wheat Chromosome 3A: Bridging Gaps between QTLs and Underlying Genes

Authors

Sachin Rustgi, Washington State University
Mustafa N. Shafqat, Washington State University
Neeraj Kumar, Washington State UniversityFollow
P. Stephen Baenziger, University of Nebraska-LincolnFollow
M. Liakat Ali, University of Nebraska-Lincoln
Ismail M. Dweikat, University of Nebraska-LincolnFollow
B. Todd Campbell, USDA, Agricultural Research ServiceFollow
Kulvinder Singh Gill, Washington State UniversityFollow

ORCID IDs

P. Stephen Baenziger

Date of this Version

2013

Citation

Rustgi S, Shafqat MN, Kumar N, Baenziger PS, Ali ML, et al. (2013) Genetic Dissection of Yield and Its Component Traits Using High-Density Composite Map of Wheat Chromosome 3A: Bridging Gaps between QTLs and Underlying Genes. PLoS ONE 8(7): e70526. doi:10.1371/journal.pone.0070526

Comments

Copyright 2013 Rustgi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Earlier we identified wheat (Triticum aestivum L.) chromosome 3A as a major determinant of grain yield and its component traits. In the present study, a high-density genetic linkage map of 81 chromosome 3A-specific markers was developed to increase the precision of previously identified yield component QTLs, and to map QTLs for biomass-related traits. Many of the previously identified QTLs for yield and its component traits were confirmed and were localized to narrower intervals. Four novel QTLs one each for shoot biomass (Xcfa2262-Xbcd366), total biomass (wPt2740-Xcfa2076), kernels/spike (KPS) (Xwmc664-Xbarc67), and Pseudocercosporella induced lodging (PsIL) were also detected. The major QTLs identified for grain yield (GY), KPS, grain volume weight (GVWT) and spikes per square meter (SPSM) respectively explained 23.2%, 24.2%, 20.5% and 20.2% of the phenotypic variation. Comparison of the genetic map with the integrated physical map allowed estimation of recombination frequency in the regions of interest and suggested that QTLs for grain yield detected in the marker intervals Xcdo549-Xbarc310 and Xpsp3047-Xbarc356 reside in the high-recombination regions, thus should be amenable to map-based cloning. On the other hand, QTLs for KPS and SPSM flanked by markers Xwmc664 and Xwmc489 mapped in the low-recombination region thus are not suitable for map-based cloning. Comparisons with the rice (Oryza sativa L.) genomic DNA sequence identified 11 candidate genes (CGs) for yield and yield related QTLs of which chromosomal location of two (CKX2 and GID2-like) was confirmed using wheat aneuploids. This study provides necessary information to perform high-resolution mapping for map-based cloning and for CG-based cloning of yield QTLs.