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

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.

Genetic analysis of yield and heat stress related traits in wheat (Triticum aestivum L. em. Thell) using microsatellite markers

“…These are powerful tools for many studies for genome characterization, detection of quantitative trait loci (QTL) for both abiotic and biotic stresses, evolutionary studies, and for marker assisted selection (MAS) (Peleg et al, 2008;Chu et al, 2010 andSadat et al, 2013). In bread wheat, a variety of complex traits have been subjected to QTL analysis (Borner et al, 2002;Wang et al, 2009Wang et al, , 2010Wu et al, 2010;Rustgi et al, 2013) using SSR markers. Therefore, in present investigation genetic analysis of terminal heat tolerance was conducted to identify QTLs for stress related traits of wheat.…”

Agronomically Relevant Traits Transferred to Major Crop Plants by Alien Introgressions

“…However, due to the laborious process of developing these lines only a few QTL mapping studies have so far been conducted using RICLs. In general RICLs allow more effi cient dissection of complex traits than any other commonly used biparental mapping population (Campbell et al 2003 ;Distelfeld et al 2006 ;Rustgi et al 2013 ). For instance, using an RICL carrying an introgression from T. dicoccoides (wild emmer wheat), Uauy et al ( 2006 ) identifi ed and cloned a gene ( Gpc-B1 ) responsible for senescence, grain protein, zinc, and iron content in bread wheat.…”

Mapping of QTLs conferring resistance in rice to brown planthopper, Nilaparvata lugens

“…The usefulness of molecular markers and simulation models provides avenues to generate and deploy durably resistant rice cultivars with other desirable traits on a large scale in the future (Savary et al, 2012;Ashkani et al, 2013). The high-resolution melt (HRM) analysis genotyping method, a recently developed technique that is fast, lowcost, easy and efficient with a high proportion of accurate results, has been proposed as a method for genotyping and mapping populations (Croxford et al, 2008;Han et al, 2012;Rustgi et al, 2013;Vipin et al, 2013). In the past few years, through the development of closely linked molecular markers, several QTLs or loci for BPH resistance have been identified and localized on rice chromosomes using different phenotypic systems with different populations (Alam & Cohen, 1998;Huang et al, 2001;Su et al, 2002;Soundararajan et al, 2004;Liu et al, 2009;Cheng et al, 2013) (Table 1).…”