Analysis of the chromosome 2(2H) region of barley associated with the correlated traits Fusarium head blight resistance and heading date

Abstract

Fusarium head blight (FHB) is a major disease of barley (Hordeum vulgare L.) that results in reduced grain yield and quality through the accumulation of the mycotoxin deoxynivalenol (DON). Coincident QTL for FHB severity, DON concentration, and heading date (HD) map to a region of chromosome 2(2H) designated Qrgz-2H-8. It is unclear whether disease resistance at this locus is due to a pleiotropic effect of late HD by delaying the host exposure to the pathogen or a tightly linked resistance gene. The objectives of this study were to develop a set of near isogenic lines (NILs) for the Qrgz-2H-8 region and to genetically dissect the QTL region containing the coincident traits. Two NIL populations were developed consisting of F(2)- and F(4)-derived recombinants from a cross between a BC(5) line carrying the donor parent (Chevron) alleles in the Qrgz-2H-8 region and the recurrent parent M69. Analysis of field and marker data from these NILs revealed that the Chevron alleles conditioning FHB resistance, late HD, and low DON concentration were successfully introgressed into the BC(5) parent line and were segregating among NILs. QTL analysis of the F(4)-derived population showed that the HD QTL is adjacent to the FHB QTL. Furthermore, a single NIL was identified that was similar to the resistant BC(5) parent for FHB severity and the early flowering parent M69 for HD. These results indicate that the relationship between FHB and HD at the Qrgz-2H-8 region is likely due to tight linkage rather than pleiotropy.

Performance of Single Nucleotide Polymorphisms versus Haplotypes for Genome-Wide Association Analysis in Barley

“…The strongest association was for markers on the long arm of chromosome 2H near the centromere (position 63.53 cM on the consensus map of Close et al [28]). This region has been referred to as Qrgz-2H-8 [29] and is typically associated with QTL for heading date and Fusarium Head Blight resistance [30]. Microsatellite marker GBM1023 , used by Nduulu et al [30] for genetically dissecting this region, is linked to the SNP highly associated with heading date in this study, POPA2_1399 [28].…”

Identification of RAPD Markers Linked to Digestive Amylase Genes using Near Isogenic Lines of the Silkworm,Bombyx mori

“…The sets showing RP/NIL allelic contrast and corresponding NIL/DP allelic equality for the informative DNA marker will establish the linkage of the marker with the target trait. NILs have been extensively used in crop plants for rapidly identifying the DNA markers that are tightly linked to the gene of interest, like tobacco mosaic virus resistance gene (Young and Tanksley 1989), Pseudomonas resistance gene (Martin et al 1991) in tomato, stripe resistance genes (Coram et al 2008; Yan et al 2003; Chague et al 1999), leaf rust resistance genes (Gupta et al 2005; Thatcher et al 2003), powdery mildew resistance genes (Ma et al 2004) in wheat, QTLs for root traits (Steele et al 2006), chlorophyll content (Wang et al 2008), bacterial blight resistance genes (Gu et al 2008) in rice and barley (Nduulu et al 2007), and maturity genes in soybean (Matsumara et al 2008). Similarly, in the present investigation, NILs developed in B. mori has been used for detecting DNA markers associated with high activity digestive amylase genes.…”

Potential and Optimization of Genomic Selection for Fusarium Head Blight Resistance in Six‐Row Barley

“…Genomic selection (Meuwissen et al, 2001) is a form of marker‐based selection using all marker information to make predictions of breeding or genotypic values for complex traits. The goal is to make predictions with high enough accuracy to allow parent selection based on those predictions, known as genomic estimated breeding values (GEBVs), alone.…”

“…The disadvantages of the two‐step process are (i) choosing an arbitrary statistical threshold to determine which markers contribute to the genetic prediction and (ii) estimating the effects of only a subset of markers inflates effect estimates because genetic variation accounted for by the markers left out of the model, as well as some residual variation, is absorbed by the markers included in the model. As pointed out by Whittaker et al (2000) and Meuwissen et al (2001), it would be preferable to include all available markers in the prediction model. Statistical models capable of estimating all marker effects simultaneously when the number of markers is large or larger than the number observations are needed.…”

“…These advances in statistical modeling have been at least as important to the development of GS as high‐throughput DNA sequencing and inexpensive marker platforms. Indeed, modeling all markers using GS models has proven to produce more accurate predictions than marker‐assisted selection models in both empirical and simulation studies (Meuwissen et al, 2001; Bernardo and Yu, 2007; Lorenzana and Bernardo, 2009; Heffner et al, 2009).…”