Genome-wide association mapping of black point reaction in common wheat (Triticum aestivum L.)

Abstract

BackgroundBlack point is a serious threat to wheat production and can be managed by host resistance. Marker-assisted selection (MAS) has the potential to accelerate genetic improvement of black point resistance in wheat breeding. We performed a genome-wide association study (GWAS) using the high-density wheat 90 K and 660 K single nucleotide polymorphism (SNP) assays to better understand the genetic basis of black point resistance and identify associated molecular markers.ResultsBlack point reactions were evaluated in 166 elite wheat cultivars in five environments. Twenty-five unique loci were identified on chromosomes 2A, 2B, 3A, 3B (2), 3D, 4B (2), 5A (3), 5B (3), 6A, 6B, 6D, 7A (5), 7B and 7D (2), respectively, explaining phenotypic variation ranging from 7.9 to 18.0%. The highest number of loci was detected in the A genome (11), followed by the B (10) and D (4) genomes. Among these, 13 were identified in two or more environments. Seven loci coincided with known genes or quantitative trait locus (QTL), whereas the other 18 were potentially novel loci. Linear regression showed a clear dependence of black point scores on the number of favorable alleles, suggesting that QTL pyramiding will be an effective approach to increase resistance. In silico analysis of sequences of resistance-associated SNPs identified 6 genes possibly involved in oxidase, signal transduction and stress resistance as candidate genes involved in black point reaction.ConclusionSNP markers significantly associated with black point resistance and accessions with a larger number of resistance alleles can be used to further enhance black point resistance in breeding. This study provides new insights into the genetic architecture of black point reaction.Electronic supplementary materialThe online version of this article (10.1186/s12870-017-1167-3) contains supplementary material, which is available to authorized users.

Exome association analysis sheds light onto leaf rust (Puccinia triticina) resistance genes currently used in wheat breeding (Triticum aestivum L.)

“…Even though most studies have reported that LD decays slower for the D genome (e.g. Chen et al , ; Liu et al , ; Lopes et al , ; Sukumaran et al , ), there have been also some studies showing LD decay values for the D genome that are similar to or even lower than those of the A and/or B genomes (Sehgal et al , ; Zhang et al , ). One possible explanation for this discrepancy are the differences in number of marker pairs as a function of physical distance observed in our study (Figure S7).…”

Genome‐wide association study reveals loci associated with seed longevity in common wheat (Triticum aestivum L.)

“…The diverse panel of 166 varieties of wheat ( Triticum aestivum L.)was used in the present study, comprising 144 accessions from the Yellow and Huai River Valleys Facultative Wheat Region of China, and 22 accessions from other foreign countries, including Italy, Argentina, Japan, Australia and Turkey (Liu et al, ). The 166 wheat accessions were grown at Anyang in Henan Province (2013–2014 cropping season, named as Anyang 2013), Beijing (2015–2016 cropping season, named as Beijing 2016) and Gaocheng of Hebei Province (2015–2016 cropping season, named as Gaocheng 2016).…”

“…Population structure, principal components analysis (PCA) and neighbour‐joining (Figure S2) tree analysis had also been conducted. The results indicated that the 166 wheat accessions could be divided into three subgroups (Dong et al, ; Liu et al, ).…”

“…Nowadays, GWAS is commonly used to the study on complex quantitative traits for many crops (Zhu et al, ). In wheat, GWAS has been applied to investigate the genetic basis of biotic and abiotic resistance (Liu et al, ), yield (Breseghello & Sorrells, ) and end‐use quality‐related traits (Zhai et al, ).…”

Genome-wide association mapping revealed syntenic loci QFhb-4AL and QFhb-5DL for Fusarium head blight resistance in common wheat (Triticum aestivum L.)

“…The optimal K-value was determined using the ΔK method [55].Linkage disequilibrium (LD) among markers was computed by the full matrix and sliding window options in Tassel v5.0 with the filtered SNP markers. The pairwise LD between the markers was calculated using squared allele frequency correlations r 2 , according to Liu et al [56].…”