Identification of genetic factors controlling kernel hardness and related traits in a recombinant inbred population derived from a soft × ‘extra-soft’ wheat (Triticum aestivum L.) cross

Abstract

Kernel hardness or texture, used to classify wheat (Triticum aestivum L.) into soft and hard classes, is a major determinant of milling and baking quality. Wheat genotypes in the soft class that are termed 'extra-soft' (with kernel hardness in the lower end of the spectrum) have been associated with superior end-use quality. In order to better understand the relationship between kernel hardness, milling yield, and various agronomic traits, we performed quantitative trait mapping using a recombinant inbred line population derived from a cross between a common soft wheat line and a genotype classified as an 'extra-soft' line. A total of 47 significant quantitative trait loci (QTL) (LOD ≥ 3.0) were identified for nine traits with the number of QTL affecting each trait ranging from three to nine. The percentage of phenotypic variance explained by these QTL ranged from 3.7 to 50.3%. Six QTL associated with kernel hardness and break flour yield were detected on chromosomes 1BS, 4BS, 5BS, 2DS, 4DS, and 5DL. The two most important QTL were mapped onto orthologous regions on chromosomes 4DS (Xbarc1118-Rht-D1) and 4BS (Xwmc617-Rht-B1). These results indicated that the 'extra-soft' characteristic was not controlled by the Hardness (Ha) locus on chromosome 5DS. QTL for eight agronomic traits occupied two genomic regions near semi-dwarf genes Rht-D1 on chromosome 4DS and Rht-B1 on chromosome 4BS. The clustering of these QTL is either due to the pleiotropic effects of single genes or tight linkage of genes controlling these various traits.

Exotic QTL improve grain quality in the tri-parental wheat population SW84

“…In the present study, the exotic allele was associated with an increase in grain protein content as observed by [6] and [19]. This QTL was also mapped by [18], [74], [16], [25], [84] and [85], indicating that Gpc-B1 presumably acts as a major factor to regulate high protein content in wheat.…”

“…The co-localization of QTL for both traits may be attributed to pleiotropy effects of the Pina and Pinb genes. Also [84] identified a QTL for GH on the long arm of 5D.…”

“…At the latter QTL a cluster of marker-trait associations was identified, again pointing to an omnipresent major QTL effect, which is present regardless of the family and the N level studied. This QTL was also mapped by [83], [32], [84], [39], [89], [90], [91] and [19], where the latter also identified a trait reducing, negative effect of the exotic donor allele. As stated before, this QTL most likely corresponds to the Ha locus where puroindoline a (Pina) and puroindoline b (Pinb) are located [19], [89].…”

Identification and validation of QTL for grain yield and plant water status under contrasting water treatments in fall-sown spring wheats

“…Most of the initial QTL studies were performed using biparental mapping populations, but genome-wide association studies (GWAS) have become common in recent years (Breseghello and Sorrells 2006; Wang et al 2012; Edae et al 2014; Ain et al 2015; Sukumaran et al 2015; Zanke et al 2015). Compared to biparental mapping populations, GWAS populations can be developed faster and provide access to a wider range of alleles (Zhu et al 2008).…”

Genomics of consumer traits in chapatti quality

“…Bread is made from hard wheat (Ramírez et al, 2003;Wang et al, 2012) while chapatti requires medium hard wheat for optimum quality (Austin and Ram, 1971). Bread needs about 13% protein for optimum quality (Zanoni et al, 1993), whereas chapatti requires 10%…”