Trade-offs in the genetic control of functional and nutritional quality traits in UK winter wheat

Abstract

A complex network of trade-offs exists between wheat quality and nutritional traits. We investigated the correlated relationships among several milling and baking traits as well as mineral density in refined white and whole grain flour. Our aim was to determine their pleiotropic genetic control in a multi-parent population over two trial years with direct application to practical breeding. Co-location of major quantitative trait loci (QTL) and principal component based multi-trait QTL mapping increased the power to detect QTL and revealed pleiotropic effects explaining many complementary and antagonistic trait relationships. High molecular weight glutenin subunit genes explained much of the heritable variation in important dough rheology traits, although additional QTL were detected. Several QTL, including one linked to the TaGW2 gene, controlled grain size and increased flour extraction rate. The semi-dwarf Rht-D1b allele had a positive effect on Hagberg falling number, but reduced grain size, specific weight, grain protein content and flour water absorption. Mineral nutrient concentrations were lower in Rht-D1b lines for many elements, in wholemeal and white flour, but potassium concentration was higher in Rht-D1b lines. The presence of awns increased calcium content without decreasing extraction rate, despite the negative correlation between these traits. QTL were also found that affect the relative concentrations of key mineral nutrients compared to phosphorus which may help increase bioavailability without associated anti-nutritional effects of phytic acid. Taken together these results demonstrate the potential for marker-based selection to optimise trait trade-offs and enhance wheat nutritional value by considering pleiotropic genetic effects across multiple traits.

Prediction of near‐term climate change impacts on UK wheat quality and the potential for adaptation through plant breeding

“…Of these trials, 323 were part of the Agriculture and Horticulture Development Board (AHDB) Recommended List national variety testing series between harvest years 2001 and 2020. Additional trials included evaluation of 19 high quality milling wheat varieties as part of 32 National Institute of Agricultural Botany (NIAB) membership trials between the harvest years 2009 and 2019, and trials from 2017 to 2019 described in Fradgley et al ( 2022 ) and Scott et al ( 2021 ), which included founders of multi‐parent mapping populations that were varieties in common with AHDB Recommended List trials. Finally, data for nine advanced breeding line evaluation trials as part of the breeding programme from the UK branch of the commercial breeding company Deutsche Saatveredelung (DSV) including 40 lines between 2017 and 2019 were also used.…”

“…These specific quality trait criteria include: high grain specific weight (a volumetric measurement of grain packing density), grain protein content at around 13%, high gluten strength determined using rheological tests such as Chopin alveograph (Bennett & Coppock, 1952 ) and Zeleny sedimentation (Zeleny et al, 1960 ), and the degree of starch breakdown resulting from enzyme activity, which is determined using the Hagberg falling number (HFN) test (Perten, 1964 ). Together, these traits are partly predictive of milling and baking performance (Andersson et al, 1994 ; Fradgley et al, 2022 ) determining quality class and value of wheat off‐farm, and are therefore critical traits for selection in wheat breeding. As well as genetic effects that can be selected through breeding, environmental conditions also affect wheat quality traits.…”

Form follows function in Triticeae inflorescences

“…Haplotype analysis revealed that four SNPs located in B1 promoter region are associated with awn length, grain length and thousand-grain weight ( Wang et al 2020 ). The investigation of 231 wheat lines from the NIAB MAGIC population found that the presence of awns increased the grain calcium content without decreasing the flour extraction rate, despite the negative correlation between these traits ( Fradgley et al 2022 ).…”

Robotized indoor phenotyping allows genomic prediction of adaptive traits in the field

“…However, the use in breeding of these physiological and growth-related traits faces the difficulty of their high sensitivity to environmental conditions, resulting in large genotype x environment interactions [24][25][26][27] . This difficulty is not limited to the extrapolation of trait values from indoor to field conditions: most of these traits also largely vary between fields depending on environmental conditions, making difficult the prediction of traits in one field from those measured in another field 24,28,29 . The relationship between these traits and yield is also highly depending on environmental scenarios 30,31 .…”