Biology subjects

Burt, C.

Publications and source records attributed to Burt, C..

3 recordsLinked to original sources

bioRxiv2023

Harnessing Landrace Diversity Empowers Wheat Breeding for Climate Resilience

Breeding crops resilient to climate change is urgently needed to help ensure food security. A key challenge is to harness genetic diversity to optimise adaptation, yield, stress resilience and nutrition. We examined the genetic and phenotypic diversity of the A.E. Watkins landrace collection of bread wheat (Triticum aestivum), a major global cereal, through whole-genome re-sequencing (827 Watkins landraces and 208 modern cultivars) and in-depth field evaluation spanning a decade. We discovered that modern cultivars are derived from just two of the seven ancestral groups of wheat, leaving five groups as previously untapped sources for breeding. This provides access to landrace-specific functional variations using structured germplasm, genotyping and informatics resources. Employing complementary genetic populations and approaches, we identified thousands of high-resolution quantitative trait loci (QTL) and significant marker-trait associations for major traits, revealing many Watkins-unique loci that can confer superior traits in modern wheat. Furthermore, we identified and functionally verified causative genes for climate-change adaptation, nutritional enhancement and resistance to wheat blast. Finally, we assessed the phenotypic effects of 44,338 Watkins-unique haplotypes, introgressed from 143 prioritised QTL in the context of modern cultivars, bridging the gap between landrace diversity and current breeding. This study establishes a framework for systematically utilising genetic diversity in crop improvement to achieve sustainable food security.

plant biology↗

bioRxiv2020

Type II Fusarium head blight susceptibility factor identified in wheat

Fusarium head blight (FHB) causes significant grain yield and quality reductions in wheat and barley. Most wheat varieties are incapable of preventing FHB spread through the rachis, but disease is typically limited to individually infected spikelets in barley. We point inoculated wheat lines possessing barley chromosome introgressions to test whether FHB resistance could be observed in a wheat genetic background. The most striking differential was between 4H(4D) substitution and 4H addition lines. The 4H addition line was similarly susceptible to the wheat parent, but the 4H(4D) substitution line was highly resistant, which suggests that there is an FHB susceptibility factor on wheat chromosome 4D. Point inoculation of Chinese Spring 4D ditelosomic lines demonstrated that removing 4DS results in high FHB resistance. We genotyped four Chinese Spring 4DS terminal deletion lines to better characterise the deletions in each line. FHB phenotyping indicated that lines del4DS-2 and del4DS-4, containing smaller deletions, were susceptible and had retained the susceptibility factor. Lines del4DS-3 and del4DS-1 contain larger deletions and were both significantly more resistant, and hence had presumably lost the susceptibility factor. Combining the genotyping and phenotyping results allowed us to refine the susceptibility factor to a 31.7 Mbp interval on 4DS. HighlightWe have identified a Type II Fusarium head blight susceptibility factor on the short arm of wheat chromosome 4D and refined its position to a 31.7 Mbp interval.

genetics↗

bioRxiv2018

The effects of training population design on genomic prediction accuracy in wheat

Genomic selection offers several routes for increasing genetic gain or efficiency of plant breeding programs. In various species of livestock there is empirical evidence of increased rates of genetic gain from the use of genomic selection to target different aspects of the breeders equation. Accurate predictions of genomic breeding value are central to this and the design of training sets is in turn central to achieving sufficient levels of accuracy. In summary, small numbers of close relatives and very large numbers of distant relatives are expected to enable accurate predictions.\n\nTo quantify the effect of some of the properties of training sets on the accuracy of genomic selection in crops we performed an extensive field-based winter wheat trial. In summary, this trial involved the construction of 44 F2:4 bi- and triparental populations, from which 2992 lines were grown on four field locations and yield was measured. For each line, genotype data were generated for 25,000 segregating single nucleotide polymorphism markers. The overall heritability of yield was estimated to 0.65, and estimates within individual families ranged between 0.10 and 0.85. Within cross genomic prediction accuracies of yield BLUEs were 0.125 - 0.127 using two different cross-validation approaches, and generally increased with training set size. Using related crosses in training and validation sets generally resulted in higher prediction accuracies than using unrelated crosses. The results of this study emphasize the importance of the training set design in relation to the genetic material to which the resulting prediction model is to be applied.

genetics↗