Trend, population structure, and trait mapping from 15 years of national varietal trials of UK winter wheat

Shorinola, Oluwaseyi; Simmonds, James; Wingen, Luzie U.; Uauy, Cristóbal

doi:10.1093/g3journal/jkab415

Cited by 6 publications

(3 citation statements)

References 58 publications

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“…This revealed that the physical assignment based on skim sequence data is close to 100% accurate. However, the physical assignment of markers derived from existing platforms still require information from mapping populations to help identify the correct homeolog (Shorinola et al ., 2022). To aid correct placement of markers, the mapping locations are included in Data S3.…”

Section: Discussionmentioning

confidence: 99%

Development of a next generation SNP genotyping array for wheat

Burridge,

Winfield,

Przewieslik‐Allen

et al. 2024

Plant Biotechnology Journal

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SummaryHigh‐throughput genotyping arrays have provided a cost‐effective, reliable and interoperable system for genotyping hexaploid wheat and its relatives. Existing, highly cited arrays including our 35K Wheat Breeder's array and the Illumina 90K array were designed based on a limited amount of varietal sequence diversity and with imperfect knowledge of SNP positions. Recent progress in wheat sequencing has given us access to a vast pool of SNP diversity, whilst technological improvements have allowed us to fit significantly more probes onto a 384‐well format Axiom array than previously possible. Here we describe a novel Axiom genotyping array, the ‘Triticum aestivum Next Generation’ array (TaNG), largely derived from whole genome skim sequencing of 204 elite wheat lines and 111 wheat landraces taken from the Watkins ‘Core Collection’. We used a novel haplotype optimization approach to select SNPs with the highest combined varietal discrimination and a design iteration step to test and replace SNPs which failed to convert to reliable markers. The final design with 43 372 SNPs contains a combination of haplotype‐optimized novel SNPs and legacy cross‐platform markers. We show that this design has an improved distribution of SNPs compared to previous arrays and can be used to generate genetic maps with a significantly higher number of distinct bins than our previous array. We also demonstrate the improved performance of TaNGv1.1 for Genome‐wide association studies (GWAS) and its utility for Copy Number Variation (CNV) analysis. The array is commercially available with supporting marker annotations and initial genotyping results freely available.

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Section: Discussionmentioning

confidence: 99%

Development of a next generation SNP genotyping array for wheat

Burridge,

Winfield,

Przewieslik‐Allen

et al. 2024

Plant Biotechnology Journal

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“…Soft wheat breeding in the UK has relied heavily upon Claire and Robigus as parents since their development in 1999 and 2005, respectively. A recent study found that UK winter wheats developed between 2002 and 2017 could be clustered into four distinct populations, and two of these populations were characterized by their Claire or Robigus heritage (Shorinola et al., 2022). The varieties within these population groups characterized by Claire and Robigus heritage are also almost entirely soft wheat varieties, further emphasizing the importance of these two varieties in UK soft wheat breeding.…”

Section: Discussionmentioning

confidence: 99%

Genetic control of grain amino acid composition in a UK soft wheat mapping population

et al. 2023

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Wheat (Triticum aestivum L.) is a major source of nutrients for populations across the globe, but the amino acid composition of wheat grain does not provide optimal nutrition. The nutritional value of wheat grain is limited by low concentrations of lysine (the most limiting essential amino acid) and high concentrations of free asparagine (precursor to the processing contaminant acrylamide). There are currently few available solutions for asparagine reduction and lysine biofortification through breeding. In this study, we investigated the genetic architecture controlling grain free amino acid composition and its relationship to other traits in a Robigus × Claire doubled haploid population. Multivariate analysis of amino acids and other traits showed that the two groups are largely independent of one another, with the largest effect on amino acids being from the environment. Linkage analysis of the population allowed identification of quantitative trait loci (QTL) controlling free amino acids and other traits, and this was compared against genomic prediction methods. Following identification of a QTL controlling free lysine content, wheat pangenome resources facilitated analysis of candidate genes in this region of the genome. These findings can be used to select appropriate strategies for lysine biofortification and free asparagine reduction in wheat breeding programs.

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“…Genetic gain is often used as a measure of the success in breeding for trait improvement. For example, in wheat, the genetic gains in yield and other agriculturally valuable traits have been well quantified (Mackay et al 2011 , Tadesse et al 2019 and Shorinola et al 2022 ). The introduction of genomic selection (GS) (Meuwissen et al 2001 ) in breeding programs further shortens breeding cycles, improves selection accuracy and intensity, and accelerates genetic gain (Voss-Fels et al 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Analysis of historical selection in winter wheat

et al. 2022

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Key Message Modeling of the distribution of allele frequency over year of variety release identifies major loci involved in historical breeding of winter wheat. Abstract Winter wheat is a major crop with a rich selection history in the modern era of crop breeding. Genetic gains across economically important traits like yield have been well characterized and are the major force driving its production. Winter wheat is also an excellent model for analyzing historical genetic selection. As a proof of concept, we analyze two major collections of winter wheat varieties that were bred in Western Europe from 1916 to 2010, namely the Triticeae Genome (TG) and WAGTAIL panels, which include 333 and 403 varieties, respectively. We develop and apply a selection mapping approach, Regression of Alleles on Years (RALLY), in these panels, as well as in simulated populations. RALLY maps loci under sustained historical selection by using a simple logistic model to regress allele counts on years of variety release. To control for drift-induced allele frequency change, we develop a hybrid approach of genomic control and delta control. Within the TG panel, we identify 22 significant RALLY quantitative selection loci (QSLs) and estimate the local heritabilities for 12 traits across these QSLs. By correlating predicted marker effects with RALLY regression estimates, we show that alleles whose frequencies have increased over time are heavily biased toward conferring positive yield effect, but negative effects in flowering time, lodging, plant height and grain protein content. Altogether, our results (1) demonstrate the use of RALLY to identify selected genomic regions while controlling for drift, and (2) reveal key patterns in the historical selection in winter wheat and guide its future breeding.

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Trend, population structure, and trait mapping from 15 years of national varietal trials of UK winter wheat

Cited by 6 publications

References 58 publications

Development of a next generation SNP genotyping array for wheat

Development of a next generation SNP genotyping array for wheat

Genetic control of grain amino acid composition in a UK soft wheat mapping population

Analysis of historical selection in winter wheat

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