Uncovering candidate genes involved in photosynthetic capacity using unexplored genetic variation in Spring Wheat

Abstract

To feed an ever-increasing population we must leverage advances in genomics and phenotyping to harness the variation in wheat breeding populations for traits like photosynthetic capacity which remains unoptimized. Here we survey a diverse set of wheat germplasm containing elite, introgression and synthetic derivative lines uncovering previously uncharacterized variation. We demonstrate how strategic integration of exotic material alleviates the D genome genetic bottleneck in wheat, increasing SNP rate by 62% largely due to Ae. tauschii synthetic wheat donors. Across the panel, 67% of the Ae. tauschii donor genome is represented as introgressions in elite backgrounds. We show how observed genetic variation together with hyperspectral reflectance data can be used to identify candidate genes for traits relating to photosynthetic capacity using association analysis. This demonstrates the value of genomic methods in uncovering hidden variation in wheat and how that variation can assist breeding efforts and increase our understanding of complex traits.

Keywords: Aegilops Tauschii; Triticum aestivum; GWAS; capture sequencing; exotic material; hyperspectral reflectance.

Figure 1

Genetic analysis of the HiBAP panel. (From outside to inside) (a) SNP density heatmap across the genome of loci containing < 10% missing data and >5% MAF within the HiBAP panel in 100 Kbp bins. (b) Fixation index calculated between elite background and exotic background subpopulations. (c) Genome‐wide association of flag leaf chlorophyll b content (c) Genome‐wide association of carotenoid content. Significance cut‐offs for ‐log10p of 5 and FDR correction are shown as blue and red lines respectively. SNPs in an interval above significance thresholds are shown in red.

Figure 2

Enrichment capture reveals hidden variation contributed by exotic material. Distribution of D genome polymorphic SNP markers in the HiBAP panel from (a) The 35K wheat breeders’ array and (b) PCA demonstrating the identified genetic variation using the 35K array SNPs (c) De novo SNP distribution from enrichment capture data after filtering the combined panel data for <10% missing data and a minor allele frequency (MAF) of >5%. (d) PCA demonstrating the identified genetic variation using the de novo called enrichment capture genotyping SNPs.

Figure 3

Synthetic wheat donor introgression identification in the D genome. D genome SNP density plots for (a) a representative example of HiBAP elite population and (b) an example of a member of the synthetically derived subpopulation. SNPs were binned into 500 kbp bins, demonstrating the number of SNPs that matched in position and allele of those seen in Ae. Tauschii against Chinese Spring reference genome (red) and the number of SNPs that did not match (blue).

Figure 4

Phenotypic variation in Spectral reflectance from the 149 lines: (a) The level of variation in reflectance of the visible portion of the hyperspectral reflectance data for each member of the HiBAP panel. The distribution of observed values derived from spectral indices (b) flag leaf chlorophyll a (c) flag leaf chlorophyll b and (d) flag leaf carotenoid content across all panel members where frequency relates to the number of panel members within each bin in each histogram.

Figure 5

Genome‐wide association results for total chlorophyll content. Manhattan plot showing (a) the GWA output for total chlorophyll content (R750/700), significance cut‐offs for ‐log10p of 5 and FDR correction are shown as blue and red lines respectively. (b) The same GWA output for chromosome 2B, the level of genetic linkage to the most associated SNP is depicted by a heatmap.