Crop productivity must increase at unprecedented rates to meet the needs of the growing worldwide population. Exploiting natural variation for the genetic improvement of crops plays a central role in increasing productivity. Although current genomic technologies can be used for high-throughput identification of genetic variation, methods for efficiently exploiting this genetic potential in a targeted, systematic manner are lacking. Here, we developed a haplotype-based approach to identify genetic diversity for crop improvement using genome assemblies from 15 bread wheat (Triticum aestivum) cultivars. We used stringent criteria to identify identical-by-state haplotypes and distinguish these from near-identical sequences (~99.95% identity). We showed that each cultivar shares ~59 % of its genome with other sequenced cultivars and we detected the presence of extended haplotype blocks containing hundreds to thousands of genes across all wheat chromosomes. We found that genic sequence alone was insufficient to fully differentiate between haplotypes, as were commonly used array-based genotyping chips due to their gene centric design. We successfully used this approach for focused discovery of novel haplotypes from a landrace collection and documented their potential for trait improvement in modern bread wheat. This study provides a framework for defining and exploiting haplotypes to increase the efficiency and precision of wheat breeding towards optimising the agronomic performance of this crucial crop.

Here, we defined haplotype blocks in hexaploid bread wheat using genome assemblies of cultivars representing modern-day diversity across wheat breeding programmes. We used chromosome-scale genome assemblies corresponding to 9 wheat lines (ArinaLrFor, Jagger, Julius, Lancer, Landmark, Mace, Norin61, Stanley, SY-Mattis) and the Chinese Spring RefSev1.0 assembly, alongside 5 scaffold-level assemblies corresponding to cultivars Cadenza, Claire, Paragon, Robigus and Weebill. Thirteen of these 15 lines are considered cultivars (cultivated varieties of wheat), whereas ArinaLrFor is a line derived from cultivar Arina and Chinese Spring is a landrace collected in the early 1900’s from China. To avoid multiple designations, for the purposes of this study we will refer to all 15 lines as cultivars.

The data corresponds to the underlying source data for the graphs and figures presented in Brinton et al 2020 A haplotype-led approach to increase the precision of wheat breeding Communications Biology.

# Figure 1
## Fig 1a,b,c,d
mace_v_stanley.all_6A_filtered_L20Kb_rq.delta

## Fig 1d,e,f (pink box coordinates)
whole_genome_mummer_BLAST_5000000_blocks_combined_updated_ref_coords_10g_corrected_2gap_no_spelta.tsv

## Fig 1f
### heatmaps
varieties_6A_identites_cdsbp.tab.gz
varieties_6A_identites_0bp.tab.gz
varieties_6A_identites_1000bp.tab.gz
varieties_6A_identites_2000bp.tab.gz
varieties_6A_identites_5000bp.tab.gz

### purple box coordinates
chr6A_haplotype_blocks_aln_25_gene_window_drop3.tsv

# Figure 2
## Fig 2a 
Block_slice500k_stats_Tae_5000kbp.tsv.gz
161010_Chinese_Spring_v1.0_pseudomolecules.fai

## Fig 2b,c
blocks_coverage_5000kbp.csv
combined_fai.txt
161010_Chinese_Spring_v1.0_pseudomolecules.fai

# Figure 3
## Fig 3a
Brinton et al_Supplementary Table 4_Previously_identified_QTL_on_chromosome_6A.xlsx

## Fig 3b
Supplementary_Table_2_Sheet1_6A_only.xlsx

# Figure 4
## Fig 4a
all.GP08_mm75_het3_publication01142019.vcf.gz_all6A_HCLC_GT_only.GT.FORMAT_position_only.tsv
35k_probe_set_IWGSCv1_chr6A.csv
voss_fells_marker_positions_6A.csv
pangenome_snp_dist_6A_CS_ref.tsv

## Fig 4b
35K_cerealsdb_hap_examples.csv
voss_fells_hap_examples.csv
akhunov_hap_examples.csv
haplotype_specific_markers_pangenome.csv

## Fig 4c (also in Supplementary Table 7)
pangenome_haplotype_allocations_35K_all_AK_USA_capture_vossfells_updated_May2020_FINAL_SET_TO_INCLUDE.csv

## Fig 4d (also in Supplementary Table 9)
final_haplotypes_watkins_UKRL.csv

## Fig 4e
Extended Data Table 1 (in manuscript)