Accelerating wheat breeding for end-use quality with multi-trait genomic predictions incorporating near infrared and nuclear magnetic resonance-derived phenotypes

摘要: Using NIR and NMR predictions of quality traits overcomes a major barrier for the application genomic selection to accelerate improvement in grain end-use wheat. Grain are among most important wheat breeding. These difficult breed for, as their assays require flour quantities only obtainable late breeding cycle, expensive. therefore an ideal target selection. However, large reference populations required accurate predictions, which challenging assemble these same reasons they for. Here, we use derived from near infrared (NIR) or nuclear magnetic resonance (NMR), that very small amounts flour, well measured by industry standard assay subset accessions, multi-trait approach prediction. The were 19 398 then assayed 2420 diverse accessions. accessions grown out multiple locations years, genotyped 51208 SNP. Incorporating phenotypes increased accuracy prediction traits. ranged 0 0.47 before addition NIR/NMR data, while after data added, it 0.69. Genomic reasonably robust across years