Genome-wide association mapping of Hagberg falling number, protein content, test weight, and grain yield in U.K. wheat.
Jon WhiteRajiv SharmaDavid BaldingJames CockramIan J MackayPublished in: Crop science (2022)
Association mapping using crop cultivars allows identification of genetic loci of direct relevance to breeding. Here, 150 U.K. wheat ( Triticum aestivum L.) cultivars genotyped with 23,288 single nucleotide polymorphisms (SNPs) were used for genome-wide association studies (GWAS) using historical phenotypic data for grain protein content, Hagberg falling number (HFN), test weight, and grain yield. Power calculations indicated experimental design would enable detection of quantitative trait loci (QTL) explaining ≥20% of the variation (PVE) at a relatively high power of >80%, falling to 40% for detection of a SNP with an R 2 ≥ .5 with the same QTL. Genome-wide association studies identified marker-trait associations for all four traits. For HFN ( h 2 = .89), six QTL were identified, including a major locus on chromosome 7B explaining 49% PVE and reducing HFN by 44 s. For protein content ( h 2 = 0.86), 10 QTL were found on chromosomes 1A, 2A, 2B, 3A, 3B, and 6B, together explaining 48.9% PVE. For test weight, five QTL were identified (one on 1B and four on 3B; 26.3% PVE). Finally, 14 loci were identified for grain yield ( h 2 = 0.95) on eight chromosomes (1A, 2A, 2B, 2D, 3A, 5B, 6A, 6B; 68.1% PVE), of which five were located within 16 Mbp of genetic regions previously identified as under breeder selection in European wheat. Our study demonstrates the utility of exploiting historical crop datasets, identifying genomic targets for independent validation, and ultimately for wheat genetic improvement.
Keyphrases
- genome wide association
- genome wide
- high density
- copy number
- dna methylation
- high resolution
- body mass index
- physical activity
- weight loss
- climate change
- weight gain
- protein protein
- amino acid
- binding protein
- case control
- electronic health record
- small molecule
- label free
- molecular dynamics
- mass spectrometry
- machine learning
- rna seq
- density functional theory
- artificial intelligence
- single cell