A high-density linkage map of finger millet provides QTL for blast resistance and other agronomic traits.
Thomas H Pendergast IvPeng QiDamaris Achieng OdenyMathews M DidaKatrien M DevosPublished in: The plant genome (2021)
Finger millet [Eleusine coracana (L.) Gaertn.] is a critical subsistence crop in eastern Africa and southern Asia but has few genomic resources and modern breeding programs. To aid in the understanding of finger millet genomic organization and genes underlying disease resistance and agronomically important traits, we generated a F 2:3 population from a cross between E. coracana (L.) Gaertn. subsp. coracana accession ACC 100007 and E. coracana (L.) Gaertn. subsp. africana , accession GBK 030647. Phenotypic data on morphology, yield, and blast (Magnaporthe oryzae) resistance traits were taken on a subset of the F 2:3 population in a Kenyan field trial. The F 2:3 population was genotyped via genotyping-by-sequencing (GBS) and the UGbS-Flex pipeline was used for sequence alignment, nucleotide polymorphism calling, and genetic map construction. An 18-linkage-group genetic map consisting of 5,422 markers was generated that enabled comparative genomic analyses with rice (Oryza sativa L.), foxtail millet [Setaria italica (L.) P. Beauv.], and sorghum [Sorghum bicolor (L.) Moench]. Notably, we identified conserved acrocentric homoeologous chromosomes (4A and 4B in finger millet) across all species. Significant quantitative trait loci (QTL) were discovered for flowering date, plant height, panicle number, and blast incidence and severity. Sixteen putative candidate genes that may underlie trait variation were identified. Seven LEUCINE-RICH REPEAT-CONTAINING PROTEIN genes, with homology to nucleotide-binding site leucine-rich repeat (NBS-LRR) disease resistance proteins, were found on three chromosomes under blast resistance QTL. This high-marker-density genetic map provides an important tool for plant breeding programs and identifies genomic regions and genes of critical interest for agronomic traits and blast resistance.
Keyphrases
- genome wide
- high density
- copy number
- dna methylation
- body mass index
- public health
- gene expression
- climate change
- single cell
- high throughput
- risk factors
- human immunodeficiency virus
- mass spectrometry
- south africa
- machine learning
- data analysis
- deep learning
- study protocol
- protein protein
- arabidopsis thaliana
- artificial intelligence
- binding protein