EuphyticaGenomicsPeer-reviewed journal article
GWAS and genomic selection for marker-assisted development of sucrose enriched soybean cultivars
Awais RiaziD, Qasim Saleem RazaiD, Anuj KumariD, Derek Dean, Kenani Edward Chiwina, Theresa Makawa Phiri, Julie Thomas, Ainong ShiiD
- Citations
- 12
- FWCI
- 1.89
- Field percentile
- top 16%
Abstract
Sucrose concentration in soy-derived foods is becoming a seminal trait for the production of food-grade soybeans. However, limited scientific knowledge is reported on this increasingly important breeding objective. In this study, 473 genetically diverse soybean germplasm accessions and 8,477 high-quality single nucleotide polymorphisms (SNPs) markers were utilized to pinpoint genomic regions associated with seed sucrose contents through a genome-wide association study (GWAS). A total of 75 significant SNPs (LOD [≥]6.0) were identified across GLM, FarmCPU and BLINK models, including four stable and novel SNPs (Gm03_45385087_ss715586641, Gm06_10919443_ss715592728, Gm09_45335932_ss715604570 and Gm14_10470463_ss715617454). Gene mining near 20 kb flanking genomic regions of four stable SNPs identified 23 candidate genes with the majority of them highly expressed in soybean seeds and pod shells. A sugar transporter encoding major facilitator superfamily gene (Glyma.06G132500) showing the highest expression in pod shells was also identified. Moreover, selection accuracy, efficiency and favorable alleles of 75 significantly associated SNPs were estimated for their utilization in soybean breeding programs. Furthermore, genomic predictions with three different scenarios revealed better feasibility of GWAS-derived SNPs for selection and improvement of seed sucrose concentration. These results could facilitate plant breeders in marker-assisted breeding and genomic selection of sucrose-enriched food-grade soybean cultivars for the global soy-food industry.
- assay: whole-genome sequencing
- research focus: GWAS / association mapping
- research focus: variant effect prediction
- research focus: variant calling
- research focus: benchmarking
- approach: statistical model
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