Genome Wide Association Study and Genomic Selection of Amino Acid Concentrations in Soybean Seeds

Abstract

Soybean is a major source of protein for human consumption and animal feed. Releasing new cultivars with high nutritional value is one of the major goals in soybean breeding. To achieve this goal, genome-wide association studies of seed amino acid contents were conducted based on 249 soybean accessions from China, US, Japan, and South Korea. The accessions were evaluated for 15 amino acids and genotyped by sequencing. Significant genetic variation was observed for amino acids among the accessions. Among the 231 single nucleotide polymorphisms (SNPs) significantly associated with variations in amino acid contents, fifteen SNPs localized near 14 candidate genes involving in amino acid metabolism. The amino acids were classified into two groups with five in one group and seven amino acids in the other. Correlation coefficients among the amino acids within each group were high and positive, but the correlation coefficients of amino acids between the two groups were negative. Twenty-five SNP markers associated with multiple amino acids can be used to simultaneously improve multi-amino acid concentration in soybean. Genomic selection analysis of amino acid concentration showed that selection efficiency of amino acids based on the markers significantly associated with all 15 amino acids was higher than that based on random markers or markers only associated with individual amino acid. The identified markers could facilitate selection of soybean varieties with improved seed quality.

Genome-Wide Association Study and Genomic Prediction for Soybean Cyst Nematode Resistance in USDA Common Bean (Phaseolus vulgaris) Core Collection

“…Genomic prediction accuracy, using the Pearson's correlation coefficient (r) between the GEBV and the observed values, has been the main parameter to measure the performance of GS (Jarquin et al, 2014(Jarquin et al, , 2016Zhang J. P. et al, 2016;Qin et al, 2019;Ravelombola et al, 2019Ravelombola et al, , 2020Ravelombola et al, , 2021Wen et al, 2019;Ali et al, 2020;Keller et al, 2020). The PA is affected by several factors, such as the trait itself with its heritability, marker number, and the marker associated with the trait, and is also affected by GS models, marker density, the level of LD, QTL number, the population size, and the relationship between training population and testing population (Jarquin et al, 2016;Ali et al, 2020;Keller et al, 2020).…”

“…The real GP will be lower if conducting predictions in other panels with different individuals. We have tested many traits in several crops and find it is a practical approach to do genome breeding, using GWAS-derived SNP markers (Qin et al, 2019;Ravelombola et al, 2019Ravelombola et al, , 2020Ravelombola et al, , 2021. Therefore, an approach combining MAS and GS through GEBVs, using associated SNP markers (Spindel et al, 2016;Zhang J. P. et al, 2016;Qin et al, 2019;Ravelombola et al, 2019Ravelombola et al, , 2020Ravelombola et al, , 2021Ali et al, 2020) will be a good choice to do molecular breeding for SCN resistance in common beans and, also, for other quantitative traits in other plant species.…”

“…GS is more efficient than the traditional MAS when dealing with small-effect QTL (Bernardo and Yu, 2007 ; Heffner et al, 2009 , 2011 ; Cortés et al, 2020 ). So far, genomic prediction (GP) as a GS parameter has been investigated in a dozen of crops such as maize ( Zea mays L.), rice ( Oryza sativa L.), soybean, and wheat (Bernardo and Yu, 2007 ; Heffner et al, 2009 , 2011 ; Albrecht et al, 2011 ; Jarquin et al, 2014 , 2016 ; Onogi et al, 2016 ; Xavier et al, 2016 ; Shikha et al, 2017 ; Zhang et al, 2017 ; Qin et al, 2019 ) for various agronomic traits, and abiotic and biotic stress traits. Genomic breeding value estimation in GP is the key step in GS.…”

Combining metabolomic and transcriptomic approaches to assess and improve crop quality traits

“…Indeed, as 92 amino acid-associated QTL were detected, only four were common with both dry weight-and protein-based amino acid content (Zhang et al 2018). (Qin et al (2019) detected 15 amino acid-associated SNPs located near 14 candidate genes in a GWAS analysis comprising 249 soybean accessions, which will be further validated with the objective to develop molecular markers for breeding purposes.…”

“…GWAS for amino acid content was also performed in bread wheat, rice or soybean (Chen et al 2016;Lee et al 2019;Peng et al 2018;Qin et al 2019;Sun et al 2020). A highly diverse panel of 182 accessions of Triticum aestivum was used to map 328 significant quantitative trait nucleotides with six different multi-locus models.…”