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Background: The nutritional value of soybean oil is largely influenced by the proportions of unsaturated fatty acids (FAs), including oleic acid (OA, 18:1), linoleic acid (LLA, 18:2), and linolenic acid (LNA, 18:3). Genome-wide association (GWAS) studies along with gene expression studies in soybean [Glycine max (L.) Merr.] were leveraged to dissect the genetics of unsaturated FAs. Results: A association panel of 194 diverse soybean accessions were phenotyped in 2013, 2014 and 2015 to identify Single Nucleotide Polymorphisms (SNPs) associated with OA, LLA, and LNA content, and determine putative candidate genes responsible for regulating unsaturated FAs composition. 149 SNPs that represented 73 genomic regions were found to be associated with the unsaturated FA contents in soybean seeds according to the results of GWAS. Twelve novel genes were predicted to be involved in unsaturated FA synthesis in soybean. The relationship between expression pattern of the candidate genes and the accumulation of unsaturated FAs revealed that multiple genes might be involved in unsaturated FAs regulation simultaneously but work in very different ways: Glyma.07G046200 and Glyma.20G245500 promote the OA accumulation in soybean seed in all the tested accessions; Glyma.13G68600 and Glyma.16G200200 promote the OA accumulation only in high OA germplasms; Glyma.07G151300 promotes OA accumulation in higher OA germplasms and suppresses that in lower OA germplasms; Glyma.16G003500 has the effect of increasing LLA accumulation in higher LA germplasms; Glyma.07G254500 suppresses the accumulation of LNA in lower OA germplasms; Glyma.14G194300 might be involved in the accumulation of LNA content in lower LNA germplasms. Conclusions: The beneficial alleles and candidate genes identified might be valuable for improving marker-assisted breeding efficiency and exploring the molecular mechanisms underlying unsaturated fatty acid of soybean. [ABSTRACT FROM AUTHOR]

Background: The advances of hyperspectral technology provide a new analytic means to decrease the gap of phenomics and genomics caused by the fast development of plant genomics with the next generation sequencing technology. Through hyperspectral technology, it is possible to phenotype the biochemical attributes of rice seeds and use the data for GWAS. Results: The results of correlation analysis indicated that Normalized Difference Spectral Index (NDSI) had high correlation with protein content (PC) with RNDSI2 = 0.68. Based on GWAS analysis using all the traits, NDSI was able to identify the same SNP loci as rice protein content that was measured by traditional methods. In total, hyperspectral trait NDSI identified all the 43 genes that were identified by biochemical trait PC. NDSI identified 1 extra SNP marker on chromosome 1, which annotated extra 22 genes that were not identified by PC. Kegg annotation results showed that traits NDSI annotated 3 pathways that are exactly the same as PC. The cysteine and methionine metabolic pathway identified by both NDSI and PC was reported important for biosynthesis and metabolism of some of amino acids/protein in rice seeds. Conclusion: This study combined hyperspectral technology and GWAS analysis to dissect PC of rice seeds, which was high throughput and proven to be able to apply to GWAS as a new phenotyping tool. It provided a new means to phenotype one of the important biochemical traits for the determination of rice quality that could be used for genetic studies. [ABSTRACT FROM AUTHOR]