Genome-wide association study dissects the genetic architecture of oil biosynthesis in maize kernels.

Maize kernel oil is a valuable source of nutrition. Here we extensively examine the genetic architecture of maize oil biosynthesis in a genome-wide association study using 1.03 million SNPs characterized in 368 maize inbred lines, including 'high-oil' lines. We identified 74 loci significantly associated with kernel oil concentration and fatty acid composition (P < 1.8 × 10(-6)), which we subsequently examined using expression quantitative trait loci (QTL) mapping, linkage mapping and coexpression analysis. More than half of the identified loci localized in mapped QTL intervals, and one-third of the candidate genes were annotated as enzymes in the oil metabolic pathway. The 26 loci associated with oil concentration could explain up to 83% of the phenotypic variation using a simple additive model. Our results provide insights into the genetic basis of oil biosynthesis in maize kernels and may facilitate marker-based breeding for oil quantity and quality.

[Measuring fatty acid concentration in maize grain by near-infrared reflectance spectroscopy].

The fatty acid concentrations in maize grain were analyzed with a set of 294 samples including normal inbred lines, high-oil inbred lines and high-oil recombinant inbred lines (RIL). The method of partial least squares (PLS) regression with internal cross validation was employed to develop the measuring models of near-infrared reflectance spectroscopy (NIRS) for concentrations of four major fatty acids, palmitic, stearic, oleic and linoleic acids, as well as oil concentration in maize grain. The NIRS models were accurate for oleic acid, linoleic acid and oil concentrations. The determination coefficients of these models in cross validation were 0.89, 0.88 and 0.91, respectively; the determination coefficients in external validation were 0.86, 0.84 and 0.92, respectively; and the ratio of standard deviation (SD) to root mean square error of validation (RMSEV) in both calibration and external validation sets (RSC(P)) was higher than 2.5. But the models for palmitic and stearic acid concentrations were not accurate enough with determination coefficients in cross validation and external validation lower than 0.80, and RSC(P) lower than 2.5. Further practical validation showed that the predicted results by using NIRS models for oleic acid, linoleic acid and oil concentrations were accurate and reliable, which will be a useful approach to the measurement of a large number of breeding samples during genetic improvement of oil quality and quantity in maize.