Foxtail millet (Setaria italica) is an important minor cereal crop in China. The yellow color of the de-husked grain is the most direct aspect for evaluating the foxtail millet quality. The yellow pigment mainly includes carotenoids (lutein and zeaxanthin) and flavonoids. To reveal the diversity and specificity of flavonoids in foxtail millet, we chose three high eating quality and two poor eating quality varieties as research materials. A total of 116 flavonoid metabolites were identified based on Ultra Performance Liquid Chromatography-Electrospray Ionization-Tandem Mass Spectrometry (UPLC-ESI-MS/MS) system. The tested varieties contained similar levels of flavonoid metabolites, but with each variety accumulating its unique flavonoid metabolites. A total of 33 flavonoid metabolites were identified as significantly discrepant between high eating quality and poor eating quality varieties, which were mainly in the flavonoid biosynthesis pathway and one of its branches, the flavone and flavonol biosynthesis pathway. These results showed the diversified components of flavonoids accumulated in foxtail millets and laid the foundation for further research on flavonoids and the breeding for high-quality foxtail millet varieties.

Genome-wide association study (GWAS) is widely used to identify genes involved in plants, animals and human complex traits. Generally, the identified SNP is not necessarily the causal variant, but it is rather in linkage disequilibrium (LD). One key challenge for GWAS results interpretation is to rapidly identify causal genes and provide profound evidence on how they affect the trait. Researches want to identify candidate causal variants from the most significant SNPs of GWAS in any species and on their local computer, while to complete these tasks are to be time-consuming, laborious and prone to errors and omission. To our knowledge, so far there is no tool available to solve the challenge for GWAS data very quickly. Based on the standard VCF (variant call format) format, CandiHap is developed to fast preselection candidate causal SNPs and gene(s) from GWAS by integrating LD result, SNP annotation, haplotype analysis and traits statistics of haplotypes.