QTL analysis of important agronomic traits and metabolites in foxtail millet (Setaria italica) by RIL population and widely targeted metabolome

Abstract

As a bridge between genome and phenotype, metabolome is closely related to plant growth and development. However, the research on the combination of genome, metabolome and multiple agronomic traits in foxtail millet (Setaria italica) is insufficient. Here, based on the linkage analysis of 3,452 metabolites via with high-quality genetic linkage maps, we detected a total of 1,049 metabolic quantitative trait loci (mQTLs) distributed in 11 hotspots, and 28 metabolite-related candidate genes were mined from 14 mQTLs. In addition, 136 single-environment phenotypic QTL (pQTLs) related to 63 phenotypes were identified by linkage analysis, and there were 12 hotspots on these pQTLs. We futher dissected 39 candidate genes related to agronomic traits through metabolite-phenotype correlation and gene function analysis, including Sd1 semidwarf gene, which can affect plant height by regulating GA synthesis. Combined correlation network and QTL analysis, we found that flavonoid-lignin pathway maybe closely related to plant architecture and yield in foxtail millet. For example, the correlation coefficient between apigenin 7-rutinoside and stem diameter reached 0.98, and they were co-located at 41.33-44.15 Mb of chromosome 5, further gene function analysis revealed that 5 flavonoid pathway genes, as well as Sd1, were located in this interval . Therefore, the correlation and co-localization between flavonoid-lignins and plant architecture may be due to the close linkage of their regulatory genes in millet. Besides, we also found that a combination of genomic and metabolomic for BLUP analysis can better predict plant agronomic traits than genomic or metabolomic data, independently. In conclusion, the combined analysis of mQTL and pQTL in millet have linked genetic, metabolic and agronomic traits, and is of great significance for metabolite-related molecular assisted breeding.

Predictive metabolomics of pearl millet phenotypic traits using a germplasm panel of genetic diversity

“…The distribution of qualitative traits shows the occurrence of six panicle shapes (cylindrical, conical, spindle, club, candle, and lanceolate). When examining unique features, the candle shape exhibited the highest number (58), followed by lanceolate (37), cylindrical (29), club (14), conical (10), and spindle (10) (Fig. 4b).…”

“…The grains were categorized into seven colour classes. Among these, genotypes with gray grain colour showed the highest number of unique metabolic markers (52), followed by brown-gray (42), a mixture of yellow and gray grains (29), deep gray (17), yellow (11), brown (10), and cream (7) (Fig. 4h).…”

“…Furthermore, integrating multi-omics data provided more accurate results than solely using genomic or metabolomic data. This way, incorporating genomic information in our dataset and testing different methods to determine the best t could prove to be an effective strategy (Wei et al 2023).…”

“…In the cereal crop eld, metabolite levels in the tyrosine pathway were effective predictors of yield when analysing ve hybrid rice varieties (Dan et al 2021). One step further, predictive multi-omics successfully predicted 21 agronomic traits across 197 recombinant inbred lines of foxtail millet over three distinct years (Wei et al 2023).…”

Genetic variability of F2 foxtail millet population derived from ICERI-5 and Botok-10 cross

“…Grain yield is one of the most important traits in cereals. A QTL analysis using RIL population of foxtail millet conducted by Wei et al (2022) identified a potential yield-related gene, Si035172m. The Si035172m homologous gene in rice, OsTE, is a co-activator of APC/C.…”