Dissecting the genetic architecture of polygenic nutritional traits in maize through meta-QTL analysis

Maize, as a staple crop, contributes significantly to global nutritional security. However, improving its nutritional quality, including grain zinc (GZn), grain iron (GFe), kernel oil (KO), protein quality (PQ), and content (PC), is difficult due to the complex and polygenic nature of these traits. In traditional quantitative trait loci (QTLs) mapping, different populations tested across variable environments have resulted in heterogeneous findings, highlighting the challenge of QTL instability. Therefore, we tested whether Meta-QTL (MQTL) analysis enables the identification of stable QTLs with broader allelic coverage and higher mapping resolution for effective marker-assisted selection (MAS) of complex traits. A comprehensive literature search revealed 29 mapping studies encompassing 308 QTLs for the targeted traits. A total of 34 stable MQTLs were identified, with an average CI of 4.59 cM. These MQTLs were located on all ten maize chromosomes, with phenotypic variance explained (PVE %) ranging from 7.3 % (MQTL1_2) to 49.0 % (MQTL3_2). Furthermore, the analysis revealed six MAS-friendly and five hotspot MQTLs. Besides, 591 CGs were identified underlying these MQTLs, of which 14 have known roles in grain filling, metal homeostasis, and fatty acid biosynthesis in maize. In silico analysis confirmed the tissue-specific expression of these 14 CGs. MQTL analysis effectively refined the genomic regions (4.86 folds) linked with nutritional quality and identified stable MQTLs and CGs. These findings will be useful for developing nutritionally enriched varieties through MAS and genetic engineering.